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Sample records for neural architecture underlying

  1. Investigation of possible neural architectures underlying information-geometric measures.

    Science.gov (United States)

    Tatsuno, Masami; Okada, Masato

    2004-04-01

    A novel analytical method based on information geometry was recently proposed, and this method may provide useful insights into the statistical interactions within neural groups. The link between informationgeometric measures and the structure of neural interactions has not yet been elucidated, however, because of the ill-posed nature of the problem. Here, possible neural architectures underlying information-geometric measures are investigated using an isolated pair and an isolated triplet of model neurons. By assuming the existence of equilibrium states, we derive analytically the relationship between the information-geometric parameters and these simple neural architectures. For symmetric networks, the first- and second-order information-geometric parameters represent, respectively, the external input and the underlying connections between the neurons provided that the number of neurons used in the parameter estimation in the log-linear model and the number of neurons in the network are the same. For asymmetric networks, however, these parameters are dependent on both the intrinsic connections and the external inputs to each neuron. In addition, we derive the relation between the information-geometric parameter corresponding to the two-neuron interaction and a conventional cross-correlation measure. We also show that the information-geometric parameters vary depending on the number of neurons assumed for parameter estimation in the log-linear model. This finding suggests a need to examine the information-geometric method carefully. A possible criterion for choosing an appropriate orthogonal coordinate is also discussed. This article points out the importance of a model-based approach and sheds light on the possible neural structure underlying the application of information geometry to neural network analysis.

  2. Neural architectures for stereo vision.

    Science.gov (United States)

    Parker, Andrew J; Smith, Jackson E T; Krug, Kristine

    2016-06-19

    Stereoscopic vision delivers a sense of depth based on binocular information but additionally acts as a mechanism for achieving correspondence between patterns arriving at the left and right eyes. We analyse quantitatively the cortical architecture for stereoscopic vision in two areas of macaque visual cortex. For primary visual cortex V1, the result is consistent with a module that is isotropic in cortical space with a diameter of at least 3 mm in surface extent. This implies that the module for stereo is larger than the repeat distance between ocular dominance columns in V1. By contrast, in the extrastriate cortical area V5/MT, which has a specialized architecture for stereo depth, the module for representation of stereo is about 1 mm in surface extent, so the representation of stereo in V5/MT is more compressed than V1 in terms of neural wiring of the neocortex. The surface extent estimated for stereo in V5/MT is consistent with measurements of its specialized domains for binocular disparity. Within V1, we suggest that long-range horizontal, anatomical connections form functional modules that serve both binocular and monocular pattern recognition: this common function may explain the distortion and disruption of monocular pattern vision observed in amblyopia.This article is part of the themed issue 'Vision in our three-dimensional world'. © 2016 The Authors.

  3. 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.

  4. Optimizing Neural Network Architectures Using Generalization Error Estimators

    DEFF Research Database (Denmark)

    Larsen, Jan

    1994-01-01

    This paper addresses the optimization of neural network architectures. It is suggested to optimize the architecture by selecting the model with minimal estimated averaged generalization error. We consider a least-squares (LS) criterion for estimating neural network models, i.e., the associated...... model weights are estimated by minimizing the LS criterion. The quality of a particular estimated model is measured by the average generalization error. This is defined as the expected squared prediction error on a novel input-output sample averaged over all possible training sets. An essential part...... of the suggested architecture optimization scheme is to calculate an estimate of the average generalization error. We suggest using the GEN-estimator which allows for dealing with nonlinear, incomplete models, i.e., models which are not capable of modeling the underlying nonlinear relationship perfectly. In most...

  5. An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures

    Energy Technology Data Exchange (ETDEWEB)

    Schuman, Catherine D [ORNL; Plank, James [University of Tennessee (UT); Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

    2016-01-01

    As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

  6. Getting symbols out of a neural architecture

    Science.gov (United States)

    Hummel, John E.

    2011-06-01

    Traditional connectionist networks are sharply limited as general accounts of human perception and cognition because they are unable to represent relational ideas such as loves (John, Mary) or bigger-than (Volkswagen, breadbox) in a way that allows them to be manipulated as explicitly relational structures. This paper reviews and critiques the four major responses to this problem in the modelling community: (1) reject connectionism (in any form) in favour of traditional symbolic approaches to modelling the mind; (2) reject the idea that mental representations are symbolic (i.e. reject the idea that we can represent relations); and (3) attempt to represent symbolic structures in a connectionist/neural architecture by finding a way to represent role-filler bindings. Approach (3) is further subdivided into (3a) approaches based on varieties of conjunctive coding and (3b) approaches based on dynamic role-filler binding. I will argue that (3b) is necessary to get symbolic processing out of a neural computing architecture. Specifically, I will argue that vector addition is both the best way to accomplish dynamic binding and an essential part of the proper treatment of symbols in a neural architecture.

  7. Stable architectures for deep neural networks

    Science.gov (United States)

    Haber, Eldad; Ruthotto, Lars

    2018-01-01

    Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

  8. A hardware implementation of neural network with modified HANNIBAL architecture

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Bum youb; Chung, Duck Jin [Inha University, Inchon (Korea, Republic of)

    1996-03-01

    A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). 14 refs., 10 figs., 3 tabs.

  9. Ambiguity resolution in a Neural Blackboard Architecture for sentence structure

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; Kühnberger, Kai-Uwe

    2015-01-01

    We simulate two examples of ambiguity resolution found in human language processing in a neural blackboard architecture for sentence representation and processing. The architecture also accounts for a related garden path effect. The architecture represents and processes sentences in terms of

  10. Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures

    Directory of Open Access Journals (Sweden)

    Yulia eSandamirskaya

    2014-01-01

    Full Text Available Dynamic Field Theory (DFT is an established framework for modelling embodied cognition. In DFT, elementary cognitive functions such as memory formation, formation of grounded representations, attentional processes, decision making, adaptation, and learning emerge from neuronal dynamics. The basic computational element of this framework is a Dynamic Neural Field (DNF. Under constraints on the time-scale of the dynamics, the DNF is computationally equivalent to a soft winner-take-all (WTA network, which is considered one of the basic computational units in neuronal processing. Recently, it has been shown how a WTA network may be implemented in neuromorphic hardware, such as analogue Very Large Scale Integration (VLSI device. This paper leverages the relationship between DFT and soft WTA networks to systematically revise and integrate established DFT mechanisms that have previously been spread among different architectures. In addition, I also identify some novel computational and architectural mechanisms of DFT which may be implemented in neuromorphic VLSI devices using WTA networks as an intermediate computational layer. These specific mechanisms include the stabilization of working memory, the coupling of sensory systems to motor dynamics, intentionality, and autonomous learning. I further demonstrate how all these elements may be integrated into a unified architecture to generate behavior and autonomous learning.

  11. Architecture Analysis of an FPGA-Based Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

    Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.

  12. Combinatorial structures and processing in neural blackboard architectures

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; d'Avila Garcez, Artur; Marcus, Gary F.; Miikkulainen, Risto

    2015-01-01

    We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported.

  13. Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

    Science.gov (United States)

    Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

    2017-07-03

    Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

  14. Neural Architectures for Named Entity Recognition

    OpenAIRE

    Lample, Guillaume; Ballesteros, Miguel; Subramanian, Sandeep; Kawakami, Kazuya; Dyer, Chris

    2016-01-01

    State-of-the-art named entity recognition systems/nrely heavily on hand-crafted features and/ndomain-specific knowledge in order to learn/neffectively from the small, supervised training/ncorpora that are available. In this paper, we/nintroduce two new neural architectures—one/nbased on bidirectional LSTMs and conditional/nrandom fields, and the other that constructs/nand labels segments using a transition-based/napproach inspired by shift-reduce parsers./nOur models rely on two sources of in...

  15. Neurally and mathematically motivated architecture for language and thought.

    Science.gov (United States)

    Perlovsky, L I; Ilin, R

    2010-01-01

    Neural structures of interaction between thinking and language are unknown. This paper suggests a possible architecture motivated by neural and mathematical considerations. A mathematical requirement of computability imposes significant constraints on possible architectures consistent with brain neural structure and with a wealth of psychological knowledge. How language interacts with cognition. Do we think with words, or is thinking independent from language with words being just labels for decisions? Why is language learned by the age of 5 or 7, but acquisition of knowledge represented by learning to use this language knowledge takes a lifetime? This paper discusses hierarchical aspects of language and thought and argues that high level abstract thinking is impossible without language. We discuss a mathematical technique that can model the joint language-thought architecture, while overcoming previously encountered difficulties of computability. This architecture explains a contradiction between human ability for rational thoughtful decisions and irrationality of human thinking revealed by Tversky and Kahneman; a crucial role in this contradiction might be played by language. The proposed model resolves long-standing issues: how the brain learns correct words-object associations; why animals do not talk and think like people. We propose the role played by language emotionality in its interaction with thought. We relate the mathematical model to Humboldt's "firmness" of languages; and discuss possible influence of language grammar on its emotionality. Psychological and brain imaging experiments related to the proposed model are discussed. Future theoretical and experimental research is outlined.

  16. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  17. Convolutional neural network architectures for predicting DNA-protein binding.

    Science.gov (United States)

    Zeng, Haoyang; Edwards, Matthew D; Liu, Ge; Gifford, David K

    2016-06-15

    Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. All the models analyzed are available at http://cnn.csail.mit.edu gifford@mit.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  18. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Deciphering the Cognitive and Neural Mechanisms Underlying ...

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

    Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

  20. Evolution of genetic architecture under directional selection.

    Science.gov (United States)

    Hansen, Thomas F; Alvarez-Castro, José M; Carter, Ashley J R; Hermisson, Joachim; Wagner, Günter P

    2006-08-01

    We investigate the multilinear epistatic model under mutation-limited directional selection. We confirm previous results that only directional epistasis, in which genes on average reinforce or diminish each other's effects, contribute to the initial evolution of mutational effects. Thus, either canalization or decanalization can occur under directional selection, depending on whether positive or negative epistasis is prevalent. We then focus on the evolution of the epistatic coefficients themselves. In the absence of higher-order epistasis, positive pairwise epistasis will tend to weaken relative to additive effects, while negative pairwise epistasis will tend to become strengthened. Positive third-order epistasis will counteract these effects, while negative third-order epistasis will reinforce them. More generally, gene interactions of all orders have an inherent tendency for negative changes under directional selection, which can only be modified by higher-order directional epistasis. We identify three types of nonadditive quasi-equilibrium architectures that, although not strictly stable, can be maintained for an extended time: (1) nondirectional epistatic architectures; (2) canalized architectures with strong epistasis; and (3) near-additive architectures in which additive effects keep increasing relative to epistasis.

  1. Architecture flow diagrams under teamwork reg sign

    Energy Technology Data Exchange (ETDEWEB)

    Nicinski, T.

    1992-02-01

    The Teamwork CASE tool allows Data Flow Diagrams (DFDs) to be maintained for structured analysis. Fermilab has extended teamwork under UNIX{trademark} to permit Hatley and Pirbhai Architecture Flow Diagrams (AFDs) to be associated with DFDs and subsequently maintained. This extension, called TWKAFD, allows a user to open an AFD, graphically edit it, and replace it into a TWKAFD maintained library. Other aspects of Hatley and Pirbhai's methodology are supported. This paper presents a quick tutorial on Architecture Diagrams. It then describes the user's view of TWKAFD, the experience incorporating it into teamwork, and the successes with using the Architecture Diagram methodology along with the shortcomings of using the teamwork/TWKAFD tool. 8 refs.

  2. A modular architecture for transparent computation in recurrent neural networks.

    Science.gov (United States)

    Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

    2017-01-01

    Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Obtaining Cross Modal Similarity Metric with Deep Neural Architecture

    Directory of Open Access Journals (Sweden)

    Ruifan Li

    2015-01-01

    Full Text Available Analyzing complex system with multimodal data, such as image and text, has recently received tremendous attention. Modeling the relationship between different modalities is the key to address this problem. Motivated by recent successful applications of deep neural learning in unimodal data, in this paper, we propose a computational deep neural architecture, bimodal deep architecture (BDA for measuring the similarity between different modalities. Our proposed BDA architecture has three closely related consecutive components. For image and text modalities, the first component can be constructed using some popular feature extraction methods in their individual modalities. The second component has two types of stacked restricted Boltzmann machines (RBMs. Specifically, for image modality a binary-binary RBM is stacked over a Gaussian-binary RBM; for text modality a binary-binary RBM is stacked over a replicated softmax RBM. In the third component, we come up with a variant autoencoder with a predefined loss function for discriminatively learning the regularity between different modalities. We show experimentally the effectiveness of our approach to the task of classifying image tags on public available datasets.

  4. SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2012-07-01

    Full Text Available After production and operations, finance and investments are one of the mostfrequent areas of neural network applications in business. The lack of standardizedparadigms that can determine the efficiency of certain NN architectures in a particularproblem domain is still present. The selection of NN architecture needs to take intoconsideration the type of the problem, the nature of the data in the model, as well as somestrategies based on result comparison. The paper describes previous research in that areaand suggests a forward strategy for selecting best NN algorithm and structure. Since thestrategy includes both parameter-based and variable-based testings, it can be used forselecting NN architectures as well as for extracting models. The backpropagation, radialbasis,modular, LVQ and probabilistic neural network algorithms were used on twoindependent sets: stock market and credit scoring data. The results show that neuralnetworks give better accuracy comparing to multiple regression and logistic regressionmodels. Since it is model-independant, the strategy can be used by researchers andprofessionals in other areas of application.

  5. TUTORIAL: Neural blackboard architectures: the realization of compositionality and systematicity in neural networks

    Science.gov (United States)

    de Kamps, Marc; van der Velde, Frank

    2006-03-01

    In this paper, we will first introduce the notions of systematicity and combinatorial productivity and we will argue that these notions are essential for human cognition and probably for every agent that needs to be able to deal with novel, unexpected situations in a complex environment. Agents that use compositional representations are faced with the so-called binding problem and the question of how to create neural network architectures that can deal with it is essential for understanding higher level cognition. Moreover, an architecture that can solve this problem is likely to scale better with problem size than other neural network architectures. Then, we will discuss object-based attention. The influence of spatial attention is well known, but there is solid evidence for object-based attention as well. We will discuss experiments that demonstrate object-based attention and will discuss a model that can explain the data of these experiments very well. The model strongly suggests that this mode of attention provides a neural basis for parallel search. Next, we will show a model for binding in visual cortex. This model is based on a so-called neural blackboard architecture, where higher cortical areas act as processors, specialized for specific features of a visual stimulus, and lower visual areas act as a blackboard for communication between these processors. This implies that lower visual areas are involved in more than bottom-up visual processing, something which already was apparent from the large number of recurrent connections from higher to lower visual areas. This model identifies a specific role for these feedback connections. Finally, we will discuss the experimental evidence that exists for this architecture. .

  6. An architecture for designing fuzzy logic controllers using neural networks

    Science.gov (United States)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  7. Optical dual-scale architecture for neural image recognition.

    Science.gov (United States)

    Neifeld, M A

    1995-09-10

    A novel neural-network architecture that combines image data reduction with focus of attention to achieve reduced training cost, improved noise tolerance, and better generalization performance than comparable conventional networks for image-recognition tasks is presented. The dual-scale architecture is amenable to optical implementation, and an example optical system is demonstrated. For one example problem, the best-case improvements of the dual-scale network over its conventional counterpart were found through simulation to be a factor of 6.7 in training cost, 67.3% in noise tolerance, and 61.6% in generalization to distortions. The dual-scale network is also applied to one instance of a human face recognition problem.

