WorldWideScience

Sample records for neural net connections

  1. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

    Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse.......Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse....

  2. The process of learning in neural net models with Poisson and Gauss connectivities.

    Science.gov (United States)

    Sivridis, L; Kotini, A; Anninos, P

    2008-01-01

    In this study we examined the dynamic behavior of isolated and non-isolated neural networks with chemical markers that follow a Poisson or Gauss distribution of connectivity. The Poisson distribution shows higher activity in comparison to the Gauss distribution although the latter has more connections that obliterated due to randomness. We examined 57 hematoxylin and eosin stained sections from an equal number of autopsy specimens with a diagnosis of "cerebral matter within normal limits". Neural counting was carried out in 5 continuous optic fields, with the use of a simple optical microscope connected to a computer (software programmer Nikon Act-1 vers-2). The number of neurons that corresponded to a surface was equal to 0.15 mm(2). There was a gradual reduction in the number of neurons as age increased. A mean value of 45.8 neurons /0.15 mm(2) was observed within the age range 21-25, 33 neurons /0.15 mm(2) within the age range 41-45, 19.3 neurons /0.15 mm(2) within the age range 56-60 years. After the age of 60 it was observed that the number of neurons per unit area stopped decreasing. A correlation was observed between these experimental findings and the theoretical neural model developed by professor Anninos and his colleagues. Equivalence between the mean numbers of neurons of the above mentioned age groups and the highest possible number of synaptic connections per neuron (highest number of synaptic connections corresponded to the age group 21-25) was created. We then used both inhibitory and excitatory post-synaptic potentials and applied these values to the Poisson and Gauss distributions, whereas the neuron threshold was varied between 3 and 5. According to the obtained phase diagrams, the hysteresis loops decrease as age increases. These findings were significant as the hysteresis loops can be regarded as the basis for short-term memory.

  3. Building Neural Net Software

    OpenAIRE

    Neto, João Pedro; Costa, José Félix

    1999-01-01

    In a recent paper [Neto et al. 97] we showed that programming languages can be translated on recurrent (analog, rational weighted) neural nets. The goal was not efficiency but simplicity. Indeed we used a number-theoretic approach to machine programming, where (integer) numbers were coded in a unary fashion, introducing a exponential slow down in the computations, with respect to a two-symbol tape Turing machine. Implementation of programming languages in neural nets turns to be not only theo...

  4. CDMA and TDMA based neural nets.

    Science.gov (United States)

    Herrero, J C

    2001-06-01

    CDMA and TDMA telecommunication techniques were established long time ago, but they have acquired a renewed presence due to the rapidly increasing mobile phones demand. In this paper, we are going to see they are suitable for neural nets, if we leave the concept "connection" between processing units and we adopt the concept "messages" exchanged between them. This may open the door to neural nets with a higher number of processing units and flexible configuration.

  5. Texture Based Image Analysis With Neural Nets

    Science.gov (United States)

    Ilovici, Irina S.; Ong, Hoo-Tee; Ostrander, Kim E.

    1990-03-01

    In this paper, we combine direct image statistics and spatial frequency domain techniques with a neural net model to analyze texture based images. The resultant optimal texture features obtained from the direct and transformed image form the exemplar pattern of the neural net. The proposed approach introduces an automated texture analysis applied to metallography for determining the cooling rate and mechanical working of the materials. The results suggest that the proposed method enhances the practical applications of neural nets and texture extraction features.

  6. Classification using Bayesian neural nets

    NARCIS (Netherlands)

    J.C. Bioch (Cor); O. van der Meer; R. Potharst (Rob)

    1995-01-01

    textabstractRecently, Bayesian methods have been proposed for neural networks to solve regression and classification problems. These methods claim to overcome some difficulties encountered in the standard approach such as overfitting. However, an implementation of the full Bayesian approach to

  7. Neural Nets for Scene Analysis

    Science.gov (United States)

    1992-09-01

    decision boundaries produced for the arificial database when prototypes are Se- feature 1 lected from reduced training set. ly selected from the 383...CLASSIFIER HIT MISS MOPOGIA CORRELATION LOW-LEVEL VISION IVARL&MCE NEURAL NE. (O D ILER) SE CORRELATION REUCE ETC.(OR I F RS)DI4ENSIONAIM AND TRAINING...A) = J11’, + tOi2Z2 + 61311’ (4) SPE Vol. 1608 mitalwg’t Robots and Coniutef Vision X (991)/501 - "X,, ,v ) X 1112 1P Pa P2 P2 .. 2 33 CL AS INPUT

  8. Fast neural net simulation with a DSP processor array.

    Science.gov (United States)

    Muller, U A; Gunzinger, A; Guggenbuhl, W

    1995-01-01

    This paper describes the implementation of a fast neural net simulator on a novel parallel distributed-memory computer. A 60-processor system, named MUSIC (multiprocessor system with intelligent communication), is operational and runs the backpropagation algorithm at a speed of 330 million connection updates per second (continuous weight update) using 32-b floating-point precision. This is equal to 1.4 Gflops sustained performance. The complete system with 3.8 Gflops peak performance consumes less than 800 W of electrical power and fits into a 19-in rack. While reaching the speed of modern supercomputers, MUSIC still can be used as a personal desktop computer at a researcher's own disposal. In neural net simulation, this gives a computing performance to a single user which was unthinkable before. The system's real-time interfaces make it especially useful for embedded applications.

  9. Document analysis with neural net circuits

    Science.gov (United States)

    Graf, Hans Peter

    1994-01-01

    Document analysis is one of the main applications of machine vision today and offers great opportunities for neural net circuits. Despite more and more data processing with computers, the number of paper documents is still increasing rapidly. A fast translation of data from paper into electronic format is needed almost everywhere, and when done manually, this is a time consuming process. Markets range from small scanners for personal use to high-volume document analysis systems, such as address readers for the postal service or check processing systems for banks. A major concern with present systems is the accuracy of the automatic interpretation. Today's algorithms fail miserably when noise is present, when print quality is poor, or when the layout is complex. A common approach to circumvent these problems is to restrict the variations of the documents handled by a system. In our laboratory, we had the best luck with circuits implementing basic functions, such as convolutions, that can be used in many different algorithms. To illustrate the flexibility of this approach, three applications of the NET32K circuit are described in this short viewgraph presentation: locating address blocks, cleaning document images by removing noise, and locating areas of interest in personal checks to improve image compression. Several of the ideas realized in this circuit that were inspired by neural nets, such as analog computation with a low resolution, resulted in a chip that is well suited for real-world document analysis applications and that compares favorably with alternative, 'conventional' circuits.

  10. Beyond Pattern Recognition With Neural Nets

    Science.gov (United States)

    Arsenault, Henri H.; Macukow, Bohdan

    1989-02-01

    Neural networks are finding many areas of application. Although they are particularly well-suited for applications related to associative recall such as content-addressable memories, neural nets can perform many other applications ranging from logic operations to the solution of optimization problems. The training of a recently introduced model to perform boolean logical operations such as XOR is described. Such simple systems can be combined to perform any complex boolean operation. Any complex task consisting of parallel and serial operations including fuzzy logic that can be described in terms of input-output relations can be accomplished by combining modules such as the ones described here. The fact that some modules can carry out their functions even when their inputs contain erroneous data, and the fact that each module can carry out its functions in parallel with itself and other modules promises some interesting applications.

  11. Perineuronal net, CSPG receptor and their regulation of neural plasticity.

    Science.gov (United States)

    Miao, Qing-Long; Ye, Qian; Zhang, Xiao-Hui

    2014-08-25

    Perineuronal nets (PNNs) are reticular structures resulting from the aggregation of extracellular matrix (ECM) molecules around the cell body and proximal neurite of specific population of neurons in the central nervous system (CNS). Since the first description of PNNs by Camillo Golgi in 1883, the molecular composition, developmental formation and potential functions of these specialized extracellular matrix structures have only been intensively studied over the last few decades. The main components of PNNs are hyaluronan (HA), chondroitin sulfate proteoglycans (CSPGs) of the lectican family, link proteins and tenascin-R. PNNs appear late in neural development, inversely correlating with the level of neural plasticity. PNNs have long been hypothesized to play a role in stabilizing the extracellular milieu, which secures the characteristic features of enveloped neurons and protects them from the influence of malicious agents. Aberrant PNN signaling can lead to CNS dysfunctions like epilepsy, stroke and Alzheimer's disease. On the other hand, PNNs create a barrier which constrains the neural plasticity and counteracts the regeneration after nerve injury. Digestion of PNNs with chondroitinase ABC accelerates functional recovery from the spinal cord injury and restores activity-dependent mechanisms for modifying neuronal connections in the adult animals, indicating that PNN is an important regulator of neural plasticity. Here, we review recent progress in the studies on the formation of PNNs during early development and the identification of CSPG receptor - an essential molecular component of PNN signaling, along with a discussion on their unique regulatory roles in neural plasticity.

  12. Real-time applications of neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.

  13. Accelerator diagnosis and control by Neural Nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

  14. 22nd Italian Workshop on Neural Nets

    CERN Document Server

    Bassis, Simone; Esposito, Anna; Morabito, Francesco

    2013-01-01

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

  15. Optical neural net for classifying imaging spectrometer data

    Science.gov (United States)

    Barnard, Etienne; Casasent, David P.

    1989-01-01

    The problem of determining the composition of an unknown input mixture from its measured spectrum, given the spectra of a number of elements, is studied. The Hopfield minimization procedure was used to express the determination of the compositions as a problem suitable for solution by neural nets. A mathematical description of the problem was developed and used as a basis for a neural network solution and an optical implementation.

  16. The Development of Animal Behavior: From Lorenz to Neural Nets

    Science.gov (United States)

    Bolhuis, Johan J.

    In the study of behavioral development both causal and functional approaches have been used, and they often overlap. The concept of ontogenetic adaptations suggests that each developmental phase involves unique adaptations to the environment of the developing animal. The functional concept of optimal outbreeding has led to further experimental evidence and theoretical models concerning the role of sexual imprinting in the evolutionary process of sexual selection. From a causal perspective it has been proposed that behavioral ontogeny involves the development of various kinds of perceptual, motor, and central mechanisms and the formation of connections among them. This framework has been tested for a number of complex behavior systems such as hunger and dustbathing. Imprinting is often seen as a model system for behavioral development in general. Recent advances in imprinting research have been the result of an interdisciplinary effort involving ethology, neuroscience, and experimental psychology, with a continual interplay between these approaches. The imprinting results are consistent with Lorenz' early intuitive suggestions and are also reflected in the architecture of recent neural net models.

  17. Classification of handwritten digits using a RAM neural net architecture

    DEFF Research Database (Denmark)

    Jørgensen, T.M.

    1997-01-01

    Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results...

  18. Translating feedforward neural nets to SOM-like maps

    NARCIS (Netherlands)

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

    A major disadvantage of feedforward neural networks is still the difficulty to gain insight into their internal functionality. This is much less the case for, e.g., nets that are trained unsupervised, such as Kohonen’s self-organizing feature maps (SOMs). These offer a direct view into the stored

  19. Squaroglitter: A 3,4-Connected Carbon Net

    KAUST Repository

    Prasad, Dasari L. V. K.

    2013-08-13

    Theoretical calculations are presented on a new hypothetical 3,4-connected carbon net (called squaroglitter) incorporating 1,4 cyclohexadiene units. The structure has tetragonal space group P4/mmm (No. 123) symmetry. The optimized geometry shows normal distances, except for some elongated bonds in the cyclobutane ring substructures in the network. Squaroglitter has an indirect bandgap of about 1.0 eV. The hypothetical lattice, whose density is close to graphite, is more stable than other 3,4-connected carbon nets. A relationship to a (4,4)nanotube is explored, as is a potential threading of the lattice with metal needles. © 2013 American Chemical Society.

  20. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    Science.gov (United States)

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. On sparsely connected optimal neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V. [Los Alamos National Lab., NM (United States); Draghici, S. [Wayne State Univ., Detroit, MI (United States)

    1997-10-01

    This paper uses two different approaches to show that VLSI- and size-optimal discrete neural networks are obtained for small fan-in values. These have applications to hardware implementations of neural networks, but also reveal an intrinsic limitation of digital VLSI technology: its inability to cope with highly connected structures. The first approach is based on implementing F{sub n,m} functions. The authors show that this class of functions can be implemented in VLSI-optimal (i.e., minimizing AT{sup 2}) neural networks of small constant fan-ins. In order to estimate the area (A) and the delay (T) of such networks, the following cost functions will be used: (i) the connectivity and the number-of-bits for representing the weights and thresholds--for good estimates of the area; and (ii) the fan-ins and the length of the wires--for good approximates of the delay. The second approach is based on implementing Boolean functions for which the classical Shannon`s decomposition can be used. Such a solution has already been used to prove bounds on the size of fan-in 2 neural networks. They will generalize the result presented there to arbitrary fan-in, and prove that the size is minimized by small fan-in values. Finally, a size-optimal neural network of small constant fan-ins will be suggested for F{sub n,m} functions.

  2. Computation and control with neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  3. Artificial neural nets application in the cotton yarn industry

    Directory of Open Access Journals (Sweden)

    Gilberto Clóvis Antoneli

    2016-06-01

    Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

  4. Neural Net Gains Estimation Based on an Equivalent Model

    Directory of Open Access Journals (Sweden)

    Karen Alicia Aguilar Cruz

    2016-01-01

    Full Text Available A model of an Equivalent Artificial Neural Net (EANN describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN. The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB the factors based on the functional error and the reference signal built with the past information of the system.

  5. Neural system modeling and simulation using Hybrid Functional Petri Net.

    Science.gov (United States)

    Tang, Yin; Wang, Fei

    2012-02-01

    The Petri net formalism has been proved to be powerful in biological modeling. It not only boasts of a most intuitive graphical presentation but also combines the methods of classical systems biology with the discrete modeling technique. Hybrid Functional Petri Net (HFPN) was proposed specially for biological system modeling. An array of well-constructed biological models using HFPN yielded very interesting results. In this paper, we propose a method to represent neural system behavior, where biochemistry and electrical chemistry are both included using the Petri net formalism. We built a model for the adrenergic system using HFPN and employed quantitative analysis. Our simulation results match the biological data well, showing that the model is very effective. Predictions made on our model further manifest the modeling power of HFPN and improve the understanding of the adrenergic system. The file of our model and more results with their analysis are available in our supplementary material.

  6. Knowledge synthesis with maps of neural connectivity

    Directory of Open Access Journals (Sweden)

    Marcelo eTallis

    2011-11-01

    Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

  7. Unfolding code for neutron spectrometry based on neural nets technology

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)

    2012-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  8. Webs, cell assemblies, and chunking in neural nets: introduction.

    Science.gov (United States)

    Wickelgren, W A

    1999-03-01

    This introduction to Wickelgren (1992), describes a theory of idea representation and learning in the cerebral cortex and seven properties of Hebb's (1949) formulation of cell assemblies that have played a major role in all such neural net models. Ideas are represented in the cerebral cortex by webs (innate cell assemblies), using sparse coding with sparse, all-or-none, innate linking. Recruiting a web to represent a new idea is called chunking. The innate links that bind the neurons of a web are basal dendritic synapses. Learning modifies the apical dendritic synapses that associate neurons in one web to neurons in another web.

  9. Rod-Shaped Neural Units for Aligned 3D Neural Network Connection.

    Science.gov (United States)

    Kato-Negishi, Midori; Onoe, Hiroaki; Ito, Akane; Takeuchi, Shoji

    2017-08-01

    This paper proposes neural tissue units with aligned nerve fibers (called rod-shaped neural units) that connect neural networks with aligned neurons. To make the proposed units, 3D fiber-shaped neural tissues covered with a calcium alginate hydrogel layer are prepared with a microfluidic system and are cut in an accurate and reproducible manner. These units have aligned nerve fibers inside the hydrogel layer and connectable points on both ends. By connecting the units with a poly(dimethylsiloxane) guide, 3D neural tissues can be constructed and maintained for more than two weeks of culture. In addition, neural networks can be formed between the different neural units via synaptic connections. Experimental results indicate that the proposed rod-shaped neural units are effective tools for the construction of spatially complex connections with aligned nerve fibers in vitro. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Stability Training for Convolutional Neural Nets in LArTPC

    Science.gov (United States)

    Lindsay, Matt; Wongjirad, Taritree

    2017-01-01

    Convolutional Neural Nets (CNNs) are the state of the art for many problems in computer vision and are a promising method for classifying interactions in Liquid Argon Time Projection Chambers (LArTPCs) used in neutrino oscillation experiments. Despite the good performance of CNN's, they are not without drawbacks, chief among them is vulnerability to noise and small perturbations to the input. One solution to this problem is a modification to the learning process called Stability Training developed by Zheng et al. We verify existing work and demonstrate volatility caused by simple Gaussian noise and also that the volatility can be nearly eliminated with Stability Training. We then go further and show that a traditional CNN is also vulnerable to realistic experimental noise and that a stability trained CNN remains accurate despite noise. This further adds to the optimism for CNNs for work in LArTPCs and other applications.

  11. A taxonomy of Deep Convolutional Neural Nets for Computer Vision

    Directory of Open Access Journals (Sweden)

    Suraj eSrinivas

    2016-01-01

    Full Text Available Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs. We start with AlexNet'' as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.

  12. Multilayer neural-net robot controller with guaranteed tracking performance.

    Science.gov (United States)

    Lewis, F L; Yegildirek, A; Liu, K

    1996-01-01

    A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error/passivity approach. No off-line learning phase is needed for the proposed NN controller and the weights are easily initialized. The nonlinear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed-loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second-order forward-propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced.

  13. Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR

    Directory of Open Access Journals (Sweden)

    Bernard P. Zeigler

    2017-01-01

    Full Text Available In the context of the modeling and simulation of neural nets, we formulate definitions for the behavioral realization of memoryless functions. The definitions of realization are substantively different for deterministic and stochastic systems constructed of neuron-inspired components. In contrast to earlier generations of neural net models, third generation spiking neural nets exhibit important temporal and dynamic properties, and random neural nets provide alternative probabilistic approaches. Our definitions of realization are based on the Discrete Event System Specification (DEVS formalism that fundamentally include temporal and probabilistic characteristics of neuron system inputs, state, and outputs. The realizations that we construct—in particular for the Exclusive Or (XOR logic gate—provide insight into the temporal and probabilistic characteristics that real neural systems might display. Our results provide a solid system-theoretical foundation and simulation modeling framework for the high-performance computational support of such applications.

  14. Neuron-Glia Interactions in Neural Plasticity: Contributions of Neural Extracellular Matrix and Perineuronal Nets

    Directory of Open Access Journals (Sweden)

    Egor Dzyubenko

    2016-01-01

    Full Text Available Synapses are specialized structures that mediate rapid and efficient signal transmission between neurons and are surrounded by glial cells. Astrocytes develop an intimate association with synapses in the central nervous system (CNS and contribute to the regulation of ion and neurotransmitter concentrations. Together with neurons, they shape intercellular space to provide a stable milieu for neuronal activity. Extracellular matrix (ECM components are synthesized by both neurons and astrocytes and play an important role in the formation, maintenance, and function of synapses in the CNS. The components of the ECM have been detected near glial processes, which abut onto the CNS synaptic unit, where they are part of the specialized macromolecular assemblies, termed perineuronal nets (PNNs. PNNs have originally been discovered by Golgi and represent a molecular scaffold deposited in the interface between the astrocyte and subsets of neurons in the vicinity of the synapse. Recent reports strongly suggest that PNNs are tightly involved in the regulation of synaptic plasticity. Moreover, several studies have implicated PNNs and the neural ECM in neuropsychiatric diseases. Here, we highlight current concepts relating to neural ECM and PNNs and describe an in vitro approach that allows for the investigation of ECM functions for synaptogenesis.

  15. Estimation of Effectivty Connectivity via Data-Driven Neural Modeling

    Directory of Open Access Journals (Sweden)

    Dean Robert Freestone

    2014-11-01

    Full Text Available This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used the track the mechanisms involved in seizure initiation and termination.

  16. Coherency and connectivity in oscillating neural networks: linear partialization analysis

    NARCIS (Netherlands)

    Kalitzin, S.; van Dijk, B. W.; Spekreijse, H.; van Leeuwen, W. A.

    1997-01-01

    This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their

  17. Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

    Science.gov (United States)

    Decker, Arthur J.

    2004-01-01

    Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

  18. ER fluid applications to vibration control devices and an adaptive neural-net controller

    Science.gov (United States)

    Morishita, Shin; Ura, Tamaki

    1993-07-01

    Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

  19. Neural-net based real-time economic dispatch for thermal power plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  20. A novel method linking neural connectivity to behavioral fluctuations: Behavior-regressed connectivity.

    Science.gov (United States)

    Passaro, Antony D; Vettel, Jean M; McDaniel, Jonathan; Lawhern, Vernon; Franaszczuk, Piotr J; Gordon, Stephen M

    2017-03-01

    During an experimental session, behavioral performance fluctuates, yet most neuroimaging analyses of functional connectivity derive a single connectivity pattern. These conventional connectivity approaches assume that since the underlying behavior of the task remains constant, the connectivity pattern is also constant. We introduce a novel method, behavior-regressed connectivity (BRC), to directly examine behavioral fluctuations within an experimental session and capture their relationship to changes in functional connectivity. This method employs the weighted phase lag index (WPLI) applied to a window of trials with a weighting function. Using two datasets, the BRC results are compared to conventional connectivity results during two time windows: the one second before stimulus onset to identify predictive relationships, and the one second after onset to capture task-dependent relationships. In both tasks, we replicate the expected results for the conventional connectivity analysis, and extend our understanding of the brain-behavior relationship using the BRC analysis, demonstrating subject-specific BRC maps that correspond to both positive and negative relationships with behavior. Comparison with Existing Method(s): Conventional connectivity analyses assume a consistent relationship between behaviors and functional connectivity, but the BRC method examines performance variability within an experimental session to understand dynamic connectivity and transient behavior. The BRC approach examines connectivity as it covaries with behavior to complement the knowledge of underlying neural activity derived from conventional connectivity analyses. Within this framework, BRC may be implemented for the purpose of understanding performance variability both within and between participants. Published by Elsevier B.V.

  1. Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net

    OpenAIRE

    Ghosal, Sayan; Ray, Nilanjan

    2016-01-01

    Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the registration pipeline. The upper bound SSD (UB-SSD) enhances the original deformable model parameter space by adding a heatmap output from FCNN. Nex...

  2. EU-PolarNet: Connecting Science with Society

    Science.gov (United States)

    Biebow, N.

    2015-12-01

    The rapid changes occurring in the Polar Regions are significantly influencing global climate with consequences for global society. European and international polar research has contributed critical knowledge to identifying the processes behind these rapid changes but datasets from the Polar Regions are still insufficient to fully understand and more effectively predict the effects of change on our climate and society. This situation can only be improved by a more holistic integrated scientific approach, a higher degree of coordination of polar research and closer cooperation with all relevant actors on an international level. The objectives of EU-PolarNet are to establish an on-going dialogue between policy-makers, business and industry leaders, local communities and scientists to increase mutual understanding and identify new ways of working that will deliver economic and societal benefits. The results of this dialogue will be brought together in an Integrated European Research Programme that will be co-designed with all relevant stakeholders and coordinated with the activities of polar research nations beyond Europe. This programme will be accompanied by a feasible implementation plan to provide the Polar community with the capability to define the nature of environmental risks so that governments can design policy measures to mitigate them and businesses and other stakeholders benefit from the opportunities that are opening up in the Polar Regions.

  3. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...

  4. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...... generation of pikes. When a stimulus is applied to the network, the spontaneous rings may prevail and hamper detection of the effects of the stimulus. Therefore, the spontaneous rings cannot be ignored and the response latency has to be detected on top of a background signal. Everything becomes more dicult...

  5. Fast neural-net based fake track rejection

    CERN Document Server

    De Cian, Michel; Seyfert, Paul; Stahl, Sascha

    2017-01-01

    A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, is fast enough to fit into the CPU time budget of the software trigger farm. It allows reducing the fake rate and consequently the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment.

  6. ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

    Energy Technology Data Exchange (ETDEWEB)

    Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav; Hodas, Nathan O.

    2017-12-08

    With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.

  7. Intelligent control based on fuzzy logic and neural net theory

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  8. Geometrical approach to neural net control of movements and posture

    Science.gov (United States)

    Pellionisz, A. J.; Ramos, C. F.

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

  9. Neuromodulatory connectivity defines the structure of a behavioral neural network.

    Science.gov (United States)

    Diao, Feici; Elliott, Amicia D; Diao, Fengqiu; Shah, Sarav; White, Benjamin H

    2017-11-22

    Neural networks are typically defined by their synaptic connectivity, yet synaptic wiring diagrams often provide limited insight into network function. This is due partly to the importance of non-synaptic communication by neuromodulators, which can dynamically reconfigure circuit activity to alter its output. Here, we systematically map the patterns of neuromodulatory connectivity in a network that governs a developmentally critical behavioral sequence in Drosophila. This sequence, which mediates pupal ecdysis, is governed by the serial release of several key factors, which act both somatically as hormones and within the brain as neuromodulators. By identifying and characterizing the functions of the neuronal targets of these factors, we find that they define hierarchically organized layers of the network controlling the pupal ecdysis sequence: a modular input layer, an intermediate central pattern generating layer, and a motor output layer. Mapping neuromodulatory connections in this system thus defines the functional architecture of the network.

  10. Reorganization of the Connectivity between Elementary Functions – A Model Relating Conscious States to Neural Connections

    Directory of Open Access Journals (Sweden)

    Jesper Mogensen

    2017-04-01

    Full Text Available In the present paper it is argued that the “neural correlate of consciousness” (NCC does not appear to be a separate “module” – but an aspect of information processing within the neural substrate of various cognitive processes. Consequently, NCC can only be addressed adequately within frameworks that model the general relationship between neural processes and mental states – and take into account the dynamic connectivity of the brain. We presently offer the REFGEN (general reorganization of elementary functions model as such a framework. This model builds upon and expands the REF (reorganization of elementary functions and REFCON (of elementary functions and consciousness models. All three models integrate the relationship between the neural and mental layers of description via the construction of an intermediate level dealing with computational states. The importance of experience based organization of neural and cognitive processes is stressed. The models assume that the mechanisms of consciousness are in principle the same as the basic mechanisms of all aspects of cognition – when information is processed to a sufficiently “high level” it becomes available to conscious experience. The NCC is within the REFGEN model seen as aspects of the dynamic and experience driven reorganizations of the synaptic connectivity between the neurocognitive “building blocks” of the model – the elementary functions.

  11. Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots

    Directory of Open Access Journals (Sweden)

    Nicholas Livingston

    2016-12-01

    Full Text Available While modularity is thought to be central for the evolution of complexity and evolvability, it remains unclear how systems boot-strap themselves into modularity from random or fully integrated starting conditions. Clune et al. (2013 suggested that a positive correlation between sparsity and modularity is the prime cause of this transition. We sought to test the generality of this modularity-sparsity hypothesis by testing it for the first time in physically embodied robots. A population of ten Tadros — autonomous, surface-swimming robots propelled by a flapping tail — was used. Individuals varied only in the structure of their neural net control, a 2 x 6 x 2 network with recurrence in the hidden layer. Each of the 60 possible connections was coded in the genome, and could achieve one of three states: -1, 0, 1. Inputs were two light-dependent resistors and outputs were two motor control variables to the flapping tail, one for the frequency of the flapping and the other for the turning offset. Each Tadro was tested separately in a circular tank lit by a single overhead light source. Fitness was the amount of light gathered by a vertically oriented sensor that was disconnected from the controller net. Reproduction was asexual, with the top performer cloned and then all individuals entered into a roulette wheel selection process, with genomes mutated to create the offspring. The starting population of networks was randomly generated. Over ten generations, the population’s mean fitness increased two-fold. This evolution occurred in spite of an unintentional integer overflow problem in recurrent nodes in the hidden layer that caused outputs to oscillate. Our investigation of the oscillatory behavior showed that the mutual information of inputs and outputs was sufficient for the reactive behaviors observed. While we had predicted that both modularity and sparsity would follow the same trend as fitness, neither did so. Instead, selection gradients

  12. Development of a neural net paradigm that predicts simulator sickness

    Energy Technology Data Exchange (ETDEWEB)

    Allgood, G.O.

    1993-03-01

    A disease exists that affects pilots and aircrew members who use Navy Operational Flight Training Systems. This malady, commonly referred to as simulator sickness and whose symptomatology closely aligns with that of motion sickness, can compromise the use of these systems because of a reduced utilization factor, negative transfer of training, and reduction in combat readiness. A report is submitted that develops an artificial neural network (ANN) and behavioral model that predicts the onset and level of simulator sickness in the pilots and aircrews who sue these systems. It is proposed that the paradigm could be implemented in real time as a biofeedback monitor to reduce the risk to users of these systems. The model captures the neurophysiological impact of use (human-machine interaction) by developing a structure that maps the associative and nonassociative behavioral patterns (learned expectations) and vestibular (otolith and semicircular canals of the inner ear) and tactile interaction, derived from system acceleration profiles, onto an abstract space that predicts simulator sickness for a given training flight.

  13. NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

    Directory of Open Access Journals (Sweden)

    Min Peng

    2016-10-01

    Full Text Available Near-infrared (NIR face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN for NIR face recognition (specifically face identification in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

  14. Identification of neural connectivity signatures of autism using machine learning

    Directory of Open Access Journals (Sweden)

    Gopikrishna eDeshpande

    2013-10-01

    Full Text Available Alterations in neural connectivity have been suggested as a signature of the pathobiology of autism. Although disrupted correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the directional causal influence between brain regions is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind in 15 high-functioning adolescents and adults with autism (ASD and 15 typically developing (TD controls. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. Causal brain connectivity obtained from a multivariate autoregressive model, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant’s group membership (ASD or TD. We found a maximum classification accuracy of 95.9 % with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between ASD and TD groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point towards the fact that alterations in causal brain connectivity in individuals with ASD could serve as a potential non-invasive neuroimaging signature for autism

  15. Self-Organizing Neural-Net Control of Ship's Horizontal Motion

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X J; Zhao, X R [Automation College of Harbin Engineering University, Harbin 150001 (China)

    2006-10-15

    This paper describes the concept and an example of an adaptive neural-net controller system for ship's horizontal motion. The system consists of two parts, a real-world part and an imaginary-world part. The real-world part is a feedback control system for the actual ship. In the imaginary-world part, the model of ship and the controller are adjusted continuously in order to deal with changes of dynamic properties caused by disturbances and so on. In this paper, the adaptability of the controller system is investigated by controlling ship's horizontal motion including roll, yaw and sway. The results of simulation indicate that with selforganizing neural-net control, the mean square error of roll angle and yaw angle reduce to 0.92{sup 0}, and 0.74{sup 0} respectively. The control effect of SONC is better than conventional LQG controller.

  16. Training for Micrographia Alters Neural Connectivity in Parkinson's Disease

    Directory of Open Access Journals (Sweden)

    Evelien Nackaerts

    2018-01-01

    Full Text Available Despite recent advances in clarifying the neural networks underlying rehabilitation in Parkinson's disease (PD, the impact of prolonged motor learning interventions on brain connectivity in people with PD is currently unknown. Therefore, the objective of this study was to compare cortical network changes after 6 weeks of visually cued handwriting training (= experimental with a placebo intervention to address micrographia, a common problem in PD. Twenty seven early Parkinson's patients on dopaminergic medication performed a pre-writing task in both the presence and absence of visual cues during behavioral tests and during fMRI. Subsequently, patients were randomized to the experimental (N = 13 or placebo intervention (N = 14 both lasting 6 weeks, after which they underwent the same testing procedure. We used dynamic causal modeling to compare the neural network dynamics in both groups before and after training. Most importantly, intensive writing training propagated connectivity via the left hemispheric visuomotor stream to an increased coupling with the supplementary motor area, not witnessed in the placebo group. Training enhanced communication in the left visuomotor integration system in line with the learned visually steered training. Notably, this pattern was apparent irrespective of the presence of cues, suggesting transfer from cued to uncued handwriting. We conclude that in early PD intensive motor skill learning, which led to clinical improvement, alters cortical network functioning. We showed for the first time in a placebo-controlled design that it remains possible to enhance the drive to the supplementary motor area through motor learning.

  17. Alterations in neural connectivity in preterm children at school age.

    Science.gov (United States)

    Gozzo, Yeisid; Vohr, Betty; Lacadie, Cheryl; Hampson, Michelle; Katz, Karol H; Maller-Kesselman, Jill; Schneider, Karen C; Peterson, Bradley S; Rajeevan, Nallakkandi; Makuch, Robert W; Constable, R Todd; Ment, Laura R

    2009-11-01

    Converging data suggest recovery from injury in the preterm brain. We used functional magnetic resonance imaging (fMRI) to test the hypothesis that cerebral connectivity involving Wernicke's area and other important cortical language regions would differ between preterm (PT) and term (T) control school age children during performance of an auditory language task. Fifty-four PT children (600-1250 g birth weight) and 24 T controls were evaluated using an fMRI passive language task and neurodevelopmental assessments including: the Wechsler Intelligence Scale for Children - III (WISC-III), the Peabody Individual Achievement Test - Revised (PIAT-R) and the Peabody Picture Vocabulary Test - Revised (PPVT-R) at 8 years of age. Neural activity was assessed for language processing and the data were evaluated for connectivity and correlations to cognitive outcomes. We found that PT subjects scored significantly lower on all components of the WISC-III (planguage function at school age differently than T controls; these alterations may represent a delay in maturation of neural networks or the engagement of alternate circuits for language processing.

  18. Identification of neural connectivity signatures of autism using machine learning.

    Science.gov (United States)

    Deshpande, Gopikrishna; Libero, Lauren E; Sreenivasan, Karthik R; Deshpande, Hrishikesh D; Kana, Rajesh K

    2013-01-01

    Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of

  19. Deep neural nets as a method for quantitative structure-activity relationships.

    Science.gov (United States)

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

  20. Adolescent nicotine induces persisting changes in development of neural connectivity.

    Science.gov (United States)

    Smith, Robert F; McDonald, Craig G; Bergstrom, Hadley C; Ehlinger, Daniel G; Brielmaier, Jennifer M

    2015-08-01

    Adolescent nicotine induces persisting changes in development of neural connectivity. A large number of brain changes occur during adolescence as the CNS matures. These changes suggest that the adolescent brain may still be susceptible to developmental alterations by substances which impact its growth. Here we review recent studies on adolescent nicotine which show that the adolescent brain is differentially sensitive to nicotine-induced alterations in dendritic elaboration, in several brain areas associated with processing reinforcement and emotion, specifically including nucleus accumbens, medial prefrontal cortex, basolateral amygdala, bed nucleus of the stria terminalis, and dentate gyrus. Both sensitivity to nicotine, and specific areas responding to nicotine, differ between adolescent and adult rats, and dendritic changes in response to adolescent nicotine persist into adulthood. Areas sensitive to, and not sensitive to, structural remodeling induced by adolescent nicotine suggest that the remodeling generally corresponds to the extended amygdala. Evidence suggests that dendritic remodeling is accompanied by persisting changes in synaptic connectivity. Modeling, electrophysiological, neurochemical, and behavioral data are consistent with the implication of our anatomical studies showing that adolescent nicotine induces persisting changes in neural connectivity. Emerging data thus suggest that early adolescence is a period when nicotine consumption, presumably mediated by nicotine-elicited changes in patterns of synaptic activity, can sculpt late brain development, with consequent effects on synaptic interconnection patterns and behavior regulation. Adolescent nicotine may induce a more addiction-prone phenotype, and the structures altered by nicotine also subserve some emotional and cognitive functions, which may also be altered. We suggest that dendritic elaboration and associated changes are mediated by activity-dependent synaptogenesis, acting in part

  1. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

    Full Text Available In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1 early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2 subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circuits are “hard-wired” in a purely activity-independent manner. This conclusion has been challenged recently through the use of new transgenic tools employed in the powerful Drosophila system, which have allowed unprecedented temporal control and single neuron imaging resolution. These recent studies reveal that activity-dependent mechanisms are indeed required to refine circuit maps in Drosophila during precise, restricted windows of late-phase development. Such mechanisms of circuit refinement may be key to understanding a number of human neurological diseases, including developmental disorders such as Fragile X syndrome (FXS and autism, which are hypothesized to result from defects in synaptic connectivity and activity-dependent circuit function. This review focuses on our current understanding of activity-dependent synaptic connectivity in Drosophila, primarily through analyzing the role of the fragile X mental retardation protein (FMRP in the Drosophila FXS disease model. The particular emphasis of this review is on the expanding array of new genetically-encoded tools that are allowing cellular events and molecular players to be dissected with ever greater precision and detail.

  2. MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

    OpenAIRE

    Sengupta, Sailik; Chakraborti, Tathagata; Kambhampati, Subbarao

    2017-01-01

    Recent works on gradient-based attacks and universal perturbations can adversarially modify images to bring down the accuracy of state-of-the-art classification techniques based on deep neural networks to as low as 10\\% on popular datasets like MNIST and ImageNet. The design of general defense strategies against a wide range of such attacks remains a challenging problem. In this paper, we derive inspiration from recent advances in the fields of cybersecurity and multi-agent systems and propos...

  3. k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification

    Directory of Open Access Journals (Sweden)

    Blaž Meden

    2018-01-01

    Full Text Available Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.

  4. Neural-Net Based Optical NDE Method for Structural Health Monitoring

    Science.gov (United States)

    Decker, Arthur J.; Weiland, Kenneth E.

    2003-01-01

    This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

  5. 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.

  6. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  7. Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero

    Directory of Open Access Journals (Sweden)

    Moriah E. Thomason

    2015-02-01

    Full Text Available Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development.

  8. Door and cabinet recognition using convolutional neural nets and real-time method fusion for handle detection and grasping

    DEFF Research Database (Denmark)

    Maurin, Adrian Llopart; Ravn, Ole; Andersen, Nils Axel

    2017-01-01

    In this paper we present a new method that robustly identifies doors, cabinets and their respective handles, with special emphasis on extracting useful features from handles to be then manipulated. The novelty of this system relies on the combination of a Convolutional Neural Net (CNN), as a form...

  9. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  10. The necessity of connection structures in neural models of variable binding

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc

    2015-01-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1–11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other (‘connectivity based’) type uses dedicated connections structures to

  11. Neural-net based coordinated stabilizing control for the exciter and governor loops of low head hydropower plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Novicevic, M.; Dobrijevic, D.; Babic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States); Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

    1995-12-01

    This paper presents a design technique of a new adaptive optimal controller of the low head hydropower plant using artificial neural networks (ANN). The adaptive controller is to operate in real time to improve the generating unit transients through the exciter input, the guide vane position and the runner blade position. The new design procedure is based on self-organization and the predictive estimation capabilities of neural-nets implemented through the cluster-wise segmented associative memory scheme. The developed neural-net based controller (NNC) whose control signals are adjusted using the on-line measurements, can offer better damping effects for generator oscillations over a wide range of operating conditions than conventional controllers. Digital simulations of hydropower plant equipped with low head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-space optimal control and neural-net based control are presented. Results obtained on the non-linear mathematical model demonstrate that the effects of the NNC closely agree with those obtained using the state-space multivariable discrete-time optimal controllers.