  8. A neural architecture for nonlinear adaptive filtering of time series

    DEFF Research Database (Denmark)

    Hoffmann, Nils; Larsen, Jan

    1991-01-01

    A neural architecture for adaptive filtering which incorporates a modularization principle is proposed. It facilitates a sparse parameterization, i.e. fewer parameters have to be estimated in a supervised training procedure. The main idea is to use a preprocessor which determines the dimension...... of the polynominals by scaling and limiting the inputs signals. The nonlinearity is constructed from Chebychev polynominals. The authors apply a second-order algorithm for updating the weights for adaptive nonlinearities. Finally the simulations indicate that the two kinds of preprocessing tend to complement each...

  9. Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction.

    Science.gov (United States)

    Sivakumar, Seshadri; Sivakumar, Shyamala

    2017-09-25

    This paper introduces a discrete-time recurrent neural network architecture using triangular feedback weight matrices that allows a simplified approach to ensuring network and training stability. The triangular structure of the weight matrices is exploited to readily ensure that the eigenvalues of the feedback weight matrix represented by the block diagonal elements lie on the unit circle in the complex z-plane by updating these weights based on the differential of the angular error variable. Such placement of the eigenvalues together with the extended close interaction between state variables facilitated by the nondiagonal triangular elements, enhances the learning ability of the proposed architecture. Simulation results show that the proposed architecture is highly effective in time-series prediction tasks associated with nonlinear and chaotic dynamic systems with underlying oscillatory modes. This modular architecture with dual upper and lower triangular feedback weight matrices mimics fully recurrent network architectures, while maintaining learning stability with a simplified training process. While training, the block-diagonal weights (hence the eigenvalues) of the dual triangular matrices are constrained to the same values during weight updates aimed at minimizing the possibility of overfitting. The dual triangular architecture also exploits the benefit of parsing the input and selectively applying the parsed inputs to the two subnetworks to facilitate enhanced learning performance.

  10. Use of genetic algorithms for encoding efficient neural network architectures: neurocomputer implementation

    Science.gov (United States)

    James, Jason; Dagli, Cihan H.

    1995-04-01

    In this study an attempt is being made to encode the architecture of a neural network in a chromosome string for evolving robust, fast-learning, minimal neural network architectures through genetic algorithms. Various attributes affecting the learning of the network are represented as genes. The performance of the networks is used as the fitness value. Neural network architecture design concepts are initially demonstrated using a backpropagation architecture with the standard data set of Rosenberg and Sejnowski for text to speech conversion on Adaptive Solutions Inc.'s CNAPS Neuro-Computer. The architectures obtained are compared with the one reported in the literature for the standard data set used. The study concludes by providing some insights regarding the architecture encoding for other artificial neural network paradigms.

  11. Neural Architecture for Feature Binding in Visual Working Memory.

    Science.gov (United States)

    Schneegans, Sebastian; Bays, Paul M

    2017-04-05

    Binding refers to the operation that groups different features together into objects. We propose a neural architecture for feature binding in visual working memory that employs populations of neurons with conjunction responses. We tested this model using cued recall tasks, in which subjects had to memorize object arrays composed of simple visual features (color, orientation, and location). After a brief delay, one feature of one item was given as a cue, and the observer had to report, on a continuous scale, one or two other features of the cued item. Binding failure in this task is associated with swap errors, in which observers report an item other than the one indicated by the cue. We observed that the probability of swapping two items strongly correlated with the items' similarity in the cue feature dimension, and found a strong correlation between swap errors occurring in spatial and nonspatial report. The neural model explains both swap errors and response variability as results of decoding noisy neural activity, and can account for the behavioral results in quantitative detail. We then used the model to compare alternative mechanisms for binding nonspatial features. We found the behavioral results fully consistent with a model in which nonspatial features are bound exclusively via their shared location, with no indication of direct binding between color and orientation. These results provide evidence for a special role of location in feature binding, and the model explains how this special role could be realized in the neural system.SIGNIFICANCE STATEMENT The problem of feature binding is of central importance in understanding the mechanisms of working memory. How do we remember not only that we saw a red and a round object, but that these features belong together to a single object rather than to different objects in our environment? Here we present evidence for a neural mechanism for feature binding in working memory, based on encoding of visual information

  12. Learning speaker-specific characteristics with a deep neural architecture.

    Science.gov (United States)

    Chen, Ke; Salman, Ahmad

    2011-11-01

    Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach.

  13. SPANNER: A Self-Repairing Spiking Neural Network Hardware Architecture.

    Science.gov (United States)

    Liu, Junxiu; Harkin, Jim; Maguire, Liam P; McDaid, Liam J; Wade, John J

    2017-03-06

    Recent research has shown that a glial cell of astrocyte underpins a self-repair mechanism in the human brain, where spiking neurons provide direct and indirect feedbacks to presynaptic terminals. These feedbacks modulate the synaptic transmission probability of release (PR). When synaptic faults occur, the neuron becomes silent or near silent due to the low PR of synapses; whereby the PRs of remaining healthy synapses are then increased by the indirect feedback from the astrocyte cell. In this paper, a novel hardware architecture of Self-rePAiring spiking Neural NEtwoRk (SPANNER) is proposed, which mimics this self-repairing capability in the human brain. This paper demonstrates that the hardware can self-detect and self-repair synaptic faults without the conventional components for the fault detection and fault repairing. Experimental results show that SPANNER can maintain the system performance with fault densities of up to 40%, and more importantly SPANNER has only a 20% performance degradation when the self-repairing architecture is significantly damaged at a fault density of 80%.

  14. Neural Dynamics Underlying Event-Related Potentials

    Science.gov (United States)

    Shah, Ankoor S.; Bressler, Steven L.; Knuth, Kevin H.; Ding, Ming-Zhou; Mehta, Ashesh D.; Ulbert, Istvan; Schroeder, Charles E.

    2003-01-01

    There are two opposing hypotheses about the brain mechanisms underlying sensory event-related potentials (ERPs). One holds that sensory ERPs are generated by phase resetting of ongoing electroencephalographic (EEG) activity, and the other that they result from signal averaging of stimulus-evoked neural responses. We tested several contrasting predictions of these hypotheses by direct intracortical analysis of neural activity in monkeys. Our findings clearly demonstrate evoked response contributions to the sensory ERP in the monkey, and they suggest the likelihood that a mixed (Evoked/Phase Resetting) model may account for the generation of scalp ERPs in humans.

  15. Quantum perceptron over a field and neural network architecture selection in a quantum computer.

    Science.gov (United States)

    da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa

    2016-04-01

    In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Neural network architecture for cognitive navigation in dynamic environments.

    Science.gov (United States)

    Villacorta-Atienza, José Antonio; Makarov, Valeri A

    2013-12-01

    Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.

  17. Electromyostimulation training effects on neural drive and muscle architecture.

    Science.gov (United States)

    Gondin, Julien; Guette, Marie; Ballay, Yves; Martin, Alain

    2005-08-01

    The purpose of the study was to investigate the effect of 4 and 8 wk of electromyostimulation (EMS) training on both muscular and neural adaptations of the knee extensor muscles. Twenty males were divided into the electrostimulated group (EG, N = 12) and the control group (CG, N = 8). The training program consisted of 32 sessions of isometric EMS over an 8-wk period. All subjects were tested at baseline (B) and retested after 4 (WK4) and 8 (WK8) wk of EMS training. The EMG activity and muscle activation obtained under maximal voluntary contractions (MVC) was used to assess neural adaptations. Torque and EMG responses obtained under electrically evoked contractions, muscle anatomical cross-sectional area (ACSA), and vastus lateralis (VL) pennation angle, both measured by ultrasonography imaging, were examined to analyze muscular changes. At WK8, knee extensor MVC significantly increased by 27% (P < 0.001) and was accompanied by an increase in muscle activation (+6%, P < 0.01), quadriceps muscle ACSA (+6%, P < 0.001), and VL pennation angle (+14%, P < 0.001). A significant increase in normalized EMG activity of both VL and vastus medialis (VM) muscles (+69 and +39%, respectively, P < 0.001) but not of rectus femoris (RF) muscle was also found at WK8. The ACSA of the VL, VM, and vastus intermedius muscles significantly increased at WK8 (5-8%, P < 0.001) but not at WK4, whereas no changes occurred in the RF muscle. We concluded that the voluntary torque gains obtained after EMS training could be attributed to both muscular and neural adaptations. Both changes selectively involved the monoarticular vastii muscles.

  18. 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.

  19. Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

    2016-01-01

    Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

  20. The Architectural Heritage: A Market Under Construction.

    Science.gov (United States)

    Kalck, Paul; Pillemont, Jacques

    2002-01-01

    From preservation of historical monuments to repair of existing buildings, architectural heritage seems to be a market with a future for France's building industry. The public's enthusiasm, along with greater appreciation of the "value" of cultural goods and their integration into a framework of economic development offer a favorable…

  1. Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures

    OpenAIRE

    Deming, Laura; Targ, Sasha; Sauder, Nate; Almeida, Diogo; Ye, Chun Jimmie

    2016-01-01

    Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we develop a novel search algorithm, applicable across domains, that discovers an optimal architecture which simultaneously learns general genom...

  2. An Improved PMSM Drive Architecture Based on BFO and Neural Network

    Directory of Open Access Journals (Sweden)

    Flah Aymen

    2013-04-01

    Full Text Available In this paper, an improved robust vector control strategy is designed to drive the Permanent magnet synchronous motor in a wide speed range mode. The designed control method guarantees the precision and robustness of speed regulation performance by using recurrent neural network architecture. The stator current controller parameter tuning problems, which characterize this control strategy, are resolved using a bacterial foraging optimization algorithm to find the optimal parameters of the current controllers used. A field weakening control algorithm generates an adaptive magnetizing current command to achieve the desired high speed mode. The robustness and effectiveness of the global control scheme are verified through computer simulations established under a Matlab-Simulink environment.

  3. A dual-pathway neural architecture for specific temporal prediction.

    Science.gov (United States)

    Schwartze, Michael; Kotz, Sonja A

    2013-12-01

    Efficient behavior depends in part on the ability to predict the type and the timing of events in the environment. Specific temporal predictions require an internal representation of the temporal structure of events. Here we propose that temporal prediction recruits adaptive and non-adaptive oscillatory mechanisms involved in establishing such an internal representation. Partial structural and functional convergence of the underlying mechanisms allows speculation about an extended subcortico-cortical network. This network develops around a dual-pathway architecture, which establishes the basis for preparing the organism for perceptual integration, for the generation of specific temporal predictions, and for optimizing the brain's allocation of its limited resources. Key to these functions is rapid cerebellar transmission of an adaptively-filtered, event-based representation of temporal structure. Rapid cerebellar transmission engages a pathway comprising connections from early sensory processing stages to the cerebellum and from there to the thalamus, effectively bypassing more central stages of classical sensory pathways. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Improving the Neural GPU Architecture for Algorithm Learning

    OpenAIRE

    Freivalds, Karlis; Liepins, Renars

    2017-01-01

    Algorithm learning is a core problem in artificial intelligence with significant implications on automation level that can be achieved by machines. Recently deep learning methods are emerging for synthesizing an algorithm from its input-output examples, the most successful being the Neural GPU, capable of learning multiplication. We present several improvements to the Neural GPU that substantially reduces training time and improves generalization. We introduce a technique of general applicabi...

  5. Photosensitive-polyimide based method for fabricating various neural electrode architectures

    Science.gov (United States)

    Kato, Yasuhiro X.; Furukawa, Shigeto; Samejima, Kazuyuki; Hironaka, Naoyuki; Kashino, Makio

    2012-01-01

    An extensive photosensitive-polyimide (PSPI)-based method for designing and fabricating various neural electrode architectures was developed. The method aims to broaden the design flexibility and expand the fabrication capability for neural electrodes to improve the quality of recorded signals and integrate other functions. After characterizing PSPI's properties for micromachining processes, we successfully designed and fabricated various neural electrodes even on a non-flat substrate using only one PSPI as an insulation material and without the time-consuming dry etching processes. The fabricated neural electrodes were an electrocorticogram (ECoG) electrode, a mesh intracortical electrode with a unique lattice-like mesh structure to fixate neural tissue, and a guide cannula electrode with recording microelectrodes placed on the curved surface of a guide cannula as a microdialysis probe. In vivo neural recordings using anesthetized rats demonstrated that these electrodes can be used to record neural activities repeatedly without any breakage and mechanical failures, which potentially promises stable recordings for long periods of time. These successes make us believe that this PSPI-based fabrication is a powerful method, permitting flexible design, and easy optimization of electrode architectures for a variety of electrophysiological experimental research with improved neural recording performance. PMID:22719725

  6. Self-growing neural network architecture using crisp and fuzzy entropy

    Science.gov (United States)

    Cios, Krzysztof J.

    1992-01-01

    The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed.

  7. Reconfigurable embedded system architecture for next-generation Neural Signal Processing.

    Science.gov (United States)

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2010-01-01

    This work presents a new architectural framework for next generation Neural Signal Processing (NSP). The essential features of the NSP hardware platform include scalability, reconfigurability, real-time processing ability and data storage. This proposed framework has been implemented in a proof-of-concept NSP prototype using an embedded system architecture synthesized in a Xilinx(®)Virtex(®)5 development board. The prototype includes a threshold-based spike detector and a fuzzy logic-based spike sorter.

  8. Neural computing architectures: The design of brain-like machines

    Energy Technology Data Exchange (ETDEWEB)

    Aleksander, I.

    1989-01-01

    Theoretical and applications aspects of neural-network (NN) computers are discussed in chapters contributed by European experts. Topics addressed include speech recognition based on topology-preserving neural maps, neural-map applications, backpropagation in nonfeedforward NNs, a parallel-distributed-processing learning approach to natural language, the learning capabilities of Boolean NNs, the logic of connectionist systems, and a probabilistic-logic NN for associative learning. Consideration is given to N-tuple sampling and genetic algorithms for speech recognition; the dynamic behavior of Boolean NNs; statistical mechanics and NNs; digital NNs, matched filters, and optical implementations; heteroassociative NNs using cabling vs link-disabling local modification rules; and the generation of movement trajectories in primates and robots. Also provided is an overview of parallel distributed processing.

  9. Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

    Energy Technology Data Exchange (ETDEWEB)

    Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

    2015-01-01

    Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

  10. 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...

  11. A self-organized artificial neural network architecture for sensory integration with applications to letter-phoneme integration

    OpenAIRE

    Jantvik, Tamas; Gustafsson, Lennart; Paplinski, Andrew

    2011-01-01

    The multimodal self-organizing network (MMSON), an artificial neural network architecture carrying out sensory integration, is presented here. The architecture is designed using neurophysiological findings and imaging studies that pertain to sensory integration and consists of interconnected lattices of artificial neurons. In this artificial neural architecture, the degree of recognition of stimuli, that is, the perceived reliability of stimuli in the various subnetworks, is included in the c...

  12. Neural cache: a low-power online digital spike-sorting architecture.

    Science.gov (United States)

    Peng, Chung-Ching; Sabharwal, Pawan; Bashirullah, Rizwan

    2008-01-01

    Transmitting large amounts of data sensed by multi-electrode array devices is widely considered to be a challenging problem in designing implantable neural recording systems. Spike sorting is an important step to reducing the data bandwidth before wireless data transmission. The feasibility of spike sorting algorithms in scaled CMOS technologies, which typically operate on low frequency neural spikes, is determined largely by its power consumption, a dominant portion of which is leakage power. Our goal is to explore energy saving architectures for online spike sorting without sacrificing performance. In the face of non-stationary neural data, training is not always a feasible option. We present a low-power architecture for real-time online spike sorting that does not require any training period and has the capability to quickly respond to the changing spike shapes.

  13. Space-time system architecture for the neural optical computing

    Science.gov (United States)

    Lo, Yee-Man V.

    1991-02-01

    The brain can perform the tasks of associative recall detection recognition and optimization. In this paper space-time system field models of the brain are introduced. They are called the space-time maximum likelihood associative memory system (ST-ML-AMS) and the space-time adaptive learning system (ST-ALS). Performance of the system is analyzed using the probability of error in memory recall (PEMR) and the space-time neural capacity (ST-NC). 1.

  14. Neural Mechanisms Underlying Compensatory and Noncompensatory Strategies in Risky Choice

    NARCIS (Netherlands)

    van Duijvenvoorde, A.C.K.; Figner, B.; Weeda, W.D.; van der Molen, M.W.; Jansen, B.R.J.; Huizenga, H.M.

    Individuals may differ systematically in their applied decision strategies, which has critical implications for decision neuroscience but is yet scarcely studied. Our study's main focus was therefore to investigate the neural mechanisms underlying compensatory versus noncompensatory strategies in

  15. Neural mechanisms underlying compensatory and noncompensatory strategies in risky choice

    NARCIS (Netherlands)

    Duijvenvoorde, A.C.K. van; Figner, B.; Weeda, W.D.; Molen, M.W. van der; Jansen, B.R.J.; Huizenga, H.M.

    2016-01-01

    Individuals may differ systematically in their applied decision strategies, which has critical implications for decision neuroscience but is yet scarcely studied. Our study's main focus was therefore to investigate the neural mechanisms underlying compensatory versus noncompensatory strategies in

  16. Experiments on neural network architectures for fuzzy logic

    Science.gov (United States)

    Keller, James M.

    1991-01-01

    The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

  17. Antagonistic neural networks underlying differentiated leadership roles

    OpenAIRE

    Richard Eleftherios Boyatzis; Kylie eRochford; Anthony Ian Jack

    2014-01-01

    The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950’s. Recent research in neuroscience suggests that the division between task oriented and socio-emotional oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks -- the Task Positive Network (TPN) and the Default Mode Network (DMN). Neural activity in ...

  18. A Study of Artificial Neural Network Architectures for Othello Evaluation Functions

    Science.gov (United States)

    Binkley, Kevin J.; Seehart, Ken; Hagiwara, Masafumi

    In this study, we use temporal difference learning (TDL) to investigate the ability of 20 different artificial neural network (ANN) architectures to learn othello game board evaluation functions. The ANN evaluation functions are applied to create a strong othello player using only 1-ply search. In addition to comparing many of the ANN architectures seen in the literature, we introduce several new architectures that consider the game board symmetry. Both embedding the game board symmetry into the network architecture through weight sharing and the outright removal of symmetry through symmetry removal are explored. Experiments varying the number of inputs per game board square from one to three, the number of hidden nodes, and number of hidden layers are also performed. We found it advantageous to consider game board symmetry in the form of symmetry by weight sharing; and that an input encoding of three inputs per square outperformed the one input per square encoding that is commonly seen in the literature. Furthermore, architectures with only one hidden layer were strongly outperformed by architectures with multiple hidden layers. A standard weighted-square board heuristic evaluation function from the literature was used to evaluate the quality of the trained ANN othello players. One of the ANN architectures introduced in this study, an ANN implementing weight sharing and consisting of three hidden layers, using only a 1-ply search, outperformed a weighted-square test heuristic player using a 6-ply minimax search.

  19. Contextual and Developmental Differences in the Neural Architecture of Cognitive Control.

    Science.gov (United States)

    Petrican, Raluca; Grady, Cheryl L

    2017-08-09

    Because both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample (N = 359, 22-36 years, 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses. Dynamically, a context-free signature emerged as stronger segregation of internal cognition (default mode) and environmentally driven control (salience, cingulo-opercular) systems. A dynamic social-affective context-specific signature was observed most clearly in the visual system. Stable connectivity analyses revealed both context-free (greater default mode segregation) and context-specific (greater frontoparietal segregation for higher cognitive load; greater attentional and environmentally driven control system segregation for greater reward value) signatures of inhibition. Superior inhibition in more mature adulthood was typified by reduced segregation in the default network with increasing reward value and increased ventral attention but reduced cingulo-opercular and subcortical system segregation with increasing cognitive load. Failure to evidence this neural profile after the age of 30 predicted poorer life functioning. Our results suggest that distinguishable neural mechanisms underlie individual differences in cognitive control during different young adult stages and across tasks, thereby underscoring the importance of better understanding the interplay among dispositional, developmental, and contextual factors in shaping adaptive versus maladaptive patterns of thought and behavior.SIGNIFICANCE STATEMENT The brain's functional

  20. Architecture flow diagrams under teamwork{reg_sign}

    Energy Technology Data Exchange (ETDEWEB)

    Nicinski, T.

    1992-02-01

    The Teamwork CASE tool allows Data Flow Diagrams (DFDs) to be maintained for structured analysis. Fermilab has extended teamwork under UNIX{trademark} to permit Hatley and Pirbhai Architecture Flow Diagrams (AFDs) to be associated with DFDs and subsequently maintained. This extension, called TWKAFD, allows a user to open an AFD, graphically edit it, and replace it into a TWKAFD maintained library. Other aspects of Hatley and Pirbhai`s methodology are supported. This paper presents a quick tutorial on Architecture Diagrams. It then describes the user`s view of TWKAFD, the experience incorporating it into teamwork, and the successes with using the Architecture Diagram methodology along with the shortcomings of using the teamwork/TWKAFD tool. 8 refs.

  1. The neural component-process architecture of endogenously generated emotion.

    Science.gov (United States)

    Engen, Haakon G; Kanske, Philipp; Singer, Tania

    2017-02-01

    Despite the ubiquity of endogenous emotions and their role in both resilience and pathology, the processes supporting their generation are largely unknown. We propose a neural component process model of endogenous generation of emotion (EGE) and test it in two functional magnetic resonance imaging (fMRI) experiments (N = 32/293) where participants generated and regulated positive and negative emotions based on internal representations, usin self-chosen generation methods. EGE activated nodes of salience (SN), default mode (DMN) and frontoparietal control (FPCN) networks. Component processes implemented by these networks were established by investigating their functional associations, activation dynamics and integration. SN activation correlated with subjective affect, with midbrain nodes exclusively distinguishing between positive and negative affect intensity, showing dynamics consistent generation of core affect. Dorsomedial DMN, together with ventral anterior insula, formed a pathway supporting multiple generation methods, with activation dynamics suggesting it is involved in the generation of elaborated experiential representations. SN and DMN both coupled to left frontal FPCN which in turn was associated with both subjective affect and representation formation, consistent with FPCN supporting the executive coordination of the generation process. These results provide a foundation for research into endogenous emotion in normal, pathological and optimal function. © The Author (2016). Published by Oxford University Press.

  2. Seafloor classification using echo- waveforms: A method employing hybrid neural network architecture

    Digital Repository Service at National Institute of Oceanography (India)

    Chakraborty, B.; Mahale, V.; DeSouza, C.; Das, P.

    , neural network architecture, seafloor classification, self-organizing feature map (SOFM). I. INTRODUCTION S EAFLOOR classification and characterization using re- mote high-frequency acoustic system has been recognized as a useful tool (see [1...] and references therein). The seafloor’s characteristics are extremely complicated due to variations of the many parameters at different scales. The parameters include sediment grain size, relief height at the water–sediment inter- face, and variations within...

  3. Natural neural projection dynamics underlying social behavior.

    Science.gov (United States)

    Gunaydin, Lisa A; Grosenick, Logan; Finkelstein, Joel C; Kauvar, Isaac V; Fenno, Lief E; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J; Airan, Raag D; Zalocusky, Kelly A; Tye, Kay M; Anikeeva, Polina; Malenka, Robert C; Deisseroth, Karl

    2014-06-19

    Social interaction is a complex behavior essential for many species and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social, but not novel object, interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type 1 dopamine receptor signaling downstream in the NAc. Direct observation of deep projection-specific activity in this way captures a fundamental and previously inaccessible dimension of mammalian circuit dynamics. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Approach to design neural cryptography: a generalized architecture and a heuristic rule.

    Science.gov (United States)

    Mu, Nankun; Liao, Xiaofeng; Huang, Tingwen

    2013-06-01

    Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results.

  5. The human infant brain: A neural architecture able to learn language.

    Science.gov (United States)

    Dehaene-Lambertz, Ghislaine

    2017-02-01

    To understand the type of neural computations that may explain how human infants acquire their native language in only a few months, the study of their neural architecture is necessary. The development of brain imaging techniques has opened the possibilities of studying human infants without discomfort, and although these studies are still sparse, several characteristics are noticeable in the human infant's brain: first, parallel and hierarchical processing pathways are observed before intense exposure to speech with an efficient temporal coding in the left hemisphere and, second, frontal regions are involved from the start in infants' cognition. These observations are certainly not sufficient to explain language acquisition but illustrate a new approach that relies on a better description of infants' brain activity during linguistic tasks, which is compared to results in animals and human adults to clarify the neural bases of language in humans.

  6. Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

    Science.gov (United States)

    Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

    2017-06-01

    We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

  7. Network architecture underlying maximal separation of neuronal representations

    Directory of Open Access Journals (Sweden)

    Ron A Jortner

    2013-01-01

    Full Text Available One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism’s surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe–mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon-cells, and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network-parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a way to easily construct ecologically meaningful representations from this code.

  8. Urban landscape architecture design under the view of sustainable development

    Science.gov (United States)

    Chen, WeiLin

    2017-08-01

    The concept of sustainable development in modern city landscape design advocates landscape architecture, which is the main development direction in the field of landscape design. They are also effective measures to promote the sustainable development of city garden. Based on this, combined with the connotation of sustainable development and sustainable design, this paper analyzes and discusses the design of urban landscape under the concept of sustainable development.

  9. A new constructive algorithm for architectural and functional adaptation of artificial neural networks.

    Science.gov (United States)

    Islam, Md Monirul; Sattar, Md Abdus; Amin, Md Faijul; Yao, Xin; Murase, Kazuyuki

    2009-12-01

    The generalization ability of artificial neural networks (ANNs) is greatly dependent on their architectures. Constructive algorithms provide an attractive automatic way of determining a near-optimal ANN architecture for a given problem. Several such algorithms have been proposed in the literature and shown their effectiveness. This paper presents a new constructive algorithm (NCA) in automatically determining ANN architectures. Unlike most previous studies on determining ANN architectures, NCA puts emphasis on architectural adaptation and functional adaptation in its architecture determination process. It uses a constructive approach to determine the number of hidden layers in an ANN and of neurons in each hidden layer. To achieve functional adaptation, NCA trains hidden neurons in the ANN by using different training sets that were created by employing a similar concept used in the boosting algorithm. The purpose of using different training sets is to encourage hidden neurons to learn different parts or aspects of the training data so that the ANN can learn the whole training data in a better way. In this paper, the convergence and computational issues of NCA are analytically studied. The computational complexity of NCA is found to be O(W xP(t) xtau), where W is the number of weights in the ANN, P(t) is the number of training examples, and tau is the number of training epochs. This complexity has the same order as what the backpropagation learning algorithm requires for training a fixed ANN architecture. A set of eight classification and two approximation benchmark problems was used to evaluate the performance of NCA. The experimental results show that NCA can produce ANN architectures with fewer hidden neurons and better generalization ability compared to existing constructive and nonconstructive algorithms.

  10. Antagonistic neural networks underlying differentiated leadership roles.

    Science.gov (United States)

    Boyatzis, Richard E; Rochford, Kylie; Jack, Anthony I

    2014-01-01

    The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950s. Recent research in neuroscience suggests that the division between task-oriented and socio-emotional-oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks - the task-positive network (TPN) and the default mode network (DMN). Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task-oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions, and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success.

  11. Antagonistic Neural Networks Underlying Differentiated Leadership Roles

    Directory of Open Access Journals (Sweden)

    Richard Eleftherios Boyatzis

    2014-03-01

    Full Text Available The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950’s. Recent research in neuroscience suggests that the division between task oriented and socio-emotional oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks -- the Task Positive Network (TPN and the Default Mode Network (DMN. Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success.

  12. Antagonistic neural networks underlying differentiated leadership roles

    Science.gov (United States)

    Boyatzis, Richard E.; Rochford, Kylie; Jack, Anthony I.

    2014-01-01

    The emergence of two distinct leadership roles, the task leader and the socio-emotional leader, has been documented in the leadership literature since the 1950s. Recent research in neuroscience suggests that the division between task-oriented and socio-emotional-oriented roles derives from a fundamental feature of our neurobiology: an antagonistic relationship between two large-scale cortical networks – the task-positive network (TPN) and the default mode network (DMN). Neural activity in TPN tends to inhibit activity in the DMN, and vice versa. The TPN is important for problem solving, focusing of attention, making decisions, and control of action. The DMN plays a central role in emotional self-awareness, social cognition, and ethical decision making. It is also strongly linked to creativity and openness to new ideas. Because activation of the TPN tends to suppress activity in the DMN, an over-emphasis on task-oriented leadership may prove deleterious to social and emotional aspects of leadership. Similarly, an overemphasis on the DMN would result in difficulty focusing attention, making decisions, and solving known problems. In this paper, we will review major streams of theory and research on leadership roles in the context of recent findings from neuroscience and psychology. We conclude by suggesting that emerging research challenges the assumption that role differentiation is both natural and necessary, in particular when openness to new ideas, people, emotions, and ethical concerns are important to success. PMID:24624074

  13. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  14. A framework for plasticity implementation on the SpiNNaker neural architecture

    Directory of Open Access Journals (Sweden)

    Francesco eGalluppi

    2015-01-01

    Full Text Available Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform.The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP, voltage-dependent STDP, and the rate-based BCM rule.We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

  15. A framework for plasticity implementation on the SpiNNaker neural architecture.

    Science.gov (United States)

    Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

    2014-01-01

    Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

  16. Developmental and architectural principles of the lateral-line neural map

    Science.gov (United States)

    Pujol-Martí, Jesús; López-Schier, Hernán

    2013-01-01

    The transmission and central representation of sensory cues through the accurate construction of neural maps is essential for animals to react to environmental stimuli. Structural diversity of sensorineural maps along a continuum between discrete- and continuous-map architectures can influence behavior. The mechanosensory lateral line of fishes and amphibians, for example, detects complex hydrodynamics occurring around the animal body. It triggers innate fast escape reactions but also modulates complex navigation behaviors that require constant knowledge about the environment. The aim of this article is to summarize recent work in the zebrafish that has shed light on the development and structure of the lateralis neural map, which is helping to understand how individual sensory modalities generate appropriate behavioral responses to the sensory context. PMID:23532704

  17. Developmental and Architectural Principles of the Lateral-line Neural Map

    Directory of Open Access Journals (Sweden)

    Hernan eLopez-Schier

    2013-03-01

    Full Text Available The transmission and central representation of sensory cues through the accurate construction of neural maps is essential for animals to react to environmental stimuli. Structural diversity of sensorineural maps along a continuum between discrete- and continuous-map architectures can influence behavior. The mechanosensory lateral line of fishes and amphibians, for example, detects complex hydrodynamics occurring around the animal body. It. It triggers innate fast escape reactions but also modulates complex navigation behaviors that require constant knowledge about the environment. The aim of this article is to summarize recent work in the zebrafish that has shed light on the development and structure of the lateralis neural map, which is helping to understand how individual sensory modalities generate appropriate behavioral responses to the sensory context.