  12. NIRExpNet: Three-Stream 3D Convolutional Neural Network for Near Infrared Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Zhan Wu

    2017-11-01

    Full Text Available Facial expression recognition (FER under active near-infrared (NIR illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of NIRExpNet makes it possible to extract automatically, not just spatial features, but also, temporal features. The design of multiple streams of the NIRExpNet enables it to fuse local and global facial expression features. To avoid over-fitting, the NIRExpNet has a moderate size to suit the Oulu-CASIA NIR facial expression database that is a medium-size database. Experimental results show that the proposed NIRExpNet outperforms some previous state-of-art methods, such as Histogram of Oriented Gradient to 3D (HOG 3D, Local binary patterns from three orthogonal planes (LBP-TOP, deep temporal appearance-geometry network (DTAGN, and adapt 3D Convolutional Neural Networks (3D CNN DAP.

  13. Quest for highly connected metal-organic framework platforms: rare-earth polynuclear clusters versatility meets net topology needs.

    Science.gov (United States)

    Alezi, Dalal; Peedikakkal, Abdul Malik P; Weseliński, Łukasz J; Guillerm, Vincent; Belmabkhout, Youssef; Cairns, Amy J; Chen, Zhijie; Wojtas, Łukasz; Eddaoudi, Mohamed

    2015-04-29

    Gaining control over the assembly of highly porous rare-earth (RE) based metal-organic frameworks (MOFs) remains challenging. Here we report the latest discoveries on our continuous quest for highly connected nets. The topological exploration based on the noncompatibility of a 12-connected RE polynuclear carboxylate-based cluster, points of extension matching the 12 vertices of the cuboctahedron (cuo), with 3-connected organic ligands led to the discovery of two fascinating and highly connected minimal edge-transitive nets, pek and aea. The reduced symmetry of the employed triangular tricarboxylate ligand, as compared to the prototype highly symmetrical 1,3,5-benzene(tris)benzoic acid guided the concurrent occurrence of nonanuclear [RE9(μ3-OH)12(μ3-O)2(O2C-)12] and hexanuclear [RE6(OH)8(O2C-)8] carboxylate-based clusters as 12-connected and 8-connected molecular building blocks in the structure of a 3-periodic pek-MOF based on a novel (3,8,12)-c trinodal net. The use of a tricarboxylate ligand with modified angles between carboxylate moieties led to the formation of a second MOF containing solely nonanuclear clusters and exhibiting once more a novel and a highly connected (3,12,12)-c trinodal net with aea topology. Notably, it is the first time that RE-MOFs with double six-membered ring (d6R) secondary building units are isolated, representing therefore a critical step forward toward the design of novel and highly coordinated materials using the supermolecular building layer approach while considering the d6Rs as building pillars. Lastly, the potential of these new MOFs for gas separation/storage was investigated by performing gas adsorption studies of various probe gas molecules over a wide range of pressures. Noticeably, pek-MOF-1 showed excellent volumetric CO2 and CH4 uptakes at high pressures.

  14. Quest for Highly-connected MOF Platforms: Rare-Earth Polynuclear Clusters Versatility Meets Net Topology Needs.

    KAUST Repository

    Alezi, Dalal

    2015-04-07

    Gaining control over the assembly of highly porous rare-earth (RE) based metal-organic frameworks (MOFs) remains challenging. Here we report the latest discoveries on our continuous quest for highly-connected nets. The topological exploration based on the non-compatibility of 12-connected RE polynuclear carboxylate-based cluster, points of extension matching the 12 vertices of the cuboctahedron (cuo), with 3-connected organic ligands led to the discovery of two fascinating and highly-connected minimal edge-transitive nets, pek and aea. The reduced symmetry of the employed triangular tricarboxylate ligand, as compared to the prototype highly symmetrical 1,3,5-benzene(tris)benzoic acid guided the concurrent occurrence of nonanuclear [RE9(μ3-OH)12(μ3-O)2(O2C–)12] and hexanuclear [RE6(OH)8(O2C–)8] carboxylate-based clusters as 12-connected and 8-connected molecular building blocks in the structure of a 3-periodic pek-MOF based on a novel (3,8,12)-c trinodal net. The use of a tricarboxylate ligand with modified angles between carboxylate moieties led to the formation of a second MOF containing solely nonanuclear clusters and exhibiting once more a novel and a highly-connected (3,12,12)-c trinodal net with aea topology. Notably, it is the first time that RE-MOFs with double six-membered ring (d6R) secondary building units are isolated, representing therefore a critical step forward toward the design of novel and highly coordinated materials using the supermolecular building layer approach while considering the d6Rs as building pillars. Lastly, the potential of these new MOFs for gas separation/storage was investigated by performing gas adsorption studies of various probe gas molecules over a wide range of pressures. Noticeably, pek-MOF-1 showed excellent volumetric CO2 and CH4 uptakes at high pressures.

  15. Estimação do volume de árvores utilizando redes neurais artificiais Estimate of tree volume using artificial neural nets

    Directory of Open Access Journals (Sweden)

    Eric Bastos Gorgens

    2009-12-01

    Full Text Available Rede neural artificial consiste em um conjunto de unidades que contêm funções matemáticas, unidas por pesos. As redes são capazes de aprender, mediante modificação dos pesos sinápticos, e generalizar o aprendizado para outros arquivos desconhecidos. O projeto de redes neurais é composto por três etapas: pré-processamento, processamento e, por fim, pós-processamento dos dados. Um dos problemas clássicos que podem ser abordados por redes é a aproximação de funções. Nesse grupo, pode-se incluir a estimação do volume de árvores. Foram utilizados quatro arquiteturas diferentes, cinco pré-processamentos e duas funções de ativação. As redes que se apresentaram estatisticamente iguais aos dados observados também foram analisadas quanto ao resíduo e à distribuição dos volumes e comparadas com a estimação de volume pelo modelo de Schumacher e Hall. As redes neurais formadas por neurônios, cuja função de ativação era exponencial, apresentaram estimativas estatisticamente iguais aos dados observados. As redes treinadas com os dados normalizados pelo método da interpolação linear e equalizados tiveram melhor desempenho na estimação.The artificial neural network consists of a set of units containing mathematical functions connected by weights. Such nets are capable of learning by means of synaptic weight modification, generalizing learning for other unknown archives. The neural network project comprises three stages: pre-processing, processing and post-processing of data. One of the classical problems approached by networks is function approximation. Tree volume estimate can be included in this group. Four different architectures, five pre-processings and two activation functions were used. The nets which were statistically similar to the observed data were also analyzed in relation to residue and volume and compared to the volume estimate provided by the Schumacher and Hall equation. The neural nets formed by

  16. A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data.

    Science.gov (United States)

    Mishchenko, Yuriy; Paninski, Liam

    2012-10-01

    In recent years, the problem of reconstructing the connectivity in large neural circuits ("connectomics") has re-emerged as one of the main objectives of neuroscience. Classically, reconstructions of neural connectivity have been approached anatomically, using electron or light microscopy and histological tracing methods. This paper describes a statistical approach for connectivity reconstruction that relies on relatively easy-to-obtain measurements using fluorescent probes such as synaptic markers, cytoplasmic dyes, transsynaptic tracers, or activity-dependent dyes. We describe the possible design of these experiments and develop a Bayesian framework for extracting synaptic neural connectivity from such data. We show that the statistical reconstruction problem can be formulated naturally as a tractable L₁-regularized quadratic optimization. As a concrete example, we consider a realistic hypothetical connectivity reconstruction experiment in C. elegans, a popular neuroscience model where a complete wiring diagram has been previously obtained based on long-term electron microscopy work. We show that the new statistical approach could lead to an orders of magnitude reduction in experimental effort in reconstructing the connectivity in this circuit. We further demonstrate that the spatial heterogeneity and biological variability in the connectivity matrix--not just the "average" connectivity--can also be estimated using the same method.

  17. Visually-salient contour detection using a V1 neural model with horizontal connections

    CERN Document Server

    Loxley, P N

    2011-01-01

    A convolution model which accounts for neural activity dynamics in the primary visual cortex is derived and used to detect visually salient contours in images. Image inputs to the model are modulated by long-range horizontal connections, allowing contextual effects in the image to determine visual saliency, i.e. line segments arranged in a closed contour elicit a larger neural response than line segments forming background clutter. The model is tested on 3 types of contour, including a line, a circular closed contour, and a non-circular closed contour. Using a modified association field to describe horizontal connections the model is found to perform well for different parameter values. For each type of contour a different facilitation mechanism is found. Operating as a feed-forward network, the model assigns saliency by increasing the neural activity of line segments facilitated by the horizontal connections. Alternatively, operating as a feedback network, the model can achieve further improvement over sever...

  18. Synaptic organizations and dynamical properties of weakly connected neural oscillators. I. Analysis of a canonical model.

    Science.gov (United States)

    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

  19. Measuring symmetry, asymmetry and randomness in neural network connectivity.

    Directory of Open Access Journals (Sweden)

    Umberto Esposito

    Full Text Available Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.

  20. Prediction of Disease Causing Non-Synonymous SNPs by the Artificial Neural Network Predictor NetDiseaseSNP

    DEFF Research Database (Denmark)

    Johansen, Morten Bo; Gonzalez-Izarzugaza, Jose Maria; Brunak, Søren

    2013-01-01

    We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features...... assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates...... cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further...

  1. Alterations in neural connectivity in preterm children at school age

    OpenAIRE

    Gozzo, Yeisid; Vohr, Betty; Lacadie, Cheryl; Hampson, Michelle; Katz, Karol H.; Maller-Kesselman, Jill; Schneider, Karen C.; Peterson, Bradley S.; Rajeevan, Nallakkandi; Makuch, Robert W.; Constable, R. Todd; Ment, Laura R.

    2009-01-01

    Converging data suggest recovery from injury in the preterm brain. We used functional Magnetic Resonance Imaging (fMRI) to test the hypothesis that cerebral connectivity involving Wernicke’s area and other important cortical language regions would differ between preterm (PT) and term (T) control school age children during performance of an auditory language task. Fifty-four PT children (600 – 1250 g birth weight) and 24 T controls were evaluated using an fMRI passive language task and neurode...

  2. The connections between neural crest development and neuroblastoma.

    Science.gov (United States)

    Jiang, Manrong; Stanke, Jennifer; Lahti, Jill M

    2011-01-01

    Neuroblastoma (NB), the most common extracranial solid tumor in childhood, is an extremely heterogeneous disease both biologically and clinically. Although significant progress has been made in identifying molecular and genetic markers for NB, this disease remains an enigmatic challenge. Since NB is thought to be an embryonal tumor that is derived from precursor cells of the peripheral (sympathetic) nervous system, understanding the development of normal sympathetic nervous system may highlight abnormal events that contribute to NB initiation. Therefore, this review focuses on the development of the peripheral trunk neural crest, the current understanding of how developmental factors may contribute to NB and on recent advances in the identification of important genetic lesions and signaling pathways involved in NB tumorigenesis and metastasis. Finally, we discuss how future advances in identification of molecular alterations in NB may lead to more effective, less toxic therapies, and improve the prognosis for NB patients. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

    OpenAIRE

    Ling Li; Jin-Xiang Zhang; Tao Jiang

    2011-01-01

    BACKGROUND: Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. MET...

  4. The biocultural paradigm: the neural connection between science and mysticism.

    Science.gov (United States)

    de Nicolas, A T

    1998-01-01

    New discoveries in perceptual psychology, brain chemistry, brain evolution, brain development, ethology, cultural anthropology, the more recent work of MacLean on the structure of the brains and the discovery by Gazzaniga of the role of the, so-called, "interpreter module," are the foundations of a new paradigm on human cortical information processing, called by its discoverer, Dr. M. Colavito, the "biocultural paradigm." This paradigm shows that biology and culture act on one another as the conditioning parameters of neurocultural information. Through mutual interaction biology in humans becomes culture, and vice versa, culture opens and stimulates the neural passages of the brains, accounting thus for the varieties of brains in humans, and for cultural diversity. Culture conditions and stimulates biology, while biology conditions and makes culture possible. Cultures and brains may be distinguished from one another through identification with certain functions or combination of functions that are exercised habitually, or become neural hard-wire through repetition or habit. This new model has replaced older and simpler models of the nature/ nurture controversy, such as the unextended rational substance of Descartes, the tabula rasa of Locke, the associated-matrix of Hume, the passive, reinforcement-driven animal of Skinner, and the genetically hard-wired robot of the sociobiologists. However, elements of these earlier models are included in the new one, but the conversation about human experience has changed, and, therefore, the human images of ourselves. This change was forced on scientists by the importance of the conditionality of the experience of "I" and "not-I" as described by Alex Comfort in his book I and That, and was introduced in the conversations some of us already had with each other. This article focuses on the "I" and "not-I" experiences with a description of the "not-I" or "oceanic" or "mystical" experience to clarify the new paradigm of

  5. Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders.

    Directory of Open Access Journals (Sweden)

    Michelle Hampson

    2010-08-01

    Full Text Available There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

  6. Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

    Science.gov (United States)

    Salehi, Seyed Sadegh Mohseni; Erdogmus, Deniz; Gholipour, Ali

    2017-06-28

    Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is crucial for the accuracy of the entire brain analysis process. State-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry; therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent and registration-free brain extraction tool in this study, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3D image information without the need for computationally expensive 3D convolutions, and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI

  7. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

    Directory of Open Access Journals (Sweden)

    Zixuan Cang

    2017-07-01

    Full Text Available Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH method. ESPH represents 3D complex geometry by one-dimensional (1D topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN. We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes.weilab.math.msu.edu/TDL/.

  8. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

    Science.gov (United States)

    2017-01-01

    Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

  9. What Is Lost During Dreamless Sleep: The Relationship Between Neural Connectivity Patterns and Consciousness

    Directory of Open Access Journals (Sweden)

    Michaela Klimova

    2014-09-01

    Full Text Available Non-rapid eye movement (NREM sleep is characterised by reduced consciousness; thus, studying its neural characteristics acts as a useful indication of what is needed for conscious experience. The integrated information theory (Tononi, 2008 states that the ability of different thalamocortical regions to interact is crucial for consciousness, thereby motivating research concerning connectivity changes in the thalamocortical system that accompany changing consciousness levels. This review aims to discuss investigations of functional connectivity of resting-state and large-scale brain networks, applying correlational approaches to neuroimaging data as well as studies that used brain stimulation to investigate effective connectivity. Most findings suggest a reorganisation of functional brain networks where inter-region connectivity is reduced and intra-region connectivity is stronger in deep sleep than wakefulness.

  10. Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

    Science.gov (United States)

    Liu, Chao; Brattico, Elvira; Abu-jamous, Basel; Pereira, Carlos S.; Jacobsen, Thomas; Nandi, Asoke K.

    2017-01-01

    People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions – one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music. PMID:29311874

  11. Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

    Directory of Open Access Journals (Sweden)

    Chao Liu

    2017-12-01

    Full Text Available People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM, to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions – one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only. During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music.

  12. Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

    Science.gov (United States)

    Decker, Arthur J.

    1999-01-01

    Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

  13. The necessity of connection structures in neural models of variable binding.

    Science.gov (United States)

    van der Velde, Frank; de Kamps, Marc

    2015-08-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

  14. Competition and Cooperation in Neural Nets : U.S.-Japan Joint Seminar

    CERN Document Server

    Arbib, Michael

    1982-01-01

    The human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a "competition and cooperation" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers ...

  15. Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

    DEFF Research Database (Denmark)

    Liu, Chao; Brattico, Elvira; Abu-Jamous, Basel

    2017-01-01

    People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing...... of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward...... processing. Participants listened to music under three conditions - one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory...

  16. The neural changes in connectivity of the voice network during voice pitch perturbation.

    Science.gov (United States)

    Flagmeier, Sabina G; Ray, Kimberly L; Parkinson, Amy L; Li, Karl; Vargas, Robert; Price, Larry R; Laird, Angela R; Larson, Charles R; Robin, Donald A

    2014-05-01

    Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was processed as an error in vocalization, altered connections between right STG and left STG. Other regions that revealed differences in connectivity during error detection and correction included bilateral inferior frontal gyrus, and the primary and pre motor cortices. Results indicated that STG plays a critical role in voice control, specifically, during error detection and correction. Additionally, pitch perturbation elicits changes in the voice network that suggest the right hemisphere is critical to pitch modulation. Published by Elsevier Inc.

  17. Optics-Only Calibration of a Neural-Net Based Optical NDE Method for Structural Health Monitoring

    Science.gov (United States)

    Decker, Arthur J.

    2004-01-01

    A calibration process is presented that uses optical measurements alone to calibrate a neural-net based NDE method. The method itself detects small changes in the vibration mode shapes of structures. The optics-only calibration process confirms previous work that the sensitivity to vibration-amplitude changes can be as small as 10 nanometers. A more practical value in an NDE service laboratory is shown to be 50 nanometers. Both model-generated and experimental calibrations are demonstrated using two implementations of the calibration technique. The implementations are based on previously published demonstrations of the NDE method and an alternative calibration procedure that depends on comparing neural-net and point sensor measurements. The optics-only calibration method, unlike the alternative method, does not require modifications of the structure being tested or the creation of calibration objects. The calibration process can be used to test improvements in the NDE process and to develop a vibration-mode-independence of damagedetection sensitivity. The calibration effort was intended to support NASA s objective to promote safety in the operations of ground test facilities or aviation safety, in general, by allowing the detection of the gradual onset of structural changes and damage.

  18. THE CONNECTION IDENTIFICATION BETWEEN THE NET INVESTMENTS IN HOTELS AND RESTORANTS AND TOURISTIC ACCOMODATION CAPACITY BY USING THE ANOVA METHOD

    Directory of Open Access Journals (Sweden)

    Elena STAN

    2009-12-01

    Full Text Available In the purpose of giving the answers to customers’ harsh exigencies, in the Romanian tourism development hasto be taking into account especially the “accommodation” component. The dimension of technical and material base ofaccommodation can be express through: units’ number, rooms’ number, places number. The most used is “placesnumber” indicator. Nowadays as regarding the tourism Romanian investments there are special concerns caused bypeculiar determinations. The study aim is represented by identifying of a connection existence between net investmentsin hotels and restaurants and tourism accommodation capacity, registered among 2002 -2007period in Romania, byusing the dispersion analysis ANOVA method.

  19. An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

    Science.gov (United States)

    2012-01-01

    Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

  20. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case.

    Science.gov (United States)

    Russ, Thomas A; Ramakrishnan, Cartic; Hovy, Eduard H; Bota, Mihail; Burns, Gully A P C

    2011-08-22

    We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain

  1. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

    Directory of Open Access Journals (Sweden)

    Bota Mihail

    2011-08-01

    Full Text Available Abstract Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871 that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED based on experimental variables and their interdependencies. The software has three parts: (a the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger

  2. Applying Artificial Neural Networks to Estimate Net Radiation at Surface Using the Synergy between GERB-SEVIRI and Ground Data

    Science.gov (United States)

    Geraldo Ferreira, A.; Soria, Emilio; Lopez-Baeza, Ernesto; Vila, Joan; Serrano, Antonio J.; Martinez, Marcelino; Velazquez Blazquez, Almudena; Clerbaux, Nicolas

    This paper describes the results obtained using Artificial Neural Networks (AAN) models to estimate the diurnal cycle of net radiation (Rn) at surface. The data used as input parameter in the AAN model were that measured by Geostationary Earth Radiation Budget (GERB-1) instrument, on board Meteosat 9 satellite. The data concerning Rn at the surface were collected at the Valencia Anchor Station (VAS), a ground reference meteorological station for the validation of low spatial resolution sensors situated near de city of Valencia, Spain. This data refers to the periods July 31st -August 6th 2006 and June 19th -August 18th 2007. Both, GERB-1 and VAS data are used to train and validate the AAN model. The same data set is also used to develop and validate a Multivariate Linear Regression (MLR) model. A comparison between the estimates provided by the AAN and the MLR models has been carried out; the results obtained with the neural model outperform the linear model. Moreover, the low values of the error indexes show that neural models can be used as an alternative methodology to make atmospheric corrections.

  3. Two unusual 12-connected metal–organic coordination polymers with fcu net

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Sheng-Qi [Institute of Polyoxometalate Chemistry, Department of Chemistry, Northeast Normal University, Changchun, Jilin 130024 (China); Tianjin Key Laboratory of Environmental Remediation and Pollution Control, Nankai University, Tianjin 300071 (China); Tian, Dan; Luo, Yu-Hui [Institute of Polyoxometalate Chemistry, Department of Chemistry, Northeast Normal University, Changchun, Jilin 130024 (China); Chen, Xin [School of Pharmaceutical and Life Sciences, Changzhou University, Changzhou, Jiangsu, 213164 (China); Zhang, Hong, E-mail: zhangh@nenu.edu.cn [Institute of Polyoxometalate Chemistry, Department of Chemistry, Northeast Normal University, Changchun, Jilin 130024 (China)

    2013-09-15

    Two new three-dimensional 12-connected metal–organic coordination polymers, [Zn{sub 2}(bptc)(H{sub 2}O)]·C{sub 2}H{sub 5}OH·H{sub 2}O (1) and [Cd{sub 4}(bptc){sub 2}(bbi)(H{sub 2}O)]·H{sub 2}O (2) (H4bptc=biphenyl-2,5,2′,5′-tetracarboxylic acid, bbi=1,1′-(1,4-butanediyl)bis(imidazole)), have been solvothermally synthesized and structurally characterized by single crystal X-ray diffraction analyses. All compounds are also characterized by elemental analyses, IR spectra, thermogravimetric (TG) analyses and X-ray powder diffraction (XRD). Topological analysis indicates that both 1 and 2 are 12-connected frameworks with fcu topology, which are based on cuboid cage and rob-like (Cd3) subunit as 12-connected nodes, respectively. Furthermore, the luminescence properties of the two compounds were discussed in detail. - Graphical abstract: Two new compounds with unusual 12-connected fcu topology display intriguing structural feature, as well as luminescence property. Display Omitted - Highlights: • Two new 3D metal–organic coordination polymers based on biphenyl-2,5,2′,5′-tetracarboxylic acid ligand have been synthesized. • Two compounds exhibit rare 12-connected fcu topology. • Photoluminescent property at room temperature has been investigated.

  4. Two unusual 12-connected metal-organic coordination polymers with fcu net

    Science.gov (United States)

    Guo, Sheng-Qi; Tian, Dan; Luo, Yu-Hui; Chen, Xin; Zhang, Hong

    2013-09-01

    Two new three-dimensional 12-connected metal-organic coordination polymers, [Zn2(bptc)(H2O)]·C2H5OH·H2O (1) and [Cd4(bptc)2(bbi)(H2O)]·H2O (2) (H4bptc=biphenyl-2,5,2',5'-tetracarboxylic acid, bbi=1,1'-(1,4-butanediyl)bis(imidazole)), have been solvothermally synthesized and structurally characterized by single crystal X-ray diffraction analyses. All compounds are also characterized by elemental analyses, IR spectra, thermogravimetric (TG) analyses and X-ray powder diffraction (XRD). Topological analysis indicates that both 1 and 2 are 12-connected frameworks with fcu topology, which are based on cuboid cage and rob-like {Cd3} subunit as 12-connected nodes, respectively. Furthermore, the luminescence properties of the two compounds were discussed in detail.

  5. Increased functional connectivity in intrinsic neural networks in individuals with aniridia

    Science.gov (United States)

    Pierce, Jordan E.; Krafft, Cynthia E.; Rodrigue, Amanda L.; Bobilev, Anastasia M.; Lauderdale, James D.; McDowell, Jennifer E.

    2014-01-01

    Mutations affecting the PAX6 gene result in aniridia, a condition characterized by the lack of an iris and other panocular defects. Among humans with aniridia, structural abnormalities also have been reported within the brain. The current study examined the functional implications of these deficits through “resting state” or task-free functional magnetic resonance imaging (fMRI) in 12 individuals with aniridia and 12 healthy age- and gender-matched controls. Using independent components analysis (ICA) and dual regression, individual patterns of functional connectivity associated with three intrinsic connectivity networks (ICNs; executive control, primary visual, and default mode) were compared across groups. In all three analyses, the aniridia group exhibited regions of greater connectivity correlated with the network, while the controls did not show any such regions. These differences suggest that individuals with aniridia recruit additional neural regions to supplement function in critical intrinsic networks, possibly due to inherent structural or sensory abnormalities related to the disorder. PMID:25566032

  6. Increased functional connectivity in intrinsic neural networks in individuals with aniridia

    Directory of Open Access Journals (Sweden)

    Jordan Elisabeth Pierce

    2014-12-01

    Full Text Available Mutations affecting the PAX6 gene result in aniridia, a condition characterized by the lack of an iris and other panocular defects. Among humans with aniridia, structural abnormalities also have been reported within the brain. The current study examined the functional implications of these deficits through resting state or task-free functional magnetic resonance imaging in 12 individuals with aniridia and 12 healthy age- and gender-matched controls. Using independent components analysis and dual regression, individual patterns of functional connectivity associated with three intrinsic connectivity networks (executive control, primary visual, and default mode were compared across groups. In all three analyses, the aniridia group exhibited regions of greater connectivity correlated with the network, while the controls did not show any such regions. These differences suggest that individuals with aniridia recruit additional neural regions to supplement function in critical intrinsic networks, possibly due to inherent structural or sensory abnormalities related to the disorder.

  7. Mobility Sensitivity Analysis for LTE-Advanced HetNet Deployments with Dual Connectivity

    DEFF Research Database (Denmark)

    Barbera, Simone; Gimenez, Lucas Chavarria; Sanchez, Maria Laura Luque

    2015-01-01

    A mobility performance sensitivity analysis is presented for Dual Connectivity cases where the users can be served simultaneously by a macro and a small cell. The performance is assessed for the 3GPP generic simulation scenario and for two site specific cases, with the aim of comparing the results...

  8. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

  9. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Application of viral vectors to the study of neural connectivities and neural circuits in the marmoset brain.

    Science.gov (United States)

    Watakabe, Akiya; Sadakane, Osamu; Hata, Katsusuke; Ohtsuka, Masanari; Takaji, Masafumi; Yamamori, Tetsuo

    2017-03-01

    It is important to study the neural connectivities and functions in primates. For this purpose, it is critical to be able to transfer genes to certain neurons in the primate brain so that we can image the neuronal signals and analyze the function of the transferred gene. Toward this end, our team has been developing gene transfer systems using viral vectors. In this review, we summarize our current achievements as follows. 1) We compared the features of gene transfer using five different AAV serotypes in combination with three different promoters, namely, CMV, mouse CaMKII (CaMKII), and human synapsin 1 (hSyn1), in the marmoset cortex with those in the mouse and macaque cortices. 2) We used target-specific double-infection techniques in combination with TET-ON and TET-OFF using lentiviral retrograde vectors for enhanced visualization of neural connections. 3) We used an AAV-mediated gene transfer method to study the transcriptional control for amplifying fluorescent signals using the TET/TRE system in the primate neocortex. We also established systems for shRNA mediated gene targeting in a neocortical region where a gene is significantly expressed and for expressing the gene using the CMV promoter for an unexpressed neocortical area in the primate cortex using AAV vectors to understand the regulation of downstream genes. Our findings have demonstrated the feasibility of using viral vector mediated gene transfer systems for the study of primate cortical circuits using the marmoset as an animal model. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 354-372, 2017. © 2016 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc.

  11. Processing of different types of social threat in shyness: Preliminary findings of distinct functional neural connectivity.

    Science.gov (United States)

    Tang, Alva; Beaton, Elliott A; Tatham, Erica; Schulkin, Jay; Hall, Geoffrey B; Schmidt, Louis A

    2016-01-01

    Current theory suggests that the processing of different types of threat is supported by distinct neural networks. Here we tested whether there are distinct neural correlates associated with different types of threat processing in shyness. Using fMRI and multivariate techniques, we compared neural responses and functional connectivity during the processing of imminent (i.e., congruent angry/angry face pairs) and ambiguous (i.e., incongruent angry/neutral face pairs) social threat in young adults selected for high and low shyness. To both types of threat processing, non-shy adults recruited a right medial prefrontal cortex (mPFC) network encompassing nodes of the default mode network involved in automatic emotion regulation, whereas shy adults recruited a right dorsal anterior cingulate cortex (dACC) network encompassing nodes of the frontoparietal network that instantiate active attentional and cognitive control. Furthermore, in shy adults, the mPFC interacted with the dACC network for ambiguous threat, but with a distinct network encompassing nodes of the salience network for imminent threat. These preliminary results expand our understanding of right mPFC function associated with temperamental shyness. They also provide initial evidence for differential neural networks associated with shy and non-shy profiles in the context of different types of social threat processing.

  12. Probabilistic and Other Neural Nets in Multi-Hole Probe Calibration and Flow Angularity Pattern Recognition

    Science.gov (United States)

    Baskaran, Subbiah; Ramachandran, Narayanan; Noever, David

    1998-01-01

    The use of probabilistic (PNN) and multilayer feed forward (MLFNN) neural networks are investigated for calibration of multi-hole pressure probes and the prediction of associated flow angularity patterns in test flow fields. Both types of networks are studied in detail for their calibration and prediction characteristics. The current formalism can be applied to any multi-hole probe, however the test results for the most commonly used five-hole Cone and Prism probe types alone are reported in this article.

  13. On the connection between level of education and the neural circuitry of emotion perception

    Directory of Open Access Journals (Sweden)

    Liliana Ramona Demenescu

    2014-10-01

    Full Text Available Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of the NESDA fMRI study, we compared two groups of fourteen healthy volunteers with intermediate and high educational attainment, matched for age and gender. The data concerned event-related functional magnetic resonance imaging of brain activation during perception of facial emotional expressions. The region of interest analysis showed stronger right amygdala activation to facial expressions in participants with lower relative to higher educational attainment. The psychophysiological interaction analysis revealed that participants with higher educational attainment exhibited stronger right amygdala – right insula connectivity during perception of emotional and neutral facial expressions. This exploratory study suggests the relevance of educational attainment on the neural mechanism of facial expression processing.

  14. Abnormal Resting-State Neural Activity and Connectivity of Fatigue in Parkinson's Disease.

    Science.gov (United States)

    Zhang, Jie-Jin; Ding, Jian; Li, Jun-Yi; Wang, Min; Yuan, Yong-Sheng; Zhang, Li; Jiang, Si-Ming; Wang, Xi-Xi; Zhu, Lin; Zhang, Ke-Zhong

    2017-03-01

    Fatigue is a common burdensome problem in patients with Parkinson's disease (PD), but its pathophysiological mechanisms are poorly understood. This study aimed at investigating the neural substrates of fatigue in patients with PD. A total of 17 PD patients with fatigue, 32 PD patients without fatigue, and 25 matched healthy controls were recruited. The 9-item fatigue severity scale (FSS) was used for fatigue screening and severity rating. Resting-state functional magnetic resonance imaging (RS-fMRI) data were obtained from all subjects. Amplitude of low-frequency fluctuations (ALFF) was used to measure regional brain activity, and functional connectivity (FC) was applied to investigate functional connectivity at a network level. PD-related fatigue was associated with ALFF changes in right middle frontal gyrus within the attention network and in left insula as well as right midcingulate cortex within the salience network. FC analysis revealed that above three regions showing ALFF differences had altered functional connectivity mainly in the temporal, parietal, and motor cortices. Our findings do reveal that abnormal regional brain activity within attention and salience network and altered FC of above abnormal regions are involved in neural mechanism of fatigue in patients with PD. © 2017 John Wiley & Sons Ltd.

  15. A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks

    Science.gov (United States)

    Messé, Arnaud; Hütt, Marc-Thorsten; König, Peter; Hilgetag, Claus C.

    2015-01-01

    The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is a central focus in brain network science. It is an open question, however, how strongly the SC-FC relationship depends on specific topological features of brain networks or the models used for describing excitable dynamics. Using a basic model of discrete excitable units that follow a susceptible - excited - refractory dynamic cycle (SER model), we here analyze how functional connectivity is shaped by the topological features of a neural network, in particular its modularity. We compared the results obtained by the SER model with corresponding simulations by another well established dynamic mechanism, the Fitzhugh-Nagumo model, in order to explore general features of the SC-FC relationship. We showed that apparent discrepancies between the results produced by the two models can be resolved by adjusting the time window of integration of co-activations from which the FC is derived, providing a clearer distinction between co-activations and sequential activations. Thus, network modularity appears as an important factor shaping the FC-SC relationship across different dynamic models.

  16. Effects of mindfulness based stress reduction therapy on subjective bother and neural connectivity in chronic tinnitus.

    Science.gov (United States)

    Roland, Lauren T; Lenze, Eric J; Hardin, Frances Mei; Kallogjeri, Dorina; Nicklaus, Joyce; Wineland, Andre M; Fendell, Ginny; Peelle, Jonathan E; Piccirillo, Jay F

    2015-05-01

    To evaluate the impact of a Mindfulness Based Stress Reduction (MBSR) program in patients with chronic bothersome tinnitus on the (1) severity of symptoms of tinnitus and (2) functional connectivity in neural attention networks. Open-label interventional pilot study. Outpatient academic medical center. A total of 13 adult participants with a median age of 55 years, suffering from bothersome tinnitus. An 8-week MBSR program was conducted by a trained MBSR instructor. The primary outcome measure was the difference in patient-reported tinnitus symptoms using the Tinnitus Handicap Index (THI) and Tinnitus Functional Index (TFI) between pre-intervention, post-MBSR, and 4-week post-MBSR assessments. Secondary outcomes included change in measurements of depression, anxiety, mindfulness, and cognitive abilities. Functional connectivity magnetic resonance imaging (MRI) was performed at pre- and post-MBSR intervention time points to serve as a neuroimaging biomarker of critical cortical networks. Scores on the THI and TFI showed statistically significant and clinically meaningful improvement over the course of the study with a median ΔTHI of -16 and median ΔTFI of -14.8 between baseline and 4-week follow-up scores. Except for depression, there was no significant change in any of the secondary outcome measures. Analysis of the resting state functional connectivity MRI (rs-fcMRI) data showed increased connectivity in the post-MBSR group in attention networks but not the default network. Participation in an MBSR program is associated with decreased severity in tinnitus symptoms and depression and connectivity changes in neural attention networks. MBSR is a promising treatment option for chronic bothersome tinnitus that is both noninvasive and inexpensive. © American Academy of Otolaryngology-Head and Neck Surgery Foundation 2015.

  17. Dual Connectivity in LTE HetNets with Split Control- and User-Plane

    DEFF Research Database (Denmark)

    Zakrzewska, Anna; López-Pérez, David; Kucera, Stepan

    2013-01-01

    Recently, a new network architecture with split control-plane and user-plane has been proposed and gained a lot of momentum in the standardisation of Long Term Evolution (LTE) Release 12. In this new network architecture, the controlplane, which transmits system information and handles user...... a detailed description of our dual connectivity framework based on the latest LTE-Advanced enhancements, in which macrocellassisted (MA) small cells use different channel state informationreference signals (CSI-RS) to differentiate among each other and allow User Equipment (UE) to take adequate measurements...

  18. Cell biology in neuroscience: Architects in neural circuit design: glia control neuron numbers and connectivity.

    Science.gov (United States)

    Corty, Megan M; Freeman, Marc R

    2013-11-11

    Glia serve many important functions in the mature nervous system. In addition, these diverse cells have emerged as essential participants in nearly all aspects of neural development. Improved techniques to study neurons in the absence of glia, and to visualize and manipulate glia in vivo, have greatly expanded our knowledge of glial biology and neuron-glia interactions during development. Exciting studies in the last decade have begun to identify the cellular and molecular mechanisms by which glia exert control over neuronal circuit formation. Recent findings illustrate the importance of glial cells in shaping the nervous system by controlling the number and connectivity of neurons.

  19. Fast neural-net based fake track rejection in the LHCb reconstruction

    CERN Document Server

    De Cian, Michel; Seyfert, Paul; Stahl, Sascha

    2017-01-01

    A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, retains more than 99 % of well reconstructed tracks while reducing the number of fake tracks by 60 %. It is fast enough to fit into the CPU time budget of the software trigger farm and thus reduces the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment in Run II of the LHC.

  20. LOGIC WITH EXCEPTION ON THE ALGEBRA OF FOURIER-DUAL OPERATIONS: NEURAL NET MECHANISM OF COGNITIVE DISSONANCE REDUCING

    Directory of Open Access Journals (Sweden)

    A. V. Pavlov

    2014-01-01

    Full Text Available A mechanism of cognitive dissonance reducing is demonstrated with approach for non-monotonic fuzzy-valued logics by Fourier-holography technique implementation developing. Cognitive dissonance occurs under perceiving of new information that contradicts to the existing subjective pattern of the outside world, represented by double Fourier-transform cascade with a hologram – neural layers interconnections matrix of inner information representation and logical conclusion. The hologram implements monotonic logic according to “General Modus Ponens” rule. New information is represented by a hologram of exclusion that implements interconnections of logical conclusion and exclusion for neural layers. The latter are linked by Fourier transform that determines duality of the algebra forming operations of conjunction and disjunction. Hologram of exclusion forms conclusion that is dual to the “General Modus Ponens” conclusion. It is shown, that trained for the main rule and exclusion system can be represented by two-layered neural network with separate interconnection matrixes for direct and inverse iterations. The network energy function is involved determining the cyclic dynamics character; dissipative factor causing convergence type of the dynamics is analyzed. Both “General Modus Ponens” and exclusion holograms recording conditions on the dynamics and convergence of the system are demonstrated. The system converges to a stable status, in which logical conclusion doesn’t depend on the inner information. Such kind of dynamics, leading to tolerance forming, is typical for ordinary kind of thinking, aimed at inner pattern of outside world stability. For scientific kind of thinking, aimed at adequacy of the inner pattern of the world, a mechanism is needed to stop the net relaxation; the mechanism has to be external relative to the model of logic. Computer simulation results for the learning conditions adequate to real holograms recording are

  1. Robust Detection of Impaired Resting State Functional Connectivity Networks in Alzheimer's Disease Using Elastic Net Regularized Regression

    Science.gov (United States)

    Teipel, Stefan J.; Grothe, Michel J.; Metzger, Coraline D.; Grimmer, Timo; Sorg, Christian; Ewers, Michael; Franzmeier, Nicolai; Meisenzahl, Eva; Klöppel, Stefan; Borchardt, Viola; Walter, Martin; Dyrba, Martin

    2017-01-01

    The large number of multicollinear regional features that are provided by resting state (rs) fMRI data requires robust feature selection to uncover consistent networks of functional disconnection in Alzheimer's disease (AD). Here, we compared elastic net regularized and classical stepwise logistic regression in respect to consistency of feature selection and diagnostic accuracy using rs-fMRI data from four centers of the “German resting-state initiative for diagnostic biomarkers” (psymri.org), comprising 53 AD patients and 118 age and sex matched healthy controls. Using all possible pairs of correlations between the time series of rs-fMRI signal from 84 functionally defined brain regions as the initial set of predictor variables, we calculated accuracy of group discrimination and consistency of feature selection with bootstrap cross-validation. Mean areas under the receiver operating characteristic curves as measure of diagnostic accuracy were 0.70 in unregularized and 0.80 in regularized regression. Elastic net regression was insensitive to scanner effects and recovered a consistent network of functional connectivity decline in AD that encompassed parts of the dorsal default mode as well as brain regions involved in attention, executive control, and language processing. Stepwise logistic regression found no consistent network of AD related functional connectivity decline. Regularized regression has high potential to increase diagnostic accuracy and consistency of feature selection from multicollinear functional neuroimaging data in AD. Our findings suggest an extended network of functional alterations in AD, but the diagnostic accuracy of rs-fMRI in this multicenter setting did not reach the benchmark defined for a useful biomarker of AD. PMID:28101051

  2. From image edges to geons to viewpoint-invariant object models: a neural net implementation

    Science.gov (United States)

    Biederman, Irving; Hummel, John E.; Gerhardstein, Peter C.; Cooper, Eric E.