  18. Neural Circuitry and Plasticity Mechanisms Underlying Delay Eyeblink Conditioning

    Science.gov (United States)

    Freeman, John H.; Steinmetz, Adam B.

    2011-01-01

    Pavlovian eyeblink conditioning has been used extensively as a model system for examining the neural mechanisms underlying associative learning. Delay eyeblink conditioning depends on the intermediate cerebellum ipsilateral to the conditioned eye. Evidence favors a two-site plasticity model within the cerebellum with long-term depression of…

  19. The probabilistic neural network architecture for high speed classification of remotely sensed imagery

    Science.gov (United States)

    Chettri, Samir R.; Cromp, Robert F.

    1993-01-01

    In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

  20. A remote password authentication scheme for multiserver architecture using neural networks.

    Science.gov (United States)

    Li, L H; Lin, L C; Hwang, M S

    2001-01-01

    Conventional remote password authentication schemes allow a serviceable server to authenticate the legitimacy of a remote login user. However, these schemes are not used for multiserver architecture environments. We present a remote password authentication scheme for multiserver environments. The password authentication system is a pattern classification system based on an artificial neural network. In this scheme, the users only remember user identity and password numbers to log in to various servers. Users can freely choose their password. Furthermore, the system is not required to maintain a verification table and can withstand the replay attack.

  1. Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

    Science.gov (United States)

    Obeso, Abraham Montoya; Benois-Pineau, Jenny; Acosta, Alejandro Álvaro Ramirez; Vázquez, Mireya Saraí García

    2017-01-01

    We propose a convolutional neural network to classify images of buildings using sparse features at the network's input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks.

  2. Neural Network Spectral Robustness under Perturbations of the Underlying Graph.

    Science.gov (United States)

    Rădulescu, Anca

    2016-01-01

    Recent studies have been using graph-theoretical approaches to model complex networks (such as social, infrastructural, or biological networks) and how their hardwired circuitry relates to their dynamic evolution in time. Understanding how configuration reflects on the coupled behavior in a system of dynamic nodes can be of great importance, for example, in the context of how the brain connectome is affecting brain function. However, the effect of connectivity patterns on network dynamics is far from being fully understood. We study the connections between edge configuration and dynamics in a simple oriented network composed of two interconnected cliques (representative of brain feedback regulatory circuitry). In this article our main goal is to study the spectra of the graph adjacency and Laplacian matrices, with a focus on three aspects in particular: (1) the sensitivity and robustness of the spectrum in response to varying the intra- and intermodular edge density, (2) the effects on the spectrum of perturbing the edge configuration while keeping the densities fixed, and (3) the effects of increasing the network size. We study some tractable aspects analytically, then simulate more general results numerically, thus aiming to motivate and explain our further work on the effect of these patterns on the network temporal dynamics and phase transitions. We discuss the implications of such results to modeling brain connectomics. We suggest potential applications to understanding synaptic restructuring in learning networks and the effects of network configuration on function of regulatory neural circuits.

  3. Neural mechanisms underlying morphine withdrawal in addicted patients: a review

    Directory of Open Access Journals (Sweden)

    Nima Babhadiashar

    2015-06-01

    Full Text Available Morphine is one of the most potent alkaloid in opium, which has substantial medical uses and needs and it is the first active principle purified from herbal source. Morphine has commonly been used for relief of moderate to severe pain as it acts directly on the central nervous system; nonetheless, its chronic abuse increases tolerance and physical dependence, which is commonly known as opiate addiction. Morphine withdrawal syndrome is physiological and behavioral symptoms that stem from prolonged exposure to morphine. A majority of brain regions are hypofunctional over prolonged abstinence and acute morphine withdrawal. Furthermore, several neural mechanisms are likely to contribute to morphine withdrawal. The present review summarizes the literature pertaining to neural mechanisms underlying morphine withdrawal. Despite the fact that morphine withdrawal is a complex process, it is suggested that neural mechanisms play key roles in morphine withdrawal.

  4. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment

    Science.gov (United States)

    Kwak, No-Sang; Müller, Klaus-Robert

    2017-01-01

    The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN’s robust, accurate decoding abilities. PMID:28225827

  5. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment.

    Science.gov (United States)

    Kwak, No-Sang; Müller, Klaus-Robert; Lee, Seong-Whan

    2017-01-01

    The robust analysis of neural signals is a challenging problem. Here, we contribute a convolutional neural network (CNN) for the robust classification of a steady-state visual evoked potentials (SSVEPs) paradigm. We measure electroencephalogram (EEG)-based SSVEPs for a brain-controlled exoskeleton under ambulatory conditions in which numerous artifacts may deteriorate decoding. The proposed CNN is shown to achieve reliable performance under these challenging conditions. To validate the proposed method, we have acquired an SSVEP dataset under two conditions: 1) a static environment, in a standing position while fixated into a lower-limb exoskeleton and 2) an ambulatory environment, walking along a test course wearing the exoskeleton (here, artifacts are most challenging). The proposed CNN is compared to a standard neural network and other state-of-the-art methods for SSVEP decoding (i.e., a canonical correlation analysis (CCA)-based classifier, a multivariate synchronization index (MSI), a CCA combined with k-nearest neighbors (CCA-KNN) classifier) in an offline analysis. We found highly encouraging SSVEP decoding results for the CNN architecture, surpassing those of other methods with classification rates of 99.28% and 94.03% in the static and ambulatory conditions, respectively. A subsequent analysis inspects the representation found by the CNN at each layer and can thus contribute to a better understanding of the CNN's robust, accurate decoding abilities.

  6. Integrating Verbal and Nonverbal Communication in a Dynamic Neural Field Architecture for Human–Robot Interaction

    Science.gov (United States)

    Bicho, Estela; Louro, Luís; Erlhagen, Wolfram

    2010-01-01

    How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer's motor system are crucially involved in our ability to understand actions of others’, to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human–robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human–robot team has to construct toy objects from their components. The experiments focus on the robot's capacity to anticipate the user's needs and to detect and communicate unexpected events that may occur during joint task execution. PMID:20725504

  7. Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction

    Directory of Open Access Journals (Sweden)

    Estela Bicho

    2010-05-01

    Full Text Available How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer's motor system are crucially involved in our ability to understand actions of others', to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human-robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and non-verbal communication skills of the robot in a joint assembly task in which the human-robot team has to construct toy objects from their components. The experiments focus on the robot’s capacity to anticipate the user’s needs and to detect and communicate unexpected events that may occur during joint task execution.

  8. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    Directory of Open Access Journals (Sweden)

    S Safinaz

    2017-08-01

    Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

  9. A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language.

    Science.gov (United States)

    Golosio, Bruno; Cangelosi, Angelo; Gamotina, Olesya; Masala, Giovanni Luca

    2015-01-01

    Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.

  10. Optimization of neural network architecture for classification of radar jamming FM signals

    Science.gov (United States)

    Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

    2017-05-01

    The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

  11. Optimizationof neural network architecture using genetic programming improvesdetection and modeling of gene-gene interactions in studies of humandiseases

    Directory of Open Access Journals (Sweden)

    Hahn Lance W

    2003-07-01

    Full Text Available Abstract Background Appropriate definitionof neural network architecture prior to data analysis is crucialfor successful data mining. This can be challenging when the underlyingmodel of the data is unknown. The goal of this study was to determinewhether optimizing neural network architecture using genetic programmingas a machine learning strategy would improve the ability of neural networksto model and detect nonlinear interactions among genes in studiesof common human diseases. Results Using simulateddata, we show that a genetic programming optimized neural network approachis able to model gene-gene interactions as well as a traditionalback propagation neural network. Furthermore, the genetic programmingoptimized neural network is better than the traditional back propagationneural network approach in terms of predictive ability and powerto detect gene-gene interactions when non-functional polymorphismsare present. Conclusion This study suggeststhat a machine learning strategy for optimizing neural network architecturemay be preferable to traditional trial-and-error approaches forthe identification and characterization of gene-gene interactionsin common, complex human diseases.

  12. Evolution of network architecture in a granular material under compression.

    Science.gov (United States)

    Papadopoulos, Lia; Puckett, James G; Daniels, Karen E; Bassett, Danielle S

    2016-09-01

    As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study

  13. Evolution of network architecture in a granular material under compression

    Science.gov (United States)

    Bassett, Danielle

    As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. However, capturing and characterizing the dynamic nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. Here, we utilize multilayer networks as a framework for directly quantifying the evolution of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and inter-particle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the reconfiguration and evolution of this structure throughout the compression process. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be done by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than consideration of the local inter-particle forces alone. The results discussed throughout this study suggest that these novel network science techniques may provide a direct way to compare and classify data from systems under different external conditions or with different physical makeup. National Science Foundation (BCS-1441502, PHY-1554488, and BCS-1631550).

  14. The Functional Architecture of the Brain Underlies Strategic Deception in Impression Management

    Directory of Open Access Journals (Sweden)

    Qiang Luo

    2017-11-01

    Full Text Available Impression management, as one of the most essential skills of social function, impacts one's survival and success in human societies. However, the neural architecture underpinning this social skill remains poorly understood. By employing a two-person bargaining game, we exposed three strategies involving distinct cognitive processes for social impression management with different levels of strategic deception. We utilized a novel adaptation of Granger causality accounting for signal-dependent noise (SDN, which captured the directional connectivity underlying the impression management during the bargaining game. We found that the sophisticated strategists engaged stronger directional connectivity from both dorsal anterior cingulate cortex and retrosplenial cortex to rostral prefrontal cortex, and the strengths of these directional influences were associated with higher level of deception during the game. Using the directional connectivity as a neural signature, we identified the strategic deception with 80% accuracy by a machine-learning classifier. These results suggest that different social strategies are supported by distinct patterns of directional connectivity among key brain regions for social cognition.

  15. Concept hierarchy memory model: a neural architecture for conceptual knowledge representation, learning, and commonsense reasoning.

    Science.gov (United States)

    Tan, A H; Soon, H S

    1996-07-01

    This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance.

  16. Neural Architecture of Hunger-Dependent Multisensory Decision Making in C. elegans.

    Science.gov (United States)

    Ghosh, D Dipon; Sanders, Tom; Hong, Soonwook; McCurdy, Li Yan; Chase, Daniel L; Cohen, Netta; Koelle, Michael R; Nitabach, Michael N

    2016-12-07

    Little is known about how animals integrate multiple sensory inputs in natural environments to balance avoidance of danger with approach to things of value. Furthermore, the mechanistic link between internal physiological state and threat-reward decision making remains poorly understood. Here we confronted C. elegans worms with the decision whether to cross a hyperosmotic barrier presenting the threat of desiccation to reach a source of food odor. We identified a specific interneuron that controls this decision via top-down extrasynaptic aminergic potentiation of the primary osmosensory neurons to increase their sensitivity to the barrier. We also establish that food deprivation increases the worm's willingness to cross the dangerous barrier by suppressing this pathway. These studies reveal a potentially general neural circuit architecture for internal state control of threat-reward decision making. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity

    Directory of Open Access Journals (Sweden)

    Oliver Lomp

    2017-04-01

    Full Text Available Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object’s pose, aligning the learned view with current input. The system is based on neural dynamics, computationally operating in real time, and can handle dynamic scenes directly off live video input. In a scenario with 30 everyday objects, the system achieves recognition rates of 87.2% from a single training view for each object, while also estimating pose quite precisely. We further demonstrate that the system can track moving objects, and that it can segment the visual array, selecting and recognizing one object while suppressing input from another known object in the immediate vicinity. Evaluation on the COIL-100 dataset, in which objects are depicted from different viewing angles, revealed recognition rates of 91.1% on the first 30 objects, each learned from four training views.

  18. A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity.

    Science.gov (United States)

    Lomp, Oliver; Faubel, Christian; Schöner, Gregor

    2017-01-01

    Handling objects or interacting with a human user about objects on a shared tabletop requires that objects be identified after learning from a small number of views and that object pose be estimated. We present a neurally inspired architecture that learns object instances by storing features extracted from a single view of each object. Input features are color and edge histograms from a localized area that is updated during processing. The system finds the best-matching view for the object in a novel input image while concurrently estimating the object's pose, aligning the learned view with current input. The system is based on neural dynamics, computationally operating in real time, and can handle dynamic scenes directly off live video input. In a scenario with 30 everyday objects, the system achieves recognition rates of 87.2% from a single training view for each object, while also estimating pose quite precisely. We further demonstrate that the system can track moving objects, and that it can segment the visual array, selecting and recognizing one object while suppressing input from another known object in the immediate vicinity. Evaluation on the COIL-100 dataset, in which objects are depicted from different viewing angles, revealed recognition rates of 91.1% on the first 30 objects, each learned from four training views.

  19. The Evolving Neural and Genetic Architecture of Vertebrate Olfaction.

    Science.gov (United States)

    Bear, Daniel M; Lassance, Jean-Marc; Hoekstra, Hopi E; Datta, Sandeep Robert

    2016-10-24

    Evolution sculpts the olfactory nervous system in response to the unique sensory challenges facing each species. In vertebrates, dramatic and diverse adaptations to the chemical environment are possible because of the hierarchical structure of the olfactory receptor (OR) gene superfamily: expansion or contraction of OR subfamilies accompanies major changes in habitat and lifestyle; independent selection on OR subfamilies can permit local adaptation or conserved chemical communication; and genetic variation in single OR genes can alter odor percepts and behaviors driven by precise chemical cues. However, this genetic flexibility contrasts with the relatively fixed neural architecture of the vertebrate olfactory system, which requires that new olfactory receptors integrate into segregated and functionally distinct neural pathways. This organization allows evolution to couple critical chemical signals with selectively advantageous responses, but also constrains relationships between olfactory receptors and behavior. The coevolution of the OR repertoire and the olfactory system therefore reveals general principles of how the brain solves specific sensory problems and how it adapts to new ones. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Neural circuitry underlying affective response to peer feedback in adolescence.

    Science.gov (United States)

    Guyer, Amanda E; Choate, Victoria R; Pine, Daniel S; Nelson, Eric E

    2012-01-01

    Peer feedback affects adolescents' behaviors, cognitions and emotions. We examined neural circuitry underlying adolescents' emotional response to peer feedback using a functional neuroimaging paradigm whereby, 36 adolescents (aged 9-17 years) believed they would interact with unknown peers postscan. Neural activity was expected to vary based on adolescents' perceptions of peers and feedback type. Ventrolateral prefrontal cortex (vlPFC) activity was found when adolescents indicated how they felt following feedback (acceptance or rejection) from peers of low vs high interest. Greater activation in both cortical (e.g. superior temporal gyrus, insula, anterior cingulate) and subcortical (e.g. striatum, thalamus) regions emerged in response to acceptance vs rejection feedback. Response to acceptance also varied by age and gender in similar regions (e.g. superior temporal gyrus, fusiform, insula), with greater age-related increases in activation to acceptance vs rejection for females than males. Affective response to rejection vs acceptance did not yield significantly greater neural activity in any region. vlPFC response suggests cognitive flexibility in reappraising initial perceptions of peers following feedback. Striatal response suggests that acceptance is a potent social reward for adolescents, an interpretation supported by more positive self-reported affective response to acceptance than rejection from high- but not low-interest peers.