    1992-03-01

    Three striking and fundamental characteristics of human shape recognition are its invariance with viewpoint in depth (including scale), its tolerance of unfamiliarity, and its robustness with the actual contours present in an image (as long as the same convex parts [geons] can be activated). These characteristics are expressed in an implemented neural network model (Hummel & Biederman, 1992) that takes a line drawing of an object as input and generates a structural description of geons and their relations which is then used for object classification. The model's capacity for structural description derives from its solution to the dynamic binding problem of neural networks: independent units representing an object's parts (in terms of their shape attributes and interrelations) are bound temporarily when those attributes occur in conjunction in the system's input. Temporary conjunctions of attributes are represented by synchronized activity among the units representing those attributes. Specifically, the model induces temporal correlation in the firing of activated units to: (1) parse images into their constituent parts; (2) bind together the attributes of a part; and (3) determine the relations among the parts and bind them to the parts to which they apply. Because it conjoins independent units temporarily, dynamic binding allows tremendous economy of representation, and permits the representation to reflect an object's attribute structure. The model's recognition performance conforms well to recent results from shape priming experiments. Moreover, the manner in which the model's performance degrades due to accidental synchrony produced by an excess of phase sets suggests a basis for a theory of visual attention.

  3. Synaptic organizations and dynamical properties of weakly connected neural oscillators. II. Learning phase information.

    Science.gov (United States)

    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.

  4. BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

    Science.gov (United States)

    Santara, Anirban; Mani, Kaustubh; Hatwar, Pranoot; Singh, Ankit; Garg, Ankur; Padia, Kirti; Mitra, Pabitra

    2017-09-01

    Deep learning based landcover classification algorithms have recently been proposed in literature. In hyperspectral images (HSI) they face the challenges of large dimensionality, spatial variability of spectral signatures and scarcity of labeled data. In this article we propose an end-to-end deep learning architecture that extracts band specific spectral-spatial features and performs landcover classification. The architecture has fewer independent connection weights and thus requires lesser number of training data. The method is found to outperform the highest reported accuracies on popular hyperspectral image data sets.

  5. Using a multi-port architecture of neural-net associative memory based on the equivalency paradigm for parallel cluster image analysis and self-learning

    Science.gov (United States)

    Krasilenko, Vladimir G.; Lazarev, Alexander A.; Grabovlyak, Sveta K.; Nikitovich, Diana V.

    2013-01-01

    We consider equivalency models, including matrix-matrix and matrix-tensor and with the dual adaptive-weighted correlation, multi-port neural-net auto-associative and hetero-associative memory (MP NN AAM and HAP), which are equivalency paradigm and the theoretical basis of our work. We make a brief overview of the possible implementations of the MP NN AAM and of their architectures proposed and investigated earlier by us. The main base unit of such architectures is a matrix-matrix or matrix-tensor equivalentor. We show that the MP NN AAM based on the equivalency paradigm and optoelectronic architectures with space-time integration and parallel-serial 2D images processing have advantages such as increased memory capacity (more than ten times of the number of neurons!), high performance in different modes (1010 - 1012 connections per second!) And the ability to process, store and associatively recognize highly correlated images. Next, we show that with minor modifications, such MP NN AAM can be successfully used for highperformance parallel clustering processing of images. We show simulation results of using these modifications for clustering and learning models and algorithms for cluster analysis of specific images and divide them into categories of the array. Show example of a cluster division of 32 images (40x32 pixels) letters and graphics for 12 clusters with simultaneous formation of the output-weighted space allocated images for each cluster. We discuss algorithms for learning and self-learning in such structures and their comparative evaluations based on Mathcad simulations are made. It is shown that, unlike the traditional Kohonen self-organizing maps, time of learning in the proposed structures of multi-port neuronet classifier/clusterizer (MP NN C) on the basis of equivalency paradigm, due to their multi-port, decreases by orders and can be, in some cases, just a few epochs. Estimates show that in the test clustering of 32 1280- element images into 12

  6. BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures.

    Science.gov (United States)

    Mehta, Raghav; Majumdar, Aabhas; Sivaswamy, Jayanthi

    2017-04-01

    Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is [Formula: see text] and [Formula: see text] for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary.

  7. HAWC Analysis of the Crab Nebula Using Neural-Net Energy Reconstruction

    Science.gov (United States)

    Marinelli, Samuel; HAWC Collaboration

    2017-01-01

    The HAWC (High-Altitude Water-Cherenkov) experiment is a TeV γ-ray observatory located 4100 m above sea level on the Sierra Negra mountain in Puebla, Mexico. The detector consists of 300 water-filled tanks, each instrumented with 4 photomuliplier tubes that utilize the water-Cherenkov technique to detect atmospheric air showers produced by cosmic γ rays. Construction of HAWC was completed in March, 2015. The experiment's wide field of view (2 sr) and high duty cycle (> 95 %) make it a powerful survey instrument sensitive to pulsar wind nebulae, supernova remnants, active galactic nuclei, and other γ-ray sources. The mechanisms of particle acceleration at these sources can be studied by analyzing their energy spectra. To this end, we have developed an event-by-event energy-reconstruction algorithm employing an artificial neural network to estimate energies of primary γ rays. The Crab Nebula, the brightest source of TeV photons, makes an excellent calibration source for this technique. We will present preliminary results from an analysis of the Crab energy spectrum using this new energy-reconstruction method. This work was supported by the National Science Foundation.

  8. Data Normalization to Accelerate Training for Linear Neural Net to Predict Tropical Cyclone Tracks

    Directory of Open Access Journals (Sweden)

    Jian Jin

    2015-01-01

    Full Text Available When pure linear neural network (PLNN is used to predict tropical cyclone tracks (TCTs in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (−1, 1 and (0, 1; with normal distribution method, each variable’s mean and standard deviation pair is set to (0, 1 and (100, 1. We present the following results: (1 data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2 mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually.

  9. Altered neural connectivity in adult female rats exposed to early life social stress.

    Science.gov (United States)

    Nephew, Benjamin C; Huang, Wei; Poirier, Guillaume L; Payne, Laurellee; King, Jean A

    2017-01-01

    The use of a variety of neuroanatomical techniques has led to a greater understanding of the adverse effects of stress on psychiatric health. One recent advance that has been particularly valuable is the development of resting state functional connectivity (RSFC) in clinical studies. The current study investigates changes in RSFC in F1 adult female rats exposed to the early life chronic social stress (ECSS) of the daily introduction of a novel male intruder to the cage of their F0 mothers while the F1 pups are in the cage. This ECSS for the F1 animals consists of depressed maternal care from their F0 mothers and exposure to conflict between their F0 mothers and intruder males. Analyses of the functional connectivity data in ECSS exposed adult females versus control females reveal broad changes in the limbic and reward systems, the salience and introspective socioaffective networks, and several additional stress and social behavior associated nuclei. Substantial changes in connectivity were found in the prefrontal cortex, nucleus accumbens, hippocampus, and somatosensory cortex. The current rodent RSFC data support the hypothesis that the exposure to early life social stress has long term effects on neural connectivity in numerous social behavior, stress, and depression relevant brain nuclei. Future conscious rodent RSFC studies can build on the wealth of data generated from previous neuroanatomical studies of early life stress and enhance translational connectivity between animal and human fMRI studies in the development of novel preventative measures and treatments. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Connecting the New World. Nets, mobility and progress in the Age of Alexander von Humboldt

    Directory of Open Access Journals (Sweden)

    Moritz von Brescius

    2012-12-01

    Beschleunigung“ analytisch verbunden werden, da diese Faktoren eine zentrale Rolle für die „Verwandlung der Welt“ im 19. Jahrhundert spielten. Abstract This article explores the link between the profound technological transformations of the nineteenth century and the life and work of the Prussian scholar Alexander von Humboldt (1769-1859. It analyses how Humboldt sought to appropriate the revolutionary new communication and transportation technologies of the time in order to integrate the American continent into global networks of commercial, intellectual and material exchange. Recent scholarship on Humboldt’s expedition to the New World (1799-1804 has claimed that his descriptions of tropical landscapes opened up South America to a range of „transformative interventions“ (Pratt by European capitalists and investors. These studies, however, have not analysed the motivations underlying Humboldt’s support for such intrusions into nature. Furthermore, they have not explored the role that such projects played in shaping Humboldt’s understanding of the forces behind the progress of societies. To comprehend Humboldt’s approval for human interventions in America’s natural world, this study first explores the role that eighteenth-century theories of progress and the notion of geographical determinism played in shaping his conception of civilisational development. It will look at concrete examples of transformative interventions in the American hemisphere that were actively proposed by Humboldt and intended to overcome natural obstacles to human interaction. These were the use of steamships, electric telegraphy, railroads and large-scale canals that together enabled global trade and communication to occur at an unprecedented pace. All these contemporary innovations will be linked to the four motifs of nets, mobility, progress and acceleration, which were driving forces behind the „transformation of the world“ that took place in the course of the nineteenth century. Resumen

  11. Neural connectivity moderates the association between sleep and impulsivity in adolescents

    Directory of Open Access Journals (Sweden)

    Sarah M. Tashjian

    2017-10-01

    Full Text Available Adolescence is characterized by chronic insufficient sleep and extensive brain development, but the relation between adolescent sleep and brain function remains unclear. We report the first functional magnetic resonance imaging study to investigate functional connectivity as a moderator between sleep and impulsivity, a problematic behavior during this developmental period. Naturalistic differences in sleep have not yet been explored as treatable contributors to adolescent impulsivity. Although public and scientific attention focuses on sleep duration, we report individual differences in sleep quality, not duration, in fifty-five adolescents (ages 14–18 yielded significant differences in functional connectivity between the prefrontal cortex and default mode network. Poor sleep quality was related to greater affect-related impulsivity among adolescents with low, but not high, connectivity, suggesting neural functioning relates to individual differences linking sleep quality and impulsivity. Response inhibition and cognitive impulsivity were not related to sleep quality, suggesting that sleep has a greater impact on affect-related impulsivity. Exploring environmental contributors of poor sleep quality, we demonstrated pillow comfort was uniquely related to sleep quality over age, sex, and income, a promising advance ripe for intervention.

  12. Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy

    Science.gov (United States)

    Amaral, Selene da Rocha; Baccalá, Luiz A.; Barbosa, Leonardo S.; Caticha, Nestor

    2017-06-01

    Proper neural connectivity inference has become essential for understanding cognitive processes associated with human brain function. Its efficacy is often hampered by the curse of dimensionality. In the electroencephalogram case, which is a noninvasive electrophysiological monitoring technique to record electrical activity of the brain, a possible way around this is to replace multichannel electrode information with dipole reconstructed data. We use a method based on maximum entropy and the renormalization group to infer the position of the sources, whose success hinges on transmitting information from low- to high-resolution representations of the cortex. The performance of this method compares favorably to other available source inference algorithms, which are ranked here in terms of their performance with respect to directed connectivity inference by using artificially generated dynamic data. We examine some representative scenarios comprising different numbers of dynamically connected dipoles over distinct cortical surface positions and under different sensor noise impairment levels. The overall conclusion is that inverse problem solutions do not affect the correct inference of the direction of the flow of information as long as the equivalent dipole sources are correctly found.

  13. Generation of daily solar irradiation by means of artificial neural net works

    Energy Technology Data Exchange (ETDEWEB)

    Siqueira, Adalberto N.; Tiba, Chigueru; Fraidenraich, Naum [Departamento de Energia Nuclear, da Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, 1000 - CDU, CEP 50.740-540 Recife, Pernambuco (Brazil)

    2010-11-15

    The present study proposes the utilization of Artificial Neural Networks (ANN) as an alternative for generating synthetic series of daily solar irradiation. The sequences were generated from the use of daily temporal series of a group of meteorological variables that were measured simultaneously. The data used were measured between the years of 1998 and 2006 in two temperate climate localities of Brazil, Ilha Solteira (Sao Paulo) and Pelotas (Rio Grande do Sul). The estimates were taken for the months of January, April, July and October, through two models which are distinguished regarding the use or nonuse of measured bright sunshine hours as an input variable. An evaluation of the performance of the 56 months of solar irradiation generated by way of ANN showed that by using the measured bright sunshine hours as an input variable (model 1), the RMSE obtained were less or equal to 23.2% being that of those, although 43 of those months presented RMSE less or equal to 12.3%. In the case of the model that did not use the measured bright sunshine hours but used a daylight length (model 2), RMSE were obtained that varied from 8.5% to 37.5%, although 38 of those months presented RMSE less or equal to 20.0%. A comparison of the monthly series for all of the years, achieved by means of the Kolmogorov-Smirnov test (to a confidence level of 99%), demonstrated that of the 16 series generated by ANN model only two, obtained by model 2 for the months of April and July in Pelotas, presented significant difference in relation to the distributions of the measured series and that all mean deviations obtained were inferior to 0.39 MJ/m{sup 2}. It was also verified that the two ANN models were able to reproduce the principal statistical characteristics of the frequency distributions of the measured series such as: mean, mode, asymmetry and Kurtosis. (author)

  14. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved

    2017-01-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  15. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

    Science.gov (United States)

    Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

    2013-08-01

    As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

  16. Maximizng the sensitivity of a low threshold VHE gamma ray telescope by the use of neural nets and other methods

    Energy Technology Data Exchange (ETDEWEB)

    Kertzman, M.P. (Department of Physics and Astronomy, DePauw University Greencastle, Indiana 46135 (USA)); Sembroski, G.H. (Department of Physcis, Purdue University West Lafayette, Indiana 47907 (USA))

    1991-04-05

    Detailed 3-dimensional Monte-Carlo computer simulations of the Cherenkov photons produced by VHE (10 GeV to 10 TeV) gamma ray and proton induced air shower cascades are used to calculate the sensitivity and threshold of a ground-based, single-mount, multi-mirror, single photo-electron sensitive gamma ray telescope. Such a telescope is designed to have the lowest possible energy threshold for gamma ray induced air showers for a given light collection area. The sensitivity and energy threshold of this design are determined for various triggering configurations, and the sources and properties of background triggers are investigated. In particular, it is found that up to 40% of the background triggers are due to single muons produced by proton induced showers with primary energies in the 25 to 75 GeV range. Two methods for increasing the sensitivity of such a telescope by discrimination against the single muon induced triggers are investigated. The first uses small outrider telescopes triggering in coincidence with the main telescope. The second uses software implemented neural nets trained to identify muon induced triggers by use of the temporal shape of the Cherenkov light pulse.

  17. Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

    Science.gov (United States)

    Li, Ling; Zhang, Jin-Xiang; Jiang, Tao

    2011-01-01

    Background Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. Methodology/Principal Findings In this study, we recorded electroencephalography (EEG) from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF) memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP) at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4–8 Hz), alpha- (8–12 Hz), beta- (12–32 Hz), and gamma- (32–40 Hz) frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF) WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. Conclusions/Significance We suggest that the differences in theta- and alpha- bands between LVF and RVF conditions in

  18. Visual working memory load-related changes in neural activity and functional connectivity.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: Visual working memory (VWM helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we recorded electroencephalography (EEG from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4-8 Hz, alpha- (8-12 Hz, beta- (12-32 Hz, and gamma- (32-40 Hz frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. CONCLUSIONS/SIGNIFICANCE: We suggest that the differences in theta- and alpha- bands between LVF and RVF

  19. Tracting the neural basis of music: Deficient structural connectivity underlying acquired amusia.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Särkämö, Teppo; Leo, Vera; Rodríguez-Fornells, Antoni; Saunavaara, Jani; Parkkola, Riitta; Soinila, Seppo

    2017-12-01

    Acquired amusia provides a unique opportunity to investigate the fundamental neural architectures of musical processing due to the transition from a functioning to defective music processing system. Yet, the white matter (WM) deficits in amusia remain systematically unexplored. To evaluate which WM structures form the neural basis for acquired amusia and its recovery, we studied 42 stroke patients longitudinally at acute, 3-month, and 6-month post-stroke stages using DTI [tract-based spatial statistics (TBSS) and deterministic tractography (DT)] and the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Non-recovered amusia was associated with structural damage and subsequent degeneration in multiple WM tracts including the right inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and frontal aslant tract (FAT), as well as in the corpus callosum (CC) and its posterior part (tapetum). In a linear regression analysis, the volume of the right IFOF was the main predictor of MBEA performance across time. Overall, our results provide a comprehensive picture of the large-scale deficits in intra- and interhemispheric structural connectivity underlying amusia, and conversely highlight which pathways are crucial for normal music perception. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Altered neural connectivity in excitatory and inhibitory cortical circuits in autism

    Directory of Open Access Journals (Sweden)

    Basilis eZikopoulos

    2013-09-01

    Full Text Available Converging evidence from diverse studies suggests that atypical brain connectivity in autism affects in distinct ways short- and long-range cortical pathways, disrupting neural communication and the balance of excitation and inhibition. This hypothesis is based mostly on functional non-invasive studies that show atypical synchronization and connectivity patterns between cortical areas in children and adults with autism. Indirect methods to study the course and integrity of major brain pathways at low resolution show changes in fractional anisotropy or diffusivity of the white matter in autism. Findings in post-mortem brains of adults with autism provide evidence of changes in the fine structure of axons below prefrontal cortices, which communicate over short- or long-range pathways with other cortices and subcortical structures. Here we focus on evidence of cellular and axon features that likely underlie the changes in short- and long-range communication in autism. We review recent findings of changes in the shape, thickness, and volume of brain areas, cytoarchitecture, neuronal morphology, cellular elements, and structural and neurochemical features of individual axons in the white matter, where pathology is evident even in gross images. We relate cellular and molecular features to imaging and genetic studies that highlight a variety of polymorphisms and epigenetic factors that primarily affect neurite growth and synapse formation and function in autism. We report preliminary findings of changes in autism in the ratio of distinct types of inhibitory neurons in prefrontal cortex, known to shape network dynamics and the balance of excitation and inhibition. Finally we present a model that synthesizes diverse findings by relating them to developmental events, with a goal to identify common processes that perturb development in autism and affect neural communication, reflected in altered patterns of attention, social interactions, and language.

  1. Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field potentials.

    Science.gov (United States)

    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

  2. Self-organization of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Clark, J.W.; Winston, J.V.; Rafelski, J.

    1984-05-14

    The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (brainwashing) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conducive to the simulation of memory and learning phenomena. 18 references, 2 figures.

  3. Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations

    Directory of Open Access Journals (Sweden)

    Tianqi Wang

    2017-01-01

    Full Text Available Major psychiatric disorders, including attention deficit hyperactivity disorder (ADHD, autism (AUT, bipolar disorder (BD, major depressive disorder (MDD, and schizophrenia (SZ, are highly heritable and polygenic. Evidence suggests that these five disorders have both shared and distinct genetic risks and neural connectivity abnormalities. To measure aggregate genetic risks, the polygenic risk score (PGRS was computed. Two independent general populations (N = 360 and N = 323 were separately examined to investigate whether the cross-disorder PGRS and PGRS for a specific disorder were associated with individual variability in functional connectivity. Consistent altered functional connectivity was found with the bilateral insula: for the left supplementary motor area and the left superior temporal gyrus with the cross-disorder PGRS, for the left insula and right middle and superior temporal lobe associated with the PGRS for autism, for the bilateral midbrain, posterior cingulate, cuneus, and precuneus associated with the PGRS for BD, and for the left angular gyrus and the left dorsolateral prefrontal cortex associated with the PGRS for schizophrenia. No significant functional connectivity was found associated with the PGRS for ADHD and MDD. Our findings indicated that genetic effects on the cross-disorder and disorder-specific neural connectivity of common genetic risk loci are detectable in the general population. Our findings also indicated that polygenic risk contributes to the main neurobiological phenotypes of psychiatric disorders and that identifying cross-disorder and specific functional connectivity related to polygenic risks may elucidate the neural pathways for these disorders.

  4. Adults with high social anhedonia have altered neural connectivity with ventral lateral prefrontal cortex when processing positive social signals

    Directory of Open Access Journals (Sweden)

    Hong eYin

    2015-08-01

    Full Text Available Social anhedonia (SA is a debilitating characteristic of schizophrenia and a vulnerability for developing schizophrenia among people at risk. Prior work (Hooker et al, 2014 has revealed neural deficits in ventral lateral prefrontal cortex (VLPFC during processing of positive emotion in a community sample of people with high social anhedonia. Deficits in VLPFC neural activity are related to worse self-reported schizophrenia-spectrum symptoms and worse mood and behavior after social stress. In the current study, psychophysiological interaction (PPI analysis was applied to investigate the neural mechanisms mediated by VLPFC during emotion processing. PPI analysis revealed that, compared to low SA controls, participants with high SA displayed reduced VLPFC integration, specifically reduced connectivity between VLPFC and premotor cortex, inferior parietal and posterior temporal regions when viewing positive relative to neutral emotion. Across all participants, connectivity between VLPFC and inferior parietal region when viewing positive (versus neutral emotion was significantly correlated with measures of emotion management and attentional control. Additionally connectivity between VLPFC and superior temporal sulcus was related to reward and pleasure anticipation, and connectivity between VLPFC and inferior temporal sulcus correlated with attentional control measure. Our results suggest that impairments to VLPFC mediated neural circuitry underlie the cognitive and emotional deficits.

  5. Exponential distance distribution of connected neurons in simulations of two-dimensional in vitro neural network development

    Science.gov (United States)

    Lv, Zhi-Song; Zhu, Chen-Ping; Nie, Pei; Zhao, Jing; Yang, Hui-Jie; Wang, Yan-Jun; Hu, Chin-Kun

    2017-06-01

    The distribution of the geometric distances of connected neurons is a practical factor underlying neural networks in the brain. It can affect the brain's dynamic properties at the ground level. Karbowski derived a power-law decay distribution that has not yet been verified by experiment. In this work, we check its validity using simulations with a phenomenological model. Based on the in vitro two-dimensional development of neural networks in culture vessels by Ito, we match the synapse number saturation time to obtain suitable parameters for the development process, then determine the distribution of distances between connected neurons under such conditions. Our simulations obtain a clear exponential distribution instead of a power-law one, which indicates that Karbowski's conclusion is invalid, at least for the case of in vitro neural network development in two-dimensional culture vessels.

  6. Identification of Sparse Neural Functional Connectivity using Penalized Likelihood Estimation and Basis Functions

    Science.gov (United States)

    Song, Dong; Wang, Haonan; Tu, Catherine Y.; Marmarelis, Vasilis Z.; Hampson, Robert E.; Deadwyler, Sam A.; Berger, Theodore W.

    2013-01-01

    One key problem in computational neuroscience and neural engineering is the identification and modeling of functional connectivity in the brain using spike train data. To reduce model complexity, alleviate overfitting, and thus facilitate model interpretation, sparse representation and estimation of functional connectivity is needed. Sparsities include global sparsity, which captures the sparse connectivities between neurons, and local sparsity, which reflects the active temporal ranges of the input-output dynamical interactions. In this paper, we formulate a generalized functional additive model (GFAM) and develop the associated penalized likelihood estimation methods for such a modeling problem. A GFAM consists of a set of basis functions convolving the input signals, and a link function generating the firing probability of the output neuron from the summation of the convolutions weighted by the sought model coefficients. Model sparsities are achieved by using various penalized likelihood estimations and basis functions. Specifically, we introduce two variations of the GFAM using a global basis (e.g., Laguerre basis) and group LASSO estimation, and a local basis (e.g., B-spline basis) and group bridge estimation, respectively. We further develop an optimization method based on quadratic approximation of the likelihood function for the estimation of these models. Simulation and experimental results show that both group-LASSO-Laguerre and group-bridge-B-spline can capture faithfully the global sparsities, while the latter can replicate accurately and simultaneously both global and local sparsities. The sparse models outperform the full models estimated with the standard maximum likelihood method in out-of-sample predictions. PMID:23674048

  7. NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    Chirayath, Ved

    2017-01-01

    In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration

  8. EuroWordNet : a multilingual database of autonomous and language-specific wordnets connected via Inter-Lingual-Index

    NARCIS (Netherlands)

    Vossen, P.T.J.M.

    2004-01-01

    This paper describes the multilingual design of the EuroWordNet database. The EuroWordNet database stores wordnets as autonomous language -specific structures that are interconnected via an Inter-Lingual-Index (ILI). In this paper, we discuss the possibilities to create mappings from each wordnet

  9. Tracking by Neural Nets

    CERN Document Server

    Jofrehei, Arash

    2015-01-01

    Current track reconstruction methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast. Simulation might not be as realistic as real data but tracking efficiency is 100 percent for that while by using real data we would probably be limited to current efficiency. The fact that this approach can be a lot faster and even more efficient than current methods by using simulation data can make it a great alternative for current track reconstruction methods used in both triggering and tracking.

  10. In search of neural mechanisms of mirror neuron dysfunction in schizophrenia: resting state functional connectivity approach.

    Science.gov (United States)

    Zaytseva, Yuliya; Bendova, Marie; Garakh, Zhanna; Tintera, Jaroslav; Rydlo, Jan; Spaniel, Filip; Horacek, Jiri

    2015-09-01

    It has been repeatedly shown that schizophrenia patients have immense alterations in goal-directed behaviour, social cognition, and social interactions, cognitive abilities that are presumably driven by the mirror neurons system (MNS). However, the neural bases of these deficits still remain unclear. Along with the task-related fMRI and EEG research tapping into the mirror neuron system, the characteristics of the resting state activity in the particular areas that encompass mirror neurons might be of interest as they obviously determine the baseline of the neuronal activity. Using resting state fMRI, we investigated resting state functional connectivity (FC) in four predefined brain structures, ROIs (inferior frontal gyrus, superior parietal lobule, premotor cortex and superior temporal gyrus), known for their mirror neurons activity, in 12 patients with first psychotic episode and 12 matched healthy individuals. As a specific hypothesis, based on the knowledge of the anatomical inputs of thalamus to all preselected ROIs, we have investigated the FC between thalamus and the ROIs. Of all ROIs included, seed-to-voxel connectivity analysis revealed significantly decreased FC only in left posterior superior temporal gyrus (STG) and the areas in visual cortex and cerebellum in patients as compared to controls. Using ROI-to-ROI analysis (thalamus and selected ROIs), we have found an increased FC of STG and bilateral thalamus whereas the FC of these areas was decreased in controls. Our results suggest that: (1) schizophrenia patients exhibit FC of STG which corresponds to the previously reported changes of superior temporal gyrus in schizophrenia and might contribute to the disturbances of specific functions, such as emotional processing or spatial awareness; (2) as the thalamus plays a pivotal role in the sensory gating, providing the filtering of the redundant stimulation, the observed hyperconnectivity between the thalami and the STGs in patients with schizophrenia

  11. Power spectrum of the rectified EMG: when and why is rectification beneficial for identifying neural connectivity?

    Science.gov (United States)

    Negro, Francesco; Keenan, Kevin; Farina, Dario

    2015-06-01

    Objective. The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons. Approach. We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals. Main results. Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs. Significance. This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.

  12. Llgl1 Connects Cell Polarity with Cell-Cell Adhesion in Embryonic Neural Stem Cells.

    Science.gov (United States)

    Jossin, Yves; Lee, Minhui; Klezovitch, Olga; Kon, Elif; Cossard, Alexia; Lien, Wen-Hui; Fernandez, Tania E; Cooper, Jonathan A; Vasioukhin, Valera

    2017-06-05

    Malformations of the cerebral cortex (MCCs) are devastating developmental disorders. We report here that mice with embryonic neural stem-cell-specific deletion of Llgl1 (Nestin-Cre/Llgl1fl/fl), a mammalian ortholog of the Drosophila cell polarity gene lgl, exhibit MCCs resembling severe periventricular heterotopia (PH). Immunohistochemical analyses and live cortical imaging of PH formation revealed that disruption of apical junctional complexes (AJCs) was responsible for PH in Nestin-Cre/Llgl1fl/fl brains. While it is well known that cell polarity proteins govern the formation of AJCs, the exact mechanisms remain unclear. We show that LLGL1 directly binds to and promotes internalization of N-cadherin, and N-cadherin/LLGL1 interaction is inhibited by atypical protein kinase C-mediated phosphorylation of LLGL1, restricting the accumulation of AJCs to the basolateral-apical boundary. Disruption of the N-cadherin-LLGL1 interaction during cortical development in vivo is sufficient for PH. These findings reveal a mechanism responsible for the physical and functional connection between cell polarity and cell-cell adhesion machineries in mammalian cells. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Discovery and introduction of a (3,18)-connected net as an ideal blueprint for the design of metal-organic frameworks

    KAUST Repository

    Guillerm, Vincent

    2014-06-29

    Metal-organic frameworks (MOFs) are a promising class of porous materials because it is possible to mutually control their porous structure, composition and functionality. However, it is still a challenge to predict the network topology of such framework materials prior to their synthesis. Here we use a new rare earth (RE) nonanuclear carboxylate-based cluster as an 18-connected molecular building block to form a gea-MOF (gea-MOF-1) based on a (3,18)-connected net. We then utilized this gea net as a blueprint to design and assemble another MOF (gea-MOF-2). In gea-MOF-2, the 18-connected RE clusters are replaced by metal-organic polyhedra, peripherally functionalized so as to have the same connectivity as the RE clusters. These metal-organic polyhedra act as supermolecular building blocks when they form gea-MOF-2. The discovery of a (3,18)-connected MOF followed by deliberate transposition of its topology to a predesigned second MOF with a different chemical system validates the prospective rational design of MOFs. © 2014 Macmillan Publishers Limited. All rights reserved.

  14. Discovery and introduction of a (3,18)-connected net as an ideal blueprint for the design of metal-organic frameworks.

    Science.gov (United States)

    Guillerm, Vincent; Weseliński, Łukasz J; Belmabkhout, Youssef; Cairns, Amy J; D'Elia, Valerio; Wojtas, Łukasz; Adil, Karim; Eddaoudi, Mohamed

    2014-08-01

    Metal-organic frameworks (MOFs) are a promising class of porous materials because it is possible to mutually control their porous structure, composition and functionality. However, it is still a challenge to predict the network topology of such framework materials prior to their synthesis. Here we use a new rare earth (RE) nonanuclear carboxylate-based cluster as an 18-connected molecular building block to form a gea-MOF (gea-MOF-1) based on a (3,18)-connected net. We then utilized this gea net as a blueprint to design and assemble another MOF (gea-MOF-2). In gea-MOF-2, the 18-connected RE clusters are replaced by metal-organic polyhedra, peripherally functionalized so as to have the same connectivity as the RE clusters. These metal-organic polyhedra act as supermolecular building blocks when they form gea-MOF-2. The discovery of a (3,18)-connected MOF followed by deliberate transposition of its topology to a predesigned second MOF with a different chemical system validates the prospective rational design of MOFs.

  15. A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

    Directory of Open Access Journals (Sweden)

    Xingang Fu

    2016-04-01

    Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.

  16. Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models.

    Science.gov (United States)

    Izhikevich, E M

    1999-01-01

    Many scientists believe that all pulse-coupled neural networks are toy models that are far away from the biological reality. We show here, however, that a huge class of biophysically detailed and biologically plausible neural-network models can be transformed into a canonical pulse-coupled form by a piece-wise continuous, possibly noninvertible, change of variables. Such transformations exist when a network satisfies a number of conditions; e.g., it is weakly connected; the neurons are Class 1 excitable (i.e., they can generate action potentials with an arbitrary small frequency); and the synapses between neurons are conventional (i.e., axo-dendritic and axo-somatic). Thus, the difference between studying the pulse-coupled model and Hodgkin-Huxley-type neural networks is just a matter of a coordinate change. Therefore, any piece of information about the pulse-coupled model is valuable since it tells something about all weakly connected networks of Class 1 neurons. For example, we show that the pulse-coupled network of identical neurons does not synchronize in-phase. This confirms Ermentrout's result that weakly connected Class 1 neurons are difficult to synchronize, regardless of the equations that describe dynamics of each cell.

  17. Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

    Directory of Open Access Journals (Sweden)

    Christopher E Hart

    2006-12-01

    Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

  18. Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

    Science.gov (United States)

    D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel

    2016-03-01

    We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

  19. Multispectral confocal microscopy images and artificial neural nets to monitor the photosensitizer uptake and degradation in Candida albicans cells

    Science.gov (United States)

    Romano, Renan A.; Pratavieira, Sebastião.; da Silva, Ana P.; Kurachi, Cristina; Guimarães, Francisco E. G.

    2017-07-01

    This study clearly demonstrates that multispectral confocal microscopy images analyzed by artificial neural networks provides a powerful tool to real-time monitoring photosensitizer uptake, as well as photochemical transformations occurred.

  20. On the Nature of the Intrinsic Connectivity of the Cat Motor Cortex: Evidence for a Recurrent Neural Network Topology

    DEFF Research Database (Denmark)

    Capaday, Charles; Ethier, C; Brizzi, L

    2009-01-01

    Capaday C, Ethier C, Brizzi L, Sik A, van Vreeswijk C, Gingras D. On the nature of the intrinsic connectivity of the cat motor cortex: evidence for a recurrent neural network topology. J Neurophysiol 102: 2131-2141, 2009. First published July 22, 2009; doi: 10.1152/jn.91319.2008. The details...... and functional significance of the intrinsic horizontal connections between neurons in the motor cortex (MCx) remain to be clarified. To further elucidate the nature of this intracortical connectivity pattern, experiments were done on the MCx of three cats. The anterograde tracer biocytin was ejected...... iontophoretically in layers II, III, and V. Some 30-50 neurons within a radius of similar to 250 mu m were thus stained. The functional output of the motor cortical point at which biocytin was injected, and of the surrounding points, was identified by microstimulation and electromyographic recordings. The axonal...

  1. Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks.

    Science.gov (United States)

    Hemmateenejad, Bahram; Ghavami, Raoof; Miri, Ramin; Shamsipur, Majtaba

    2006-02-15

    Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.

  2. NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

    DEFF Research Database (Denmark)

    Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

    2010-01-01

    is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino......β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method...... NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which...

  3. The neural changes in connectivity of the voice network during voice pitch perturbation

    OpenAIRE

    Flagmeier, Sabina G.; Ray, Kimberly L.; Parkinson, Amy L.; Li, Karl; Vargas, Robert; Price, Larry R.; Laird, Angela R.; Charles R Larson; Robin, Donald A.

    2014-01-01

    Voice control is critical to communication. To date, studies have used behavioral, electrophysiological and functional data to investigate the neural correlates of voice control using perturbation tasks, but have yet to examine the interactions of these neural regions. The goal of this study was to use structural equation modeling of functional neuroimaging data to examine network properties of voice with and without perturbation. Results showed that the presence of a pitch shift, which was p...

  4. NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

    Directory of Open Access Journals (Sweden)

    Bent Petersen

    Full Text Available UNLABELLED: β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. CONCLUSION: The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

  5. Functional connectivity among spike trains in neural assemblies during rat working memory task.

    Science.gov (United States)

    Xie, Jiacun; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2014-11-01

    Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity - evaluation with auditory habituation.

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    Peng Wang

    Full Text Available In this work we propose a biologically realistic local cortical circuit model (LCCM, based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1 activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2 realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1 besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6, there exists a parallel "short-cut" pathway (layer 4 to layer 5/6, (2 the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3 the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3 are more strongly habituated than backward connections (from Layer 5/6 to layer 4. Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG, which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.

  7. On the connection between level of education and the neural circuitry of emotion perception

    NARCIS (Netherlands)

    Demenescu, L.R.; Stan, A.; Kortekaas, R.; van der Wee, N.J.A.; Veltman, D.J.; Aleman, A.

    2014-01-01

    Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of The

  8. Mode of Effective Connectivity within a Putative Neural Network Differentiates Moral Cognitions Related to Care and Justice Ethics

    Science.gov (United States)

    Cáceda, Ricardo; James, G. Andrew; Ely, Timothy D.; Snarey, John; Kilts, Clinton D.

    2011-01-01

    Background Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. Methodology/Principal Findings Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. Conclusions/Significance These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses. PMID:21364916

  9. Mode of effective connectivity within a putative neural network differentiates moral cognitions related to care and justice ethics.

    Directory of Open Access Journals (Sweden)

    Ricardo Cáceda

    Full Text Available BACKGROUND: Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC and posterior (PCC cingulate cortex, posterior superior temporal sulcus (pSTS, insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. METHODOLOGY/PRINCIPAL FINDINGS: Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. CONCLUSIONS/SIGNIFICANCE: These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses.

  10. Speed hysteresis and noise shaping of traveling fronts in neural fields: role of local circuitry and nonlocal connectivity

    Science.gov (United States)

    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.

  11. Derivation of Surface Net Radiation at the Valencia Anchor Station from Top of the Atmosphere Gerb Fluxes by Means of Linear Models and Neural Networks

    Science.gov (United States)

    Geraldo Ferreira, A.; Lopez-Baeza, Ernesto; Velazquez Blazquez, Almudena; Soria-Olivas, Emilio; Serrano Lopez, Antonio J.; Gomez Chova, Juan

    2012-07-01

    In this work, Linear Models (LM) and Artificial Neural Networks (ANN) have been developed to estimate net radiation (RN) at the surface. The models have been developed and evaluated by using the synergy between Geostationary Earth Radiation Budget (GERB-1) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, both instruments onboard METEOSAT-9, and ``in situ'' measurements. The data used in this work, corresponding to August 2006 and June to August 2007, proceed from Top of the Atmosphere (TOA) broadband fluxes from GERB-1, every 15 min, and from net radiation at the surface measured, every 10 min, at the Valencia Anchor Station (VAS) area, having measured independently the shortwave and the longwave radiation components (downwelling and upwelling) for different land uses and land cover. The adjustment of both temporal resolutions for the satellite and in situ data was achieved by linear interpolation that showed less standard deviation than the cubic one. The LMs were developed and validated by using satellite TOA RN and ground station surface RN measurements, only considering cloudy free days selected from the ground data. The ANN model was developed both for cloudy and cloudy-free conditions using seven input variables selected for the training/validation sets, namely, hour, day, month, surface RN, solar zenith angle and TOA shortwave and longwave fluxes. Both, LMs and ANNs show remarkably good agreement when compared to surface RN measurements. Therefore, this methodology can be successfully applied to estimate RN at surface from GERB/SEVIRI data.

  12. Distinct Neural Signatures Detected for ADHD Subtypes After Controlling for Micro-Movements in Resting State Functional Connectivity MRI Data

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    Damien eFair

    2013-02-01

    Full Text Available In recent years, there has been growing enthusiasm that functional MRI could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to A examine the impact of emerging techniques for controlling for micro-movements, and B provide novel insights into the neural correlates of ADHD subtypes. Using SVM based MVPA we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C and Inattentive (ADHD-I subtypes demonstrated some overlapping (particularly sensorimotor systems, but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that rs-fcMRI data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical

  13. Neural correlates of verbal creativity: Differences in resting-state functional connectivity associated with expertise in creative writing

    Directory of Open Access Journals (Sweden)

    Martin eLotze

    2014-07-01

    Full Text Available Neural characteristics of verbal creativity as assessed by word generation tasks have been recently identified, but differences in resting-state functional connectivity (rFC between experts and non-experts in creative writing have not been reported yet. Previous electroencephalography (EEG coherence measures during rest demonstrated a decreased cooperation between brain areas in association with creative thinking ability. Here, we used resting-state functional magnetic resonance imaging to compare 20 experts in creative writing and 23 age-matched non-experts with respect to rFC strengths within a brain network previously found to be associated with creative writing. Decreased rFC for experts was found between areas 44 of both hemispheres. Increased rFC for experts was observed between right hemispheric caudate and intraparietal sulcus. Correlation analysis of verbal creativity indices with rFC values in the expert group revealed predominantly negative associations, particularly of rFC between left area 44 and left temporal pole. Overall, our data support previous findings on reduced connectivity between interhemispheric areas and increased right-hemispheric connectivity during rest in highly verbally creative individuals.

  14. Neural correlates of verbal creativity: differences in resting-state functional connectivity associated with expertise in creative writing.

    Science.gov (United States)

    Lotze, Martin; Erhard, Katharina; Neumann, Nicola; Eickhoff, Simon B; Langner, Robert

    2014-01-01

    Neural characteristics of verbal creativity as assessed by word generation tasks have been recently identified, but differences in resting-state functional connectivity (rFC) between experts and non-experts in creative writing have not been reported yet. Previous electroencephalography (EEG) coherence measures during rest demonstrated a decreased cooperation between brain areas in association with creative thinking ability. Here, we used resting-state functional magnetic resonance imaging to compare 20 experts in creative writing and 23 age-matched non-experts with respect to rFC strengths within a brain network previously found to be associated with creative writing. Decreased rFC for experts was found between areas 44 of both hemispheres. Increased rFC for experts was observed between right hemispheric caudate and intraparietal sulcus. Correlation analysis of verbal creativity indices (VCIs) with rFC values in the expert group revealed predominantly negative associations, particularly of rFC between left area 44 and left temporal pole. Overall, our data support previous findings of reduced connectivity between interhemispheric areas and increased right-hemispheric connectivity during rest in highly verbally creative individuals.

  15. Neural traces of stress: cortisol related sustained enhancement of amygdala-hippocampal functional connectivity

    Directory of Open Access Journals (Sweden)

    Sharon eVaisvaser

    2013-07-01

    Full Text Available Stressful experiences modulate neuro-circuitry function, and the temporal trajectory of these alterations, elapsing from early disturbances to late recovery, heavily influences resilience and vulnerability to stress. Such effects of stress may depend on processes that are engaged during resting-state, through active recollection of past experiences and anticipation of future events, all known to involve the default mode network (DMN. By inducing social stress and acquiring resting-state fMRI before stress, immediately following it, and two hours later, we expanded the time-window for examining the trajectory of the stress response. Throughout the study repeated cortisol samplings and self-reports of stress levels were obtained from 51 healthy young males. Post-stress alterations were investigated by whole brain resting-state functional connectivity of two central hubs of the DMN: the posterior cingulate cortex and hippocampus. Results indicate a 'recovery' pattern of DMN connectivity, in which all alterations, ascribed to the intervening stress, returned to pre-stress levels. The only exception to this pattern was a stress-induced rise in amygdala-hippocampal connectivity, which was sustained for as long as two hours following stress induction. Furthermore, this sustained enhancement of limbic connectivity was inversely correlated to individual stress-induced cortisol responsiveness (AUCi and characterized only the group lacking such increased cortisol (i.e., non-responders. Our observations provide evidence of a prolonged post-stress response profile, characterized by both the comprehensive balance of most DMN functional connections and the distinct time and cortisol dependent ascent of intra-limbic connectivity. These novel insights into neuro-endocrine relations are another milestone in the ongoing search for individual markers in stress-related psychopathologies.

  16. Symbiosis of a telemedicine and neural net's project as a new way of the decision of medical problems

    Science.gov (United States)

    Kasimov, Oleg V.; Karchenova, Elena V.; Maximova, Irina L.

    2007-05-01

    The new approach to training doctors which specialty means skill to distinguish various images - for example, doctors of beam diagnostics, pathologists, hematologists is possible. Telemedicine by means of opportunities of the Internet and video-conference is capable to create expert databases in the several world centers. Neural Networks (the Programs, being a part of the Artificial Intellect) - are trained to give out variants of possible interpretations of the set image on the basis of these expert databases. And the doctors trained the above-named specialties, spend not years and not tens years for achievement of an expert level of professionalism, saving time and greater means and societies for training. Having an opportunity diagnostics at the highest level, the medicine improves quality of a life of the patient, also saving its means.

  17. Identifying functional connectivity in large-scale neural ensemble recordings: a multiscale data mining approach.

    Science.gov (United States)

    Eldawlatly, Seif; Jin, Rong; Oweiss, Karim G

    2009-02-01

    Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

  18. Impact of acoustic coordinated reset neuromodulation on effective connectivity in a neural network of phantom sound.

    Science.gov (United States)

    Silchenko, Alexander N; Adamchic, Ilya; Hauptmann, Christian; Tass, Peter A

    2013-08-15

    Chronic subjective tinnitus is an auditory phantom phenomenon characterized by abnormal neuronal synchrony in the central auditory system. As recently shown in a proof of concept clinical trial, acoustic coordinated reset (CR) neuromodulation causes a significant relief of tinnitus symptoms combined with a significant decrease of pathological oscillatory activity in a network comprising auditory and non-auditory brain areas. The objective of the present study was to analyze whether CR therapy caused an alteration of the effective connectivity in a tinnitus related network of localized EEG brain sources. To determine which connections matter, in a first step, we considered a larger network of brain sources previously associated with tinnitus. To that network we applied a data-driven approach, combining empirical mode decomposition and partial directed coherence analysis, in patients with bilateral tinnitus before and after 12 weeks of CR therapy as well as in healthy controls. To increase the signal-to-noise ratio, we focused on the good responders, classified by a reliable-change-index (RCI). Prior to CR therapy and compared to the healthy controls, the good responders showed a significantly increased connectivity between the left primary cortex auditory cortex and the posterior cingulate cortex in the gamma and delta bands together with a significantly decreased effective connectivity between the right primary auditory cortex and the dorsolateral prefrontal cortex in the alpha band. Intriguingly, after 12 weeks of CR therapy most of the pathological interactions were gone, so that the connectivity patterns of good responders and healthy controls became statistically indistinguishable. In addition, we used dynamic causal modeling (DCM) to examine the types of interactions which were altered by CR therapy. Our DCM results show that CR therapy specifically counteracted the imbalance of excitation and inhibition. CR significantly weakened the excitatory connection

  19. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    Directory of Open Access Journals (Sweden)

    A. Novellino

    2007-01-01

    Full Text Available One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA, to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

  20. New lanthanide hybrid as clustered infinite nanotunnel with 3D Ln-O-Ln framework and (3,4)-connected net.

    Science.gov (United States)

    Huang, You-gui; Wu, Ben-lai; Yuan, Da-qiang; Xu, Yan-qing; Jiang, Fei-long; Hong, Mao-chun

    2007-02-19

    A series of new lanthanide hybrids [Ln3(mu-OH)4 (2,5-pydc)(2,5-Hpydc)3(H2O)4]n (Ln = Gd (1), Dy (2), Er (3), Eu (4), Sm (5), Yb (6), Y (7); 2,5-pydc=pyridine-2,5-dicarboxylate), as clustered lanthanide oxide ring tunnels with helical dodecahedral chains and fully 3D Ln-O-Ln connectivity, has been hydrothermally synthesized and characterized. The inorganic skeleton of the hybrid can be specified by the Schläfli symbol (6210)2 (64102) as a single 3D (3,4)-connected net. The luminescence properties have been studied, and the results showed that the Dy(III) (2) and Eu(III) (4) complexes exhibited sensitized luminescence in the visible region. Variable-temperature magnetic susceptibility measurements of 1-6 showed that the complexes 1-3 are nearly paramagnets, whereas the depopulation of the Stark levels in complexes 4-6 leads to a continuous decrease in mu(eff) when the sample is cooled from 300 to 2 K.

  1. Theory of Mind and the Whole Brain Functional Connectivity: Behavioral and Neural Evidences with the Amsterdam Resting State Questionnaire.

    Science.gov (United States)

    Marchetti, Antonella; Baglio, Francesca; Costantini, Isa; Dipasquale, Ottavia; Savazzi, Federica; Nemni, Raffaello; Sangiuliano Intra, Francesca; Tagliabue, Semira; Valle, Annalisa; Massaro, Davide; Castelli, Ilaria

    2015-01-01

    A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the "stream of consciousness" of James, is now known in the psychological literature as "Mind-Wandering." Although of great interest, this construct has been scarcely investigated so far. Diaz et al. (2013) created the Amsterdam Resting State Questionnaire (ARSQ), composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM), self, planning, sleepiness, comfort, and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N = 28) divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes) with higher functional connectivity (FC) with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.

  2. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

    Science.gov (United States)

    Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

    2017-01-01

    The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre

  3. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

    Directory of Open Access Journals (Sweden)

    Xinyu Guo

    2017-08-01

    Full Text Available The whole-brain functional connectivity (FC pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes. Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150. Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross

  4. Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets

    Directory of Open Access Journals (Sweden)

    Felix Rembold

    2013-03-01

    Full Text Available For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI. Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+ images and official agricultural statistics. Encouraging results were obtained demonstrating the potential of the proposed approach. For example, the spatial distribution of winter crop acreage at sub-pixel level was mapped with a cross-validated coefficient of determination of 0.8 with respect to the reference information from high resolution imagery. For the eight years for which

  5. Neural signature of coma revealed by posteromedial cortex connection density analysis

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    Briguita Malagurski

    2017-01-01

    A complex pattern of decreased and increased connections was observed, suggesting a network imbalance between internal/external processing systems, within PMC during coma. The number of PMC voxels with hypo-CD positive correlation showed a significant negative association with the CRS-R score, notwithstanding aetiology. Traumatic injury specifically appeared to be associated with a greater prevalence of hyper-connected (negative correlation voxels, which was inversely associated with patient neurological outcome. A logistic regression model using the number of hypo-CD positive and hyper-CD negative correlations, accurately permitted patient's outcome prediction (AUC = 0.906, 95%IC = 0.795–1. These points might reflect adaptive plasticity mechanism and pave the way for innovative prognosis and therapeutics methods.

  6. Neural connectivity moderates the association between sleep and impulsivity in adolescents

    OpenAIRE

    Sarah M. Tashjian; Diane Goldenberg; Adriana Galván

    2017-01-01

    Adolescence is characterized by chronic insufficient sleep and extensive brain development, but the relation between adolescent sleep and brain function remains unclear. We report the first functional magnetic resonance imaging study to investigate functional connectivity as a moderator between sleep and impulsivity, a problematic behavior during this developmental period. Naturalistic differences in sleep have not yet been explored as treatable contributors to adolescent impulsivity. Althoug...

  7. Resilience and cross-network connectivity: A neural model for post-trauma survival.

    Science.gov (United States)

    Brunetti, Marcella; Marzetti, Laura; Sepede, Gianna; Zappasodi, Filippo; Pizzella, Vittorio; Sarchione, Fabiola; Vellante, Federica; Martinotti, Giovanni; Di Giannantonio, Massimo

    2017-07-03

    Literature on the neurobiological bases of Post-Traumatic Stress Disorder (PTSD) considers medial Prefrontal cortex (mPFC), a core region of the Default Mode Network (DMN), as a region involved in response regulation to stressors. Disrupted functioning of the DMN has been recognized at the basis of the pathophysiology of a number of mental disorders. Furthermore, in the evaluation of the protective factors to trauma consequence, an important role has been assigned to resilience. Our aim was to investigate the specific relation of resilience and PTSD symptoms severity with resting state brain connectivity in a traumatized population using magnetoencephalography (MEG), a non-invasive imaging technique with high temporal resolution and documented advantages in clinical applications. Nineteen Trauma Exposed non-PTSD (TENP) and 19 PTSD patients participated to a resting state MEG session. MEG functional connectivity of mPFC seed to the whole brain was calculated. Correlation between mPFC functional connectivity and Clinician Administered PTSD Scale (CAPS) or Connor-Davidson Resilience Scale (CD-RISC) total score was also assessed. In the whole group, it has been evidenced that the higher was the resilience, the lower was the cross-network connectivity between DMN and Salience Network (SN) nodes. Contrarily, in the TENP group, the negative correlation between resilience and DMN-SN cross-interaction disappeared, suggesting a protective role of resilience for brain functioning. Regarding our findings as a continuum between healthy and pathological after trauma outcomes, we could suggest a link between resilience and the good dialogue between the networks needed to face a traumatic event and its long-term consequence on individuals' lives. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. The Unification Space implemented as a localist neural net: predictions and error-tolerance in a constraint-based parser.

    Science.gov (United States)

    Vosse, Theo; Kempen, Gerard

    2009-12-01

    We introduce a novel computer implementation of the Unification-Space parser (Vosse and Kempen in Cognition 75:105-143, 2000) in the form of a localist neural network whose dynamics is based on interactive activation and inhibition. The wiring of the network is determined by Performance Grammar (Kempen and Harbusch in Verb constructions in German and Dutch. Benjamins, Amsterdam, 2003), a lexicalist formalism with feature unification as binding operation. While the network is processing input word strings incrementally, the evolving shape of parse trees is represented in the form of changing patterns of activation in nodes that code for syntactic properties of words and phrases, and for the grammatical functions they fulfill. The system is capable, at least qualitatively and rudimentarily, of simulating several important dynamic aspects of human syntactic parsing, including garden-path phenomena and reanalysis, effects of complexity (various types of clause embeddings), fault-tolerance in case of unification failures and unknown words, and predictive parsing (expectation-based analysis, surprisal effects). English is the target language of the parser described.

  9. Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

    Science.gov (United States)

    Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

    2014-04-01

    Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

  10. Preterm birth results in alterations in neural connectivity at age 16 years.

    Science.gov (United States)

    Mullen, Katherine M; Vohr, Betty R; Katz, Karol H; Schneider, Karen C; Lacadie, Cheryl; Hampson, Michelle; Makuch, Robert W; Reiss, Allan L; Constable, R Todd; Ment, Laura R

    2011-02-14

    Very low birth weight preterm (PT) children are at high risk for brain injury. Employing diffusion tensor imaging (DTI), we tested the hypothesis that PT adolescents would demonstrate microstructural white matter disorganization relative to term controls at 16 years of age. Forty-four PT subjects (600-1250 g birth weight) without neonatal brain injury and 41 term controls were evaluated at age 16 years with DTI, the Wechsler Intelligence Scale for Children-III (WISC), the Peabody Picture Vocabulary Test-Revised (PPVT), and the Comprehensive Test of Phonological Processing (CTOPP). PT subjects scored lower than term subjects on WISC full scale (p=0.003), verbal (p=0.043), and performance IQ tests (p=0.001), as well as CTOPP phonological awareness (p=0.004), but scored comparably to term subjects on PPVT and CTOPP Rapid Naming tests. PT subjects had lower fractional anisotropy (FA) values in multiple regions including bilateral uncinate fasciculi (left: p=0.01; right: p=0.004), bilateral external capsules (left: planguage task) in the PT subjects (left: r=0.314, p=0.038; right: r=0.336, p=0.026). FA values in the left and right arcuate fasciculi correlated with CTOPP Rapid Naming scores (a phonologic task) in the PT subjects (left: r=0.424, p=0.004; right: r=0.301, p=0.047). These data support for the first time that dual pathways underlying language function are present in PT adolescents. The striking bilateral dorsal correlations for the PT group suggest that prematurely born subjects rely more heavily on the right hemisphere than typically developing adults for performance of phonological language tasks. These findings may represent either a delay in maturation or the engagement of alternative neural pathways for language in the developing PT brain. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. Effects of gratitude meditation on neural network functional connectivity and brain-heart coupling.

    Science.gov (United States)

    Kyeong, Sunghyon; Kim, Joohan; Kim, Dae Jin; Kim, Hesun Erin; Kim, Jae-Jin

    2017-07-11

    A sense of gratitude is a powerful and positive experience that can promote a happier life, whereas resentment is associated with life dissatisfaction. To explore the effects of gratitude and resentment on mental well-being, we acquired functional magnetic resonance imaging and heart rate (HR) data before, during, and after the gratitude and resentment interventions. Functional connectivity (FC) analysis was conducted to identify the modulatory effects of gratitude on the default mode, emotion, and reward-motivation networks. The average HR was significantly lower during the gratitude intervention than during the resentment intervention. Temporostriatal FC showed a positive correlation with HR during the gratitude intervention, but not during the resentment intervention. Temporostriatal resting-state FC was significantly decreased after the gratitude intervention compared to the resentment intervention. After the gratitude intervention, resting-state FC of the amygdala with the right dorsomedial prefrontal cortex and left dorsal anterior cingulate cortex were positively correlated with anxiety scale and depression scale, respectively. Taken together, our findings shed light on the effect of gratitude meditation on an individual's mental well-being, and indicate that it may be a means of improving both emotion regulation and self-motivation by modulating resting-state FC in emotion and motivation-related brain regions.

  12. Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading.

    Science.gov (United States)

    Cao, Fan; Perfetti, Charles A

    2016-01-01

    Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG) is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level.

  13. Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading.

    Directory of Open Access Journals (Sweden)

    Fan Cao

    Full Text Available Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level.

  14. Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter.

    Science.gov (United States)

    Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo

    2015-09-01

    This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.

  15. Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Suliang Ma

    2016-11-01

    Full Text Available Photovoltaic (PV systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP. Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL non-linear controller combined with an artificial neural network (ANN is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

  16. Net4Care Ecosystem Website

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Hansen, Klaus Marius; Rasmussen, Morten

    2012-01-01

    is a tele-monitoring scenario in which Net4Care clients are deployed in a gateway in private homes. Medical devices then connect to these gateways and transmit their observations to a Net4Care server. In turn the Net4Care server creates valid clinical HL7 documents, stores them in a national XDS repository...

  17. Neural Plasticity in Human Brain Connectivity: The Effects of Long Term Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson’s Disease

    Science.gov (United States)

    van Hartevelt, Tim J.; Cabral, Joana; Deco, Gustavo; Møller, Arne; Green, Alexander L.; Aziz, Tipu Z.; Kringelbach, Morten L.

    2014-01-01

    Background Positive clinical outcomes are now well established for deep brain stimulation, but little is known about the effects of long-term deep brain stimulation on brain structural and functional connectivity. Here, we used the rare opportunity to acquire pre- and postoperative diffusion tensor imaging in a patient undergoing deep brain stimulation in bilateral subthalamic nuclei for Parkinson’s Disease. This allowed us to analyse the differences in structural connectivity before and after deep brain stimulation. Further, a computational model of spontaneous brain activity was used to estimate the changes in functional connectivity arising from the specific changes in structural connectivity. Results We found significant localised structural changes as a result of long-term deep brain stimulation. These changes were found in sensory-motor, prefrontal/limbic, and olfactory brain regions which are known to be affected in Parkinson’s Disease. The nature of these changes was an increase of nodal efficiency in most areas and a decrease of nodal efficiency in the precentral sensory-motor area. Importantly, the computational model clearly shows the impact of deep brain stimulation-induced structural alterations on functional brain changes, which is to shift the neural dynamics back towards a healthy regime. The results demonstrate that deep brain stimulation in Parkinson’s Disease leads to a topological reorganisation towards healthy bifurcation of the functional networks measured in controls, which suggests a potential neural mechanism for the alleviation of symptoms. Conclusions The findings suggest that long-term deep brain stimulation has not only restorative effects on the structural connectivity, but also affects the functional connectivity at a global level. Overall, our results support causal changes in human neural plasticity after long-term deep brain stimulation and may help to identify the underlying mechanisms of deep brain stimulation. PMID

  18. Neural plasticity in human brain connectivity: the effects of long term deep brain stimulation of the subthalamic nucleus in Parkinson's disease.

    Science.gov (United States)

    van Hartevelt, Tim J; Cabral, Joana; Deco, Gustavo; Møller, Arne; Green, Alexander L; Aziz, Tipu Z; Kringelbach, Morten L

    2014-01-01

    Positive clinical outcomes are now well established for deep brain stimulation, but little is known about the effects of long-term deep brain stimulation on brain structural and functional connectivity. Here, we used the rare opportunity to acquire pre- and postoperative diffusion tensor imaging in a patient undergoing deep brain stimulation in bilateral subthalamic nuclei for Parkinson's Disease. This allowed us to analyse the differences in structural connectivity before and after deep brain stimulation. Further, a computational model of spontaneous brain activity was used to estimate the changes in functional connectivity arising from the specific changes in structural connectivity. We found significant localised structural changes as a result of long-term deep brain stimulation. These changes were found in sensory-motor, prefrontal/limbic, and olfactory brain regions which are known to be affected in Parkinson's Disease. The nature of these changes was an increase of nodal efficiency in most areas and a decrease of nodal efficiency in the precentral sensory-motor area. Importantly, the computational model clearly shows the impact of deep brain stimulation-induced structural alterations on functional brain changes, which is to shift the neural dynamics back towards a healthy regime. The results demonstrate that deep brain stimulation in Parkinson's Disease leads to a topological reorganisation towards healthy bifurcation of the functional networks measured in controls, which suggests a potential neural mechanism for the alleviation of symptoms. The findings suggest that long-term deep brain stimulation has not only restorative effects on the structural connectivity, but also affects the functional connectivity at a global level. Overall, our results support causal changes in human neural plasticity after long-term deep brain stimulation and may help to identify the underlying mechanisms of deep brain stimulation.

  19. Semantic Networks and Neural Nets.

    Science.gov (United States)

    1984-06-01

    TRAGEDIES . If John likes SCIENCE-FICTION more than SHAKESPEAREAN - TRAGEDIES then it is easy to see how SCIENCE-FICTION will be chosen as the answer...manner it is easy to see how in subsequent steps the fod may converge to [LITERARY-KIND ’AI with the choices being SCIENCE-FICTION and SHAKESPEAREAN

  20. Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state.

    Science.gov (United States)

    Sotero, Roberto C; Shmuel, Amir

    2012-06-01

    Several studies posit energy as a constraint on the coding and processing of information in the brain due to the high cost of resting and evoked cortical activity. This suggestion has been addressed theoretically with models of a single neuron and two coupled neurons. Neural mass models (NMMs) address mean-field based modeling of the activity and interactions between populations of neurons rather than a few neurons. NMMs have been widely employed for studying the generation of EEG rhythms, and more recently as frameworks for integrated models of neurophysiology and functional MRI (fMRI) responses. To date, the consequences of energy constraints on the activity and interactions of ensembles of neurons have not been addressed. Here we aim to study the impact of constraining energy consumption during the resting-state on NMM parameters. To this end, we first linearized the model, then used stochastic control theory by introducing a quadratic cost function, which transforms the NMM into a stochastic linear quadratic regulator (LQR). Solving the LQR problem introduces a regime in which the NMM parameters, specifically the effective connectivities between neuronal populations, must vary with time. This is in contrast to current NMMs, which assume a constant parameter set for a given condition or task. We further simulated energy-constrained stochastic control of a specific NMM, the Wilson and Cowan model of two coupled neuronal populations, one of which is excitatory and the other inhibitory. These simulations demonstrate that with varying weights of the energy-cost function, the NMM parameters show different time-varying behavior. We conclude that constraining NMMs according to energy consumption may create more realistic models. We further propose to employ linear NMMs with time-varying parameters as an alternative to traditional nonlinear NMMs with constant parameters.

  1. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework.

    Science.gov (United States)

    Deng, Lei; Jiao, Peng; Pei, Jing; Wu, Zhenzhi; Li, Guoqi

    2018-02-02

    Although deep neural networks (DNNs) are being a revolutionary power to open up the AI era, the notoriously huge hardware overhead has challenged their applications. Recently, several binary and ternary networks, in which the costly multiply-accumulate operations can be replaced by accumulations or even binary logic operations, make the on-chip training of DNNs quite promising. Therefore there is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR. More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity

  2. Neural network-based estimates of Southern Ocean net community production from in situ O2 / Ar and satellite observation: a methodological study

    Science.gov (United States)

    Chang, C.-H.; Johnson, N. C.; Cassar, N.

    2014-06-01

    Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of one to two weeks and with six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This nonparametric approach is based entirely on the observed statistical relationships between NCP and the predictors and, therefore, is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published, independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the Subtropical Front in the Atlantic, Indian, and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, the Patagonian Shelf, the Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 17.9 mmol C m-2 d-1, falls within the range of 8.3 to 24 mmol

  3. Neural network-based estimates of Southern Ocean net community production from in-situ O2 / Ar and satellite observation: a methodological study

    Science.gov (United States)

    Chang, C.-H.; Johnson, N. C.; Cassar, N.

    2013-10-01

    Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of 1-2 weeks, and six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This non-parametric approach is based entirely on the observed statistical relationships between NCP and the predictors, and therefore is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November-March NCP climatology reveals a pronounced zonal band of high NCP roughly following the subtropical front in the Atlantic, Indian and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, Patagonian Shelf, Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air-sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 14 mmol C m-2 d-1, falls within the range of 8.3-24 mmol C m-2 d-1 from other model

  4. Differential functional connectivity within an emotion regulation neural network among individuals resilient and susceptible to the depressogenic effects of early life stress.

    Science.gov (United States)

    Cisler, J M; James, G A; Tripathi, S; Mletzko, T; Heim, C; Hu, X P; Mayberg, H S; Nemeroff, C B; Kilts, C D

    2013-03-01

    Early life stress (ELS) is a significant risk factor for depression. The effects of ELS exposure on neural network organization have not been differentiated from the effect of depression. Furthermore, many individuals exposed to ELS do not develop depression, yet the network organization patterns differentiating resiliency versus susceptibility to the depressogenic effects of ELS are not clear. Women aged 18-44 years with either a history of ELS and no history of depression (n = 7), a history of ELS and current or past depression (n = 19), or a history of neither ELS nor depression (n = 12) underwent a resting-state 3-T functional magnetic resonance imaging (fMRI) scan. An emotion regulation brain network consisting of 21 nodes was described using graph analyses and compared between groups. Group differences in network topology involved decreased global connectivity and hub-like properties for the right ventrolateral prefrontal cortex (vlPFC) and decreased local network connectivity for the dorsal anterior cingulate cortex (dACC) among resilient individuals. Decreased local connectivity and increased hub-like properties of the left amygdala, decreased hub-like properties of the dACC and decreased local connectivity of the left vlPFC were observed among susceptible individuals. Regression analyses suggested that the severity of ELS (measured by self-report) correlated negatively with global connectivity and hub-like qualities for the left dorsolateral PFC (dlPFC). These preliminary results suggest functional neural connectivity patterns specific to ELS exposure and resiliency versus susceptibility to the depressogenic effects of ELS exposure.

  5. Connective-Tissue Growth Factor (CTGF/CCN2 Induces Astrogenesis and Fibronectin Expression of Embryonic Neural Cells In Vitro.

    Directory of Open Access Journals (Sweden)

    Fabio A Mendes

    Full Text Available Connective-tissue growth factor (CTGF is a modular secreted protein implicated in multiple cellular events such as chondrogenesis, skeletogenesis, angiogenesis and wound healing. CTGF contains four different structural modules. This modular organization is characteristic of members of the CCN family. The acronym was derived from the first three members discovered, cysteine-rich 61 (CYR61, CTGF and nephroblastoma overexpressed (NOV. CTGF is implicated as a mediator of important cell processes such as adhesion, migration, proliferation and differentiation. Extensive data have shown that CTGF interacts particularly with the TGFβ, WNT and MAPK signaling pathways. The capacity of CTGF to interact with different growth factors lends it an important role during early and late development, especially in the anterior region of the embryo. ctgf knockout mice have several cranio-facial defects, and the skeletal system is also greatly affected due to an impairment of the vascular-system development during chondrogenesis. This study, for the first time, indicated that CTGF is a potent inductor of gliogenesis during development. Our results showed that in vitro addition of recombinant CTGF protein to an embryonic mouse neural precursor cell culture increased the number of GFAP- and GFAP/Nestin-positive cells. Surprisingly, CTGF also increased the number of Sox2-positive cells. Moreover, this induction seemed not to involve cell proliferation. In addition, exogenous CTGF activated p44/42 but not p38 or JNK MAPK signaling, and increased the expression and deposition of the fibronectin extracellular matrix protein. Finally, CTGF was also able to induce GFAP as well as Nestin expression in a human malignant glioma stem cell line, suggesting a possible role in the differentiation process of gliomas. These results implicate ctgf as a key gene for astrogenesis during development, and suggest that its mechanism may involve activation of p44/42 MAPK signaling

  6. Neural connectivity during reward expectation dissociates psychopathic criminals from non-criminal individuals with high impulsive/antisocial psychopathic traits

    NARCIS (Netherlands)

    Geurts, D.E.; Borries, K. von; Volman, I.; Bulten, B.H.; Cools, R.; Verkes, R.J.

    2016-01-01

    Criminal behaviour poses a big challenge for society. A thorough understanding of the neurobiological mechanisms underlying criminality could optimize its prevention and management. Specifically,elucidating the neural mechanisms underpinning reward expectation might be pivotal to understanding

  7. Program Aids Simulation Of Neural Networks

    Science.gov (United States)

    Baffes, Paul T.

    1990-01-01

    Computer program NETS - Tool for Development and Evaluation of Neural Networks - provides simulation of neural-network algorithms plus software environment for development of such algorithms. Enables user to customize patterns of connections between layers of network, and provides features for saving weight values of network, providing for more precise control over learning process. Consists of translating problem into format using input/output pairs, designing network configuration for problem, and finally training network with input/output pairs until acceptable error reached. Written in C.

  8. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

    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.

  9. Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.

    Science.gov (United States)

    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.

  10. The Uniframe .Net Web Service Discovery Service

    National Research Council Canada - National Science Library

    Berbeco, Robert W

    2003-01-01

    Microsoft .NET allows the creation of distributed systems in a seamless manner Within NET small, discrete applications, referred to as Web services, are utilized to connect to each other or larger applications...

  11. Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings

    Directory of Open Access Journals (Sweden)

    Daan van Rooij

    2015-01-01

    Discussion: Subjects with ADHD fail to integrate activation within the response inhibition network and to inhibit connectivity with task-irrelevant regions. Unaffected siblings show similar alterations only during failed stop trials, as well as unique suppression of motor areas, suggesting compensatory strategies. These findings support the role of altered functional connectivity in understanding the neurobiology and familial transmission of ADHD.

  12. Neural mechanism of activity spread in the cat motor cortex and its relation to the intrinsic connectivity

    DEFF Research Database (Denmark)

    Capaday, Charles; van Vreeswijk, Carl; Ethier, Christian

    2011-01-01

    NON TECHNICAL SUMMARY{NBSP}: The motor cortex (MCx) is an important brain region that initiates and controls voluntary movements. Neurons in MCx are anatomically connected by recurrent (feedback) networks. This connectivity pattern allows neurons to communicate reciprocally with each other potent...

  13. Brief Report: Anomalous Neural Deactivations and Functional Connectivity during Receptive Language in Autism Spectrum Disorder--A Functional MRI Study

    Science.gov (United States)

    Karten, Ariel; Hirsch, Joy

    2015-01-01

    Neural mechanisms that underlie language disability in autism spectrum disorder (ASD) have been associated with reduced excitatory processes observed as positive blood oxygen level dependent (BOLD) responses. However, negative BOLD responses (NBR) associated with language and inhibitory processes have been less studied in ASD. In this study,…

  14. The neural basis of trait self-esteem revealed by the amplitude of low-frequency fluctuations and resting state functional connectivity.

    Science.gov (United States)

    Pan, Weigang; Liu, Congcong; Yang, Qian; Gu, Yan; Yin, Shouhang; Chen, Antao

    2016-03-01

    Self-esteem is an affective, self-evaluation of oneself and has a significant effect on mental and behavioral health. Although research has focused on the neural substrates of self-esteem, little is known about the spontaneous brain activity that is associated with trait self-esteem (TSE) during the resting state. In this study, we used the resting-state functional magnetic resonance imaging (fMRI) signal of the amplitude of low-frequency fluctuations (ALFFs) and resting state functional connectivity (RSFC) to identify TSE-related regions and networks. We found that a higher level of TSE was associated with higher ALFFs in the left ventral medial prefrontal cortex (vmPFC) and lower ALFFs in the left cuneus/lingual gyrus and right lingual gyrus. RSFC analyses revealed that the strengths of functional connectivity between the left vmPFC and bilateral hippocampus were positively correlated with TSE; however, the connections between the left vmPFC and right inferior frontal gyrus and posterior superior temporal sulcus were negatively associated with TSE. Furthermore, the strengths of functional connectivity between the left cuneus/lingual gyrus and right dorsolateral prefrontal cortex and anterior cingulate cortex were positively related to TSE. These findings indicate that TSE is linked to core regions in the default mode network and social cognition network, which is involved in self-referential processing, autobiographical memory and social cognition. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  15. Net Locality

    DEFF Research Database (Denmark)

    de Souza e Silva, Adriana Araujo; Gordon, Eric

    Provides an introduction to the new theory of Net Locality and the profound effect on individuals and societies when everything is located or locatable. Describes net locality as an emerging form of location awareness central to all aspects of digital media, from mobile phones, to Google Maps...... of emerging technologies, from GeoCities to GPS, Wi-Fi, Wiki Me, and Google Android....

  16. Net Neutrality

    DEFF Research Database (Denmark)

    Savin, Andrej

    2017-01-01

    Repealing “net neutrality” in the US will have no bearing on Internet freedom or security there or anywhere else.......Repealing “net neutrality” in the US will have no bearing on Internet freedom or security there or anywhere else....

  17. Symbolic processing in neural networks

    OpenAIRE

    Neto, João Pedro; Hava T Siegelmann; Costa,J.Félix

    2003-01-01

    In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, thro...

  18. Competitiveness of grid-connected solar electricity in Sweden - as seen from the perspective of the utilities and the net owners; Konkurrenskraft foer naetansluten solel i Sverige - sett ur kraftfoeretagens och naetaegarnas perspektiv

    Energy Technology Data Exchange (ETDEWEB)

    Carlstedt, Nils-Eric [Vattenfall Power Consultant AB, Stockholm (Sweden); Karlsson, Bjoern; Kjellsson, Elisabeth; Samuelsson, Olof [Faculty of Engineering (LTH), Lund University, Lund (Sweden); Neij, Lena [International Institute for Industrial Environmental Economics, Lund University, Lund (Sweden)

    2006-12-15

    The objective of this report was to analyse the competitiveness of grid-connected solar power in Sweden - and specifically the competitiveness for energy companies and net owners. In theory, solar power could to a large extent fulfil the electricity demand in Sweden, especially in the summer. However, the high cost of solar cells is a major barrier to implementation. Future technology development and increased efficiency could, however, lead to important cost reductions. The question is if such expected cost reductions would make grid-connected solar power a preferable investment option for energy companies and an interesting alternative for the net owners. The results of the study show that solar power will not be a competitive alternative for the energy companies in Sweden, not in 2020 and probably not in 2050. Other alternatives such as new investments in wind turbines and bio-mass based technology options will be producing electricity at a lower cost. Moreover, solar power will have an unfavourable production profile, generating power in the summertime when less needed. However, by using the reservoirs of the hydro power systems in Sweden as storage capacity, approximately 5 TWh solar power could be allowed in the Swedish electricity system. The results of the study indicate that solar power could have a positive effect on the electricity distribution system since distributed generation will result in lower losses in the system. Moreover, solar power will be produced during daytime when the electricity demand will peak. One of the main challenges for the net owners would be to design the net in such a way that the net and the solar cells could work together in the best possible way. Another challenge would be the high cost for connecting the solar cells to the grid; this cost needs to be reduced. Looking instead at the house-owners as possible investors, solar cells appear as a much more attractive alternative for the future, the value of the solar power is

  19. Analysis of Salinity Intrusion in the San Francisco Bay-Delta Using a GA-Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

    Science.gov (United States)

    Thompson, D. E.; Rajkumar, T.

    2002-12-01

    The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Suisan Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Suisan Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located along the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay / Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with DSM2 is that the numerical simulation takes roughly 16 hours to complete a prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple gauging stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Suisan Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the Bay-Delta is strongly tidally driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects

  20. Reduced neural connectivity but increased task-related activity during working memory in de novo Parkinson patients

    NARCIS (Netherlands)

    Trujillo, James P; Gerrits, Niels J H M; Veltman, Dick J; Berendse, Henk W; van der Werf, Ysbrand D; van den Heuvel, Odile A

    OBJECTIVE: Patients with Parkinson's disease (PD) often suffer from impairments in executive functions, such as working memory deficits. It is widely held that dopamine depletion in the striatum contributes to these impairments through decreased activity and connectivity between task-related brain

  1. The Circadian Clock Gene Period1 Connects the Molecular Clock to Neural Activity in the Suprachiasmatic Nucleus.

    Science.gov (United States)

    Kudo, Takashi; Block, Gene D; Colwell, Christopher S

    2015-01-01

    The neural activity patterns of suprachiasmatic nucleus (SCN) neurons are dynamically regulated throughout the circadian cycle with highest levels of spontaneous action potentials during the day. These rhythms in electrical activity are critical for the function of the circadian timing system and yet the mechanisms by which the molecular clockwork drives changes in the membrane are not well understood. In this study, we sought to examine how the clock gene Period1 (Per1) regulates the electrical activity in the mouse SCN by transiently and selectively decreasing levels of PER1 through use of an antisense oligodeoxynucleotide. We found that this treatment effectively reduced SCN neural activity. Direct current injection to restore the normal membrane potential partially, but not completely, returned firing rate to normal levels. The antisense treatment also reduced baseline [Ca(2+)]i levels as measured by Fura2 imaging technique. Whole cell patch clamp recording techniques were used to examine which specific potassium currents were altered by the treatment. These recordings revealed that the large conductance [Ca(2+)]i-activated potassium currents were reduced in antisense-treated neurons and that blocking this current mimicked the effects of the anti-sense on SCN firing rate. These results indicate that the circadian clock gene Per1 alters firing rate in SCN neurons and raise the possibility that the large conductance [Ca(2+)]i-activated channel is one of the targets. © The Author(s) 2015.