  1. Distinct neural networks underlying empathy for pleasant and unpleasant touch.

    Science.gov (United States)

    Lamm, Claus; Silani, Giorgia; Singer, Tania

    2015-09-01

    In spite of considerable progress in the understanding of the neural mechanisms underlying the experience of empathy, the majority of previous investigations have focused on how we share negative affective states (and in particular pain) of others, whereas only few studies have targeted empathy for positive emotions. This bias has precluded addressing one of the central tenets of the shared representations account of empathy, which is that different networks should be engaged when empathizing with emotions that are represented on different neural levels. The aim of the present study was to overcome this limitation and to test whether empathy for pleasant and unpleasant affective touch is underpinned by different neural networks. To this end we used functional magnetic resonance imaging (fMRI), with two independent replication experiments (N = 18, N = 32), and a novel paradigm enabling the joint investigation of first-hand and vicarious responses to pleasant and unpleasant affect induced via visuo-tactile stimulation. This revealed that empathy is subserved by distinct neural networks, with those regions recruited in the first-hand experience of positive or negative affective states also being specifically recruited when empathizing with these respective states in others. More specifically, the first-hand and vicarious experience of pleasant touch commonly recruited medial orbitofrontal cortex (OFC), while unpleasant touch was associated with shared activation in the right fronto-insular cortex. The observation that specifically tailored subsystems of the human brain are engaged to share positive versus negative touch of others brings fresh evidence to one of the major goals of the social neuroscience of empathy: to identify which specific aspects of the affective states of others are shared, and what role this plays in enabling the understanding of the emotions of others. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Experimental study and artificial neural network modeling of tartrazine removal by photocatalytic process under solar light.

    Science.gov (United States)

    Sebti, Aicha; Souahi, Fatiha; Mohellebi, Faroudja; Igoud, Sadek

    2017-07-01

    This research focuses on the application of an artificial neural network (ANN) to predict the removal efficiency of tartrazine from simulated wastewater using a photocatalytic process under solar illumination. A program is developed in Matlab software to optimize the neural network architecture and select the suitable combination of training algorithm, activation function and hidden neurons number. The experimental results of a batch reactor operated under different conditions of pH, TiO2 concentration, initial organic pollutant concentration and solar radiation intensity are used to train, validate and test the networks. While negligible mineralization is demonstrated, the experimental results show that under sunlight irradiation, 85% of tartrazine is removed after 300 min using only 0.3 g/L of TiO2 powder. Therefore, irradiation time is prolonged and almost 66% of total organic carbon is reduced after 15 hours. ANN 5-8-1 with Bayesian regulation back-propagation algorithm and hyperbolic tangent sigmoid transfer function is found to be able to predict the response with high accuracy. In addition, the connection weights approach is used to assess the importance contribution of each input variable on the ANN model response. Among the five experimental parameters, the irradiation time has the greatest effect on the removal efficiency of tartrazine.

  3. Functional modular architecture underlying attentional control in aging.

    Science.gov (United States)

    Monge, Zachary A; Geib, Benjamin R; Siciliano, Rachel E; Packard, Lauren E; Tallman, Catherine W; Madden, David J

    2017-07-15

    Previous research suggests that age-related differences in attention reflect the interaction of top-down and bottom-up processes, but the cognitive and neural mechanisms underlying this interaction remain an active area of research. Here, within a sample of community-dwelling adults 19-78 years of age, we used diffusion reaction time (RT) modeling and multivariate functional connectivity to investigate the behavioral components and whole-brain functional networks, respectively, underlying bottom-up and top-down attentional processes during conjunction visual search. During functional MRI scanning, participants completed a conjunction visual search task in which each display contained one item that was larger than the other items (i.e., a size singleton) but was not informative regarding target identity. This design allowed us to examine in the RT components and functional network measures the influence of (a) additional bottom-up guidance when the target served as the size singleton, relative to when the distractor served as the size singleton (i.e., size singleton effect) and (b) top-down processes during target detection (i.e., target detection effect; target present vs. absent trials). We found that the size singleton effect (i.e., increased bottom-up guidance) was associated with RT components related to decision and nondecision processes, but these effects did not vary with age. Also, a modularity analysis revealed that frontoparietal module connectivity was important for both the size singleton and target detection effects, but this module became central to the networks through different mechanisms for each effect. Lastly, participants 42 years of age and older, in service of the target detection effect, relied more on between-frontoparietal module connections. Our results further elucidate mechanisms through which frontoparietal regions support attentional control and how these mechanisms vary in relation to adult age. Copyright © 2017 Elsevier Inc. All

  4. IRAF and STSDAS under the new ALPHA architecture

    Science.gov (United States)

    Zarate, N. R.

    1992-01-01

    Digital's next generation RISC architecture, known as ALPHA, presents many IRAF system portability questions and challenges to both site managers and end users. DEC promises to support the ULTRIX, VMS, and OSF/1 operating systems, which should allow IRAF to be ported to the new architecture at either the program executable level (using VEST), or at the source level, where IRAF can be tuned for greater performance. These notes highlight some of the details of porting IRAF to OpenVMS on the ALPHA architecture.

  5. Neural mechanisms underlying melodic perception and memory for pitch.

    Science.gov (United States)

    Zatorre, R J; Evans, A C; Meyer, E

    1994-04-01

    The neural correlates of music perception were studied by measuring cerebral blood flow (CBF) changes with positron emission tomography (PET). Twelve volunteers were scanned using the bolus water method under four separate conditions: (1) listening to a sequence of noise bursts, (2) listening to unfamiliar tonal melodies, (3) comparing the pitch of the first two notes of the same set of melodies, and (4) comparing the pitch of the first and last notes of the melodies. The latter two conditions were designed to investigate short-term pitch retention under low or high memory load, respectively. Subtraction of the obtained PET images, superimposed on matched MRI scans, provides anatomical localization of CBF changes associated with specific cognitive functions. Listening to melodies, relative to acoustically matched noise sequences, resulted in CBF increases in the right superior temporal and right occipital cortices. Pitch judgments of the first two notes of each melody, relative to passive listening to the same stimuli, resulted in right frontal-lobe activation. Analysis of the high memory load condition relative to passive listening revealed the participation of a number of cortical and subcortical regions, notably in the right frontal and right temporal lobes, as well as in parietal and insular cortex. Both pitch judgment conditions also revealed CBF decreases within the left primary auditory cortex. We conclude that specialized neural systems in the right superior temporal cortex participate in perceptual analysis of melodies; pitch comparisons are effected via a neural network that includes right prefrontal cortex, but active retention of pitch involves the interaction of right temporal and frontal cortices.

  6. A self-organized artificial neural network architecture for sensory integration with applications to letter-phoneme integration.

    Science.gov (United States)

    Jantvik, Tamas; Gustafsson, Lennart; Papliński, Andrew P

    2011-08-01

    The multimodal self-organizing network (MMSON), an artificial neural network architecture carrying out sensory integration, is presented here. The architecture is designed using neurophysiological findings and imaging studies that pertain to sensory integration and consists of interconnected lattices of artificial neurons. In this artificial neural architecture, the degree of recognition of stimuli, that is, the perceived reliability of stimuli in the various subnetworks, is included in the computation. The MMSON's behavior is compared to aspects of brain function that deal with sensory integration. According to human behavioral studies, integration of signals from sensory receptors of different modalities enhances perception of objects and events and also reduces time to detection. In neocortex, integration takes place in bimodal and multimodal association areas and result, not only in feedback-mediated enhanced unimodal perception and shortened reaction time, but also in robust bimodal or multimodal percepts. Simulation data from the presented artificial neural network architecture show that it replicates these important psychological and neuroscientific characteristics of sensory integration.

  7. Hearing loss impacts neural alpha oscillations under adverse listening conditions

    Directory of Open Access Journals (Sweden)

    Eline Borch Petersen

    2015-02-01

    Full Text Available Degradations in external, acoustic stimulation have long been suspected to increase the load on working memory. One neural signature of working memory load is enhanced power of alpha oscillations (6 ‒ 12 Hz. However, it is unknown to what extent common internal, auditory degradation, that is, hearing impairment, affects the neural mechanisms of working memory when audibility has been ensured via amplification. Using an adapted auditory Sternberg paradigm, we varied the orthogonal factors memory load and background noise level, while the electroencephalogram (EEG was recorded. In each trial, participants were presented with 2, 4, or 6 spoken digits embedded in one of three different levels of background noise. After a stimulus-free delay interval, participants indicated whether a probe digit had appeared in the sequence of digits. Participants were healthy older adults (62 – 86 years, with normal to moderately impaired hearing. Importantly, the background noise levels were individually adjusted and participants were wearing hearing aids to equalize audibility across participants. Irrespective of hearing loss, behavioral performance improved with lower memory load and also with lower levels of background noise. Interestingly, the alpha power in the stimulus-free delay interval was dependent on the interplay between task demands (memory load and noise level and hearing loss; while alpha power increased with hearing loss during low and intermediate levels of memory load and background noise, it dropped for participants with the relatively most severe hearing loss under the highest memory load and background noise level. These findings suggest that adaptive neural mechanisms for coping with adverse listening conditions break down for higher degrees of hearing loss, even when adequate hearing aid amplification is in place.

  8. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Science.gov (United States)

    Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

    2016-01-01

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance

  9. Design of silicon brains in the nano-CMOS era: spiking neurons, learning synapses and neural architecture optimization.

    Science.gov (United States)

    Cassidy, Andrew S; Georgiou, Julius; Andreou, Andreas G

    2013-09-01

    We present a design framework for neuromorphic architectures in the nano-CMOS era. Our approach to the design of spiking neurons and STDP learning circuits relies on parallel computational structures where neurons are abstracted as digital arithmetic logic units and communication processors. Using this approach, we have developed arrays of silicon neurons that scale to millions of neurons in a single state-of-the-art Field Programmable Gate Array (FPGA). We demonstrate the validity of the design methodology through the implementation of cortical development in a circuit of spiking neurons, STDP synapses, and neural architecture optimization. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Ontogeny of neural circuits underlying spatial memory in the rat

    Directory of Open Access Journals (Sweden)

    James Alexander Ainge

    2012-03-01

    Full Text Available Spatial memory is a well characterised psychological function in both humans and rodents. The combined computations of a network of systems including place cells in the hippocampus, grid cells in the medial entorhinal cortex and head direction cells found in numerous structures in the brain have been suggested to form the neural instantiation of the cognitive map as first described by Tolman in 1948. However, while our understanding of the neural mechanisms underlying spatial representations in adults is relatively sophisticated, we know substantially less about how this network develops in young animals. In this article we review studies examining the developmental timescale that these systems follow. Electrophysiological recordings from very young rats show that directional information is at adult levels at the outset of navigational experience. The systems supporting allocentric memory, however, take longer to mature. This is consistent with behavioural studies of young rats which show that spatial memory based on head direction develops very early but that allocentric spatial memory takes longer to mature. We go on to report new data demonstrating that memory for associations between objects and their spatial locations is slower to develop than memory for objects alone. This is again consistent with previous reports suggesting that adult like spatial representations have a protracted development in rats and also suggests that the systems involved in processing non-spatial stimuli come online earlier.

  11. Neural basis of increased costly norm enforcement under adversity.

    Science.gov (United States)

    Wu, Yan; Yu, Hongbo; Shen, Bo; Yu, Rongjun; Zhou, Zhiheng; Zhang, Guoping; Jiang, Yushi; Zhou, Xiaolin

    2014-12-01

    Humans are willing to punish norm violations even at a substantial personal cost. Using fMRI and a variant of the ultimatum game and functional magnetic resonance imaging, we investigated how the brain differentially responds to fairness in loss and gain domains. Participants (responders) received offers from anonymous partners indicating a division of an amount of monetary gain or loss. If they accept, both get their shares according to the division; if they reject, both get nothing or lose the entire stake. We used a computational model to derive perceived fairness of offers and participant-specific inequity aversion. Behaviorally, participants were more likely to reject unfair offers in the loss (vs gain) domain. Neurally, the positive correlation between fairness and activation in ventral striatum was reduced, whereas the negative correlations between fairness and activations in dorsolateral prefrontal cortex were enhanced in the loss domain. Moreover, rejection-related dorsal striatum activation was higher in the loss domain. Furthermore, the gain-loss domain modulates costly punishment only when unfair behavior was directed toward the participants and not when it was directed toward others. These findings provide neural and computational accounts of increased costly norm enforcement under adversity and advanced our understanding of the context-dependent nature of fairness preference. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  12. A Neural Network Architecture For Rapid Model Indexing In Computer Vision Systems

    Science.gov (United States)

    Pawlicki, Ted

    1988-03-01

    Models of objects stored in memory have been shown to be useful for guiding the processing of computer vision systems. A major consideration in such systems, however, is how stored models are initially accessed and indexed by the system. As the number of stored models increases, the time required to search memory for the correct model becomes high. Parallel distributed, connectionist, neural networks' have been shown to have appealing content addressable memory properties. This paper discusses an architecture for efficient storage and reference of model memories stored as stable patterns of activity in a parallel, distributed, connectionist, neural network. The emergent properties of content addressability and resistance to noise are exploited to perform indexing of the appropriate object centered model from image centered primitives. The system consists of three network modules each of which represent information relative to a different frame of reference. The model memory network is a large state space vector where fields in the vector correspond to ordered component objects and relative, object based spatial relationships between the component objects. The component assertion network represents evidence about the existence of object primitives in the input image. It establishes local frames of reference for object primitives relative to the image based frame of reference. The spatial relationship constraint network is an intermediate representation which enables the association between the object based and the image based frames of reference. This intermediate level represents information about possible object orderings and establishes relative spatial relationships from the image based information in the component assertion network below. It is also constrained by the lawful object orderings in the model memory network above. The system design is consistent with current psychological theories of recognition by component. It also seems to support Marr's notions

  13. Cortical Neural Activity Predicts Sensory Acuity Under Optogenetic Manipulation.

    Science.gov (United States)

    Briguglio, John J; Aizenberg, Mark; Balasubramanian, Vijay; Geffen, Maria N

    2018-02-21

    Excitatory and inhibitory neurons in the mammalian sensory cortex form interconnected circuits that control cortical stimulus selectivity and sensory acuity. Theoretical studies have predicted that suppression of inhibition in such excitatory-inhibitory networks can lead to either an increase or, paradoxically, a decrease in excitatory neuronal firing, with consequent effects on stimulus selectivity. We tested whether modulation of inhibition or excitation in the auditory cortex of male mice could evoke such a variety of effects in tone-evoked responses and in behavioral frequency discrimination acuity. We found that, indeed, the effects of optogenetic manipulation on stimulus selectivity and behavior varied in both magnitude and sign across subjects, possibly reflecting differences in circuitry or expression of optogenetic factors. Changes in neural population responses consistently predicted behavioral changes for individuals separately, including improvement and impairment in acuity. This correlation between cortical and behavioral change demonstrates that, despite the complex and varied effects that these manipulations can have on neuronal dynamics, the resulting changes in cortical activity account for accompanying changes in behavioral acuity. SIGNIFICANCE STATEMENT Excitatory and inhibitory interactions determine stimulus specificity and tuning in sensory cortex, thereby controlling perceptual discrimination acuity. Modeling has predicted that suppressing the activity of inhibitory neurons can lead to increased or, paradoxically, decreased excitatory activity depending on the architecture of the network. Here, we capitalized on differences between subjects to test whether suppressing/activating inhibition and excitation can in fact exhibit such paradoxical effects for both stimulus sensitivity and behavioral discriminability. Indeed, the same optogenetic manipulation in the auditory cortex of different mice could improve or impair frequency discrimination

  14. Incorporation of Tenascin-C into the Extracellular Matrix by Periostin Underlies an Extracellular Meshwork Architecture*

    OpenAIRE

    Kii, Isao; Nishiyama, Takashi; Li, Minqi; Matsumoto, Ken-ichi; Saito, Mitsuru; Amizuka, Norio; Kudo, Akira

    2009-01-01

    Extracellular matrix (ECM) underlies a complicated multicellular architecture that is subjected to significant forces from mechanical environment. Although various components of the ECM have been enumerated, mechanisms that evolve the sophisticated ECM architecture remain to be addressed. Here we show that periostin, a matricellular protein, promotes incorporation of tenascin-C into the ECM and organizes a meshwork architecture of the ECM. We found that both periostin null mice and tenascin-C...