  2. Determination of the Most Important Soil Parameters Affecting the Availability of Phosphorus in Sistan Plain, Using Connection Weight Method in Neural Networks

    Directory of Open Access Journals (Sweden)

    H. Mir

    2016-09-01

    neurons in the hidden layer were calculated based on the trial and error method and finally the best structure was selected according to the highest R2 and the lowest RMSE value. Moreover, quantifying the importance of variables in the neural network was done through employing connection weight approach. In this method, the connection weights of input-hidden and hidden-output neurons were used to indicate the significance of variables. Results and Discussion: The values of the coefficient of variation for the soil properties were in the range of 5.66 for pH (the lowest and 69.90 for available phosphorus (the highest. The high variation of the available phosphorus could be due to the different amounts of phosphorus fertilizers consumption and their diverse rate of conversion to less soluble forms. The validation results of regression and neural network methods showed that the latter technique was more accurate compared with the multivariate linear regression method, in the estimation of available phosphorus, as multi-layer perceptron neural network with 4-6-1 layout predicts nearly 90% of available phosphorus variability using soil properties (percentage of clay, organic matter, calcium carbonate and the amount of pH; however, the obtained regression equation could explain only 43% of phosphorus variances. The reasons for this could be: 1 considering nonlinear relations between the variables in the artificial neural network method, and 2 less sensitivity of this method to the existence of error in input data, comparing with the regression method. The values of R2 and RMSE were 0.43 and 11.23, respectively for training the multivariate linear regression method and 0.91 and 4.28, respectively for training the artificial neural network method. From the investigated soil properties in the current study, the percentage of organic matter and clay were entered in the regression model, and the values of standardized regression coefficient (beta showed that the first variable is

  3. Ketamine modulates subgenual cingulate connectivity with the memory-related neural circuit—a mechanism of relevance to resistant depression?

    Directory of Open Access Journals (Sweden)

    Jing J. Wong

    2016-02-01

    Full Text Available Background. Ketamine has been reported to have efficacy as an antidepressant in several studies of treatment-resistant depression. In this study, we investigate whether an acute administration of ketamine leads to reductions in the functional connectivity of subgenual anterior cingulate cortex (sgACC with other brain regions. Methods. Thirteen right-handed healthy male subjects underwent a 15 min resting state fMRI with an infusion of intravenous ketamine (target blood level = 150 ng/ml starting at 5 min. We used a seed region centred on the sgACC and assessed functional connectivity before and during ketamine administration. Results. Before ketamine administration, positive coupling with the sgACC seed region was observed in a large cluster encompassing the anterior cingulate and negative coupling was observed with the anterior cerebellum. Following ketamine administration, sgACC activity became negatively correlated with the brainstem, hippocampus, parahippocampal gyrus, retrosplenial cortex, and thalamus. Discussion. Ketamine reduced functional connectivity of the sgACC with brain regions implicated in emotion, memory and mind wandering. It is possible the therapeutic effects of ketamine may be mediated via this mechanism, although further work is required to test this hypothesis.

  4. Part 2-The firings of many neurons and their density; the neural network its connections and field of firings.

    Science.gov (United States)

    Saaty, Thomas

    2017-02-01

    This paper is concerned with the firing of many neurons and the synthesis of these firings to develop functions and their transforms which relate chemical and electrical phenomena to the physical world. The density of such functions in the most general spaces that we encounter allows us to use linear combinations of them to approximate arbitrarily close to any phenomenon we encounter, imagine or think about. Absence of the technology needed to represent all the senses and the mathematical difficulty of making geometric representations of functions of a complex and of more general division algebra variables make it difficult to validate the mathematical outcome of this approach to neural firings. But we think that this problem will be solved in the not-too-distant future when at least the senses of smell, taste and touch would have been so mathematized that it is possible to instill these qualities in robots in some fashion. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. A re-assessment of long distance growth and connectivity of neural stem cells after severe spinal cord injury

    Science.gov (United States)

    Sharp, Kelli G.; Yee, Kelly Matsudaira; Steward, Oswald

    2014-01-01

    As part of the NIH “Facilities of Research Excellence—Spinal Cord Injury” project to support independent replication, we repeated key parts of a study reporting robust engraftment of neural stem cells (NSCs) treated with growth factors after complete spinal cord transection in rats. Rats (n = 20) received complete transections at thoracic level 3 (T3) and 2 weeks later received NSC transplants in a fibrin matrix with a growth factor cocktail using 2 different transplantation methods (with and without removal of scar tissue). Control rats (n = 9) received transections only. Hindlimb locomotor function was assessed with the BBB scale. Nine weeks post injury, reticulospinal tract axons were traced in 6 rats by injecting BDA into the reticular formation. Transplants grew to fill the lesion cavity in most rats although grafts made with scar tissue removal had large central cavities. Grafts blended extensively with host tissue obliterating the astroglial boundary at the cut ends, but in most cases there was a well-defined partition within the graft that separated rostral and caudal parts of the graft. In some cases, the partition contained non-neuronal scar tissue. There was extensive outgrowth of GFP labeled axons from the graft, but there was minimal ingrowth of host axons into the graft revealed by tract tracing and immunocy-tochemistry for 5HT. There were no statistically significant differences between transplant and control groups in the degree of locomotor recovery. Our results confirm the previous report that NSC transplants can fill lesion cavities and robustly extend axons, but reveal that most grafts do not create a continuous bridge of neural tissue between rostral and caudal segments. PMID:24747827

  6. RESTful NET

    CERN Document Server

    Flanders, Jon

    2008-01-01

    RESTful .NET is the first book that teaches Windows developers to build RESTful web services using the latest Microsoft tools. Written by Windows Communication Foundation (WFC) expert Jon Flanders, this hands-on tutorial demonstrates how you can use WCF and other components of the .NET 3.5 Framework to build, deploy and use REST-based web services in a variety of application scenarios. RESTful architecture offers a simpler approach to building web services than SOAP, SOA, and the cumbersome WS- stack. And WCF has proven to be a flexible technology for building distributed systems not necessa

  7. Petri Nets

    Indian Academy of Sciences (India)

    Associate Professor of. Computer Science and. Automation at the Indian. Institute of Science,. Bangalore. His research interests are broadly in the areas of stochastic modeling and scheduling methodologies for future factories; and object oriented modeling. GENERAL I ARTICLE. Petri Nets. 1. Overview and Foundations.

  8. Petri Nets

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 4; Issue 8. Petri Nets - Overview and Foundations. Y Narahari. General Article Volume 4 Issue 8 August 1999 pp ... Author Affiliations. Y Narahari1. Department ot Computer Science and Automation, Indian Institute of Science, Bangalore 560 012, India.

  9. Decreased functional connectivity and disrupted neural network in the prefrontal cortex of affective disorders: A resting-state fNIRS study.

    Science.gov (United States)

    Zhu, Huilin; Xu, Jie; Li, Jiangxue; Peng, Hongjun; Cai, Tingting; Li, Xinge; Wu, Shijing; Cao, Wei; He, Sailing

    2017-10-15

    Affective disorders (AD) have been conceptualized as neural network-level diseases. In this study, we utilized functional near infrared spectroscopy (fNIRS) to investigate the spontaneous hemodynamic activities in the prefrontal cortex (PFC) of the AD patients with or without medications. 42 optical channels were applied to cover the superior frontal gyrus (SFG), middle frontal gyrus (MFG), and inferior frontal gyrus (IFG), which constitute one of the most important affective networks of the brain. We performed resting-state measurements on 28 patients who were diagnosed as having AD and 30 healthy controls (HC). Raw fNIRS data were preprocessed with independent component analysis (ICA) and a band-pass filter to remove artifacts and physiological noise. By systematically analyzing the intra-regional, intrahemispheric, and interhemispheric connectivities based on the spontaneous oscillations of Δ[HbO], our results indicated that patients with AD exhibited significantly reduced intra-regional and symmetrically interhemispheric connectivities in the PFC when compared to HC. More specifically, relative to HC, AD patients showed significantly lower locally functional connectivity in the right IFG, and poor long-distance connectivity between bilateral IFG. In addition, AD patients without medication presented more disrupted cortical organizations in the PFC, and the severity of self-reported symptoms of depression was negatively correlated with the strength of intra-regional and symmetrically interhemispheric connectivity in the PFC. Regarding the measuring technique, fNIRS has restricted measurement depth and spatial resolution. During the study, the subgroups of AD, such as major depressive disorder, bipolar, comorbidity, or non-comorbidity, dosage of psychotropic drugs, as well as different types of pharmacological responses were not distinguished and systematically compared. Furthermore, due to the limitation of the research design, it was still not very clear how

  10. A newly identified mouse hypothalamic area having bidirectional neural connections with the lateral septum: the perifornical area of the anterior hypothalamus rich in chondroitin sulfate proteoglycans.

    Science.gov (United States)

    Horii-Hayashi, Noriko; Sasagawa, Takayo; Hashimoto, Takashi; Kaneko, Takeshi; Takeuchi, Kosei; Nishi, Mayumi

    2015-09-01

    While previous studies and brain atlases divide the hypothalamus into many nuclei and areas, uncharacterised regions remain. Here, we report a new region in the mouse anterior hypothalamus (AH), a triangular-shaped perifornical area of the anterior hypothalamus (PeFAH) between the paraventricular hypothalamic nucleus and fornix, that abundantly expresses chondroitin sulfate proteoglycans (CSPGs). The PeFAH strongly stained with markers for chondroitin sulfate/CSPGs such as Wisteria floribunda agglutinin and antibodies against aggrecan and chondroitin 6 sulfate. Nissl-stained sections of the PeFAH clearly distinguished it as a region of comparatively low density compared to neighboring regions, the paraventricular nucleus and central division of the anterior hypothalamic area. Immunohistochemical and DNA microarray analyses suggested that PeFAH contains sparsely distributed calretinin-positive neurons and a compact cluster of enkephalinergic neurons. Neuronal tract tracing revealed that both enkephalin- and calretinin-positive neurons project to the lateral septum (LS), while the PeFAH receives input from calbindin-positive LS neurons. These results suggest bidirectional connections between the PeFAH and LS. Considering neuronal subtype and projection, part of PeFAH that includes a cluster of enkephalinergic neurons is similar to the rat perifornical nucleus and guinea pig magnocellular dorsal nucleus. Finally, we examined c-Fos expression after several types of stimuli and found that PeFAH neuronal activity was increased by psychological but not homeostatic stressors. These findings suggest that the PeFAH is a source of enkephalin peptides in the LS and indicate that bidirectional neural connections between these regions may participate in controlling responses to psychological stressors. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  11. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-07-01

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

  12. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

    Science.gov (United States)

    Zhang, Zhen; Ma, Cheng; Zhu, Rong

    2016-10-14

    High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  13. Neural network optimization, components, and design selection

    Science.gov (United States)

    Weller, Scott W.

    1990-07-01

    Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and noncontrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of muting and classification types of optimization problems. Neural Networks are constructed from neurons, which in electronics or software attempt to model but are not constrained by the real thing, i.e., neurons in our gray matter. Neurons are simple processing units connected to many other neurons over pathways which modify the incoming signals. A single synthetic neuron typically sums its weighted inputs, runs this sum through a non-linear function, and produces an output. In the brain, neurons are connected in a complex topology: in hardware/software the topology is typically much simpler, with neurons lying side by side, forming layers of neurons which connect to the layer of neurons which receive their outputs. This simplistic model is much easier to construct than the real thing, and yet can solve real problems. The information in a network, or its "memory", is completely contained in the weights on the connections from one neuron to another. Establishing these weights is called "training" the network. Some networks are trained by design -- once constructed no further learning takes place. Other types of networks require iterative training once wired up, but are not trainable once taught Still other types of networks can continue to learn after initial construction. The main benefit to using Neural Networks is their ability to work with conflicting or incomplete ("fuzzy") data sets. This ability and its usefulness will become evident in the following

  14. Neural network technologies

    Science.gov (United States)

    Villarreal, James A.

    1991-01-01

    A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

  15. Understanding Net Zero Energy Buildings

    DEFF Research Database (Denmark)

    Salom, Jaume; Widén, Joakim; Candanedo, José

    2011-01-01

    Although several alternative definitions exist, a Net-Zero Energy Building (Net ZEB) can be succinctly described as a grid-connected building that generates as much energy as it uses over a year. The “net-zero” balance is attained by applying energy conservation and efficiency measures...... and by incorporating renewable energy systems. While based on annual balances, a complete description of a Net ZEB requires examining the system at smaller time-scales. This assessment should address: (a) the relationship between power generation and building loads and (b) the resulting interaction with the power grid....... This paper presents and categorizes quantitative indicators suitable to describe both aspects of the building’s performance. These indicators, named LMGI - Load Matching and Grid Interaction indicators, are easily quantifiable and could complement the output variables of existing building simulation tools...

  16. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    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...

  17. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    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...

  18. Isolated unit tests in .Net

    OpenAIRE

    Haukilehto, Tero

    2013-01-01

    In this thesis isolation in unit testing is studied to get a precise picture of the isolation frameworks available for .Net environment. At the beginning testing is discussed in theory with the benefits and the problems it may have been linked with. The theory includes software development in general in connection with testing. Theory of isolation is also described before the actual isolation frameworks are represented. Common frameworks are described in more detail and comparable informa...

  19. Experiments and simulation of a net closing mechanism for tether-net capture of space debris

    Science.gov (United States)

    Sharf, Inna; Thomsen, Benjamin; Botta, Eleonora M.; Misra, Arun K.

    2017-10-01

    This research addresses the design and testing of a debris containment system for use in a tether-net approach to space debris removal. The tether-net active debris removal involves the ejection of a net from a spacecraft by applying impulses to masses on the net, subsequent expansion of the net, the envelopment and capture of the debris target, and the de-orbiting of the debris via a tether to the chaser spacecraft. To ensure a debris removal mission's success, it is important that the debris be successfully captured and then, secured within the net. To this end, we present a concept for a net closing mechanism, which we believe will permit consistently successful debris capture via a simple and unobtrusive design. This net closing system functions by extending the main tether connecting the chaser spacecraft and the net vertex to the perimeter and around the perimeter of the net, allowing the tether to actuate closure of the net in a manner similar to a cinch cord. A particular embodiment of the design in a laboratory test-bed is described: the test-bed itself is comprised of a scaled-down tether-net, a supporting frame and a mock-up debris. Experiments conducted with the facility demonstrate the practicality of the net closing system. A model of the net closure concept has been integrated into the previously developed dynamics simulator of the chaser/tether-net/debris system. Simulations under tether tensioning conditions demonstrate the effectiveness of the closure concept for debris containment, in the gravity-free environment of space, for a realistic debris target. The on-ground experimental test-bed is also used to showcase its utility for validating the dynamics simulation of the net deployment, and a full-scale automated setup would make possible a range of validation studies of other aspects of a tether-net debris capture mission.

  20. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  1. Bayesian regularization of neural networks.

    Science.gov (United States)

    Burden, Frank; Winkler, Dave

    2008-01-01

    Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of a ridge regression. The advantage of BRANNs is that the models are robust and the validation process, which scales as O(N2) in normal regression methods, such as back propagation, is unnecessary. These networks provide solutions to a number of problems that arise in QSAR modeling, such as choice of model, robustness of model, choice of validation set, size of validation effort, and optimization of network architecture. They are difficult to overtrain, since evidence procedures provide an objective Bayesian criterion for stopping training. They are also difficult to overfit, because the BRANN calculates and trains on a number of effective network parameters or weights, effectively turning off those that are not relevant. This effective number is usually considerably smaller than the number of weights in a standard fully connected back-propagation neural net. Automatic relevance determination (ARD) of the input variables can be used with BRANNs, and this allows the network to "estimate" the importance of each input. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables for modeling the activity data. This chapter outlines the equations that define the BRANN method plus a flowchart for producing a BRANN-QSAR model. Some results of the use of BRANNs on a number of data sets are illustrated and compared with other linear and nonlinear models.

  2. NA-NET numerical analysis net

    Energy Technology Data Exchange (ETDEWEB)

    Dongarra, J. [Tennessee Univ., Knoxville, TN (United States). Dept. of Computer Science]|[Oak Ridge National Lab., TN (United States); Rosener, B. [Tennessee Univ., Knoxville, TN (United States). Dept. of Computer Science

    1991-12-01

    This report describes a facility called NA-NET created to allow numerical analysts (na) an easy method of communicating with one another. The main advantage of the NA-NET is uniformity of addressing. All mail is addressed to the Internet host ``na-net.ornl.gov`` at Oak Ridge National Laboratory. Hence, members of the NA-NET do not need to remember complicated addresses or even where a member is currently located. As long as moving members change their e-mail address in the NA-NET everything works smoothly. The NA-NET system is currently located at Oak Ridge National Laboratory. It is running on the same machine that serves netlib. Netlib is a separate facility that distributes mathematical software via electronic mail. For more information on netlib consult, or send the one-line message ``send index`` to netlib{at}ornl.gov. The following report describes the current NA-NET system from both a user`s perspective and from an implementation perspective. Currently, there are over 2100 members in the NA-NET. An average of 110 mail messages pass through this facility daily.

  3. NA-NET numerical analysis net

    Energy Technology Data Exchange (ETDEWEB)

    Dongarra, J. (Tennessee Univ., Knoxville, TN (United States). Dept. of Computer Science Oak Ridge National Lab., TN (United States)); Rosener, B. (Tennessee Univ., Knoxville, TN (United States). Dept. of Computer Science)

    1991-12-01

    This report describes a facility called NA-NET created to allow numerical analysts (na) an easy method of communicating with one another. The main advantage of the NA-NET is uniformity of addressing. All mail is addressed to the Internet host na-net.ornl.gov'' at Oak Ridge National Laboratory. Hence, members of the NA-NET do not need to remember complicated addresses or even where a member is currently located. As long as moving members change their e-mail address in the NA-NET everything works smoothly. The NA-NET system is currently located at Oak Ridge National Laboratory. It is running on the same machine that serves netlib. Netlib is a separate facility that distributes mathematical software via electronic mail. For more information on netlib consult, or send the one-line message send index'' to netlib{at}ornl.gov. The following report describes the current NA-NET system from both a user's perspective and from an implementation perspective. Currently, there are over 2100 members in the NA-NET. An average of 110 mail messages pass through this facility daily.

  4. Evidence of adaptations of locomotor neural drive in response to enhanced intermuscular connectivity between the triceps surae muscles of the rat

    OpenAIRE

    Bernabei, Michel; van Dieën, Jaap H.; Maas, Huub

    2017-01-01

    The aims of this study were to investigate changes 1) in the coordination of activation of the triceps surae muscle group, and 2) in muscle belly length of soleus (SO) and lateral gastrocnemius (LG) during locomotion (trotting) in response to increased stiffness of intermuscular connective tissues in the rat. We measured muscle activation and muscle belly lengths, as well as hindlimb kinematics, before and after an artificial enhancement of the connectivity between SO and LG muscles obtained ...

  5. Evidence of adaptations of locomotor neural drive in response to enhanced intermuscular connectivity between the triceps surae muscles of the rat.

    Science.gov (United States)

    Bernabei, Michel; van Dieën, Jaap H; Maas, Huub

    2017-09-01

    The aims of this study were to investigate changes 1) in the coordination of activation of the triceps surae muscle group, and 2) in muscle belly length of soleus (SO) and lateral gastrocnemius (LG) during locomotion (trotting) in response to increased stiffness of intermuscular connective tissues in the rat. We measured muscle activation and muscle belly lengths, as well as hindlimb kinematics, before and after an artificial enhancement of the connectivity between SO and LG muscles obtained by implanting a tissue-integrating surgical mesh at the muscles' interface. We found that SO muscle activation decreased to 62%, while activation of LG and medial gastrocnemius muscles increased to 134 and 125%, respectively, compared with the levels measured preintervention. Although secondary additional or amplified activation bursts were observed with enhanced connectivity, the primary pattern of activation over the stride and the burst duration were not affected by the intervention. Similar muscle length changes after manipulation were observed, suggesting that length feedback from spindle receptors within SO and LG was not affected by the connectivity enhancement. We conclude that peripheral mechanical constraints given by morphological (re)organization of connective tissues linking synergists are taken into account by the central nervous system. The observed shift in activity toward the gastrocnemius muscles after the intervention suggests that these larger muscles are preferentially recruited when the soleus has a similar mechanical disadvantage in that it produces an unwanted flexion moment around the knee.NEW & NOTEWORTHY Connective tissue linkages between muscle-tendon units may act as an additional mechanical constraint on the musculoskeletal system, thereby reducing the spectrum of solutions for performing a motor task. We found that intermuscular coordination changes following intermuscular connectivity enhancement. Besides showing that the extent of such

  6. Neural Plasticity in Human Brain Connectivity: The Effects of Long Term Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease

    OpenAIRE

    van Hartevelt, Tim J; Joana Cabral; Gustavo Deco; Arne Møller; Green, Alexander L.; Aziz, Tipu Z.; Morten L Kringelbach

    2014-01-01

    Background: Positive clinical outcomes are now well established for deep brain stimulation, but little is known about the effects of long-term deep brain stimulation on brain structural and functional connectivity. Here, we used the rare opportunity to acquire pre- and postoperative diffusion tensor imaging in a patient undergoing deep brain stimulation in bilateral subthalamic nuclei for Parkinson’s Disease. This allowed us to analyse the differences in structural connectivity before and aft...

  7. Flexible body control using neural networks

    Science.gov (United States)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  8. The KM3NeT project

    Energy Technology Data Exchange (ETDEWEB)

    Katz, U.F., E-mail: katz@physik.uni-erlangen.d [Erlangen Centre for Astroparticle Physics (ECAP), University of Erlangen-Nuernberg, Erwin-Rommel-Str. 1, 91058 Erlangen (Germany)

    2011-01-21

    The KM3NeT research infrastructure in the deep Mediterranean Sea will host a multi-cubic-kilometre neutrino telescope and provide connectivity for continuous, long-term measurements of earth and sea sciences, such as geology, marine biology and oceanography. The KM3NeT neutrino telescope will complement the IceCube telescope currently being installed at the South Pole in its field of view and surpass its sensitivity by a substantial factor. In this document the major aspects of the KM3NeT technical design are described and the expected physics sensitivity is discussed. Finally, the expected time line towards construction is presented.

  9. Net Ecosystem Carbon Flux

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Net Ecosystem Carbon Flux is defined as the year-over-year change in Total Ecosystem Carbon Stock, or the net rate of carbon exchange between an ecosystem and the...

  10. Professional Enterprise NET

    CERN Document Server

    Arking, Jon

    2010-01-01

    Comprehensive coverage to help experienced .NET developers create flexible, extensible enterprise application code If you're an experienced Microsoft .NET developer, you'll find in this book a road map to the latest enterprise development methodologies. It covers the tools you will use in addition to Visual Studio, including Spring.NET and nUnit, and applies to development with ASP.NET, C#, VB, Office (VBA), and database. You will find comprehensive coverage of the tools and practices that professional .NET developers need to master in order to build enterprise more flexible, testable, and ext

  11. Evidence of adaptations of locomotor neural drive in response to enhanced intermuscular connectivity between the triceps surae muscles of the rat

    NARCIS (Netherlands)

    Bernabei, Michel; van Dieën, Jaap H.; Maas, Huub

    2017-01-01

    The aims of this study were to investigate changes 1) in the coordination of activation of the triceps surae muscle group, and 2) in muscle belly length of soleus (SO) and lateral gastrocnemius (LG) during locomotion (trotting) in response to increased stiffness of intermuscular connective tissues

  12. Large-scale directional connections among multi resting-state neural networks in human brain: a functional MRI and Bayesian network modeling study.

    Science.gov (United States)

    Li, Rui; Chen, Kewei; Fleisher, Adam S; Reiman, Eric M; Yao, Li; Wu, Xia

    2011-06-01

    This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Neural network optimization, components, and design selection

    Science.gov (United States)

    Weller, Scott W.

    1991-01-01

    Neural Networks are part of a revived technology which has received a lot of hype in recent years. As is apt to happen in any hyped technology, jargon and predictions make its assimilation and application difficult. Nevertheless, Neural Networks have found use in a number of areas, working on non-trivial and non-contrived problems. For example, one net has been trained to "read", translating English text into phoneme sequences. Other applications of Neural Networks include data base manipulation and the solving of routing and classification types of optimization problems. It was their use in optimization that got me involved with Neural Networks. As it turned out, "optimization" used in this context was somewhat misleading, because while some network configurations could indeed solve certain kinds of optimization problems, the configuring or "training" of a Neural Network itself is an optimization problem, and most of the literature which talked about Neural Nets and optimization in the same breath did not speak to my goal of using Neural Nets to help solve lens optimization problems. I did eventually apply Neural Network to lens optimization, and I will touch on those results. The application of Neural Nets to the problem of lens selection was much more successful, and those results will dominate this paper.

  14. WaveNet

    Science.gov (United States)

    2015-10-30

    Coastal Inlets Research Program WaveNet WaveNet is a web-based, Graphical-User-Interface ( GUI ) data management tool developed for Corps coastal...generates tabular and graphical information for project planning and design documents. The WaveNet is a web-based GUI designed to provide users with a...data from different sources, and employs a combination of Fortran, Python and Matlab codes to process and analyze data for USACE applications

  15. Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt

    1991-01-01

    This paper describes how Coloured Petri Nets (CP-nets) have been developed — from being a promising theoretical model to being a full-fledged language for the design, specification, simulation, validation and implementation of large software systems (and other systems in which human beings and...... use of CP-nets — because it means that the function representation and the translations (which are a bit mathematically complex) no longer are parts of the basic definition of CP-nets. Instead they are parts of the invariant method (which anyway demands considerable mathematical skills...

  16. Game Coloured Petri Nets

    DEFF Research Database (Denmark)

    Westergaard, Michael

    2006-01-01

    This paper introduces the notion of game coloured Petri nets. This allows the modeler to explicitly model what parts of the model comprise the modeled system and what parts are the environment of the modeled system. We give the formal definition of game coloured Petri nets, a means of reachability...... analysis of this net class, and an application of game coloured Petri nets to automatically generate easy-to-understand visualizations of the model by exploiting the knowledge that some parts of the model are not interesting from a visualization perspective (i.e. they are part of the environment...

  17. Programming NET Web Services

    CERN Document Server

    Ferrara, Alex

    2007-01-01

    Web services are poised to become a key technology for a wide range of Internet-enabled applications, spanning everything from straight B2B systems to mobile devices and proprietary in-house software. While there are several tools and platforms that can be used for building web services, developers are finding a powerful tool in Microsoft's .NET Framework and Visual Studio .NET. Designed from scratch to support the development of web services, the .NET Framework simplifies the process--programmers find that tasks that took an hour using the SOAP Toolkit take just minutes. Programming .NET

  18. Annotating Coloured Petri Nets

    DEFF Research Database (Denmark)

    Lindstrøm, Bo; Wells, Lisa Marie

    2002-01-01

    -net. An example of such auxiliary information is a counter which is associated with a token to be able to do performance analysis. Modifying colour sets and arc inscriptions in a CP-net to support a specific use may lead to creation of several slightly different CP-nets – only to support the different uses...... a method which makes it possible to associate auxiliary information, called annotations, with tokens without modifying the colour sets of the CP-net. Annotations are pieces of information that are not essential for determining the behaviour of the system being modelled, but are rather added to support...

  19. Homology Groups of a Pipeline Petri Net

    Directory of Open Access Journals (Sweden)

    A. A. Husainov

    2013-01-01

    Full Text Available Petri net is said to be elementary if every place can contain no more than one token. In this paper, it is studied topological properties of the elementary Petri net for a pipeline consisting of n functional devices. If the work of the functional devices is considered continuous, we can come to some topological space of “intermediate” states. In the paper, it is calculated the homology groups of this topological space. By induction on n, using the Addition Sequence for homology groups of semicubical sets, it is proved that in dimension 0 and 1 the integer homology groups of these nets are equal to the group of integers, and in the remaining dimensions are zero. Directed homology groups are studied. A connection of these groups with deadlocks and newsletters is found. This helps to prove that all directed homology groups of the pipeline elementary Petri nets are zeroth.

  20. Entrainment of Arteriole Vasomotor Fluctuations by Neural Activity Is a Basis of Blood-Oxygenation-Level-Dependent "Resting-State" Connectivity.

    Science.gov (United States)

    Mateo, Celine; Knutsen, Per M; Tsai, Philbert S; Shih, Andy Y; Kleinfeld, David

    2017-11-15

    Resting-state signals in blood-oxygenation-level-dependent (BOLD) imaging are used to parcellate brain regions and define "functional connections" between regions. Yet a physiological link between fluctuations in blood oxygenation with those in neuronal signaling pathways is missing. We present evidence from studies on mouse cortex that modulation of vasomotion, i.e., intrinsic ultra-slow (0.1 Hz) fluctuations in arteriole diameter, provides this link. First, ultra-slow fluctuations in neuronal signaling, which occur as an envelope over γ-band activity, entrains vasomotion. Second, optogenetic manipulations confirm that entrainment is unidirectional. Third, co-fluctuations in the diameter of pairs of arterioles within the same hemisphere diminish to chance for separations >1.4 mm. Yet the diameters of arterioles in distant (>5 mm), mirrored transhemispheric sites strongly co-fluctuate; these correlations are diminished in acallosal mice. Fourth, fluctuations in arteriole diameter coherently drive fluctuations in blood oxygenation. Thus, entrainment of vasomotion links neuronal pathways to functional connections. Copyright © 2017. Published by Elsevier Inc.

  1. Artificial Neural Networks·

    Indian Academy of Sciences (India)

    differences between biological neural networks (BNNs) of the brain and ANN s. A thorough understanding of ... neurons. Artificial neural models are loosely based on biology since a complete understanding of the .... A learning scheme for updating a neuron's connections (weights) was proposed by Donald Hebb in 1949.

  2. Using NetLogo in the Data Farming Environment

    OpenAIRE

    Koehler, Matthew

    2006-01-01

    from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 1 Workshop 13 NetLogo is a freely available agent-based modeling environment being developed by Northwestern University’s Center for Connected Learning (ccl.northwestern.edu/ netlogø). NetLogo is an excellent environment for creating simpler or smaller-scale agent-based models or prototyping more complex models. NetLogo’s strengths include using a very easy to learn and flexible ...

  3. Handbook of Brain Connectivity

    CERN Document Server

    Jirsa, Viktor K

    2007-01-01

    Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring struct...

  4. Net zero water

    CSIR Research Space (South Africa)

    Lindeque, M

    2013-01-01

    Full Text Available Is it possible to develop a building that uses a net zero amount of water? In recent years it has become evident that it is possible to have buildings that use a net zero amount of electricity. This is possible when the building is taken off...

  5. SolNet

    DEFF Research Database (Denmark)

    Jordan, Ulrike; Vajen, Klaus; Bales, Chris

    2014-01-01

    SolNet, founded in 2006, is the first coordinated International PhD education program on Solar Thermal Engineering. The SolNet network is coordinated by the Institute of Thermal Engineering at Kassel University, Germany. The network offers PhD courses on solar heating and cooling, conference...

  6. High serotonin levels during brain development alter the structural input-output connectivity of neural networks in the rat somatosensory layer IV

    Directory of Open Access Journals (Sweden)

    Stéphanie eMiceli

    2013-06-01

    Full Text Available Homeostatic regulation of serotonin (5-HT concentration is critical for normal topographical organization and development of thalamocortical (TC afferent circuits. Down-regulation of the serotonin transporter (SERT and the consequent impaired reuptake of 5-HT at the synapse, results in a reduced terminal branching of developing TC afferents within the primary somatosensory cortex (S1. Despite the presence of multiple genetic models, the effect of high extracellular 5-HT levels on the structure and function of developing intracortical neural networks is far from being understood. Here, using juvenile SERT knockout (SERT-/- rats we investigated, in vitro, the effect of increased 5-HT levels on the structural organization of (i the thalamocortical projections of the ventroposteromedial thalamic nucleus towards S1, (ii the general barrel-field pattern and (iii the electrophysiological and morphological properties of the excitatory cell population in layer IV of S1 (spiny stellate and pyramidal cells. Our results confirmed previous findings that high levels of 5-HT during development lead to a reduction of the topographical precision of TCA projections towards the barrel cortex. Also, the barrel pattern was altered but not abolished in SERT-/- rats. In layer IV, both excitatory spiny stellate and pyramidal cells showed a significantly reduced intracolumnar organization of their axonal projections. In addition, the layer IV spiny stellate cells gave rise to a prominent projection towards the infragranular layer Vb. Our findings point to a structural and functional reorganization, of TCAs, as well as early stage intracortical microcircuitry, following the disruption of 5-HT reuptake during critical developmental periods. The increased projection pattern of the layer IV neurons suggests that the intracortical network changes are not limited to the main entry layer IV but may also affect the subsequent stages of the canonical circuits of the barrel

  7. A new class of methods for functional connectivity estimation

    Science.gov (United States)

    Lin, Wutu

    Measuring functional connectivity from neural recordings is important in understanding processing in cortical networks. The covariance-based methods are the current golden standard for functional connectivity estimation. However, the link between the pair-wise correlations and the physiological connections inside the neural network is unclear. Therefore, the power of inferring physiological basis from functional connectivity estimation is limited. To build a stronger tie and better understand the relationship between functional connectivity and physiological neural network, we need (1) a realistic model to simulate different types of neural recordings with known ground truth for benchmarking; (2) a new functional connectivity method that produce estimations closely reflecting the physiological basis. In this thesis, (1) I tune a spiking neural network model to match with human sleep EEG data, (2) introduce a new class of methods for estimating connectivity from different kinds of neural signals and provide theory proof for its superiority, (3) apply it to simulated fMRI data as an application.

  8. Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions

    Science.gov (United States)

    Clark, John W.; Rafelski, Johann; Winston, Jeffrey V.

    1985-07-01

    Aspects of brain function are examined in terms of a nonlinear dynamical system of highly interconnected neuron-like binary decision elements. The model neurons operate synchronously in discrete time, according to deterministic or probabilistic equations of motion. Plasticity of the nervous system, which underlies such cognitive collective phenomena as adaptive development, learning, and memory, is represented by temporal modification of interneuronal connection strengths depending on momentary or recent neural activity. A formal basis is presented for the construction of local plasticity algorithms, or connection-modification routines, spanning a large class. To build an intuitive understanding of the behavior of discrete-time network models, extensive computer simulations have been carried out (a) for nets with fixed, quasirandom connectivity and (b) for nets with connections that evolve under one or another choice of plasticity algorithm. From the former experiments, insights are gained concerning the spontaneous emergence of order in the form of cyclic modes of neuronal activity. In the course of the latter experiments, a simple plasticity routine (“brainwashing,” or “anti-learning”) was identified which, applied to nets with initially quasirandom connectivity, creates model networks which provide more felicitous starting points for computer experiments on the engramming of content-addressable memories and on learning more generally. The potential relevance of this algorithm to developmental neurobiology and to sleep states is discussed. The model considered is at the same time a synthesis of earlier synchronous neural-network models and an elaboration upon them; accordingly, the present article offers both a focused review of the dynamical properties of such systems and a selection of new findings derived from computer simulation.

  9. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  10. Spectral and bispectral feature-extraction neural networks for texture classification

    Science.gov (United States)

    Kameyama, Keisuke; Kosugi, Yukio

    1997-10-01

    A neural network model (Kernel Modifying Neural Network: KM Net) specialized for image texture classification, which unifies the filtering kernels for feature extraction and the layered network classifier, will be introduced. The KM Net consists of a layer of convolution kernels that are constrained to be 2D Gabor filters to guarantee efficient spectral feature localization. The KM Net enables an automated feature extraction in multi-channel texture classification through simultaneous modification of the Gabor kernel parameters (central frequency and bandwidth) and the connection weights of the subsequent classifier layers by a backpropagation-based training rule. The capability of the model and its training rule was verified via segmentation of common texture mosaic images. In comparison with the conventional multi-channel filtering method which uses numerous filters to cover the spatial frequency domain, the proposed strategy can greatly reduce the computational cost both in feature extraction and classification. Since the adaptive Gabor filtering scheme is also applicable to band selection in moment spectra of higher orders, the network model was extended for adaptive bispectral filtering for extraction of the phase relation among the frequency components. The ability of this Bispectral KM Net was demonstrated in the discrimination of visually discriminable synthetic textures with identical local power spectral distributions.

  11. Pro NET Best Practices

    CERN Document Server

    Ritchie, Stephen D

    2011-01-01

    Pro .NET Best Practices is a practical reference to the best practices that you can apply to your .NET projects today. You will learn standards, techniques, and conventions that are sharply focused, realistic and helpful for achieving results, steering clear of unproven, idealistic, and impractical recommendations. Pro .NET Best Practices covers a broad range of practices and principles that development experts agree are the right ways to develop software, which includes continuous integration, automated testing, automated deployment, and code analysis. Whether the solution is from a free and

  12. Getting to Net Zero

    Energy Technology Data Exchange (ETDEWEB)

    2016-09-01

    The technology necessary to build net zero energy buildings (NZEBs) is ready and available today, however, building to net zero energy performance levels can be challenging. Energy efficiency measures, onsite energy generation resources, load matching and grid interaction, climatic factors, and local policies vary from location to location and require unique methods of constructing NZEBs. It is recommended that Components start looking into how to construct and operate NZEBs now as there is a learning curve to net zero construction and FY 2020 is just around the corner.

  13. Instant Lucene.NET

    CERN Document Server

    Heydt, Michael

    2013-01-01

    Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. A step-by-step guide that helps you to index, search, and retrieve unstructured data with the help of Lucene.NET.Instant Lucene.NET How-to is essential for developers new to Lucene and Lucene.NET who are looking to get an immediate foundational understanding of how to use the library in their application. It's assumed you have programming experience in C# already, but not that you have experience with search techniques such as information retrieval theory (although there will be a l

  14. [Neural codes for perception].

    Science.gov (United States)

    Romo, R; Salinas, E; Hernández, A; Zainos, A; Lemus, L; de Lafuente, V; Luna, R

    This article describes experiments designed to show the neural codes associated with the perception and processing of tactile information. The results of these experiments have shown the neural activity correlated with tactile perception. The neurones of the primary somatosensory cortex (S1) represent the physical attributes of tactile perception. We found that these representations correlated with tactile perception. By means of intracortical microstimulation we demonstrated the causal relationship between S1 activity and tactile perception. In the motor areas of the frontal lobe is to be found the connection between sensorial and motor representation whilst decisions are being taken. S1 generates neural representations of the somatosensory stimuli which seen to be sufficient for tactile perception. These neural representations are subsequently processed by central areas to S1 and seem useful in perception, memory and decision making.

  15. Net Zero Energy Buildings

    DEFF Research Database (Denmark)

    Marszal, Anna Joanna; Bourrelle, Julien S.; Musall, Eike

    2010-01-01

    and identify possible renewable energy supply options which may be considered in calculations. Finally, the gap between the methodology proposed by each organisation and their respective national building code is assessed; providing an overview of the possible changes building codes will need to undergo......The international cooperation project IEA SHC Task 40 / ECBCS Annex 52 “Towards Net Zero Energy Solar Buildings”, attempts to develop a common understanding and to set up the basis for an international definition framework of Net Zero Energy Buildings (Net ZEBs). The understanding of such buildings...... parameters used in the calculations are discussed and the various renewable supply options considered in the methodologies are summarised graphically. Thus, the paper helps to understand different existing approaches to calculate energy balance in Net ZEBs, highlights the importance of variables selection...

  16. PhysioNet

    Data.gov (United States)

    U.S. Department of Health & Human Services — The PhysioNet Resource is intended to stimulate current research and new investigations in the study of complex biomedical and physiologic signals. It offers free...

  17. NetSig

    DEFF Research Database (Denmark)

    Horn, Heiko; Lawrence, Michael S; Chouinard, Candace R

    2018-01-01

    Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (Net......Sig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that Net......Sig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified...

  18. TideNet

    Science.gov (United States)

    2015-10-30

    query tide data sources in a desired geographic region of USA and its territories (Figure 1). Users can select a tide data source through the Google Map ...select data sources according to the desired geographic region. It uses the Google Map interface to display data from different sources. Recent...Coastal Inlets Research Program TideNet The TideNet is a web-based Graphical User Interface (GUI) that provides users with GIS mapping tools to

  19. Interaction Nets in Russian

    OpenAIRE

    Salikhmetov, Anton

    2013-01-01

    Draft translation to Russian of Chapter 7, Interaction-Based Models of Computation, from Models of Computation: An Introduction to Computability Theory by Maribel Fernandez. "In this chapter, we study interaction nets, a model of computation that can be seen as a representative of a class of models based on the notion of 'computation as interaction'. Interaction nets are a graphical model of computation devised by Yves Lafont in 1990 as a generalisation of the proof structures of linear logic...

  20. Programming NET 35

    CERN Document Server

    Liberty, Jesse

    2009-01-01

    Bestselling author Jesse Liberty and industry expert Alex Horovitz uncover the common threads that unite the .NET 3.5 technologies, so you can benefit from the best practices and architectural patterns baked into the new Microsoft frameworks. The book offers a Grand Tour" of .NET 3.5 that describes how the principal technologies can be used together, with Ajax, to build modern n-tier and service-oriented applications. "

  1. -Net Approach to Sensor -Coverage

    Directory of Open Access Journals (Sweden)

    Fusco Giordano

    2010-01-01

    Full Text Available Wireless sensors rely on battery power, and in many applications it is difficult or prohibitive to replace them. Hence, in order to prolongate the system's lifetime, some sensors can be kept inactive while others perform all the tasks. In this paper, we study the -coverage problem of activating the minimum number of sensors to ensure that every point in the area is covered by at least sensors. This ensures higher fault tolerance, robustness, and improves many operations, among which position detection and intrusion detection. The -coverage problem is trivially NP-complete, and hence we can only provide approximation algorithms. In this paper, we present an algorithm based on an extension of the classical -net technique. This method gives an -approximation, where is the number of sensors in an optimal solution. We do not make any particular assumption on the shape of the areas covered by each sensor, besides that they must be closed, connected, and without holes.

  2. Waterscape determinants of net mercury methylation in a tropical wetland.

    Science.gov (United States)

    Lázaro, Wilkinson L; Díez, Sergi; da Silva, Carolina J; Ignácio, Áurea R A; Guimarães, Jean R D

    2016-10-01

    The periphyton associated with freshwater macrophyte roots is the main site of Hg methylation in different wetland environments in the world. The aim of this study was to test the use of connectivity metrics of water bodies, in the context of patches, in a tropical waterscape wetland (Guapore River, Amazonia, Brazil) as a predictor of potential net methylmercury (MeHg) production by periphyton communities. We sampled 15 lakes with different patterns of lateral connectivity with the main river channel, performing net mercury methylation potential tests in incubations with local water and Eichhornia crassipes root-periphyton samples, using (203)HgCl2 as a tracer. Physico-chemical variables, landscape data (morphological characteristics, land use, and lateral connection type of water bodies) using GIS resources and field data were analyzed with Generalized Additive Models (GAM). The net Me(203)Hg production (as % of total added (203)Hg) was expressive (6.2-25.6%) showing that periphyton is an important matrix in MeHg production. The model that best explained the variation in the net Me(203)Hg production (76%) was built by the variables: connection type, total phosphorus and dissolved organic carbon (DOC) in water (AICc=48.324, p=0.001). Connection type factor was the best factor to model fit (r(2)=0.32; p=0.008) and temporarily connected lakes had higher rates of net mercury methylation. Both DOC and total phosphorus showed positive significant covariation with the net methylation rates (r(2)=0.26; p=0.008 and r(2)=0.21; p=0.012 respectively). Our study suggests a strong relationship between rates of net MeHg production in this tropical area and the type of water body and its hydrological connectivity within the waterscape. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    Schroeder, Alex

    2015-11-01

    The Connected Traveler project is a multi-disciplinary undertaking that seeks to validate potential for transformative transportation system energy savings by incentivizing efficient traveler behavior. This poster outlines various aspects of the Connected Traveler project, including market opportunity, understanding traveler behavior and decision-making, automation and connectivity, and a projected timeline for Connected Traveler's key milestones.

  4. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

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

    1990-01-01

    A report is presented on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal...... understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given......, and an explanation facility designed to help neural signal understanding is described. The results are compared to those obtained with a knowledge-based signal interpretation system using the same instrument and data...

  5. La plataforma .NET

    OpenAIRE

    Fornas Estrada, Miquel

    2008-01-01

    L'aparició de la plataforma .NET Framework ha suposat un canvi molt important en la forma de crear i distribuir aplicacions, degut a que incorpora una sèrie d'innovacions tècniques i productives que simplifiquen molt les tasques necessàries per desenvolupar un projecte. La aparición de la plataforma. NET Framework ha supuesto un cambio muy importante en la forma de crear y distribuir aplicaciones, debido a que incorpora una serie de innovaciones técnicas y productivas que simplifican mucho...

  6. Biological Petri Nets

    CERN Document Server

    Wingender, E

    2011-01-01

    It was suggested some years ago that Petri nets might be well suited to modeling metabolic networks, overcoming some of the limitations encountered by the use of systems employing ODEs (ordinary differential equations). Much work has been done since then which confirms this and demonstrates the usefulness of this concept for systems biology. Petri net technology is not only intuitively understood by scientists trained in the life sciences, it also has a robust mathematical foundation and provides the required degree of flexibility. As a result it appears to be a very promising approach to mode

  7. Petri Nets-Applications

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 4; Issue 9. Petri Nets - Applications. Y Narahari. General Article Volume 4 Issue 9 September 1999 pp 44-52. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/004/09/0044-0052. Author Affiliations. Y Narahari ...

  8. Safety nets or straitjackets?

    DEFF Research Database (Denmark)

    Ilsøe, Anna

    2012-01-01

    Does regulation of working hours at national and sector level impose straitjackets, or offer safety nets to employees seeking working time flexibility? This article compares legislation and collective agreements in the metal industries of Denmark, Germany and the USA. The industry has historically...

  9. Coloured Petri Nets

    CERN Document Server

    Jensen, Kurt

    2009-01-01

    Coloured Petri Nets (CPN) is a graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role. This book introduces the constructs of the CPN modelling language and presents the related analysis methods. It provides a comprehensive road map for the practical use of CPN.

  10. Boom Booom Net Radio

    DEFF Research Database (Denmark)

    Grimshaw, Mark Nicholas; Yong, Louisa; Dobie, Ian

    1999-01-01

    of an existing Internet radio station; Boom Booom Net Radio. Whilst necessity dictates some use of technology-related terminology, wherever possible we have endeavoured to keep such jargon to a minimum and to either explain it in the text or to provide further explanation in the appended glossary....

  11. Game Theory .net.

    Science.gov (United States)

    Shor, Mikhael

    2003-01-01

    States making game theory relevant and accessible to students is challenging. Describes the primary goal of GameTheory.net is to provide interactive teaching tools. Indicates the site strives to unite educators from economics, political and computer science, and ecology by providing a repository of lecture notes and tests for courses using…

  12. Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt; Kristensen, Lars Michael

    Coloured Petri Nets (CPN) is a graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role. The development of such systems is particularly challenging because of inherent intricacies like possible nondeterminism...

  13. Neural connections between antrum and duodenum

    DEFF Research Database (Denmark)

    Kraglund, K; Schrøder, H D; Stødkilde-Jørgensen, H

    1983-01-01

    Postprandial coordination of antroduodenal motility partly takes place via intrinsic mural pathways. The nature and origin of these nerve fibers have not yet been clarified. In this investigation using fluorochromic substances injected into the antrum and duodenum it was demonstrated that common...

  14. Micro- and Nanotechnologies for Optical Neural Interfaces

    Science.gov (United States)

    Pisanello, Ferruccio; Sileo, Leonardo; De Vittorio, Massimo

    2016-01-01

    In last decade, the possibility to optically interface with the mammalian brain in vivo has allowed unprecedented investigation of functional connectivity of neural circuitry. Together with new genetic and molecular techniques to optically trigger and monitor neural activity, a new generation of optical neural interfaces is being developed, mainly thanks to the exploitation of both bottom-up and top-down nanofabrication approaches. This review highlights the role of nanotechnologies for optical neural interfaces, with particular emphasis on new devices and methodologies for optogenetic control of neural activity and unconventional methods for detection and triggering of action potentials using optically-active colloidal nanoparticles. PMID:27013939

  15. Neural-like growing networks

    Science.gov (United States)

    Yashchenko, Vitaliy A.

    2000-03-01

    On the basis of the analysis of scientific ideas reflecting the law in the structure and functioning the biological structures of a brain, and analysis and synthesis of knowledge, developed by various directions in Computer Science, also there were developed the bases of the theory of a new class neural-like growing networks, not having the analogue in world practice. In a base of neural-like growing networks the synthesis of knowledge developed by classical theories - semantic and neural of networks is. The first of them enable to form sense, as objects and connections between them in accordance with construction of the network. With thus each sense gets a separate a component of a network as top, connected to other tops. In common it quite corresponds to structure reflected in a brain, where each obvious concept is presented by certain structure and has designating symbol. Secondly, this network gets increased semantic clearness at the expense owing to formation not only connections between neural by elements, but also themselves of elements as such, i.e. here has a place not simply construction of a network by accommodation sense structures in environment neural of elements, and purely creation of most this environment, as of an equivalent of environment of memory. Thus neural-like growing networks are represented by the convenient apparatus for modeling of mechanisms of teleological thinking, as a fulfillment of certain psychophysiological of functions.

  16. Food Safety Nets:

    OpenAIRE

    Haggblade, Steven; Diallo, Boubacar; Staatz, John; Theriault, Veronique; Traoré, Abdramane

    2013-01-01

    Food and social safety nets have a history as long as human civilization. In hunter gatherer societies, food sharing is pervasive. Group members who prove unlucky in the short run, hunting or foraging, receive food from other households in anticipation of reciprocal consideration at a later time (Smith 1988). With the emergence of the first large sedentary civilizations in the Middle East, administrative systems developed specifically around food storage and distribution. The ancient Egyptian...

  17. Net technical assessment

    OpenAIRE

    Wegmann, David G.

    1989-01-01

    Approved for public release; distribution is unlimited. The present and near term military balance of power between the U.S. and the Soviet Union can be expressed in a variety of net assessments. One can examine the strategic nuclear balance, the conventional balance in Europe, the maritime balance, and many others. Such assessments are essential not only for policy making but for arms control purposes and future force structure planning. However, to project the future military balance, on...

  18. Using WordNet for Building WordNets

    CERN Document Server

    Farreres, X; Farreres, Xavier; Rodriguez, Horacio; Rigau, German

    1998-01-01

    This paper summarises a set of methodologies and techniques for the fast construction of multilingual WordNets. The English WordNet is used in this approach as a backbone for Catalan and Spanish WordNets and as a lexical knowledge resource for several subtasks.

  19. BioNet Digital Communications Framework

    Science.gov (United States)

    Gifford, Kevin; Kuzminsky, Sebastian; Williams, Shea

    2010-01-01

    BioNet v2 is a peer-to-peer middleware that enables digital communication devices to talk to each other. It provides a software development framework, standardized application, network-transparent device integration services, a flexible messaging model, and network communications for distributed applications. BioNet is an implementation of the Constellation Program Command, Control, Communications and Information (C3I) Interoperability specification, given in CxP 70022-01. The system architecture provides the necessary infrastructure for the integration of heterogeneous wired and wireless sensing and control devices into a unified data system with a standardized application interface, providing plug-and-play operation for hardware and software systems. BioNet v2 features a naming schema for mobility and coarse-grained localization information, data normalization within a network-transparent device driver framework, enabling of network communications to non-IP devices, and fine-grained application control of data subscription band width usage. BioNet directly integrates Disruption Tolerant Networking (DTN) as a communications technology, enabling networked communications with assets that are only intermittently connected including orbiting relay satellites and planetary rover vehicles.

  20. Universal approximation in p-mean by neural networks

    NARCIS (Netherlands)

    Burton, R.M; Dehling, H.G

    A feedforward neural net with d input neurons and with a single hidden layer of n neurons is given by [GRAPHICS] where a(j), theta(j), w(ji) is an element of R. In this paper we study the approximation of arbitrary functions f: R-d --> R by a neural net in an L-p(mu) norm for some finite measure mu

  1. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  2. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

    2015-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  3. Proof nets for lingusitic analysis

    NARCIS (Netherlands)

    Moot, R.C.A.

    2002-01-01

    This book investigates the possible linguistic applications of proof nets, redundancy free representations of proofs, which were introduced by Girard for linear logic. We will adapt the notion of proof net to allow the formulation of a proof net calculus which is soundand complete for the

  4. Teaching Tennis for Net Success.

    Science.gov (United States)

    Young, Bryce

    1989-01-01

    A program for teaching tennis to beginners, NET (Net Easy Teaching) is described. The program addresses three common needs shared by tennis students: active involvement in hitting the ball, clearing the net, and positive reinforcement. A sample lesson plan is included. (IAH)

  5. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  6. Master Robotic Net

    Directory of Open Access Journals (Sweden)

    Vladimir Lipunov

    2010-01-01

    Full Text Available The main goal of the MASTER-Net project is to produce a unique fast sky survey with all sky observed over a single night down to a limiting magnitude of 19-20. Such a survey will make it possible to address a number of fundamental problems: search for dark energy via the discovery and photometry of supernovae (including SNIa, search for exoplanets, microlensing effects, discovery of minor bodies in the Solar System, and space-junk monitoring. All MASTER telescopes can be guided by alerts, and we plan to observe prompt optical emission from gamma-ray bursts synchronously in several filters and in several polarization planes.

  7. Art/Net/Work

    DEFF Research Database (Denmark)

    Andersen, Christian Ulrik; Lindstrøm, Hanne

    2006-01-01

    The seminar Art|Net|Work deals with two important changes in our culture. On one side, the network has become essential in the latest technological development. The Internet has entered a new phase, Web 2.0, including the occurrence of as ‘Wiki’s’, ‘Peer-2-Peer’ distribution, user controlled...... the praxis of the artist. We see different kinds of interventions and activism (including ‘hacktivism’) using the network as a way of questioning the invisible rules that govern public and semi-public spaces. Who ‘owns’ them? What kind of social relationships do they generate? On what principle...

  8. Nonequilibrium landscape theory of neural networks

    Science.gov (United States)

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

    2013-01-01

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

  9. Building a dynamically ASP.NET 2.0 GridView control

    Directory of Open Access Journals (Sweden)

    Catalin NACHILA

    2008-01-01

    Full Text Available Microsoft Visual Studio 2005 (based on ASP.NET 2.0, the successor to Visual Studio .NET 2003 has a lot of new features and goodies designed for Web developers. This article show how a ASP.NET 2.0 control can be dynamically connected to Microsoft Access database. The delete and update operation will be implemented using a GridView control and SQL queries. The connection between the database and the .NET application will be made with OleDb Data provider, the new Access Data Source control. The SQL queries will be implemented with OleDbCommand.

  10. Helminth.net: expansions to Nematode.net and an introduction to Trematode.net

    Science.gov (United States)

    Martin, John; Rosa, Bruce A.; Ozersky, Philip; Hallsworth-Pepin, Kymberlie; Zhang, Xu; Bhonagiri-Palsikar, Veena; Tyagi, Rahul; Wang, Qi; Choi, Young-Jun; Gao, Xin; McNulty, Samantha N.; Brindley, Paul J.; Mitreva, Makedonka

    2015-01-01

    Helminth.net (http://www.helminth.net) is the new moniker for a collection of databases: Nematode.net and Trematode.net. Within this collection we provide services and resources for parasitic roundworms (nematodes) and flatworms (trematodes), collectively known as helminths. For over a decade we have provided resources for studying nematodes via our veteran site Nematode.net (http://nematode.net). In this article, (i) we provide an update on the expansions of Nematode.net that hosts omics data from 84 species and provides advanced search tools to the broad scientific community so that data can be mined in a useful and user-friendly manner and (ii) we introduce Trematode.net, a site dedicated to the dissemination of data from flukes, flatworm parasites of the class Trematoda, phylum Platyhelminthes. Trematode.net is an independent component of Helminth.net and currently hosts data from 16 species, with information ranging from genomic, functional genomic data, enzymatic pathway utilization to microbiome changes associated with helminth infections. The databases’ interface, with a sophisticated query engine as a backbone, is intended to allow users to search for multi-factorial combinations of species’ omics properties. This report describes updates to Nematode.net since its last description in NAR, 2012, and also introduces and presents its new sibling site, Trematode.net. PMID:25392426

  11. NETS FOR PEACH PROTECTED CULTIVATION

    Directory of Open Access Journals (Sweden)

    Evelia Schettini

    2012-06-01

    Full Text Available The aim of this paper was to investigate the radiometric properties of coloured nets used to protect a peach cultivation. The modifications of the solar spectral distribution, mainly in the R and FR wavelength band, influence plant photomorphogenesis by means of the phytochrome and cryptochrome. The phytochrome response is characterized in terms of radiation rate in the red wavelengths (R, 600-700 nm to that in the farred radiation (FR, 700-800 nm, i.e. the R/FR ratio. The effects of the blue radiation (B, 400-500 nm is investigated by the ratio between the blue radiation and the far-red radiation, i.e. the B/FR ratio. A BLUE net, a RED net, a YELLOW net, a PEARL net, a GREY net and a NEUTRAL net were tested in Bari (Italy, latitude 41° 05’ N. Peach trees were located in pots inside the greenhouses and in open field. The growth of the trees cultivated in open field was lower in comparison to the growth of the trees grown under the nets. The RED, PEARL, YELLOW and GREY nets increased the growth of the trees more than the other nets. The nets positively influenced the fruit characteristics, such as fruit weight and flesh firmness.

  12. Gendered Connections

    DEFF Research Database (Denmark)

    Jensen, Steffen Bo

    2009-01-01

    This article explores the gendered nature of urban politics in Cape Town by focusing on a group of female, township politicians. Employing the Deleuzian concept of `wild connectivity', it argues that these politically entrepreneurial women were able to negotiate a highly volatile urban landscape ...... of connectivity might endure, as Capetonian politics assumes a post-apartheid structure....

  13. Automated target recognition and tracking using an optical pattern recognition neural network

    Science.gov (United States)

    Chao, Tien-Hsin

    1991-01-01

    The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

  14. NetPhosBac - A predictor for Ser/Thr phosphorylation sites in bacterial proteins

    DEFF Research Database (Denmark)

    Miller, Martin Lee; Soufi, Boumediene; Jers, Carsten

    2009-01-01

    predictors on bacterial systems. We used these large bacterial datasets and neural network algorithms to create the first bacteria-specific protein phosphorylation predictor: NetPhosBac. With respect to predicting bacterial phosphorylation sites, NetPhosBac significantly outperformed all benchmark predictors....... Moreover, NetPhosBac predictions of phosphorylation sites in E. coli proteins were experimentally verified on protein and site-specific levels. In conclusion, NetPhosBac clearly illustrates the advantage of taxa-specific predictors and we hope it will provide a useful asset to the microbiological community....

  15. Neural Network Approach to Locating Cryptography in Object Code

    Energy Technology Data Exchange (ETDEWEB)

    Jason L. Wright; Milos Manic

    2009-09-01

    Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

  16. The equivalency between logic Petri workflow nets and workflow nets.

    Science.gov (United States)

    Wang, Jing; Yu, ShuXia; Du, YuYue

    2015-01-01

    Logic Petri nets (LPNs) can describe and analyze batch processing functions and passing value indeterminacy in cooperative systems. Logic Petri workflow nets (LPWNs) are proposed based on LPNs in this paper. Process mining is regarded as an important bridge between modeling and analysis of data mining and business process. Workflow nets (WF-nets) are the extension to Petri nets (PNs), and have successfully been used to process mining. Some shortcomings cannot be avoided in process mining, such as duplicate tasks, invisible tasks, and the noise of logs. The online shop in electronic commerce in this paper is modeled to prove the equivalence between LPWNs and WF-nets, and advantages of LPWNs are presented.

  17. Multiprocessor Neural Network in Healthcare.

    Science.gov (United States)

    Godó, Zoltán Attila; Kiss, Gábor; Kocsis, Dénes

    2015-01-01

    A possible way of creating a multiprocessor artificial neural network is by the use of microcontrollers. The RISC processors' high performance and the large number of I/O ports mean they are greatly suitable for creating such a system. During our research, we wanted to see if it is possible to efficiently create interaction between the artifical neural network and the natural nervous system. To achieve as much analogy to the living nervous system as possible, we created a frequency-modulated analog connection between the units. Our system is connected to the living nervous system through 128 microelectrodes. Two-way communication is provided through A/D transformation, which is even capable of testing psychopharmacons. The microcontroller-based analog artificial neural network can play a great role in medical singal processing, such as ECG, EEG etc.

  18. Accelerated training for accurate neural net based load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Borsje, H.J.; Ling, B. [Stone and Webster Advanced Systems Development Services, Inc., Boston, MA (United States)

    1995-10-01

    A fast, accurate, robust and reliable load forecast method was developed, tested and demonstrated. The achieved prediction accuracy, based on a practical input parameters, matches or exceeds that of currently used methods. The time required to train the system is orders of magnitude shorter than other methods. This gives utility personnel the tools to refine local forecasts by quickly evaluating the effect of user selectable parameters. The conventional back propagation method can accurately predict the adaptive one-hour ahead forecast with reasonable learning requirements.

  19. Intelligent control aspects of fuzzy logic and neural nets

    CERN Document Server

    Harris, C J; Brown, M

    1993-01-01

    With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent expe

  20. About connections.

    Science.gov (United States)

    Rockland, Kathleen S

    2015-01-01

    Despite the attention attracted by "connectomics", one can lose sight of the very real questions concerning "What are connections?" In the neuroimaging community, "structural" connectivity is ground truth and underlying constraint on "functional" or "effective" connectivity. It is referenced to underlying anatomy; but, as increasingly remarked, there is a large gap between the wealth of human brain mapping and the relatively scant data on actual anatomical connectivity. Moreover, connections have typically been discussed as "pairwise", point x projecting to point y (or: to points y and z), or more recently, in graph theoretical terms, as "nodes" or regions and the interconnecting "edges". This is a convenient shorthand, but tends not to capture the richness and nuance of basic anatomical properties as identified in the classic tradition of tracer studies. The present short review accordingly revisits connectional weights, heterogeneity, reciprocity, topography, and hierarchical organization, drawing on concrete examples. The emphasis is on presynaptic long-distance connections, motivated by the intention to probe current assumptions and promote discussions about further progress and synthesis.

  1. About Connections

    Directory of Open Access Journals (Sweden)

    Kathleen S Rockland

    2015-05-01

    Full Text Available Despite the attention attracted by connectomics, one can lose sight of the very real questions concerning What are connections? In the neuroimaging community, structural connectivity is ground truth and underlying constraint on functional or effective connectivity. It is referenced to underlying anatomy; but, as increasingly remarked, there is a large gap between the wealth of human brain mapping and the relatively scant data on actual anatomical connectivity. Moreover, connections have typically been discussed as pairwise, point x projecting to point y (or: to points y and z, or more recently, in graph theoretical terms, as nodes or regions and the interconnecting edges. This is a convenient shorthand, but tends not to capture the richness and nuance of basic anatomical properties as identified in the classic tradition of tracer studies. The present short review accordingly revisits connectional weights, heterogeneity, reciprocity, topography, and hierarchical organization, drawing on concrete examples. The emphasis is on presynaptic long-distance connections, motivated by the intention to probe current assumptions and promote discussions about further progress and synthesis.

  2. Minimal Edge-Transitive Nets for the Design and Construction of Metal-Organic Frameworks

    KAUST Repository

    Eddaoudi, Mohamed

    2017-04-05

    Highly-connected and minimal edge-transitive nets (with one or two kinds of edge) can be regarded as ideal blueprints for the rational design and construction of metal-organic frameworks (MOFs). Here we report and affirm the prominence of highly-connected nets as suitable targets in reticular chemistry for the design and synthesis of MOFs. Of special interest are augmented highly-connected binodal edge-transitive nets embedding a unique and precise positioning and connectivity of the net vertex figures, regarded as net-coded building units (net-cBUs). Explicitly, a definite net-cBU encompasses precise geometrical information that codes uniquely and matchlessly a selected net, a compelling perquisite for the rational design of MOFs. Interestingly, the double six-membered ring (d6R) building unit offers great prospective to be deployed as a net-cBU for the deliberate reticulation of the sole two edge-transitive nets with a vertex figure as a d6R, namely the (4,12)-coordinated shp net (square and hexagonal prism) and the (6,12)-coordinated alb net (aluminium diboride, hexagonal prism and trigonal prism). Conceivably, we envisioned and proposed various MOF structures based on the derived shp and alb nets. Gaining access to the requisite net-cBUs is essential for the successful practice of reticular chemistry; correspondingly organic and organic chemistries were deployed to afford concomitant molecular building blocks (MBBs) with the looked-for shape and connectivity. Practically, the combination of the 12-connected (12-c) rare-earth (RE) polynuclear, points of extension matching the 12 vertices of the hexagonal prism (d6R) with a 4-connected tetracarboxylate ligand or a 6-connected hexacarboxylate ligand afforded the targeted shp-MOF or alb-MOF, respectively. Intuitively, a dodecacarboxylate ligand can be conceived and purported as a compatible 12-c MBB, plausibly affording the positioning of the carbon centers of the twelve carboxylate groups on the vertices of the

  3. Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt; Kristensen, Lars Michael

    studies that illustrate the practical use of CPN modelling and validation for design, specification, simulation, verification and implementation in various application domains. Their presentation primarily aims at readers interested in the practical use of CPN. Thus all concepts and constructs are first......Coloured Petri Nets (CPN) is a graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role. The development of such systems is particularly challenging because of inherent intricacies like possible nondeterminism...... and the immense number of possible execution sequences. In this textbook, Jensen and Kristensen introduce the constructs of the CPN modelling language and present the related analysis methods in detail. They also provide a comprehensive road map for the practical use of CPN by showcasing selected industrial case...

  4. DARPA Neural Network Study: October 1987 - February 1988

    Science.gov (United States)

    1989-03-22

    8217Neural Net’ Models Allen Waxman, Boston University 10-20-1987: Mobile Robots vs. Neural Navigators 01-19-1988: Motion Computation In Vision 63...34Weight." Neurodynamics The study of the generation and propagation of synchronized neural activity in biological systems. 70 Neuron The nerve cells in...Malsburg, "Frank Rosenblatt: Principles of neurodynamics : Perceptrons and the theory of brain mechanisms," in Brain Theory, (G. Palm and A. Aertsen, eds

  5. Memristor-based neural networks

    Science.gov (United States)

    Thomas, Andy

    2013-03-01

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

  6. HR Connect

    Data.gov (United States)

    US Agency for International Development — HR Connect is the USAID HR personnel system which allows HR professionals to process HR actions related to employee's personal and position information. This system...

  7. Mathematics Connection

    African Journals Online (AJOL)

    MATHEMATICS CONNECTION aims at providing a forum topromote the development of Mathematics Education in Ghana. Articles that seekto enhance the teaching and/or learning of mathematics at all levels of theeducational system are welcome.

  8. WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL ...

    African Journals Online (AJOL)

    This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos.

  9. Towards semen quality assessment using neural networks

    DEFF Research Database (Denmark)

    Linneberg, Christian; Salamon, P.; Svarer, C.

    1994-01-01

    The paper presents the methodology and results from a neural net based classification of human sperm head morphology. The methodology uses a preprocessing scheme in which invariant Fourier descriptors are lumped into “energy” bands. The resulting networks are pruned using optimal brain damage...

  10. Cognitive And Neural Sciences Division 1992 Programs

    Science.gov (United States)

    1992-08-01

    Neuronal Micronets as Nodal Elements PRINCIPAL INVESTIGATOR: Thomas H. Brown Yale University Department of Psychology (203) 432-7008 R&T PROJECT CODE...of neural nets, and to develop a micronet architecture which captures the computations in neurons. Approach: Simulations will be conducted of the

  11. Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

    Science.gov (United States)

    Cheng, Phillip M; Malhi, Harshawn S

    2017-04-01

    The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set. The fully connected layers of two convolutional neural networks based on CaffeNet and VGGNet, previously trained on the 2012 Large Scale Visual Recognition Challenge data set, were retrained on the training set. Weights in the convolutional layers of each network were frozen to serve as fixed feature extractors. Accuracy on the test set was evaluated for each network. A radiologist experienced in abdominal ultrasound also independently classified the images in the test set into the same 11 categories. The CaffeNet network classified 77.3% of the test set images accurately (1100/1423 images), with a top-2 accuracy of 90.4% (1287/1423 images). The larger VGGNet network classified 77.9% of the test set accurately (1109/1423 images), with a top-2 accuracy of VGGNet was 89.7% (1276/1423 images). The radiologist classified 71.7% of the test set images correctly (1020/1423 images). The differences in classification accuracies between both neural networks and the radiologist were statistically significant (p neural networks may be used to construct effective classifiers for abdominal ultrasound images.

  12. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  13. NetOglyc: prediction of mucin type O-glycosylation sites based on sequence context and surface accessibility

    DEFF Research Database (Denmark)

    Hansen, Jan Erik; Lund, Ole; Tolstrup, Niels

    1998-01-01

    . A jury of artifical neural networks was trained to recognize the sequence context and surface accessibility of 299 known and verified mucin type O-glycosylation sites extracted from O-GLYCBASE. The cross-validated NetOglyc network system correctly found 83% of the glycosylated and 90% of the non...... on the amino acid sequence. The server addresses are http://www.cbs.dtu.dk/services/NetOGlyc/ and netOglyc@cbs.dtu.dk...

  14. Linear Logic on Petri Nets

    DEFF Research Database (Denmark)

    Engberg, Uffe Henrik; Winskel, Glynn

    This article shows how individual Petri nets form models of Girard's intuitionistic linear logic. It explores questions of expressiveness and completeness of linear logic with respect to this interpretation. An aim is to use Petri nets to give an understanding of linear logic and give some apprai...

  15. Reference Guide Microsoft.NET

    NARCIS (Netherlands)

    Zee M van der; Verspaij GJ; Rosbergen S; IMP; NMD

    2003-01-01

    Developers, administrators and managers can get more understanding of the .NET technology with this report. They can also make better choices how to use this technology. The report describes the results and conclusions of a study of the usability for the RIVM of this new generation .NET development

  16. Net neutrality and audiovisual services

    NARCIS (Netherlands)

    van Eijk, N.; Nikoltchev, S.

    2011-01-01

    Net neutrality is high on the European agenda. New regulations for the communication sector provide a legal framework for net neutrality and need to be implemented on both a European and a national level. The key element is not just about blocking or slowing down traffic across communication

  17. A Small Universal Petri Net

    Directory of Open Access Journals (Sweden)

    Dmitry A. Zaitsev

    2013-09-01

    Full Text Available A universal deterministic inhibitor Petri net with 14 places, 29 transitions and 138 arcs was constructed via simulation of Neary and Woods' weakly universal Turing machine with 2 states and 4 symbols; the total time complexity is exponential in the running time of their weak machine. To simulate the blank words of the weakly universal Turing machine, a couple of dedicated transitions insert their codes when reaching edges of the working zone. To complete a chain of a given Petri net encoding to be executed by the universal Petri net, a translation of a bi-tag system into a Turing machine was constructed. The constructed Petri net is universal in the standard sense; a weaker form of universality for Petri nets was not introduced in this work.

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

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

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

  19. Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    2016-06-01

    The Connected Traveler framework seeks to boost the energy efficiency of personal travel and the overall transportation system by maximizing the accuracy of predicted traveler behavior in response to real-time feedback and incentives. It is anticipated that this approach will establish a feedback loop that 'learns' traveler preferences and customizes incentives to meet or exceed energy efficiency targets by empowering individual travelers with information needed to make energy-efficient choices and reducing the complexity required to validate transportation system energy savings. This handout provides an overview of NREL's Connected Traveler project, including graphics, milestones, and contact information.

  20. Fuzzy Petri nets to model vision system decisions within a flexible manufacturing system

    Science.gov (United States)

    Hanna, Moheb M.; Buck, A. A.; Smith, R.

    1994-10-01

    The paper presents a Petri net approach to modelling, monitoring and control of the behavior of an FMS cell. The FMS cell described comprises a pick and place robot, vision system, CNC-milling machine and 3 conveyors. The work illustrates how the block diagrams in a hierarchical structure can be used to describe events at different levels of abstraction. It focuses on Fuzzy Petri nets (Fuzzy logic with Petri nets) including an artificial neural network (Fuzzy Neural Petri nets) to model and control vision system decisions and robot sequences within an FMS cell. This methodology can be used as a graphical modelling tool to monitor and control the imprecise, vague and uncertain situations, and determine the quality of the output product of an FMS cell.

  1. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...

  2. Making connections

    NARCIS (Netherlands)

    Marion Duimel

    2007-01-01

    Original title: Verbinding maken; senioren en internet. More and more older people are finding their way to the Internet. Many people aged over 50 who have only recently gone online say that a new world has opened up for them. By connecting to the Internet they have the feeling that they

  3. CMS Connect

    Science.gov (United States)

    Balcas, J.; Bockelman, B.; Gardner, R., Jr.; Hurtado Anampa, K.; Jayatilaka, B.; Aftab Khan, F.; Lannon, K.; Larson, K.; Letts, J.; Marra Da Silva, J.; Mascheroni, M.; Mason, D.; Perez-Calero Yzquierdo, A.; Tiradani, A.

    2017-10-01

    The CMS experiment collects and analyzes large amounts of data coming from high energy particle collisions produced by the Large Hadron Collider (LHC) at CERN. This involves a huge amount of real and simulated data processing that needs to be handled in batch-oriented platforms. The CMS Global Pool of computing resources provide +100K dedicated CPU cores and another 50K to 100K CPU cores from opportunistic resources for these kind of tasks and even though production and event processing analysis workflows are already managed by existing tools, there is still a lack of support to submit final stage condor-like analysis jobs familiar to Tier-3 or local Computing Facilities users into these distributed resources in an integrated (with other CMS services) and friendly way. CMS Connect is a set of computing tools and services designed to augment existing services in the CMS Physics community focusing on these kind of condor analysis jobs. It is based on the CI-Connect platform developed by the Open Science Grid and uses the CMS GlideInWMS infrastructure to transparently plug CMS global grid resources into a virtual pool accessed via a single submission machine. This paper describes the specific developments and deployment of CMS Connect beyond the CI-Connect platform in order to integrate the service with CMS specific needs, including specific Site submission, accounting of jobs and automated reporting to standard CMS monitoring resources in an effortless way to their users.

  4. High-level Petri Nets

    DEFF Research Database (Denmark)

    High-level Petri nets are now widely used in both theoretical analysis and practical modelling of concurrent systems. The main reason for the success of this class of net models is that they make it possible to obtain much more succinct and manageable descriptions than can be obtained by means...... of low-level Petri nets - while, on the other hand, they still offer a wide range of analysis methods and tools. The step from low-level nets to high-level nets can be compared to the step from assembly languages to modern programming languages with an elaborated type concept. In low-level nets...... there is only one kind of token and this means that the state of a place is described by an integer (and in many cases even by a boolean). In high-level nets each token can carry a complex information/data - which, e.g., may describe the entire state of a process or a data base. Today most practical...

  5. LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning

    OpenAIRE

    Ye, Chengxi; Zhao, Chen; Yang, Yezhou; Fermuller, Cornelia; Aloimonos, Yiannis

    2016-01-01

    LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The idea underlying its design is to provide an easy-to-understand, easy-to-use and efficient computational platform for deep learning research. The implemented framework supports major deep learning architectures such as Multilayer Perceptron Networks (MLP), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The framework also supports both CPU and GPU computation, and the switch betwe...

  6. Fuzzy neural networks: theory and applications

    Science.gov (United States)

    Gupta, Madan M.

    1994-10-01

    During recent years, significant advances have been made in two distinct technological areas: fuzzy logic and computational neural networks. The theory of fuzzy logic provides a mathematical framework to capture the uncertainties associated with human cognitive processes, such as thinking and reasoning. It also provides a mathematical morphology to emulate certain perceptual and linguistic attributes associated with human cognition. On the other hand, the computational neural network paradigms have evolved in the process of understanding the incredible learning and adaptive features of neuronal mechanisms inherent in certain biological species. Computational neural networks replicate, on a small scale, some of the computational operations observed in biological learning and adaptation. The integration of these two fields, fuzzy logic and neural networks, have given birth to an emerging technological field -- fuzzy neural networks. Fuzzy neural networks, have the potential to capture the benefits of these two fascinating fields, fuzzy logic and neural networks, into a single framework. The intent of this tutorial paper is to describe the basic notions of biological and computational neuronal morphologies, and to describe the principles and architectures of fuzzy neural networks. Towards this goal, we develop a fuzzy neural architecture based upon the notion of T-norm and T-conorm connectives. An error-based learning scheme is described for this neural structure.