  15. An artificial neural network architecture for non-parametric visual odometry in wireless capsule endoscopy

    Science.gov (United States)

    Dimas, George; Iakovidis, Dimitris K.; Karargyris, Alexandros; Ciuti, Gastone; Koulaouzidis, Anastasios

    2017-09-01

    Wireless capsule endoscopy is a non-invasive screening procedure of the gastrointestinal (GI) tract performed with an ingestible capsule endoscope (CE) of the size of a large vitamin pill. Such endoscopes are equipped with a usually low-frame-rate color camera which enables the visualization of the GI lumen and the detection of pathologies. The localization of the commercially available CEs is performed in the 3D abdominal space using radio-frequency (RF) triangulation from external sensor arrays, in combination with transit time estimation. State-of-the-art approaches, such as magnetic localization, which have been experimentally proved more accurate than the RF approach, are still at an early stage. Recently, we have demonstrated that CE localization is feasible using solely visual cues and geometric models. However, such approaches depend on camera parameters, many of which are unknown. In this paper the authors propose a novel non-parametric visual odometry (VO) approach to CE localization based on a feed-forward neural network architecture. The effectiveness of this approach in comparison to state-of-the-art geometric VO approaches is validated using a robotic-assisted in vitro experimental setup.

  16. A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control.

    Science.gov (United States)

    Donnarumma, Francesco; Prevete, Roberto; Chersi, Fabian; Pezzulo, Giovanni

    2015-09-01

    There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.

  17. 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.

  18. Towards a multi-level neural architecture that unifies self-intended and imitated arm reaching performance.

    Science.gov (United States)

    Gentili, Rodolphe J; Oh, Hyuk; Huang, Di-Wei; Katz, Garrett E; Miller, Ross H; Reggia, James A

    2014-01-01

    Dexterous arm reaching movements are a critical feature that allow human interactions with tools, the environment, and socially with others. Thus the development of a neural architecture providing unified mechanisms for actual, mental, observed and imitated actions could enhance robot performance, enhance human-robot social interactions, and inform specific human brain processes. Here we present a model, including a fronto-parietal network that implements sensorimotor transformations (inverse kinematics, workspace visuo-spatial rotations), for self-intended and imitation performance. Our findings revealed that this neural model can perform accurate and robust 3D actual/mental arm reaching while reproducing human-like kinematics. Also, using visuo-spatial remapping, the neural model can imitate arm reaching independently of a demonstrator-imitator viewpoint. This work is a first step towards providing the basis of a future neural architecture for combining cognitive and sensorimotor processing levels that will allow for multi-level mental simulation when executing actual, mental, observed, and imitated actions for dexterous arm movements.

  19. Hybrid Fuzzy Wavelet Neural Networks Architecture Based on Polynomial Neural Networks and Fuzzy Set/Relation Inference-Based Wavelet Neurons.

    Science.gov (United States)

    Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold

    2017-08-11

    This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.

  20. Neural mechanism underlying autobiographical memory modulated by remoteness and emotion

    Science.gov (United States)

    Ge, Ruiyang; Fu, Yan; Wang, DaHua; Yao, Li; Long, Zhiying

    2012-03-01

    Autobiographical memory is the ability to recollect past events from one's own life. Both emotional tone and memory remoteness can influence autobiographical memory retrieval along the time axis of one's life. Although numerous studies have been performed to investigate brain regions involved in retrieving processes of autobiographical memory, the effect of emotional tone and memory age on autobiographical memory retrieval remains to be clarified. Moreover, whether the involvement of hippocampus in consolidation of autobiographical events is time dependent or independent has been controversial. In this study, we investigated the effect of memory remoteness (factor1: recent and remote) and emotional valence (factor2: positive and negative) on neural correlates underlying autobiographical memory by using functional magnetic resonance imaging (fMRI) technique. Although all four conditions activated some common regions known as "core" regions in autobiographical memory retrieval, there are some other regions showing significantly different activation for recent versus remote and positive versus negative memories. In particular, we found that bilateral hippocampal regions were activated in the four conditions regardless of memory remoteness and emotional valence. Thus, our study confirmed some findings of previous studies and provided further evidence to support the multi-trace theory which believes that the role of hippocampus involved in autobiographical memory retrieval is time-independent and permanent in memory consolidation.

  1. Neural circuitry underlying sentence-level linguistic prosody.

    Science.gov (United States)

    Tong, Yunxia; Gandour, Jackson; Talavage, Thomas; Wong, Donald; Dzemidzic, Mario; Xu, Yisheng; Li, Xiaojian; Lowe, Mark

    2005-11-01

    This study investigates the neural substrates underlying the perception of two sentence-level prosodic phenomena in Mandarin Chinese: contrastive stress (initial vs. final emphasis position) and intonation (declarative vs. interrogative modality). In an fMRI experiment, Chinese and English listeners were asked to selectively attend to either stress or intonation in paired 3-word sentences, and make speeded-response discrimination judgments. Between-group comparisons revealed that the Chinese group exhibited significantly greater activity in the left supramarginal gyrus and posterior middle temporal gyrus relative to the English group for both tasks. These same two regions showed a leftward asymmetry in the stress task for the Chinese group only. For both language groups, rightward asymmetries were observed in the middle portion of the middle frontal gyrus across tasks. All task effects involved greater activity for the stress task as compared to intonation. A left-sided task effect was observed in the posterior middle temporal gyrus for the Chinese group only. Both language groups exhibited a task effect bilaterally in the intraparietal sulcus. These findings support the emerging view that speech prosody perception involves a dynamic interplay among widely distributed regions not only within a single hemisphere but also between the two hemispheres. This model of speech prosody processing emphasizes the role of right hemisphere regions for complex-sound analysis, whereas task-dependent regions in the left hemisphere predominate when language processing is required.

  2. Attention Modulates the Neural Processes Underlying Multisensory Integration of Emotion

    Directory of Open Access Journals (Sweden)

    Hao Tam Ho

    2011-10-01

    Full Text Available Integrating emotional information from multiple sensory modalities is generally assumed to be a pre-attentive process (de Gelder et al., 1999. This assumption, however, presupposes that the integrative process occurs independent of attention. Using event-potentials (ERP the present study investigated whether the neural processes underlying the integration of dynamic facial expression and emotional prosody is indeed unaffected by attentional manipulations. To this end, participants were presented with congruent and incongruent face-voice combinations (eg, an angry face combined with a neutral voice and performed different two-choice tasks in four consecutive blocks. Three of the tasks directed the participants' attention to emotion expressions in the face, the voice or both. The fourth task required participants to attend to the synchronicity between voice and lip movements. The results show divergent modulations of early ERP components by the different attentional manipulations. For example, when attention was directed to the face (or the voice, incongruent stimuli elicited a reduced N1 as compared to congruent stimuli. This effect was absent, when attention was diverted away from the emotionality in both face and voice suggesting that the detection of emotional incongruence already requires attention. Based on these findings, we question whether multisensory integration of emotion occurs indeed pre-attentively.

  3. [Neural mechanism underlying autistic savant and acquired savant syndrome].

    Science.gov (United States)

    Takahata, Keisuke; Kato, Motoichiro

    2008-07-01

    , especially that of the prefrontal cortex and the posterior regions of the brain. (3) Autistic models, including those based on weak central coherence theory (Frith, 1989), that focus on how savant skills emerge from an autistic brain. Based on recent neuroimaging studies of ASD, Just et al. (2004) suggested the underconnectivity theory, which emphasizes the disruption of long-range connectivity and the relative intact or even more enhanced local connectivity in the autistic brain. All the models listed above have certain advantages and shortcomings. At the end of this review, we propose another integrative model of savant syndrome. In this model, we predict an altered balance of local/global connectivity patterns that contribute to an altered functional segregation/integration ratio. In particular, we emphasize the crucial role played by the disruption of global connectivity in a parallel distributed cortical network, which might result in impairment in integrated cognitive processing, such as impairment in executive function and social cognition. On the other hand, the reduced inter-regional collaboration could lead to a disinhibitory enhancement of neural activity and connectivity in local cortical regions. In addition, enhanced connectivity in the local brain regions is partly due to the abnormal organization of the cortical network as a result of developmental and pathological states. This enhanced local connectivity results in the specialization and facilitation of low-level cognitive processing. The disruption of connectivity between the prefrontal cortex and other regions is considered to be a particularly important factor because the prefrontal region shows the most influential inhibitory control on other cortical areas. We propose that these neural mechanisms as the underlying causes for the emergence of savant ability in ASD and FTD patients.

  4. Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

    Science.gov (United States)

    Radziszewski, Kacper

    2017-10-01

    The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

  5. A simple genetic architecture underlies morphological variation in dogs.

    Science.gov (United States)

    Boyko, Adam R; Quignon, Pascale; Li, Lin; Schoenebeck, Jeffrey J; Degenhardt, Jeremiah D; Lohmueller, Kirk E; Zhao, Keyan; Brisbin, Abra; Parker, Heidi G; vonHoldt, Bridgett M; Cargill, Michele; Auton, Adam; Reynolds, Andy; Elkahloun, Abdel G; Castelhano, Marta; Mosher, Dana S; Sutter, Nathan B; Johnson, Gary S; Novembre, John; Hubisz, Melissa J; Siepel, Adam; Wayne, Robert K; Bustamante, Carlos D; Ostrander, Elaine A

    2010-08-10

    Domestic dogs exhibit tremendous phenotypic diversity, including a greater variation in body size than any other terrestrial mammal. Here, we generate a high density map of canine genetic variation by genotyping 915 dogs from 80 domestic dog breeds, 83 wild canids, and 10 outbred African shelter dogs across 60,968 single-nucleotide polymorphisms (SNPs). Coupling this genomic resource with external measurements from breed standards and individuals as well as skeletal measurements from museum specimens, we identify 51 regions of the dog genome associated with phenotypic variation among breeds in 57 traits. The complex traits include average breed body size and external body dimensions and cranial, dental, and long bone shape and size with and without allometric scaling. In contrast to the results from association mapping of quantitative traits in humans and domesticated plants, we find that across dog breeds, a small number of quantitative trait loci (dog using a database of genotyped individuals and highlight the important role human-directed selection has played in altering the genetic architecture of key traits in this important species.

  6. A simple genetic architecture underlies morphological variation in dogs.

    Directory of Open Access Journals (Sweden)

    Adam R Boyko

    2010-08-01

    Full Text Available Domestic dogs exhibit tremendous phenotypic diversity, including a greater variation in body size than any other terrestrial mammal. Here, we generate a high density map of canine genetic variation by genotyping 915 dogs from 80 domestic dog breeds, 83 wild canids, and 10 outbred African shelter dogs across 60,968 single-nucleotide polymorphisms (SNPs. Coupling this genomic resource with external measurements from breed standards and individuals as well as skeletal measurements from museum specimens, we identify 51 regions of the dog genome associated with phenotypic variation among breeds in 57 traits. The complex traits include average breed body size and external body dimensions and cranial, dental, and long bone shape and size with and without allometric scaling. In contrast to the results from association mapping of quantitative traits in humans and domesticated plants, we find that across dog breeds, a small number of quantitative trait loci (< or = 3 explain the majority of phenotypic variation for most of the traits we studied. In addition, many genomic regions show signatures of recent selection, with most of the highly differentiated regions being associated with breed-defining traits such as body size, coat characteristics, and ear floppiness. Our results demonstrate the efficacy of mapping multiple traits in the domestic dog using a database of genotyped individuals and highlight the important role human-directed selection has played in altering the genetic architecture of key traits in this important species.

  7. SNAVA-A real-time multi-FPGA multi-model spiking neural network simulation architecture.

    Science.gov (United States)

    Sripad, Athul; Sanchez, Giovanny; Zapata, Mireya; Pirrone, Vito; Dorta, Taho; Cambria, Salvatore; Marti, Albert; Krishnamourthy, Karthikeyan; Madrenas, Jordi

    2018-01-01

    Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. REMOD: a tool for analyzing and remodeling the dendritic architecture of neural cells

    Directory of Open Access Journals (Sweden)

    Panagiotis eBozelos

    2016-01-01

    Full Text Available Dendritic morphology is a key determinant of how individual neurons acquire a unique signal processing profile. The highly branched dendritic structure that originates from the cell body, explores the surrounding 3D space in a fractal-like manner, until it reaches a certain amount of complexity. Its shape undergoes significant alterations under various physiological or neuropathological conditions. Yet, despite the profound effect that these alterations can have on neuronal function, the causal relationship between the two remains largely elusive. The lack of a systematic approach for remodeling neural cells and their dendritic trees is a key limitation that contributes to this problem. Such causal relationships can be inferred via the use of large-scale neuronal models whereby the anatomical plasticity of neurons is accounted for, in order to enhance their biological relevance and hence their predictive performance. To facilitate this effort, we developed a computational tool named REMOD that allows the structural remodeling of any type of virtual neuron. REMOD is written in Python and can be accessed through a dedicated web interface that guides the user through various options to manipulate selected neuronal morphologies. REMOD can also be used to extract meaningful morphology statistics for one or multiple reconstructions, including features such as sholl analysis, total dendritic length and area, path length to the soma, centrifugal branch order, diameter tapering and more. As such, the tool can be used both for the analysis and/or the remodeling of neuronal morphologies of any type.