  7. Automated Modeling of Microwave Structures by Enhanced Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

    Full Text Available The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. In the paper, neural networks are used to approximate the behavior of a planar microwave filter (moment method, Zeland IE3D. In order to evaluate the efficiency of neural modeling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and the accuracy. Considering conclusions, methodological recommendations for including neural networks to the microwave design are formulated.

  8. Cognitive control network connectivity in adolescent women with and without a parental history of depression

    Directory of Open Access Journals (Sweden)

    Peter C. Clasen

    2014-01-01

    Conclusions: Depressed parents may transmit depression vulnerability to their adolescent daughters via alterations in functional connectivity within neural circuits that underlie cognitive control of emotional information.

  9. Pro asynchronous programming with .NET

    CERN Document Server

    Blewett, Richard; Ltd, Rock Solid Knowledge

    2014-01-01

    Pro Asynchronous Programming with .NET teaches the essential skill of asynchronous programming in .NET. It answers critical questions in .NET application development, such as: how do I keep my program responding at all times to keep my users happy how do I make the most of the available hardware how can I improve performanceIn the modern world, users expect more and more from their applications and devices, and multi-core hardware has the potential to provide it. But it takes carefully crafted code to turn that potential into responsive, scalable applications.With Pro Asynchronous Programming

  10. Conformal Nets II: Conformal Blocks

    Science.gov (United States)

    Bartels, Arthur; Douglas, Christopher L.; Henriques, André

    2017-08-01

    Conformal nets provide a mathematical formalism for conformal field theory. Associated to a conformal net with finite index, we give a construction of the `bundle of conformal blocks', a representation of the mapping class groupoid of closed topological surfaces into the category of finite-dimensional projective Hilbert spaces. We also construct infinite-dimensional spaces of conformal blocks for topological surfaces with smooth boundary. We prove that the conformal blocks satisfy a factorization formula for gluing surfaces along circles, and an analogous formula for gluing surfaces along intervals. We use this interval factorization property to give a new proof of the modularity of the category of representations of a conformal net.

  11. The Formal Semantics of Core ABS and ABS-NET

    OpenAIRE

    Palmskog, Karl

    2013-01-01

    ABS is a language and framework for modelling distributed object-oriented systems, developed in the EU FP7 HATS project. Core ABS formalizes the key parts of ABS, including the syntax, type system, and an operational semantics in the style of rewriting logic. ABS-NET is a novel operational semantics for Core ABS programs, developed as a part of work on decentralized runtime adaptation of distributed objects. ABS-NET describes program execution on top of a network of nodes connected point-to-p...

  12. 78 FR 55684 - ConnectED Workshop

    Science.gov (United States)

    2013-09-11

    ... National Telecommunications and Information Administration ConnectED Workshop AGENCY: National... in the United States to next- generation broadband. This Notice announces that the ConnectED Workshop... ConnectED Workshop will discuss the growing bandwidth needs of K-12 schools as more schools use mobile...

  13. Petri Net Tool Overview 1986

    DEFF Research Database (Denmark)

    Jensen, Kurt; Feldbrugge, Frits

    1987-01-01

    This paper provides an overview of the characteristics of all currently available net based tools. It is a compilation of information provided by tool authors or contact persons. A concise one page overview is provided as well....

  14. PolicyNet Publication System

    Data.gov (United States)

    Social Security Administration — The PolicyNet Publication System project will merge the Oracle-based Policy Repository (POMS) and the SQL-Server CAMP system (MSOM) into a new system with an Oracle...

  15. KM3NeT

    CERN Multimedia

    KM3NeT is a large scale next-generation neutrino telescope located in the deep waters of the Mediterranean Sea, optimized for the discovery of galactic neutrino sources emitting in the TeV energy region.

  16. Net Neutrality: Background and Issues

    National Research Council Canada - National Science Library

    Gilroy, Angele A

    2006-01-01

    .... The move to place restrictions on the owners of the networks that compose and provide access to the Internet, to ensure equal access and nondiscriminatory treatment, is referred to as "net neutrality...

  17. Petri Nets in Cryptographic Protocols

    DEFF Research Database (Denmark)

    Crazzolara, Federico; Winskel, Glynn

    2001-01-01

    A process language for security protocols is presented together with a semantics in terms of sets of events. The denotation of process is a set of events, and as each event specifies a set of pre and postconditions, this denotation can be viewed as a Petri net. By means of an example we illustrate...... how the Petri-net semantics can be used to prove security properties....

  18. The Economics of Net Neutrality

    OpenAIRE

    Hahn, Robert W.; Wallsten, Scott

    2006-01-01

    This essay examines the economics of "net neutrality" and broadband Internet access. We argue that mandating net neutrality would be likely to reduce economic welfare. Instead, the government should focus on creating competition in the broadband market by liberalizing more spectrum and reducing entry barriers created by certain local regulations. In cases where a broadband provider can exercise market power the government should use its antitrust enforcement authority to police anticompetitiv...

  19. Optical implementation of neural networks

    Science.gov (United States)

    Yu, Francis T. S.; Guo, Ruyan

    2002-12-01

    An adaptive optical neuro-computing (ONC) using inexpensive pocket size liquid crystal televisions (LCTVs) had been developed by the graduate students in the Electro-Optics Laboratory at The Pennsylvania State University. Although this neuro-computing has only 8×8=64 neurons, it can be easily extended to 16×20=320 neurons. The major advantages of this LCTV architecture as compared with other reported ONCs, are low cost and the flexibility to operate. To test the performance, several neural net models are used. These models are Interpattern Association, Hetero-association and unsupervised learning algorithms. The system design considerations and experimental demonstrations are also included.

  20. 26 CFR 1.904(f)-3 - Allocation of net operating losses and net capital losses.

    Science.gov (United States)

    2010-04-01

    ... 26 Internal Revenue 9 2010-04-01 2010-04-01 false Allocation of net operating losses and net....904(f)-3 Allocation of net operating losses and net capital losses. For rules relating to the allocation of net operating losses and net capital losses, see § 1.904(g)-3T. ...

  1. 29 CFR 4204.13 - Net income and net tangible assets tests.

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 9 2010-07-01 2010-07-01 false Net income and net tangible assets tests. 4204.13 Section....13 Net income and net tangible assets tests. (a) General. The criteria under this section are that either— (1) Net income test. The purchaser's average net income after taxes for its three most recent...

  2. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

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

  3. AGU Joins MentorNet to Support Young Geoscientists

    Science.gov (United States)

    Cifuentes, Inés

    2009-11-01

    AGU has joined MentorNet as a partner in a mentoring network that encourages diversity in the engineering and science professions. MentorNet matches protégés and mentors and provides mentoring advice, suggestions, and gentle reminders to keep the exchange going. This partnership makes it possible for AGU to connect student members who would like a mentor in the geosciences with members who want to mentor. Mentoring is key to encouraging young people—particularly women, Latinos, and African Americans—to become involved and stay involved with the sciences. MentorNet partners with institutions of higher education, industry, government, and professional societies to provide online programs to serve science professionals and students.

  4. Connective Versus Collective Action in Social Movements

    DEFF Research Database (Denmark)

    Lundgaard, Daniel; Razmerita, Liana

    LivesMatter and the #IceBucketChallenge by analyzing Twitter and Facebook data from key periods of these movements,through a net nographic study. In particular, this study has investigated the following research questions: How are the logics of collective and connective action reflected in online social media interactions...

  5. Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP

    Directory of Open Access Journals (Sweden)

    Stanimir Surchev

    2013-10-01

    Full Text Available Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting.

  6. Neural recording and modulation technologies

    Science.gov (United States)

    Chen, Ritchie; Canales, Andres; Anikeeva, Polina

    2017-01-01

    In the mammalian nervous system, billions of neurons connected by quadrillions of synapses exchange electrical, chemical and mechanical signals. Disruptions to this network manifest as neurological or psychiatric conditions. Despite decades of neuroscience research, our ability to treat or even to understand these conditions is limited by the capability of tools to probe the signalling complexity of the nervous system. Although orders of magnitude smaller and computationally faster than neurons, conventional substrate-bound electronics do not recapitulate the chemical and mechanical properties of neural tissue. This mismatch results in a foreign-body response and the encapsulation of devices by glial scars, suggesting that the design of an interface between the nervous system and a synthetic sensor requires additional materials innovation. Advances in genetic tools for manipulating neural activity have fuelled the demand for devices that are capable of simultaneously recording and controlling individual neurons at unprecedented scales. Recently, flexible organic electronics and bio- and nanomaterials have been developed for multifunctional and minimally invasive probes for long-term interaction with the nervous system. In this Review, we discuss the design lessons from the quarter-century-old field of neural engineering, highlight recent materials-driven progress in neural probes and look at emergent directions inspired by the principles of neural transduction.

  7. A neural network simulation package in CLIPS

    Science.gov (United States)

    Bhatnagar, Himanshu; Krolak, Patrick D.; Mcgee, Brenda J.; Coleman, John

    1990-01-01

    The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

  8. The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection

    NARCIS (Netherlands)

    Mettes, P.; Koelma, D.C.; Snoek, C.G.M.

    2016-01-01

    This paper strives for video event detection using a representation learned from deep convolutional neural networks. Different from the leading approaches, who all learn from the 1,000 classes defined in the ImageNet Large Scale Visual Recognition Challenge, we investigate how to leverage the

  9. Automatic slice identification in 3D medical images with a ConvNet regressor

    NARCIS (Netherlands)

    de Vos, Bob D.; Viergever, Max A.; de Jong, Pim A.; Išgum, Ivana

    2016-01-01

    Identification of anatomical regions of interest is a prerequisite in many medical image analysis tasks. We propose a method that automatically identifies a slice of interest (SOI) in 3D images with a convolutional neural network (ConvNet) regressor. In 150 chest CT scans two reference slices were

  10. Part 2: Prediktion, Simulering og Regulering med Neurale Netværk. Prediction, Simulation and Control using Neural Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik

    til Del 1, idet de to rapporter kan opfattes som en enhed. Herefter introduceres de grundlæggende begreber inden for prediktion, samt for mål og integralteorien. Det beskrives, hvorledes neurale net kan fungere som ulinære prediktionsmodeller og den nødvendige teori for Multi Lags Perceptronen (MLP......) samt alternative strukturer baseret på Parzen Window estimationsmetoden, præsenteres med detaljerne af analysen henlagt til appendices. Herefter demonstreres ved en simpel test, hvorledes de forskellige nettyper fungerer i prediktionsanvendelser. Herefter er neurale net anvendt til simulering behandlet...... på tilsvarende måde, dog i en lidt forkortet udgave. Til sidst behandles, hvorledes de behandlede nettyper anvendes i en regulatorstruktur baseret på såkaldte Sliding mode control. Teorien for de neurale net er her den samme som for simulering. Det konkluderes at de alternative strukturer, baseret på...

  11. On the reliability of the nervous (Nv) nets

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.; Frigo, J.R.; Moore, K.R.

    1998-12-31

    This paper investigates the reliability of a particular class of neural networks, the Nervous Nets (Nv). This is the class of nonsymmetric ring oscillator networks of inverters coupled through variable delays. They have been successfully applied to controlling walking robots, while many other applications will shortly be mentioned. The authors will then explain the robustness of Nv nets in the sense of their highly reliable functioning--which has been observed through many experiments. For doing that the authors will show that although the Nv net has an exponential number of periodic points, only a small (still exponential) part are stable, while all the others are saddle points. The ratio between the number of stable and periodic points quickly vanishes to zero as the number of nodes is increased, as opposed to classical finite state machines--where this ratio is relatively constant. These show that the Nv net will always converge quickly to a stable oscillatory state--a fact not true in general for finite state machines.

  12. Connecting dots

    DEFF Research Database (Denmark)

    Murakami, Kyoko; Jacobs, Rachel L.

    2017-01-01

    and Middleton, 1995). A reminiscence conversation is a dynamic talk-in-interaction, which can produce valuable learning experience for the participants involved. Reminiscence talk contains rich, personal, historic data that can reveal and inform family members of an unknown past. In this seminar/chapter, we......Reminiscence is a self-reflecting process on past events and experiences. Not only does it enable past experiences to be brought to light through talk, but it also creates an affective environment, which allows participants to explore and construct a representation of the self (Buchanan...... of connecting the dots of recalled moments of individual family members lives and is geared towards building a family’s shared future for posterity. Lastly, we consider a wider implication of family reminiscence in terms of human development. http://www.infoagepub.com/products/Memory-Practices-and-Learning...

  13. TimeNET Optimization Environment

    Directory of Open Access Journals (Sweden)

    Christoph Bodenstein

    2015-12-01

    Full Text Available In this paper a novel tool for simulation-based optimization and design-space exploration of Stochastic Colored Petri nets (SCPN is introduced. The working title of this tool is TimeNET Optimization Environment (TOE. Targeted users of this tool are people modeling complex systems with SCPNs in TimeNET who want to find parameter sets that are optimal for a certain performance measure (fitness function. It allows users to create and simulate sets of SCPNs and to run different optimization algorithms based on parameter variation. The development of this tool was motivated by the need to automate and speed up tests of heuristic optimization algorithms to be applied for SCPN optimization. A result caching mechanism is used to avoid recalculations.

  14. Implementing NetScaler VPX

    CERN Document Server

    Sandbu, Marius

    2014-01-01

    An easy-to-follow guide with detailed step-by step-instructions on how to implement the different key components in NetScaler, with real-world examples and sample scenarios.If you are a Citrix or network administrator who needs to implement NetScaler in your virtual environment to gain an insight on its functionality, this book is ideal for you. A basic understanding of networking and familiarity with some of the different Citrix products such as XenApp or XenDesktop is a prerequisite.

  15. Net4Care PHMR Library

    DEFF Research Database (Denmark)

    2014-01-01

    The Net4Care PHMR library contains a) A GreenCDA approach for constructing a data object representing a PHMR document: SimpleClinicalDocument, and b) A Builder which can produce a XML document representing a valid Danish PHMR (following the MedCom profile) document from the SimpleClinicalDocument......The Net4Care PHMR library contains a) A GreenCDA approach for constructing a data object representing a PHMR document: SimpleClinicalDocument, and b) A Builder which can produce a XML document representing a valid Danish PHMR (following the MedCom profile) document from the Simple...

  16. Pro DLR in NET 4

    CERN Document Server

    Wu, Chaur

    2011-01-01

    Microsoft's Dynamic Language Runtime (DLR) is a platform for running dynamic languages such as Ruby and Python on an equal footing with compiled languages such as C#. Furthermore, the runtime is the foundation for many useful software design and architecture techniques you can apply as you develop your .NET applications. Pro DLR in .NET 4 introduces you to the DLR, showing how you can use it to write software that combines dynamic and static languages, letting you choose the right tool for the job. You will learn the core DLR components such as LINQ expressions, call sites, binders, and dynami

  17. Hierarchies in Coloured Petri Nets

    DEFF Research Database (Denmark)

    Huber, Peter; Jensen, Kurt; Shapiro, Robert M.

    1991-01-01

    The paper shows how to extend Coloured Petri Nets with a hierarchy concept. The paper proposes five different hierarchy constructs, which allow the analyst to structure large CP-nets as a set of interrelated subnets (called pages). The paper discusses the properties of the proposed hierarchy...... constructs, and it illustrates them by means of two examples. The hierarchy constructs can be used for theoretical considerations, but their main use is to describe and analyse large real-world systems. All of the hierarchy constructs are supported by the editing and analysis facilities in the CPN Palette...

  18. Neural Tube Defects

    Science.gov (United States)

    ... vitamin, before and during pregnancy prevents most neural tube defects. Neural tube defects are usually diagnosed before the infant is ... or imaging tests. There is no cure for neural tube defects. The nerve damage and loss of function ...

  19. 2D neural hardware versus 3D biological ones

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

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

  20. Classification of behavior using unsupervised temporal neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Adair, K.L. [Florida State Univ., Tallahassee, FL (United States). Dept. of Computer Science; Argo, P. [Los Alamos National Lab., NM (United States)

    1998-03-01

    Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem.

  1. Characterization of NvLWamide-like neurons reveals stereotypy in Nematostella nerve net development.

    Science.gov (United States)

    Havrilak, Jamie A; Faltine-Gonzalez, Dylan; Wen, Yiling; Fodera, Daniella; Simpson, Ayanna C; Magie, Craig R; Layden, Michael J

    2017-11-15

    The organization of cnidarian nerve nets is traditionally described as diffuse with randomly arranged neurites that show minimal reproducibility between animals. However, most observations of nerve nets are conducted using cross-reactive antibodies that broadly label neurons, which potentially masks stereotyped patterns produced by individual neuronal subtypes. Additionally, many cnidarians species have overt structures such as a nerve ring, suggesting higher levels of organization and stereotypy exist, but mechanisms that generated that stereotypy are unknown. We previously demonstrated that NvLWamide-like is expressed in a small subset of the Nematostella nerve net and speculated that observing a few neurons within the developing nerve net would provide a better indication of potential stereotypy. Here we document NvLWamide-like expression more systematically. NvLWamide-like is initially expressed in the typical neurogenic salt and pepper pattern within the ectoderm at the gastrula stage, and expression expands to include endodermal salt and pepper expression at the planula larval stage. Expression persists in both ectoderm and endoderm in adults. We characterized our NvLWamide-like::mCherry transgenic reporter line to visualize neural architecture and found that NvLWamide-like is expressed in six neural subtypes identifiable by neural morphology and location. Upon completing development the numbers of neurons in each neural subtype are minimally variable between animals and the projection patterns of each subtype are consistent. Furthermore, between the juvenile polyp and adult stages the number of neurons for each subtype increases. We conclude that development of the Nematostella nerve net is stereotyped between individuals. Our data also imply that one aspect of generating adult cnidarian nervous systems is to modify the basic structural architecture generated in the juvenile by increasing neural number proportionally with size. Copyright © 2017 The Authors

  2. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  3. Connected Chemistry--Incorporating Interactive Simulations into the Chemistry Classroom.

    Science.gov (United States)

    Stieff, Mike; Wilensky, Uri

    2003-01-01

    Describes a novel modeling and simulation package and assesses its impact on students' understanding of chemistry. Connected Chemistry was implemented inside the NetLogo modeling environment. Using Connected Chemistry, students employed problem -solving techniques characterized by stronger attempts at conceptual understanding and logical…

  4. 38 CFR 17.149 - Sensori-neural aids.

    Science.gov (United States)

    2010-07-01

    ... medical treatment. (c) VA will furnish needed hearing aids to those veterans who have service-connected... 38 Pensions, Bonuses, and Veterans' Relief 1 2010-07-01 2010-07-01 false Sensori-neural aids. 17... Prosthetic, Sensory, and Rehabilitative Aids § 17.149 Sensori-neural aids. (a) Notwithstanding any other...

  5. [Glutamate signaling and neural plasticity].

    Science.gov (United States)

    Watanabe, Masahiko

    2013-07-01

    Proper functioning of the nervous system relies on the precise formation of neural circuits during development. At birth, neurons have redundant synaptic connections not only to their proper targets but also to other neighboring cells. Then, functional neural circuits are formed during early postnatal development by the selective strengthening of necessary synapses and weakening of surplus connections. Synaptic connections are also modified so that projection fields of active afferents expand at the expense of lesser ones. We have studied the molecular mechanisms underlying these activity-dependent prunings and the plasticity of synaptic circuitry using gene-engineered mice defective in the glutamatergic signaling system. NMDA-type glutamate receptors are critically involved in the establishment of the somatosensory pathway ascending from the brainstem trigeminal nucleus to the somatosensory cortex. Without NMDA receptors, whisker-related patterning fails to develop, whereas lesion-induced plasticity occurs normally during the critical period. In contrast, mice lacking the glutamate transporters GLAST or GLT1 are selectively impaired in the lesion-induced critical plasticity of cortical barrels, although whisker-related patterning itself develops normally. In the developing cerebellum, multiple climbing fibers initially innervating given Purkinje cells are eliminated one by one until mono-innervation is achieved. In this pruning process, P/Q-type Ca2+ channels expressed on Purkinje cells are critically involved by the selective strengthening of single main climbing fibers against other lesser afferents. Therefore, the activation of glutamate receptors that leads to an activity-dependent increase in the intracellular Ca2+ concentration plays a key role in the pruning of immature synaptic circuits into functional circuits. On the other hand, glutamate transporters appear to control activity-dependent plasticity among afferent fields, presumably through adjusting

  6. Statistical Physics, Neural Networks, Brain Studies

    OpenAIRE

    TOULOUSE, Gérard

    2014-01-01

    An overview of some aspects of a vast domain, located at the crossroads of physics, biology and computer science is presented: 1) During the last fifteen years, physicists advancing along various pathways have come into contact with biology (computational neurosciences) and engineering (formal neural nets). 2) This move may actually be viewed as one component in a larger picture. A prominent trend of recent years, observable over many countries, has been the establishment of interdis...

  7. D.NET case study

    International Development Research Centre (IDRC) Digital Library (Canada)

    lremy

    developing products, marketing tools and building capacity of the grass root telecentre workers. D.Net recognized that it had several ideas worth developing into small interventions that would make big differences, but resource constraints were a barrier for scaling-up these initiatives. More demands, limited resources.

  8. Surgery for GEP-NETs

    DEFF Research Database (Denmark)

    Knigge, Ulrich; Hansen, Carsten Palnæs

    2012-01-01

    Surgery is the only treatment that may cure the patient with gastroentero-pancreatic (GEP) neuroendocrine tumours (NET) and neuroendocrine carcinomas (NEC) and should always be considered as first line treatment if R0/R1 resection can be achieved. The surgical and interventional procedures for GEP...

  9. Net Neutrality in the Netherlands

    NARCIS (Netherlands)

    van Eijk, N.

    2014-01-01

    The Netherlands is among the first countries that have put specific net neutrality standards in place. The decision to implement specific regulation was influenced by at least three factors. The first was the prevailing social and academic debate, partly due to developments in the United States. The

  10. Complexity Metrics for Workflow Nets

    DEFF Research Database (Denmark)

    Lassen, Kristian Bisgaard; van der Aalst, Wil M.P.

    2009-01-01

    Process modeling languages such as EPCs, BPMN, flow charts, UML activity diagrams, Petri nets, etc.\\ are used to model business processes and to configure process-aware information systems. It is known that users have problems understanding these diagrams. In fact, even process engineers and system...

  11. Caught in the Net: Perineuronal Nets and Addiction

    Directory of Open Access Journals (Sweden)

    Megan Slaker

    2016-01-01

    Full Text Available Exposure to drugs of abuse induces plasticity in the brain and creates persistent drug-related memories. These changes in plasticity and persistent drug memories are believed to produce aberrant motivation and reinforcement contributing to addiction. Most studies have explored the effect drugs of abuse have on pre- and postsynaptic cells and astrocytes; however, more recently, attention has shifted to explore the effect these drugs have on the extracellular matrix (ECM. Within the ECM are unique structures arranged in a net-like manner, surrounding a subset of neurons called perineuronal nets (PNNs. This review focuses on drug-induced changes in PNNs, the molecules that regulate PNNs, and the expression of PNNs within brain circuitry mediating motivation, reward, and reinforcement as it pertains to addiction.

  12. Army Net Zero Prove Out. Army Net Zero Training Report

    Science.gov (United States)

    2014-11-20

    sensors were strategically placed throughout the installation by magnetically attaching them to water main valve stems. The sensors check sound...Recycle Wrap  Substitutes for Packaging Materials  Re-Use of Textiles and Linens  Setting Printers to Double-Sided Printing Net Zero Waste...can effectively achieve source reduction. Clean and Re-Use Shop Rags - Shop rags represent a large textile waste stream at many installations. As a

  13. Army Net Zero Prove Out. Net Zero Waste Best Practices

    Science.gov (United States)

    2014-11-20

    Anaerobic Digesters – Although anaerobic digestion is not a new technology and has been used on a large-scale basis in wastewater treatment , the...technology and has been used on a large-scale basis in wastewater treatment , the use of the technology should be demonstrated with other...approaches can be used for cardboard and cellulose -based packaging materials. This approach is in line with the Net Zero Waste hierarchy in terms of

  14. End-to-end unsupervised deformable image registration with a convolutional neural network

    NARCIS (Netherlands)

    de Vos, Bob D.; Berendsen, Floris; Viergever, Max A.; Staring, Marius; Išgum, Ivana

    2017-01-01

    In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial

  15. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  16. [Neural repair].

    Science.gov (United States)

    Kitada, Masaaki; Dezawa, Mari

    2008-05-01

    Recent progress of stem cell biology gives us the hope for neural repair. We have established methods to specifically induce functional Schwann cells and neurons from bone marrow stromal cells (MSCs). The effectiveness of these induced cells was evaluated by grafting them either into peripheral nerve injury, spinal cord injury, or Parkinson' s disease animal models. MSCs-derived Schwann cells supported axonal regeneration and re-constructed myelin to facilitate the functional recovery in peripheral and spinal cord injury. MSCs-derived dopaminergic neurons integrated into host striatum and contributed to behavioral repair. In this review, we introduce the differentiation potential of MSCs and finally discuss about their benefits and drawbacks of these induction systems for cell-based therapy in neuro-traumatic and neuro-degenerative diseases.

  17. The use of artificial neural networks in experimental data acquisition and aerodynamic design

    Science.gov (United States)

    Meade, Andrew J., Jr.

    1991-01-01

    It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

  18. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet...

  19. HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity

    Data.gov (United States)

    National Aeronautics and Space Administration — The Human Appropriation of Net Primary Productivity (HANPP) as a Percentage of Net Primary Productivity (NPP) portion of the Human Appropriation of Net Primary...

  20. Hydrodynamic characteristics of plane netting used for aquaculture net cages in uniform current

    National Research Council Canada - National Science Library

    DONG, SHUCHUANG; HU, FUXIANG; KUMAZAWA, TAISEI; SIODE, DAISUKE; TOKAI, TADASHI

    2016-01-01

      The hydrodynamic characteristics of polyethylene (PE) netting and chain link wire netting with different types of twine diameter and mesh size for aquaculture net cages were examined by experiments in a flume tank...

  1. Neural Networks in Antennas and Microwaves: A Practical Approach

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2001-12-01

    Full Text Available Neural networks are electronic systems which can be trained toremember behavior of a modeled structure in given operational points,and which can be used to approximate behavior of the structure out ofthe training points. These approximation abilities of neural nets aredemonstrated on modeling a frequency-selective surface, a microstriptransmission line and a microstrip dipole. Attention is turned to theaccuracy and to the efficiency of neural models. The association ofneural models and genetic algorithms, which can provide a global designtool, is discussed.

  2. Connectivity of communication networks

    CERN Document Server

    Mao, Guoqiang

    2017-01-01

    This book introduces a number of recent developments on connectivity of communication networks, ranging from connectivity of large static networks and connectivity of highly dynamic networks to connectivity of small to medium sized networks. This book also introduces some applications of connectivity studies in network optimization, in network localization, and in estimating distances between nodes. The book starts with an overview of the fundamental concepts, models, tools, and methodologies used for connectivity studies. The rest of the chapters are divided into four parts: connectivity of large static networks, connectivity of highly dynamic networks, connectivity of small to medium sized networks, and applications of connectivity studies.

  3. Morphological changes of tissue in implantation of xenopericardium and polypropylene net after surgical intervention

    Directory of Open Access Journals (Sweden)

    Titova Ye.V.

    2012-12-01

    Full Text Available The purpose: Comparative study of rehabilitation processes after implantation of synthetic and biological materials into the tissue of abdominal wall. Materials and Methods: Histological methods have been used to study tissue samples from 15 implantation area of xenopericardial plate and polypropylene net of adult rabbits in 3, 6 and 12 months after the surgery. Results: It has been found out that polypropylene net implantation in the tissue of anterior abdominal wall causes inflammation and growth of connective tissue only around it. Xenopericardial plate implantation is accompanied by lesser inflammation an3 it connects with surrounding connective tissue. Conclusion: It has been revealed that xenopericardium is more likely to use in herniologythan synthetic net. It provides greater mechanical strength of abdominal wall in the later periods after the surgical intervention.

  4. Flow Pattern Identification of Horizontal Two-Phase Refrigerant Flow Using Neural Networks

    Science.gov (United States)

    2015-12-31

    making classification difficult. Consequently, Table 5 shows neural net - work classification results for nine flow patterns. The number of runs...AFRL-RQ-WP-TP-2016-0079 FLOW PATTERN IDENTIFICATION OF HORIZONTAL TWO-PHASE REFRIGERANT FLOW USING NEURAL NETWORKS (POSTPRINT) Abdeel J... NEURAL NETWORKS (POSTPRINT) 5a. CONTRACT NUMBER In-house 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62203F 6. AUTHOR(S) Abdeel J. Roman and

  5. Design and regularization of neural networks: the optimal use of a validation set

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai; Svarer, Claus

    1996-01-01

    We derive novel algorithms for estimation of regularization parameters and for optimization of neural net architectures based on a validation set. Regularisation parameters are estimated using an iterative gradient descent scheme. Architecture optimization is performed by approximative...... combinatorial search among the relevant subsets of an initial neural network architecture by employing a validation set based optimal brain damage/surgeon (OBD/OBS) or a mean field combinatorial optimization approach. Numerical results with linear models and feed-forward neural networks demonstrate...

  6. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  7. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-11-01

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

  8. Constructive autoassociative neural network for facial recognition.

    Directory of Open Access Journals (Sweden)

    Bruno J T Fernandes

    Full Text Available Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network. CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.

  9. Gyral net: A new representation of cortical folding organization.

    Science.gov (United States)

    Chen, Hanbo; Li, Yujie; Ge, Fangfei; Li, Gang; Shen, Dinggang; Liu, Tianming

    2017-12-01

    One distinct feature of the cerebral cortex is its convex (gyri) and concave (sulci) folding patterns. Due to the remarkable complexity and variability of gyral/sulcal shapes, it has been challenging to quantitatively model their organization patterns. Inspired by the observation that the lines of gyral crests can form a connected graph on each brain hemisphere, we propose a new representation of cortical gyri/sulci organization pattern - gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the reconstructed cortical surfaces. A novel computational framework is developed to efficiently and automatically construct gyral nets from surface meshes, and four measurements are devised to quantify the folding patterns. Using an MRI dataset for autism study as a test bed, we identified reduced local connectivity cost and increased curviness of gyral net bilaterally on the parietal lobe, occipital lobe, and temporal lobe in autistic patients. Additionally, we found that the cortical thickness and the gyral straightness of gyral joints are higher than the rest of gyral crest regions. The proposed representation offers a new tool for a comprehensive and reliable characterization of the cortical folding organization. This novel computational framework will enable large-scale analyses of cortical folding patterns in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Neural plasticity across the lifespan.

    Science.gov (United States)

    Power, Jonathan D; Schlaggar, Bradley L

    2017-01-01

    An essential feature of the brain is its capacity to change. Neuroscientists use the term 'plasticity' to describe the malleability of neuronal connectivity and circuitry. How does plasticity work? A review of current data suggests that plasticity encompasses many distinct phenomena, some of which operate across most or all of the lifespan, and others that operate exclusively in early development. This essay surveys some of the key concepts related to neural plasticity, beginning with how current patterns of neural activity (e.g., as you read this essay) come to impact future patterns of activity (e.g., your memory of this essay), and then extending this framework backward into more development-specific mechanisms of plasticity. WIREs Dev Biol 2017, 6:e216. doi: 10.1002/wdev.216 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

  11. Event hierarchies in DanNet

    DEFF Research Database (Denmark)

    Pedersen, Bolette Sandford; Nimb, Sanni

    2008-01-01

    Artiklen omhandler udarbejdelsen af et verbumshierarki i det leksikalsk-semantiske ordnet, DanNet.......Artiklen omhandler udarbejdelsen af et verbumshierarki i det leksikalsk-semantiske ordnet, DanNet....

  12. Long Term RadNet Quality Data

    Data.gov (United States)

    U.S. Environmental Protection Agency — This RadNet Quality Data Asset includes all data since initiation and when ERAMS was expanded to become RadNet, name changed to reflect new mission. This includes...

  13. Neural network models: Insights and prescriptions from practical applications

    Energy Technology Data Exchange (ETDEWEB)

    Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)

    1995-12-31

    Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.

  14. Microbial activity in district cooling nets; Mikrobiell Aktivitet i Fjaerrkylenaet

    Energy Technology Data Exchange (ETDEWEB)

    Nordling, Magnus [Swedish Corrosion Inst., Stockholm (Sweden)

    2004-07-01

    Four district cooling nets with varying water quality have been investigated according to presence of microbially related problems. The aim has been to formulate recommendations regarding the water quality and regarding other procedures that might reduce the risk for biofilm formation and microbial corrosion. The method has consisted of using so called exposure containers, connected to each net. The water has been allowed to flow through the exposure containers where coupons of carbon steel have been exposed. The coupons have been withdrawn at different times, and analysed regarding the presence of biofilm and corrosion attack. Analyses have also been made regarding the amount of a number of different types of micro-organisms in the biofilm and in the district cooling water. The project has been divided in two phases. During the first phase of the project only two nets were investigated, one with municipal water and one with water of district heating quality, i.e. degassed and deionised. Biofilms could be seen on the coupons from both nets, even though the exposure time only had been 1.5 month. Considerable concentrations of micro-organisms were found in the biofilms and in the water for both nets, however much larger amounts for the net with municipal water. During the second phase of the project four nets were investigated, two with mainly municipal water and two with water of district heating quality. Here, on the other hand, it could be seen that the two nets with municipal water had micro-organisms of equivalent or lower concentrations compared to the two nets with water of district heating quality. One explanation to this is that the colouring substance pyranine is added to these two nets. Pyranine is added for the purpose of easily detecting a leakage but is at the same time a carbon compound, and as such a possible nutrient for the micro-organisms. This illustrates the importance of having the district cooling water as free from additives as possible. Other

  15. PsychoNet: a psycholinguistc commonsense ontology

    OpenAIRE

    Mohtasseb, Haytham; Ahmed, Amr

    2010-01-01

    Ontologies have been widely accepted as the most advanced knowledge representation model. This paper introduces PsychoNet, a new knowledgebase that forms the link between psycholinguistic taxonomy, existing in LIWC, and its semantic textual representation in the form of commonsense semantic ontology, represented by ConceptNet. The integration of LIWC and ConceptNet and the added functionalities facilitate employing ConceptNet in psycholinguistic studies. Furthermore, it simplifies utilization...

  16. Neural Network and Letter Recognition.

    Science.gov (United States)

    Lee, Hue Yeon

    Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C -layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken the on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the 'Gabor' transform. Pattern dependent choice of center and wavelengths of 'Gabor' filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets. The correct recognition rate of the system increases with the number of training sets and eventually saturates at a certain value. Similar recognition rates are obtained for the above three different learning algorithms. The minimum error

  17. Yarn-dyed fabric defect classification based on convolutional neural network

    Science.gov (United States)

    Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

    2017-09-01

    Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

  18. 78 FR 72451 - Net Investment Income Tax

    Science.gov (United States)

    2013-12-02

    ... Revenue Service 26 CFR Part 1 RIN 1545-BL74 Net Investment Income Tax AGENCY: Internal Revenue Service...). These regulations provide guidance on the computation of net investment income. The regulations affect... lesser of: (A) The individual's net investment income for such taxable year, or (B) the excess (if any...

  19. 47 CFR 69.302 - Net investment.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Net investment. 69.302 Section 69.302... Apportionment of Net Investment § 69.302 Net investment. (a) Investment in Accounts 2001, 1220 and Class B Rural...) Investment in Accounts 2002, 2003 and to the extent such inclusions are allowed by this Commission, Account...

  20. 47 CFR 65.450 - Net income.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Net income. 65.450 Section 65.450... OF RETURN PRESCRIPTION PROCEDURES AND METHODOLOGIES Exchange Carriers § 65.450 Net income. (a) Net income shall consist of all revenues derived from the provision of interstate telecommunications services...

  1. 47 CFR 65.500 - Net income.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Net income. 65.500 Section 65.500... OF RETURN PRESCRIPTION PROCEDURES AND METHODOLOGIES Interexchange Carriers § 65.500 Net income. The net income methodology specified in § 65.450 shall be utilized by all interexchange carriers that are...

  2. NetBeans IDE 8 cookbook

    CERN Document Server

    Salter, David

    2014-01-01

    If you're a Java developer of any level using NetBeans and want to learn how to get the most out of NetBeans, then this book is for you. Learning how to utilize NetBeans will provide a firm foundation for your Java application development.

  3. Characterizing behavioural congruences for Petri nets

    DEFF Research Database (Denmark)

    Nielsen, Mogens; Priese, Lutz; Sassone, Vladimiro

    1995-01-01

    We exploit a notion of interface for Petri nets in order to design a set of net combinators. For such a calculus of nets, we focus on the behavioural congruences arising from four simple notions of behaviour, viz., traces, maximal traces, step, and maximal step traces, and from the corresponding...

  4. 27 CFR 4.37 - Net contents.

    Science.gov (United States)

    2010-04-01

    ... the volume of wine within the container, except that the following tolerances shall be allowed: (1... THE TREASURY LIQUORS LABELING AND ADVERTISING OF WINE Labeling Requirements for Wine § 4.37 Net contents. (a) Statement of net contents. The net contents of wine for which a standard of fill is...

  5. Learning Topologies with the Growing Neural Forest.

    Science.gov (United States)

    Palomo, Esteban José; López-Rubio, Ezequiel

    2016-06-01

    In this work, a novel self-organizing model called growing neural forest (GNF) is presented. It is based on the growing neural gas (GNG), which learns a general graph with no special provisions for datasets with separated clusters. On the contrary, the proposed GNF learns a set of trees so that each tree represents a connected cluster of data. High dimensional datasets often contain large empty regions among clusters, so this proposal is better suited to them than other self-organizing models because it represents these separated clusters as connected components made of neurons. Experimental results are reported which show the self-organization capabilities of the model. Moreover, its suitability for unsupervised clustering and foreground detection applications is demonstrated. In particular, the GNF is shown to correctly discover the connected component structure of some datasets. Moreover, it outperforms some well-known foreground detectors both in quantitative and qualitative terms.

  6. Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.

    Science.gov (United States)

    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).

  7. Testing of information condensation in a model reverberating spiking neural network.

    Science.gov (United States)

    Vidybida, Alexander

    2011-06-01

    Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.

  8. Reconstruction of neutron spectra through neural networks; Reconstruccion de espectros de neutrones mediante redes neuronales

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E. [Cuerpo Academico de Radiobiologia, Estudios Nucleares, Universidad Autonoma de Zacatecas, A.P. 336, 98000 Zacatecas (Mexico)] e-mail: rvega@cantera.reduaz.mx [and others

    2003-07-01

    A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

  9. FPGA shore station demonstrator for KM3NeT

    Energy Technology Data Exchange (ETDEWEB)

    Anassontzis, E.G. [Physics Department, University of Athens (Greece); Belias, A.; Koutsoukos, S.; Koutsoumpos, V. [NESTOR Institute for Astroparticle Physics, National Observatory of Athens, 24001 Pylos (Greece); Manolopoulos, K., E-mail: kmanolo@phys.uoa.gr [NESTOR Institute for Astroparticle Physics, National Observatory of Athens, 24001 Pylos (Greece); Resvanis, L.K. [Physics Department, University of Athens (Greece); NESTOR Institute for Astroparticle Physics, National Observatory of Athens, 24001 Pylos (Greece)

    2013-10-11

    The KM3NeT readout concept is based on a point-to-point optical network connecting the 10,000 optical modules in the deep-sea neutrino telescope with the shore station. The numerous fiber optic channels arriving at the shore station will be concentrated on the shore electronics systems, which will receive, merge and time order the data, and send them to the DAQ system. Although the network functionality is bi-directional, the physical channel allocation is asymmetric; most channels are assigned to the data reception and only a few channels are used for control with data transport from shore to the telescope. We will discuss the FPGA based platform systems for the shore station and the appropriate firmware implementation for the data gathering and broadcast demands of a neutrino telescope. We will present our experiences based on FPGA evaluation platforms suitable to build a demonstrator of the KM3NeT shore station.

  10. NET 40 Generics Beginner's Guide

    CERN Document Server

    Mukherjee, Sudipta

    2012-01-01

    This is a concise, practical guide that will help you learn Generics in .NET, with lots of real world and fun-to-build examples and clear explanations. It is packed with screenshots to aid your understanding of the process. This book is aimed at beginners in Generics. It assumes some working knowledge of C# , but it isn't mandatory. The following would get the most use out of the book: Newbie C# developers struggling with Generics. Experienced C++ and Java Programmers who are migrating to C# and looking for an alternative to other generic frameworks like STL and JCF would find this book handy.

  11. The Net Reclassification Index (NRI)

    DEFF Research Database (Denmark)

    Pepe, Margaret S.; Fan, Jing; Feng, Ziding

    2015-01-01

    The Net Reclassification Index (NRI) is a very popular measure for evaluating the improvement in prediction performance gained by adding a marker to a set of baseline predictors. However, the statistical properties of this novel measure have not been explored in depth. We demonstrate the alarming...... marker is proven to erroneously yield a positive NRI. Some insight into this phenomenon is provided. Since large values for the NRI statistic may simply be due to use of poorly fitting risk models, we suggest caution in using the NRI as the basis for marker evaluation. Other measures of prediction...

  12. Grid-Connected Distributed Generation: Compensation Mechanism Basics

    Energy Technology Data Exchange (ETDEWEB)

    Aznar, Alexandra Y [National Renewable Energy Laboratory (NREL), Golden, CO (United States); ; ; ; Zinaman, Owen R [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-10-02

    This short report defines compensation mechanisms for grid-connected, behind-the-meter distributed generation (DG) systems as instruments that comprise three core elements: (1) metering and billing arrangements, (2) sell rate design, and (3) retail rate design. This report describes metering and billing arrangements, with some limited discussion of sell rate design. We detail the three possible arrangements for metering and billing of DG: net energy metering (NEM); buy all, sell all; and net billing.

  13. Part 1: Ulineær Regression med Neurale Netværk. Nonlinear Regression using Neural Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik

    baggrunden for iværksættelsen af projektet. Har beskrives forskellige publikationer, der forslår anvendelsen af neurale net inden for forskellige delområder af proceskontrol. En central erkendelse er her at de neurale net alle steder fungerer ved at approximere eller estimere forskellige relevante funktioner....... Herefter introducered de grundlæggende begreber omkring neurale net og Multi Lags Perceptronen (MLP). Den nødvendige teori for MLP til approximation og konsistent estimation af funktioner beskrives med detajler henlagt til appendix og en væsentlig ulempe, der mindsker den praktiske relevans af denne teori......Denne rapport er 1.del af den samlede dokumentation for Ph.D. arbejdet. Den samlede dokumentation består af to dele. Disse dele er: · Del 1: "Ulineære regression med neurale netværk · Del 2: "Prediktion, simulering og regulering med neurale netværk Rapporten indleder med en beskrivelse af...

  14. Memory-optimal neural network approximation

    Science.gov (United States)

    Bölcskei, Helmut; Grohs, Philipp; Kutyniok, Gitta; Petersen, Philipp

    2017-08-01

    We summarize the main results of a recent theory-developed by the authors-establishing fundamental lower bounds on the connectivity and memory requirements of deep neural networks as a function of the complexity of the function class to be approximated by the network. These bounds are shown to be achievable. Specifically, all function classes that are optimally approximated by a general class of representation systems-so-called affine systems-can be approximated by deep neural networks with minimal connectivity and memory requirements. Affine systems encompass a wealth of representation systems from applied harmonic analysis such as wavelets, shearlets, ridgelets, α-shearlets, and more generally α-molecules. This result elucidates a remarkable universality property of deep neural networks and shows that they achieve the optimum approximation properties of all affine systems combined. Finally, we present numerical experiments demonstrating that the standard stochastic gradient descent algorithm generates deep neural networks which provide close-to-optimal approximation rates at minimal connectivity. Moreover, stochastic gradient descent is found to actually learn approximations that are sparse in the representation system optimally sparsifying the function class the network is trained on.

  15. M&E-NetPay: A Micropayment System for Mobile and Electronic Commerce

    Directory of Open Access Journals (Sweden)

    Xiaodi Huang

    2016-08-01

    Full Text Available As an increasing number of people purchase goods and services online, micropayment systems are becoming particularly important for mobile and electronic commerce. We have designed and developed such a system called M&E-NetPay (Mobile and Electronic NetPay. With open interoperability and mobility, M&E-NetPay uses web services to connect brokers and vendors, providing secure, flexible and reliable credit services over the Internet. In particular, M&E-NetPay makes use of a secure, inexpensive and debit-based off-line protocol that allows vendors to interact only with customers, after validating coins. The design of the architecture and protocol of M&E-NetPay are presented, together with the implementation of its prototype in ringtone and wallpaper sites. To validate our system, we have conducted its evaluations on performance, usability and heuristics. Furthermore, we compare our system to the CORBA-based (Common Object Request Broker Architecture off-line micro-payment systems. The results have demonstrated that M&E-NetPay outperforms the .NET-based M&E-NetPay system in terms of performance and user satisfaction.

  16. An overview of Bayesian methods for neural spike train analysis.

    Science.gov (United States)

    Chen, Zhe

    2013-01-01

    Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  17. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  18. An efficient neural network approach to dynamic robot motion planning.

    Science.gov (United States)

    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.

  19. SpectralNET – an application for spectral graph analysis and visualization

    Directory of Open Access Journals (Sweden)

    Schreiber Stuart L

    2005-10-01

    Full Text Available Abstract Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices and interactions (edges that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors. Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is

  20. SpectralNET--an application for spectral graph analysis and visualization.

    Science.gov (United States)

    Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

    2005-10-19

    Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

  1. Intermodal Passenger Connectivity Database -

    Data.gov (United States)

    Department of Transportation — The Intermodal Passenger Connectivity Database (IPCD) is a nationwide data table of passenger transportation terminals, with data on the availability of connections...

  2. Multitask neurovision processor with extensive feedback and feedforward connections

    Science.gov (United States)

    Gupta, Madan M.; Knopf, George K.

    1991-11-01

    A multi-task neuro-vision parameter which performs a variety of information processing operations associated with the early stages of biological vision is presented. The network architecture of this neuro-vision processor, called the positive-negative (PN) neural processor, is loosely based on the neural activity fields exhibited by thalamic and cortical nervous tissue layers. The computational operation performed by the processor arises from the strength of the recurrent feedback among the numerous positive and negative neural computing units. By adjusting the feedback connections it is possible to generate diverse dynamic behavior that may be used for short-term visual memory (STVM), spatio-temporal filtering (STF), and pulse frequency modulation (PFM). The information attributes that are to be processes may be regulated by modifying the feedforward connections from the signal space to the neural processor.

  3. Gender Differences in Brain Functional Connectivity Density

    OpenAIRE

    Tomasi, Dardo; Volkow, Nora D.

    2011-01-01

    The neural bases of gender differences in emotional, cognitive, and socials behaviors are largely unknown. Here, magnetic resonance imaging data from 336 women and 225 men revealed a gender dimorphism in the functional organization of the brain. Consistently across five research sites, women had 14% higher local functional connectivity density (lFCD) and up to 5% higher gray matter density than men in cortical and subcortical regions. The negative power scaling of the lFCD was steeper for men...

  4. Neural simulations on multi-core architectures

    Directory of Open Access Journals (Sweden)

    Hubert Eichner

    2009-07-01

    Full Text Available Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i. e. user-transparent load balancing.

  5. Electronic device aspects of neural network memories

    Science.gov (United States)

    Lambe, J.; Moopenn, A.; Thakoor, A. P.

    1985-01-01

    The basic issues related to the electronic implementation of the neural network model (NNM) for content addressable memories are examined. A brief introduction to the principles of the NNM is followed by an analysis of the information storage of the neural network in the form of a binary connection matrix and the recall capability of such matrix memories based on a hardware simulation study. In addition, materials and device architecture issues involved in the future realization of such networks in VLSI-compatible ultrahigh-density memories are considered. A possible space application of such devices would be in the area of large-scale information storage without mechanical devices.

  6. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    In the present paper we consider the allocation of cost in connection networks. Agents have connection demands in form of pairs of locations they want to be connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection demands....... We use three axioms to characterize allocation rules that truthfully implement cost minimizing networks satisfying all connection demands in a game where: (1) a central planner announces an allocation rule and a cost estimation rule; (2) every agent reports her own connection demand as well as all...... connection costs; and, (3) the central planner selects a cost minimizing network satisfying reported connection demands based on estimated connection costs and allocates true connection costs of the selected network....

  7. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  8. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  9. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  10. Artificial neural networks as quantum associative memory

    Science.gov (United States)

    Hamilton, Kathleen; Schrock, Jonathan; Imam, Neena; Humble, Travis

    We present results related to the recall accuracy and capacity of Hopfield networks implemented on commercially available quantum annealers. The use of Hopfield networks and artificial neural networks as content-addressable memories offer robust storage and retrieval of classical information, however, implementation of these models using currently available quantum annealers faces several challenges: the limits of precision when setting synaptic weights, the effects of spurious spin-glass states and minor embedding of densely connected graphs into fixed-connectivity hardware. We consider neural networks which are less than fully-connected, and also consider neural networks which contain multiple sparsely connected clusters. We discuss the effect of weak edge dilution on the accuracy of memory recall, and discuss how the multiple clique structure affects the storage capacity. Our work focuses on storage of patterns which can be embedded into physical hardware containing n States Department of Defense and used resources of the Computational Research and Development Programs as Oak Ridge National Laboratory under Contract No. DE-AC0500OR22725 with the U. S. Department of Energy.

  11. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

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

  12. The Laplacian spectrum of neural networks

    Science.gov (United States)

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

    2014-01-01

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

  13. NETS - Danish participation. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Alsen, S. (Grontmij - Carl Bro, Glostrup (Denmark)); Theel, C. (Baltic Sea Solutions, Holeby (Denmark))

    2008-12-15

    Within the NICe-funded project 'Nordic Environmental Technology Solutions (NETS)' a new type of networking at the Nordic level was organized in order to jointly exploit the rapidly growing market potential in the environmental technology sector. The project aimed at increased and professionalized commercialization of Nordic Cleantech in energy and water business segments through 1) closer cooperation and joint marketing activities, 2) a website, 3) cleantech product information via brochures and publications 4) and participating in relevant trade fairs and other industry events. Facilitating business-to-business activities was another core task for the NETS project partners from Norway, Sweden, Finland and Denmark with the aim to encourage total solutions for combined Cleantech system offers. The project has achieved to establish a Cleantech register of 600 Nordic Cleantech companies, a network of 86 member enterprises, produced several publications and brochures for direct technology promotion and a website for direct access to company profiles and contact data. The project partners have attended 14 relevant international Cleantech trade fairs and conferences and facilitated business-to-business contacts added by capacity building offers through two company workshops. The future challenge for the project partners and Nordic Cleantech will be to coordinate the numerous efforts within the Nordic countries in order to reach concerted action and binding of member companies for reliable services, an improved visibility and knowledge exchange. With Cleantech's growing market influence and public awareness, the need to develop total solutions is increasing likewise. Marketing efforts should be encouraged cross-sectional and cross-border among the various levels of involved actors from both the public and the private sector. (au)

  14. Modeling EEG Waveforms with Semi-Supervised Deep Belief Nets: Fast Classification and Anomaly Measurement

    OpenAIRE

    Wulsin, D. F.; Gupta, J.R; Mani, R; Blanco, J. A.; Litt, B.

    2011-01-01

    Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep Belief Nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data, but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset...

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

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

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

  16. Children's Schemes for Anticipating the Validity of Nets for Solids

    Science.gov (United States)

    Wright, Vince; Smith, Ken

    2017-01-01

    There is growing acknowledgement of the importance of spatial abilities to student achievement across a broad range of domains and disciplines. Nets are one way to connect three-dimensional shapes and their two-dimensional representations and are a common focus of geometry curricula. Thirty-four students at year 6 (upper primary school) were…

  17. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  18. Application and Theory of Petri Nets

    DEFF Research Database (Denmark)

    This volume contains the proceedings of the 13th International Conference onApplication and Theory of Petri Nets, held in Sheffield, England, in June 1992. The aim of the Petri net conferences is to create a forum for discussing progress in the application and theory of Petri nets. Typically....... Balbo and W. Reisig, 18 submitted papers, and seven project papers. The submitted papers and project presentations were selectedby the programme committee and a panel of referees from a large number of submissions....

  19. Are You Neutral About Net Neutrality

    Science.gov (United States)

    2007-06-20

    Information Resources Management College National Defense University Are You Neutral About Net Neutrality ? A presentation for Systems & Software...author uses Verizon FiOS for phone, TV, and internet service 3 Agenda Net Neutrality —Through 2 Lenses Who Are the Players & What Are They Saying...Medical Treatment Mini-Case Studies Updates Closing Thoughts 4 Working Definitions of Net Neutrality "Network Neutrality" is the concept that

  20. Factors associated with mosquito net use by individuals in households owning nets in Ethiopia

    Directory of Open Access Journals (Sweden)

    Graves Patricia M

    2011-12-01

    Full Text Available Abstract Background Ownership of insecticidal mosquito nets has dramatically increased in Ethiopia since 2006, but the proportion of persons with access to such nets who use them has declined. It is important to understand individual level net use factors in the context of the home to modify programmes so as to maximize net use. Methods Generalized linear latent and mixed models (GLLAMM were used to investigate net use using individual level data from people living in net-owning households from two surveys in Ethiopia: baseline 2006 included 12,678 individuals from 2,468 households and a sub-sample of the Malaria Indicator Survey (MIS in 2007 included 14,663 individuals from 3,353 households. Individual factors (age, sex, pregnancy; net factors (condition, age, net density; household factors (number of rooms [2006] or sleeping spaces [2007], IRS, women's knowledge and school attendance [2007 only], wealth, altitude; and cluster level factors (rural or urban were investigated in univariate and multi-variable models for each survey. Results In 2006, increased net use was associated with: age 25-49 years (adjusted (a OR = 1.4, 95% confidence interval (CI 1.2-1.7 compared to children U5; female gender (aOR = 1.4; 95% CI 1.2-1.5; fewer nets with holes (Ptrend = 0.002; and increasing net density (Ptrend [all nets in HH good] = 1.6; 95% CI 1.2-2.1; increasing net density (Ptrend [per additional space] = 0.6, 95% CI 0.5-0.7; more old nets (aOR [all nets in HH older than 12 months] = 0.5; 95% CI 0.3-0.7; and increasing household altitude (Ptrend Conclusion In both surveys, net use was more likely by women, if nets had fewer holes and were at higher net per person density within households. School-age children and young adults were much less likely to use a net. Increasing availability of nets within households (i.e. increasing net density, and improving net condition while focusing on education and promotion of net use, especially in school-age children

  1. Pro Agile NET Development with Scrum

    CERN Document Server

    Blankenship, Jerrel; Millett, Scott

    2011-01-01

    Pro Agile .NET Development with SCRUM guides you through a real-world ASP.NET project and shows how agile methodology is put into practice. There is plenty of literature on the theory behind agile methodologies, but no book on the market takes the concepts of agile practices and applies these in a practical manner to an end-to-end ASP.NET project, especially the estimating, requirements and management aspects of a project. Pro Agile .NET Development with SCRUM takes you through the initial stages of a project - gathering requirements and setting up an environment - through to the development a

  2. Pro ASP.NET MVC 4

    CERN Document Server

    Freeman, Adam

    2012-01-01

    The ASP.NET MVC 4 Framework is the latest evolution of Microsoft's ASP.NET web platform. It provides a high-productivity programming model that promotes cleaner code architecture, test-driven development, and powerful extensibility, combined with all the benefits of ASP.NET. ASP.NET MVC 4 contains a number of significant advances over previous versions. New mobile and desktop templates (employing adaptive rendering) are included together with support for jQuery Mobile for the first time. New display modes allow your application to select views based on the browser that's making the request whi

  3. Professional Visual Basic 2010 and .NET 4

    CERN Document Server

    Sheldon, Bill; Sharkey, Kent

    2010-01-01

    Intermediate and advanced coverage of Visual Basic 2010 and .NET 4 for professional developers. If you've already covered the basics and want to dive deep into VB and .NET topics that professional programmers use most, this is your book. You'll find a quick review of introductory topics-always helpful-before the author team of experts moves you quickly into such topics as data access with ADO.NET, Language Integrated Query (LINQ), security, ASP.NET web programming with Visual Basic, Windows workflow, threading, and more. You'll explore all the new features of Visual Basic 2010 as well as all t

  4. NASA Net Zero Energy Buildings Roadmap

    Energy Technology Data Exchange (ETDEWEB)

    Pless, S.; Scheib, J.; Torcellini, P.; Hendron, B.; Slovensky, M.

    2014-10-01

    In preparation for the time-phased net zero energy requirement for new federal buildings starting in 2020, set forth in Executive Order 13514, NASA requested that the National Renewable Energy Laboratory (NREL) to develop a roadmap for NASA's compliance. NASA detailed a Statement of Work that requested information on strategic, organizational, and tactical aspects of net zero energy buildings. In response, this document presents a high-level approach to net zero energy planning, design, construction, and operations, based on NREL's first-hand experience procuring net zero energy construction, and based on NREL and other industry research on net zero energy feasibility. The strategic approach to net zero energy starts with an interpretation of the executive order language relating to net zero energy. Specifically, this roadmap defines a net zero energy acquisition process as one that sets an aggressive energy use intensity goal for the building in project planning, meets the reduced demand goal through energy efficiency strategies and technologies, then adds renewable energy in a prioritized manner, using building-associated, emission- free sources first, to offset the annual energy use required at the building; the net zero energy process extends through the life of the building, requiring a balance of energy use and production in each calendar year.

  5. Towards a Standard for Modular Petri Nets

    DEFF Research Database (Denmark)

    Kindler, Ekkart; Petrucci, Laure

    2009-01-01

    When designing complex systems, mechanisms for structuring, composing, and reusing system components are crucial. Today, there are many approaches for equipping Petri nets with such mechanisms. In the context of defining a standard interchange format for Petri nets, modular PNML was defined....... Moreover, we present and discuss some more advanced features of modular Petri nets that could be included in the standard. This way, we provide a formal foundation and a basis for a discussion of features to be included in the upcoming standard of a module concept for Petri nets in general and for high...

  6. Children's schemes for anticipating the validity of nets for solids

    Science.gov (United States)

    Wright, Vince; Smith, Ken

    2017-09-01

    There is growing acknowledgement of the importance of spatial abilities to student achievement across a broad range of domains and disciplines. Nets are one way to connect three-dimensional shapes and their two-dimensional representations and are a common focus of geometry curricula. Thirty-four students at year 6 (upper primary school) were interviewed on two occasions about their anticipation of whether or not given nets for the cube- and square-based pyramid would fold to form the target solid. Vergnaud's ( Journal of Mathematical Behavior, 17(2), 167-181, 1998, Human Development, 52, 83-94, 2009) four characteristics of schemes were used as a theoretical lens to analyse the data. Successful schemes depended on the interaction of operational invariants, such as strategic choice of the base, rules for action, particularly rotation of shapes, and anticipations of composites of polygons in the net forming arrangements of faces in the solid. Inferences were rare. These data suggest that students need teacher support to make inferences, in order to create transferable schemes.

  7. Temporal Dynamics of Sensorimotor Networks in Effort-Based Cost-Benefit Valuation: Early Emergence and Late Net Value Integration.

    Science.gov (United States)

    Harris, Alison; Lim, Seung-Lark

    2016-07-06

    Although physical effort can impose significant costs on decision-making, when and how effort cost information is incorporated into choice remains contested, reflecting a larger debate over the role of sensorimotor networks in specifying behavior. Serial information processing models, in which motor circuits simply implement the output of cognitive systems, hypothesize that effort cost factors into decisions relatively late, via integration with stimulus values into net (combined) value signals in dorsomedial frontal cortex (dmFC). In contrast, ethology-inspired approaches suggest a more active role for the dorsal sensorimotor stream, with effort cost signals emerging rapidly after stimulus onset. Here we investigated the time course of effort cost integration using event-related potentials in hungry human subjects while they made decisions about expending physical effort for appetitive foods. Consistent with the ethological perspective, we found that effort cost was represented from as early as 100-250 ms after stimulus onset, localized to dorsal sensorimotor regions including middle cingulate, somatosensory, and motor/premotor cortices. However, examining the same data time-locked to motor output revealed net value signals combining stimulus value and effort cost approximately -400 ms before response, originating from sensorimotor areas including dmFC, precuneus, and posterior parietal cortex. Granger causal connectivity analysis of the motor effector signal in the time leading to response showed interactions between these sensorimotor regions and ventrolateral prefrontal cortex, a structure associated with adjusting behavior-response mappings. These results suggest that rapid activation of sensorimotor regions interacts with cognitive valuation systems, producing a net value signal reflecting both physical effort and reward contingencies. Although physical effort imposes a cost on choice, when and how effort cost influences neural correlates of decision

  8. Recovery of directed intracortical connectivity from fMRI data

    Science.gov (United States)

    Gilson, Matthieu; Ritter, Petra; Deco, Gustavo

    2016-06-01

    The brain exhibits complex spatio-temporal patterns of activity. In particular, its baseline activity at rest has a specific structure: imaging techniques (e.g., fMRI, EEG and MEG) show that cortical areas experience correlated fluctuations, which is referred to as functional connectivity (FC). The present study relies on our recently developed model in which intracortical white-matter connections shape noise-driven fluctuations to reproduce FC observed in experimental data (here fMRI BOLD signal). Here noise has a functional role and represents the variability of neural activity. The model also incorporates anatomical information obtained using diffusion tensor imaging (DTI), which estimates the density of white-matter fibers (structural connectivity, SC). After optimization to match empirical FC, the model provides an estimation of the efficacies of these fibers, which we call effective connectivity (EC). EC differs from SC, as EC not only accounts for the density of neural fibers, but also the concentration of synapses formed at their end, the type of neurotransmitters associated and the excitability of target neural populations. In summary, the model combines anatomical SC and activity FC to evaluate what drives the neural dynamics, embodied in EC. EC can then be analyzed using graph theory to understand how it generates FC and to seek for functional communities among cortical areas (parcellation of 68 areas). We find that intracortical connections are not symmetric, which affects the dynamic range of cortical activity (i.e., variety of states it can exhibit).

  9. Moral transgressions corrupt neural representations of value.

    Science.gov (United States)

    Crockett, Molly J; Siegel, Jenifer Z; Kurth-Nelson, Zeb; Dayan, Peter; Dolan, Raymond J

    2017-06-01

    Moral systems universally prohibit harming others for personal gain. However, we know little about how such principles guide moral behavior. Using a task that assesses the financial cost participants ascribe to harming others versus themselves, we probed the relationship between moral behavior and neural representations of profit and pain. Most participants displayed moral preferences, placing a higher cost on harming others than themselves. Moral preferences correlated with neural responses to profit, where participants with stronger moral preferences had lower dorsal striatal responses to profit gained from harming others. Lateral prefrontal cortex encoded profit gained from harming others, but not self, and tracked the blameworthiness of harmful choices. Moral decisions also modulated functional connectivity between lateral prefrontal cortex and the profit-sensitive region of dorsal striatum. The findings suggest moral behavior in our task is linked to a neural devaluation of reward realized by a prefrontal modulation of striatal value representations.

  10. Linking netCDF Data with the Semantic Web - Enhancing Data Discovery Across Domains

    Science.gov (United States)

    Biard, J. C.; Yu, J.; Hedley, M.; Cox, S. J. D.; Leadbetter, A.; Car, N. J.; Druken, K. A.; Nativi, S.; Davis, E.

    2016-12-01

    Geophysical data communities are publishing large quantities of data across a wide variety of scientific domains which are overlapping more and more. Whilst netCDF is a common format for many of these communities, it is only one of a large number of data storage and transfer formats. One of the major challenges ahead is finding ways to leverage these diverse data sets to advance our understanding of complex problems. We describe a methodology for incorporating Resource Description Framework (RDF) triples into netCDF files called netCDF-LD (netCDF Linked Data). NetCDF-LD explicitly connects the contents of netCDF files - both data and metadata, with external web-based resources, including vocabularies, standards definitions, and data collections, and through them, a whole host of related information. This approach also preserves and enhances the self describing essence of the netCDF format and its metadata, whilst addressing the challenge of integrating various conventions into files. We present a case study illustrating how reasoning over RDF graphs can empower researchers to discover datasets across domain boundaries.

  11. Investment Valuation Analysis with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Hüseyin İNCE

    2017-07-01

    Full Text Available This paper shows that discounted cash flow and net present value, which are traditional investment valuation models, can be combined with artificial neural network model forecasting. The main inputs for the valuation models, such as revenue, costs, capital expenditure, and their growth rates, are heavily related to sector dynamics and macroeconomics. The growth rates of those inputs are related to inflation and exchange rates. Therefore, predicting inflation and exchange rates is a critical issue for the valuation output. In this paper, the Turkish economy’s inflation rate and the exchange rate of USD/TRY are forecast by artificial neural networks and implemented to the discounted cash flow model. Finally, the results are benchmarked with conventional practices.

  12. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    2015-01-01

    In the present paper we consider the allocation of costs in connection networks. Agents have connection demands in form of pairs of locations they want to have connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection...... demands. We use a few axioms to characterize allocation rules that truthfully implement cost minimizing networks satisfying all connection demands in a game where: (1) a central planner announces an allocation rule and a cost estimation rule; (2) every agent reports her own connection demand as well...... as all connection costs; (3) the central planner selects a cost minimizing network satisfying reported connection demands based on the estimated costs; and, (4) the planner allocates the true costs of the selected network. It turns out that an allocation rule satisfies the axioms if and only if relative...

  13. A Topological Perspective of Neural Network Structure

    Science.gov (United States)

    Sizemore, Ann; Giusti, Chad; Cieslak, Matthew; Grafton, Scott; Bassett, Danielle

    The wiring patterns of white matter tracts between brain regions inform functional capabilities of the neural network. Indeed, densely connected and cyclically arranged cognitive systems may communicate and thus perform distinctly. However, previously employed graph theoretical statistics are local in nature and thus insensitive to such global structure. Here we present an investigation of the structural neural network in eight healthy individuals using persistent homology. An extension of homology to weighted networks, persistent homology records both circuits and cliques (all-to-all connected subgraphs) through a repetitive thresholding process, thus perceiving structural motifs. We report structural features found across patients and discuss brain regions responsible for these patterns, finally considering the implications of such motifs in relation to cognitive function.

  14. Matrix representation of a Neural Network

    DEFF Research Database (Denmark)

    Christensen, Bjørn Klint

    Processing, by David Rummelhart (Rummelhart 1986) for an easy-to-read introduction. What the paper does explain is how a matrix representation of a neural net allows for a very simple implementation. The matrix representation is introduced in (Rummelhart 1986, chapter 9), but only for a two-layer linear...... network and the feedforward algorithm. This paper develops the idea further to three-layer non-linear networks and the backpropagation algorithm. Figure 1 shows the layout of a three-layer network. There are I input nodes, J hidden nodes and K output nodes all indexed from 0. Bias-node for the hidden...

  15. HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity

    Data.gov (United States)

    National Aeronautics and Space Administration — The Human Appropriation of Net Primary Productivity (HANPP) as a Percentage of Net Primary Product (NPP) portion of the HANPP Collection represents a map identifying...

  16. Price smarter on the Net.

    Science.gov (United States)

    Baker, W; Marn, M; Zawada, C

    2001-02-01

    Companies generally have set prices on the Internet in two ways. Many start-ups have offered untenably low prices in a rush to capture first-mover advantage. Many incumbents have simply charged the same prices on-line as they do off-line. Either way, companies are missing a big opportunity. The fundamental value of the Internet lies not in lowering prices or making them consistent but in optimizing them. After all, if it's easy for customers to compare prices on the Internet, it's also easy for companies to track customers' behavior and adjust prices accordingly. The Net lets companies optimize prices in three ways. First, it lets them set and announce prices with greater precision. Different prices can be tested easily, and customers' responses can be collected instantly. Companies can set the most profitable prices, and they can tap into previously hidden customer demand. Second, because it's so easy to change prices on the Internet, companies can adjust prices in response to even small fluctuations in market conditions, customer demand, or competitors' behavior. Third, companies can use the clickstream data and purchase histories that it collects through the Internet to segment customers quickly. Then it can offer segment-specific prices or promotions immediately. By taking full advantage of the unique possibilities afforded by the Internet to set prices with precision, adapt to changing circumstances quickly, and segment customers accurately, companies can get their pricing right. It's one of the ultimate drivers of e-business success.

  17. Feature to prototype transition in neural networks

    Science.gov (United States)

    Krotov, Dmitry; Hopfield, John

    Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

  18. Assessing Functional Neural Connectivity as an Indicator of Cognitive Performance

    Science.gov (United States)

    2015-10-12

    correct and incorrect trials. Fig. 3. Differences in graph features as a function of frequency band 4 Machine Learning Approach 4.1 Dimensionality...principal component analy- sis (PCA) dimensionality reduction procedure for each feature set. To avoid overfit - ting, we apply an identical procedure for each

  19. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  20. Flexible neural interfaces with integrated stiffening shank

    Energy Technology Data Exchange (ETDEWEB)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2017-10-17

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  1. Auditory Hallucinations in Schizophrenia Are Associated with Reduced Functional Connectivity of the Temporo-Parietal Area

    NARCIS (Netherlands)

    Vercammen, Ans; Knegtering, Henderikus; den Boer, Johann A.; Liemburg, Edith J.; Aleman, Andre

    2010-01-01

    Background: Schizophrenia has been conceptualized as a disorder of integration of neural activity across distributed networks. However, the relationship between specific symptom dimensions and patterns of functional connectivity remains unclear. The current study aimed to investigate the

  2. Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations

    Directory of Open Access Journals (Sweden)

    Maria de la Iglesia-Vaya

    2014-01-01

    These data indicate that an anomalous process of neural connectivity exists when patients with AH process emotional auditory stimuli. Additionally, a central role is suggested for the cerebellum in processing emotional stimuli in patients with persistent AH.

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

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    that occur during development that may be observable even in actual neural systems where these changes are convoluted with changes in synaptic connectivity and intrinsic neural plasticity.

  4. 78 FR 72393 - Net Investment Income Tax

    Science.gov (United States)

    2013-12-02

    ... Investment Income Tax; Final and Proposed Rules #0;#0;Federal Register / Vol. 78, No. 231 / Monday, December... Parts 1 and 602 RIN 1545-BK44 Net Investment Income Tax AGENCY: Internal Revenue Service (IRS), Treasury... Investment Income Tax and the computation of Net Investment Income. The regulations affect individuals...

  5. 77 FR 72611 - Net Investment Income Tax

    Science.gov (United States)

    2012-12-05

    ... December 5, 2012 Part V Department of the Treasury Internal Revenue Service 26 CFR Part 1 Net Investment... Investment Income Tax AGENCY: Internal Revenue Service (IRS), Treasury. ACTION: Notice of proposed rulemaking...) the individual's net investment income for such taxable year, or (B) the excess (if any) of (i) the...

  6. Net analyte signal based statistical quality control

    NARCIS (Netherlands)

    Skibsted, E.T.S.; Boelens, H.F.M.; Westerhuis, J.A.; Smilde, A.K.; Broad, N.W.; Rees, D.R.; Witte, D.T.

    2005-01-01

    Net analyte signal statistical quality control (NAS-SQC) is a new methodology to perform multivariate product quality monitoring based on the net analyte signal approach. The main advantage of NAS-SQC is that the systematic variation in the product due to the analyte (or property) of interest is

  7. Asynchronous stream processing with S-Net

    NARCIS (Netherlands)

    Grelck, C.; Scholz, S.-B.; Shafarenko, A.

    2010-01-01

    We present the rationale and design of S-Net, a coordination language for asynchronous stream processing. The language achieves a near-complete separation between the application code, written in any conventional programming language, and the coordination/communication code written in S-Net. Our

  8. Using the MVC architecture on . NET platform

    OpenAIRE

    Ježek, David

    2011-01-01

    This thesis deals with usage of MVC (Model View Controller) technology in web development on ASP.NET platform from Microsoft. Mainly it deals with latest version of framework ASP.NET MVC 3. First part describes MVC architecture and the second describes usage of MVC in certain parts of web application an comparing with PHP.

  9. Analysis of Petri Nets and Transition Systems

    Directory of Open Access Journals (Sweden)

    Eike Best

    2015-08-01

    Full Text Available This paper describes a stand-alone, no-frills tool supporting the analysis of (labelled place/transition Petri nets and the synthesis of labelled transition systems into Petri nets. It is implemented as a collection of independent, dedicated algorithms which have been designed to operate modularly, portably, extensibly, and efficiently.

  10. 27 CFR 7.27 - Net contents.

    Science.gov (United States)

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Net contents. 7.27 Section 7.27 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE BUREAU, DEPARTMENT OF... the net contents are displayed by having the same blown, branded, or burned in the container in...

  11. Petri nets and other models of concurrency

    DEFF Research Database (Denmark)

    Nielsen, Mogens; Sassone, Vladimiro

    1998-01-01

    This paper retraces, collects, and summarises contributions of the authors - in collaboration with others - on the theme of Petri nets and their categorical relationships to other models of concurrency.......This paper retraces, collects, and summarises contributions of the authors - in collaboration with others - on the theme of Petri nets and their categorical relationships to other models of concurrency....

  12. Delta Semantics Defined By Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt; Kyng, Morten; Madsen, Ole Lehrmann

    This report is identical to an earlier version of May 1978 except that Chapter 5 has been revised. A new paper: "A Petri Net Definition of a System Description Language", DAIMI, April 1979, 20 pages, extends the Petri net model to include a data state representing the program variables. Delta...

  13. Net neutrality and inflation of traffic

    NARCIS (Netherlands)

    Peitz, M.; Schütt, Florian

    Under strict net neutrality Internet service providers (ISPs) are required to carry data without any differentiation and at no cost to the content provider. We provide a simple framework with a monopoly ISP to evaluate the short-run effects of different net neutrality rules. Content differs in its

  14. Net Neutrality and Inflation of Traffic

    NARCIS (Netherlands)

    Peitz, M.; Schütt, F.

    2015-01-01

    Under strict net neutrality Internet service providers (ISPs) are required to carry data without any differentiation and at no cost to the content provider. We provide a simple framework with a monopoly ISP to evaluate different net neutrality rules. Content differs in its sensitivity to delay.

  15. The Net Neutrality Debate: The Basics

    Science.gov (United States)

    Greenfield, Rich

    2006-01-01

    Rich Greenfield examines the basics of today's net neutrality debate that is likely to be an ongoing issue for society. Greenfield states the problems inherent in the definition of "net neutrality" used by Common Cause: "Network neutrality is the principle that Internet users should be able to access any web content they choose and…

  16. Dynamic response of the thermometric net radiometer

    Science.gov (United States)

    J. D. Wilson; W. J. Massman; G. E. Swaters

    2009-01-01

    We computed the dynamic response of an idealized thermometric net radiometer, when driven by an oscillating net longwave radiation intended roughly to simulate rapid fluctuations of the radiative environment such as might be expected during field use of such devices. The study was motivated by curiosity as to whether non-linearity of the surface boundary conditions...

  17. Teaching and Learning with the Net Generation

    Science.gov (United States)

    Barnes, Kassandra; Marateo, Raymond C.; Ferris, S. Pixy

    2007-01-01

    As the Net Generation places increasingly greater demands on educators, students and teachers must jointly consider innovative ways of teaching and learning. In this, educators are supported by the fact that the Net Generation wants to learn. However, these same educators should not fail to realize that this generation learns differently from…

  18. Verification of Timed-Arc Petri Nets

    DEFF Research Database (Denmark)

    Jacobsen, Lasse; Jacobsen, Morten; Møller, Mikael Harkjær

    2011-01-01

    Timed-Arc Petri Nets (TAPN) are an extension of the classical P/T nets with continuous time. Tokens in TAPN carry an age and arcs between places and transitions are labelled with time intervals restricting the age of tokens available for transition firing. The TAPN model posses a number...

  19. A Brief Introduction to Coloured Petri Nets

    DEFF Research Database (Denmark)

    Jensen, Kurt

    1997-01-01

    Coloured Petri Nets (CP-nets or CPN) is a graphical oriented language for design, specification, simulation and verification of systems. It is in particular well- suited for systems in which communication, synchronisation and resource sharing are important. Typical examples of application areas a...

  20. Gill net and trammel net selectivity in the northern Aegean Sea, Turkey

    Directory of Open Access Journals (Sweden)

    F. Saadet Karakulak

    2008-09-01

    Full Text Available Fishing trials were carried out with gill nets and trammel nets in the northern Aegean Sea from March 2004 to February 2005. Four different mesh sizes for the gill nets and the inner panel of trammel nets (16, 18, 20 and 22 mm bar length were used. Selectivity parameters for the five most economically important species, bogue (Boops boops, annular sea bream (Diplodus annularis, striped red mullet (Mullus surmuletus, axillary sea bream (Pagellus acarne and blotched picarel (Spicara maena, caught by the two gears were estimated. The SELECT method was used to estimate the selectivity parameters of a variety of models. Catch composition and catch proportion of several species were different in gill and trammel nets. The length frequency distributions of the species caught by the two gears were significantly different. The bi-modal model selectivity curve gave the best fit for gill net and trammel net data, and there was little difference between the modal lengths of these nets. However, a clear difference was found in catching efficiency. The highest catch rates were obtained with the trammel net. Given that many discard species and small fish are caught by gill nets and trammel nets with a mesh size of 16 mm, it is clear that these nets are not appropriate for fisheries. Consequently, the best mesh size for multispecies fisheries is 18 mm. This mesh size will considerably reduce the numbers of small sized individuals and discard species in the catch.