  9. CMOS-based Stochastically Spiking Neural Network for Optimization under Uncertainties

    Science.gov (United States)

    2017-03-01

    uncertainties. We discuss a ‘scenario generation’ circuit to non- parametrically estimate/emulate statistics of uncertain cost/constraints...are explored: (1) We discuss a ‘scenario generation’ circuit to non- parametrically estimate and emulate statistics of uncertain cost/constraints...uncertainties. The discussed mixed-signal, CMOS-based architecture of stochastically spiking neural network minimizes area/power of each cell and

  10. Neural suppression of irrelevant information underlies optimal working memory performance.

    Science.gov (United States)

    Zanto, Theodore P; Gazzaley, Adam

    2009-03-11

    Our ability to focus attention on task-relevant information and ignore distractions is reflected by differential enhancement and suppression of neural activity in sensory cortex (i.e., top-down modulation). Such selective, goal-directed modulation of activity may be intimately related to memory, such that the focus of attention biases the likelihood of successfully maintaining relevant information by limiting interference from irrelevant stimuli. Despite recent studies elucidating the mechanistic overlap between attention and memory, the relationship between top-down modulation of visual processing during working memory (WM) encoding, and subsequent recognition performance has not yet been established. Here, we provide neurophysiological evidence in healthy, young adults that top-down modulation of early visual processing (memory, motion direction and color. Moreover, attention to irrelevant stimuli was reflected neurally during the WM maintenance period as an increased memory load. These results suggest that neural enhancement of relevant information is not the primary determinant of high-level performance, but rather optimal WM performance is dependent on effectively filtering irrelevant information through neural suppression to prevent overloading a limited memory capacity.

  11. Neural processing of reward magnitude under varying attentional demands.

    Science.gov (United States)

    Stoppel, Christian Michael; Boehler, Carsten Nicolas; Strumpf, Hendrik; Heinze, Hans-Jochen; Hopf, Jens-Max; Schoenfeld, Mircea Ariel

    2011-04-06

    Central to the organization of behavior is the ability to represent the magnitude of a prospective reward and the costs related to obtaining it. Therein, reward-related neural activations are discounted in dependence of the effort required to resolve a given task. Varying attentional demands of the task might however affect reward-related neural activations. Here we employed fMRI to investigate the neural representation of expected values during a monetary incentive delay task with varying attentional demands. Following a cue, indicating at the same time the difficulty (hard/easy) and the reward magnitude (high/low) of the upcoming trial, subjects performed an attention task and subsequently received feedback about their monetary reward. Consistent with previous results, activity in anterior-cingulate, insular/orbitofrontal and mesolimbic regions co-varied with the anticipated reward-magnitude, but also with the attentional requirements of the task. These activations occurred contingent on action-execution and resembled the response time pattern of the subjects. In contrast, cue-related activations, signaling the forthcoming task-requirements, were only observed within attentional control structures. These results suggest that anticipated reward-magnitude and task-related attentional demands are concurrently processed in partially overlapping neural networks of anterior-cingulate, insular/orbitofrontal, and mesolimbic regions. Copyright © 2011 Elsevier B.V. All rights reserved.

  12. Research on architecture of intelligent design platform for artificial neural network expert system

    Science.gov (United States)

    Gu, Honghong

    2017-09-01

    Based on the review of the development and current situation of CAD technology, the necessity of combination of artificial neural network and expert system, and then present an intelligent design system based on artificial neural network. Moreover, it discussed the feasibility of realization of a design-oriented expert system development tools on the basis of above combination. In addition, knowledge representation strategy and method and the solving process are given in this paper.

  13. The signal processing architecture underlying subjective reports of sensory awareness.

    Science.gov (United States)

    Maniscalco, Brian; Lau, Hakwan

    2016-01-01

    What is the relationship between perceptual information processing and subjective perceptual experience? Empirical dissociations between stimulus identification performance and subjective reports of stimulus visibility are crucial for shedding light on this question. We replicated a finding that metacontrast masking can produce such a dissociation (Lau and Passingham, 2006), and report a novel finding that this paradigm can also dissociate stimulus identification performance from the efficacy with which visibility ratings predict task performance. We explored various hypotheses about the relationship between perceptual task performance and visibility rating by implementing them in computational models and using formal model comparison techniques to assess which ones best captured the unusual patterns in the data. The models fell into three broad categories: Single Channel models, which hold that task performance and visibility ratings are based on the same underlying source of information; Dual Channel models, which hold that there are two independent processing streams that differentially contribute to task performance and visibility rating; and Hierarchical models, which hold that a late processing stage generates visibility ratings by evaluating the quality of early perceptual processing. Taking into account the quality of data fitting and model complexity, we found that Hierarchical models perform best at capturing the observed behavioral dissociations. Because current theories of visual awareness map well onto these different model structures, a formal comparison between them is a powerful approach for arbitrating between the different theories.

  14. Neural suppression of irrelevant information underlies optimal working memory performance

    OpenAIRE

    Zanto, Theodore P.; Gazzaley, Adam

    2009-01-01

    Our ability to focus attention on task-relevant information and ignore distractions is reflected by differential enhancement and suppression of neural activity in sensory cortex (i.e., top-down modulation). Such selective, goal-directed modulation of activity may be intimately related to memory, such that the focus of attention biases the likelihood of successfully maintaining relevant information by limiting interference from irrelevant stimuli. Despite recent studies elucidating the mechani...

  15. Neural ensemble dynamics underlying a long-term associative memory

    Science.gov (United States)

    Grewe, Benjamin F.; Gründemann, Jan; Kitch, Lacey J.; Lecoq, Jerome A.; Parker, Jones G.; Marshall, Jesse D.; Larkin, Margaret C.; Jercog, Pablo E.; Grenier, Francois; Li, Jin Zhong; Lüthi, Andreas; Schnitzer, Mark J.

    2017-01-01

    The brain’s ability to associate different stimuli is vital to long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala (BLA) encode associations between conditioned and unconditioned stimuli (CS, US). Using a miniature fluorescence microscope, we tracked BLA ensemble neural Ca2+ dynamics during fear learning and extinction over six days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells’ CS-evoked responses. This bi-directional plasticity mainly occurred after conditioning and reshaped the CS ensemble neural representation to gain similarity to the US-representation. During extinction training with repetitive CS presentations, the CS-representation became more distinctive without reverting to its original form. Throughout, the strength of the ensemble-encoded CS-US association predicted each mouse’s level of behavioral conditioning. These findings support a supervised learning model in which activation of the US-representation guides the transformation of the CS-representation. PMID:28329757

  16. Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures

    Directory of Open Access Journals (Sweden)

    Murat Cuhadar

    2014-03-01

    Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.

  17. The neural bases underlying social risk perception in purchase decisions.

    Science.gov (United States)

    Yokoyama, Ryoichi; Nozawa, Takayuki; Sugiura, Motoaki; Yomogida, Yukihito; Takeuchi, Hikaru; Akimoto, Yoritaka; Shibuya, Satoru; Kawashima, Ryuta

    2014-05-01

    Social considerations significantly influence daily purchase decisions, and the perception of social risk (i.e., the anticipated disapproval of others) is crucial in dissuading consumers from making purchases. However, the neural basis for consumers' perception of social risk remains undiscovered, and this novel study clarifies the relevant neural processes. A total of 26 volunteers were scanned while they evaluated purchase intention of products (purchase intention task) and their anticipation of others' disapproval for possessing a product (social risk task), using functional magnetic resonance imaging (fMRI). The fMRI data from the purchase intention task was used to identify the brain region associated with perception of social risk during purchase decision making by using subjective social risk ratings for a parametric modulation analysis. Furthermore, we aimed to explore if there was a difference between participants' purchase decisions and their explicit evaluations of social risk, with reference to the neural activity associated with social risk perception. For this, subjective social risk ratings were used for a parametric modulation analysis on fMRI data from the social risk task. Analysis of the purchase intention task revealed a significant positive correlation between ratings of social risk and activity in the anterior insula, an area of the brain that is known as part of the emotion-related network. Analysis of the social risk task revealed a significant positive correlation between ratings of social risk and activity in the temporal parietal junction and the medial prefrontal cortex, which are known as theory-of-mind regions. Our results suggest that the anterior insula processes consumers' social risk implicitly to prompt consumers not to buy socially unacceptable products, whereas ToM-related regions process such risk explicitly in considering the anticipated disapproval of others. These findings may prove helpful in understanding the mental

  18. Child Maltreatment and Neural Systems Underlying Emotion Regulation.

    Science.gov (United States)

    McLaughlin, Katie A; Peverill, Matthew; Gold, Andrea L; Alves, Sonia; Sheridan, Margaret A

    2015-09-01

    The strong associations between child maltreatment and psychopathology have generated interest in identifying neurodevelopmental processes that are disrupted following maltreatment. Previous research has focused largely on neural response to negative facial emotion. We determined whether child maltreatment was associated with neural responses during passive viewing of negative and positive emotional stimuli and effortful attempts to regulate emotional responses. A total of 42 adolescents aged 13 to 19 years, half with exposure to physical and/or sexual abuse, participated. Blood oxygen level-dependent (BOLD) response was measured during passive viewing of negative and positive emotional stimuli and attempts to modulate emotional responses using cognitive reappraisal. Maltreated adolescents exhibited heightened response in multiple nodes of the salience network, including amygdala, putamen, and anterior insula, to negative relative to neutral stimuli. During attempts to decrease responses to negative stimuli relative to passive viewing, maltreatment was associated with greater recruitment of superior frontal gyrus, dorsal anterior cingulate cortex, and frontal pole; adolescents with and without maltreatment down-regulated amygdala response to a similar degree. No associations were observed between maltreatment and neural response to positive emotional stimuli during passive viewing or effortful regulation. Child maltreatment heightens the salience of negative emotional stimuli. Although maltreated adolescents modulate amygdala responses to negative cues to a degree similar to that of non-maltreated youths, they use regions involved in effortful control to a greater degree to do so, potentially because greater effort is required to modulate heightened amygdala responses. These findings are promising, given the centrality of cognitive restructuring in trauma-focused treatments for children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry

  19. Neural Systems Underlying Individual Differences in Intertemporal Decision-making.

    Science.gov (United States)

    Elton, Amanda; Smith, Christopher T; Parrish, Michael H; Boettiger, Charlotte A

    2017-03-01

    Excessively choosing immediate over larger future rewards, or delay discounting (DD), associates with multiple clinical conditions. Individual differences in DD likely depend on variations in the activation of and functional interactions between networks, representing possible endophenotypes for associated disorders, including alcohol use disorders (AUDs). Numerous fMRI studies have probed the neural bases of DD, but investigations of large-scale networks remain scant. We addressed this gap by testing whether activation within large-scale networks during Now/Later decision-making predicts individual differences in DD. To do so, we scanned 95 social drinkers (18-40 years old; 50 women) using fMRI during hypothetical choices between small monetary amounts available "today" or larger amounts available later. We identified neural networks engaged during Now/Later choice using independent component analysis and tested the relationship between component activation and degree of DD. The activity of two components during Now/Later choice correlated with individual DD rates: A temporal lobe network positively correlated with DD, whereas a frontoparietal-striatal network negatively correlated with DD. Activation differences between these networks predicted individual differences in DD, and their negative correlation during Now/Later choice suggests functional competition. A generalized psychophysiological interactions analysis confirmed a decrease in their functional connectivity during decision-making. The functional connectivity of these two networks negatively correlates with alcohol-related harm, potentially implicating these networks in AUDs. These findings provide novel insight into the neural underpinnings of individual differences in impulsive decision-making with potential implications for addiction and related disorders in which impulsivity is a defining feature.

  20. On design and evaluation of tapped-delay neural network architectures

    DEFF Research Database (Denmark)

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

    1993-01-01

    Pruning and evaluation of tapped-delay neural networks for the sunspot benchmark series are addressed. It is shown that the generalization ability of the networks can be improved by pruning using the optimal brain damage method of Le Cun, Denker and Solla. A stop criterion for the pruning algorithm...

  1. Fixed latency on-chip interconnect for hardware spiking neural network architectures

    NARCIS (Netherlands)

    Pande, Sandeep; Morgan, Fearghal; Smit, Gerardus Johannes Maria; Bruintjes, Tom; Rutgers, J.H.; Cawley, Seamus; Harkin, Jim; McDaid, Liam

    Information in a Spiking Neural Network (SNN) is encoded as the relative timing between spikes. Distortion in spike timings can impact the accuracy of SNN operation by modifying the precise firing time of neurons within the SNN. Maintaining the integrity of spike timings is crucial for reliable

  2. Neural mechanisms underlying social conformity in an ultimatum game

    Directory of Open Access Journals (Sweden)

    Zhenyu eWei

    2013-12-01

    Full Text Available When individuals’ actions are incongruent with those of the group they belong to, they may change their initial behavior in order to conform to the group norm. This phenomenon is known as social conformity. In the present study, we used event-related functional magnetic resonance imaging (fMRI to investigate brain activity in response to group opinion during an ultimatum game. Results showed that participants changed their choices when these choices conflicted with the normative opinion of the group they were members of, especially in conditions of unfair treatment. The fMRI data revealed that a conflict with group norms activated the brain regions involved in norm violations and behavioral adjustment. Furthermore, in the reject-unfair condition, we observed that a conflict with group norms activated the medial frontal gyrus. These findings contribute to recent research examining neural mechanisms involved in detecting violations of social norms, and provide information regarding the neural representation of conformity behavior in an economic game.

  3. Neural mechanisms underlying social conformity in an ultimatum game.

    Science.gov (United States)

    Wei, Zhenyu; Zhao, Zhiying; Zheng, Yong

    2013-01-01

    When individuals' actions are incongruent with those of the group they belong to, they may change their initial behavior in order to conform to the group norm. This phenomenon is known as "social conformity." In the present study, we used event-related functional magnetic resonance imaging (fMRI) to investigate brain activity in response to group opinion during an ultimatum game. Results showed that participants changed their choices when these choices conflicted with the normative opinion of the group they were members of, especially in conditions of unfair treatment. The fMRI data revealed that a conflict with group norms activated the brain regions involved in norm violations and behavioral adjustment. Furthermore, in the reject-unfair condition, we observed that a conflict with group norms activated the medial frontal gyrus. These findings contribute to recent research examining neural mechanisms involved in detecting violations of social norms, and provide information regarding the neural representation of conformity behavior in an economic game.

  4. Architecture for a neural expert system for condition-based maintenance of blanking

    NARCIS (Netherlands)

    de Boer, Thomas W.; Klingenberg, Warse

    2008-01-01

    This paper describes a proposal for a hybrid architecture for monitoring and Condition-based Maintenance (CBM) of punching/blanking of sheet metal. Previous work shows that it is possible for certain applications (in which process parameters are sufficiently stable) to detect tool wear and other

  5. Incorporation of Tenascin-C into the Extracellular Matrix by Periostin Underlies an Extracellular Meshwork Architecture*

    Science.gov (United States)

    Kii, Isao; Nishiyama, Takashi; Li, Minqi; Matsumoto, Ken-ichi; Saito, Mitsuru; Amizuka, Norio; Kudo, Akira

    2010-01-01

    Extracellular matrix (ECM) underlies a complicated multicellular architecture that is subjected to significant forces from mechanical environment. Although various components of the ECM have been enumerated, mechanisms that evolve the sophisticated ECM architecture remain to be addressed. Here we show that periostin, a matricellular protein, promotes incorporation of tenascin-C into the ECM and organizes a meshwork architecture of the ECM. We found that both periostin null mice and tenascin-C null mice exhibited a similar phenotype, confined tibial periostitis, which possibly corresponds to medial tibial stress syndrome in human sports injuries. Periostin possessed adjacent domains that bind to tenascin-C and the other ECM protein: fibronectin and type I collagen, respectively. These adjacent domains functioned as a bridge between tenascin-C and the ECM, which increased deposition of tenascin-C on the ECM. The deposition of hexabrachions of tenascin-C may stabilize bifurcations of the ECM fibrils, which is integrated into the extracellular meshwork architecture. This study suggests a role for periostin in adaptation of the ECM architecture in the mechanical environment. PMID:19887451

  6. Incorporation of tenascin-C into the extracellular matrix by periostin underlies an extracellular meshwork architecture.

    Science.gov (United States)

    Kii, Isao; Nishiyama, Takashi; Li, Minqi; Matsumoto, Ken-Ichi; Saito, Mitsuru; Amizuka, Norio; Kudo, Akira

    2010-01-15

    Extracellular matrix (ECM) underlies a complicated multicellular architecture that is subjected to significant forces from mechanical environment. Although various components of the ECM have been enumerated, mechanisms that evolve the sophisticated ECM architecture remain to be addressed. Here we show that periostin, a matricellular protein, promotes incorporation of tenascin-C into the ECM and organizes a meshwork architecture of the ECM. We found that both periostin null mice and tenascin-C null mice exhibited a similar phenotype, confined tibial periostitis, which possibly corresponds to medial tibial stress syndrome in human sports injuries. Periostin possessed adjacent domains that bind to tenascin-C and the other ECM protein: fibronectin and type I collagen, respectively. These adjacent domains functioned as a bridge between tenascin-C and the ECM, which increased deposition of tenascin-C on the ECM. The deposition of hexabrachions of tenascin-C may stabilize bifurcations of the ECM fibrils, which is integrated into the extracellular meshwork architecture. This study suggests a role for periostin in adaptation of the ECM architecture in the mechanical environment.

  7. Neural and Behavioral Correlates of Alcohol-Induced Aggression Under Provocation

    OpenAIRE

    Gan, Gabriela; Sterzer, Philipp; Marxen, Michael; Zimmermann, Ulrich S.; Smolka, Michael N.

    2015-01-01

    Although alcohol consumption is linked to increased aggression, its neural correlates have not directly been studied in humans so far. Based on a comprehensive neurobiological model of alcohol-induced aggression, we hypothesized that alcohol-induced aggression would go along with increased amygdala and ventral striatum reactivity and impaired functioning of the prefrontal cortex (PFC) under alcohol. We measured neural and behavioral correlates of alcohol-induced aggression in a provoking vs n...

  8. Neural adaptations underlying cross-education after unilateral strength training.

    Science.gov (United States)

    Fimland, Marius S; Helgerud, Jan; Solstad, Gerd Marie; Iversen, Vegard Moe; Leivseth, Gunnar; Hoff, Jan

    2009-12-01

    The purpose of this study was to investigate the effects of 4-week (16 sessions) unilateral, maximal isometric strength training on contralateral neural adaptations. Subjects were randomised to a strength training group (TG, n = 15) or to a control group (CG, n = 11). Both legs of both groups were tested for plantar flexion maximum voluntary isometric contractions (MVCs), surface electromyogram (EMG), H-reflexes and V-waves in the soleus (SOL) and gastrocnemius medialis (GM) superimposed during MVC and normalised by the M-wave (EMG/M(SUP), H(SUP)/M(SUP), V/M(SUP), respectively), before and after the training period. For the untrained leg, the TG increased compared to the CG for MVC torque (33%, P cross-education of strength.

  9. Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations

    Directory of Open Access Journals (Sweden)

    Susmita Mall

    2013-01-01

    Full Text Available This paper investigates the solution of Ordinary Differential Equations (ODEs with initial conditions using Regression Based Algorithm (RBA and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases. Initial weights are taken as combination of random as well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.

  10. A Mobile Asset Tracking System Architecture under Mobile-Stationary Co-Existing WSNs

    Science.gov (United States)

    Kim, Tae Hyon; Jo, Hyeong Gon; Lee, Jae Shin; Kang, Soon Ju

    2012-01-01

    The tracking of multiple wireless mobile nodes is not easy with current legacy WSN technologies, due to their inherent technical complexity, especially when heavy traffic and frequent movement of mobile nodes are encountered. To enable mobile asset tracking under these legacy WSN systems, it is necessary to design a specific system architecture that can manage numerous mobile nodes attached to mobile assets. In this paper, we present a practical system architecture including a communication protocol, a three-tier network, and server-side middleware for mobile asset tracking in legacy WSNs consisting of mobile-stationary co-existing infrastructures, and we prove the functionality of this architecture through careful evaluation in a test bed. Evaluation was carried out in a microwave anechoic chamber as well as on a straight road near our office. We evaluated communication mobility performance between mobile and stationary nodes, location-awareness performance, system stability under numerous mobile node conditions, and the successful packet transfer rate according to the speed of the mobile nodes. The results indicate that the proposed architecture is sufficiently robust for application in realistic mobile asset tracking services that require a large number of mobile nodes. PMID:23242277

  11. Organic solar cells under the BHJ approach using conventional/inverted architectures

    Science.gov (United States)

    Salinas, J. F.; Salto, C.; Maldonado, J. L.; Ramos-Ortíz, G.; Rodríguez, M.; Meneses-Nava, M. A.; Barbosa-García, Oracio; Farfán, N.; Santillan, R.

    2011-08-01

    The search of clean and renewable energy sources is one of the most important challenges that mankind confronts. Recently there has been a notable interest to develop organic photovoltaic (OPV) technology as a mean of renewable energy source since it combines low-cost and easy fabrication. Most of the efforts have been directed to increase the efficiency, leaving aside the durability of the organic materials, however, a new architecture known as inverted solar cell might bring a never seen durability (years) that could make possible large scale applications of this technology. Here are presented the results we achieved using both, the conventional and inverted architectures employing as organic donor (D) the very well known semi-conducting polymer P3HT, in mixtures with the acceptor (A) fullerene PC61BM. The morphology of thin polymer films prepared by using the spin coating technique was analyzed by AFM. For the conventional architecture the cells were fabricated following the structure ITO/PEDOT:PSS/P3HT:PC61BM/Wood´s metal, where the Wood´s metal cathode is an alloy that melts at 75 °C. For the inverted architecture the structure ITO/ZnO/P3HT:PC61BM /PEDOT:PSS/(Ag, Cu or Silver paint) was used, where ITO worked as cathode by switching its work function through the introduction of ZnO nanoparticles. Under tests using Xenon lamp irradiation at 100 mW/cm2, the conventional and the inverted architectures produced efficiencies of 1.75 % and 0.5 %, respectively. For both architectures the chosen back-contact materials (Wood´s metal and silver paint) allowed us to easily make the OPVs cells without the need of vacuum steps.

  12. Normative data on development of neural and behavioral mechanisms underlying attention orienting toward social-emotional stimuli: An exploratory study

    OpenAIRE

    Lindstrom, Kara; Guyer, Amanda E; Mogg, Karin; Bradley, Brendan P.; Fox, Nathan A.; Ernst, Monique; Nelson, Eric E.; Leibenluft, Ellen; Britton, Jennifer C.; Monk, Christopher S.; Pine, Daniel S.; Bar-Haim, Yair

    2009-01-01

    The ability of positive and negative facial signals to influence attention orienting is crucial to social functioning. Given the dramatic developmental change in neural architecture supporting social function, positive and negative facial cues may influence attention orienting differently in relatively young or old individuals. However, virtually no research examines such age-related differences in the neural circuitry supporting attention orienting to emotional faces. We examined age-related...

  13. An Inquiry into the Neural Plasticity Underlying Everyday Actions

    Directory of Open Access Journals (Sweden)

    Garrett Tisdale

    2017-11-01

    Full Text Available How does the brain change with respect to how we live our daily lives? Modern studies on how specific actions affect the anatomy of the brain have shown that different actions shape the way the brain is oriented. While individual studies might point towards these effects occurring in daily actions, the concept that morphological changes occur throughout the numerous fields of neuroplasticity based on daily actions has yet to become a well established and discussed phenomena. It is the goal of this article to view a few fields of neuroplasticity to answer this overarching question and review brain imaging studies indicating such morphological changes associated with the fields of neuroplasticity and everyday actions. To achieve this goal, a systematic approach revolving around scholarly search engines was used to briefly explore each studied field of interest. In this article, the activities of music production, video game play, and sleep are analyzed indicating such morphological change. These activities show changes to the respective areas of the brain in which the tasks are processed with a trend arising from the amount of time spent performing each action. It is shown from these fields of study that this classification of relating everyday actions to morphological change through neural plasticity does hold validity with respect to experimental studies.

  14. Common neural mechanisms underlying reversal learning by reward and punishment.

    Science.gov (United States)

    Xue, Gui; Xue, Feng; Droutman, Vita; Lu, Zhong-Lin; Bechara, Antoine; Read, Stephen

    2013-01-01

    Impairments in flexible goal-directed decisions, often examined by reversal learning, are associated with behavioral abnormalities characterized by impulsiveness and disinhibition. Although the lateral orbital frontal cortex (OFC) has been consistently implicated in reversal learning, it is still unclear whether this region is involved in negative feedback processing, behavioral control, or both, and whether reward and punishment might have different effects on lateral OFC involvement. Using a relatively large sample (N = 47), and a categorical learning task with either monetary reward or moderate electric shock as feedback, we found overlapping activations in the right lateral OFC (and adjacent insula) for reward and punishment reversal learning when comparing correct reversal trials with correct acquisition trials, whereas we found overlapping activations in the right dorsolateral prefrontal cortex (DLPFC) when negative feedback signaled contingency change. The right lateral OFC and DLPFC also showed greater sensitivity to punishment than did their left homologues, indicating an asymmetry in how punishment is processed. We propose that the right lateral OFC and anterior insula are important for transforming affective feedback to behavioral adjustment, whereas the right DLPFC is involved in higher level attention control. These results provide insight into the neural mechanisms of reversal learning and behavioral flexibility, which can be leveraged to understand risky behaviors among vulnerable populations.

  15. An Innovative Architecture of UTC GPS/INS System with Improved Performance under Severe Jamming

    Directory of Open Access Journals (Sweden)

    Xueyun Wang

    2014-01-01

    Full Text Available Ultratightly coupled (UTC architecture is believed to be the best architecture for Global Positioning System (GPS and Inertial Navigation System (INS integration system due to the advanced data fusion strategy and effective mutual assistance between the subsystems. However the performance of UTC GPS/INS system will be degraded by severe jamming interference, especially when low-grade inertial measurement unit (IMU is used. To solve this problem an innovative architecture of UTC GPS/INS system is proposed. Since GPS receiver’s antijamming ability is closely related to tracking loop bandwidth, adaptive tracking loop bandwidth based on the fuzzy logics is proposed to enhance antijamming ability for GPS receiver. The bandwidth will be adapted through a fuzzy logic controller according to the calculated carrier to noise intensity ratio (C/N0. Moreover, fuzzy adaptive integration Kalman filter (IKF is developed to improve estimation accuracy of IKF when measurement noises change. A simulation platform is established to evaluate the innovative architecture and results demonstrate that the proposed scheme improves navigation performance significantly under severe jamming conditions.

  16. CPU architecture for a fast and energy-saving calculation of convolution neural networks

    Science.gov (United States)

    Knoll, Florian J.; Grelcke, Michael; Czymmek, Vitali; Holtorf, Tim; Hussmann, Stephan

    2017-06-01

    One of the most difficult problem in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user only remains the state of the art method, which is the use of a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore a new processor on the basis of a field programmable gate array (FPGA) has been developed and is optimized for the application of deep learning. This processor is presented in this paper. The processor can be adapted for a particular application (in this paper to an organic farming application). The power consumption is only a fraction of a GPU application and should therefore be well suited for energy-saving applications.

  17. Deep architecture neural network-based real-time image processing for image-guided radiotherapy.

    Science.gov (United States)

    Mori, Shinichiro

    2017-08-01

    To develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising. Setup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training. More than 6 convolutional layers with convolutional kernels >5×5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with >3 convolutions each and rCNNs with >12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality. Use of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  18. Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

    Science.gov (United States)

    Tsehay, Yohannes K.; Lay, Nathan S.; Roth, Holger R.; Wang, Xiaosong; Kwak, Jin Tae; Turkbey, Baris I.; Pinto, Peter A.; Wood, Brad J.; Summers, Ronald M.

    2017-03-01

    Prostate cancer (PCa) is the second most common cause of cancer related deaths in men. Multiparametric MRI (mpMRI) is the most accurate imaging method for PCa detection; however, it requires the expertise of experienced radiologists leading to inconsistency across readers of varying experience. To increase inter-reader agreement and sensitivity, we developed a computer-aided detection (CAD) system that can automatically detect lesions on mpMRI that readers can use as a reference. We investigated a convolutional neural network based deep-learing (DCNN) architecture to find an improved solution for PCa detection on mpMRI. We adopted a network architecture from a state-of-the-art edge detector that takes an image as an input and produces an image probability map. Two-fold cross validation along with a receiver operating characteristic (ROC) analysis and free-response ROC (FROC) were used to determine our deep-learning based prostate-CAD's (CADDL) performance. The efficacy was compared to an existing prostate CAD system that is based on hand-crafted features, which was evaluated on the same test-set. CADDL had an 86% detection rate at 20% false-positive rate while the top-down learning CAD had 80% detection rate at the same false-positive rate, which translated to 94% and 85% detection rate at 10 false-positives per patient on the FROC. A CNN based CAD is able to detect cancerous lesions on mpMRI of the prostate with results comparable to an existing prostate-CAD showing potential for further development.

  19. The dynamics of architectural complexity on coral reefs under climate change.

    Science.gov (United States)

    Bozec, Yves-Marie; Alvarez-Filip, Lorenzo; Mumby, Peter J

    2015-01-01

    One striking feature of coral reef ecosystems is the complex benthic architecture which supports diverse and abundant fauna, particularly of reef fish. Reef-building corals are in decline worldwide, with a corresponding loss of live coral cover resulting in a loss of architectural complexity. Understanding the dynamics of the reef architecture is therefore important to envision the ability of corals to maintain functional habitats in an era of climate change. Here, we develop a mechanistic model of reef topographical complexity for contemporary Caribbean reefs. The model describes the dynamics of corals and other benthic taxa under climate-driven disturbances (hurricanes and coral bleaching). Corals have a simplified shape with explicit diameter and height, allowing species-specific calculation of their colony surface and volume. Growth and the mechanical (hurricanes) and biological erosion (parrotfish) of carbonate skeletons are important in driving the pace of extension/reduction in the upper reef surface, the net outcome being quantified by a simple surface roughness index (reef rugosity). The model accurately simulated the decadal changes of coral cover observed in Cozumel (Mexico) between 1984 and 2008, and provided a realistic hindcast of coral colony-scale (1-10 m) changing rugosity over the same period. We then projected future changes of Caribbean reef rugosity in response to global warming. Under severe and frequent thermal stress, the model predicted a dramatic loss of rugosity over the next two or three decades. Critically, reefs with managed parrotfish populations were able to delay the general loss of architectural complexity, as the benefits of grazing in maintaining living coral outweighed the bioerosion of dead coral skeletons. Overall, this model provides the first explicit projections of reef rugosity in a warming climate, and highlights the need of combining local (protecting and restoring high grazing) to global (mitigation of greenhouse gas

  20. Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

    Science.gov (United States)

    Jurrus, Elizabeth; Paiva, Antonio R C; Watanabe, Shigeki; Anderson, James R; Jones, Bryan W; Whitaker, Ross T; Jorgensen, Erik M; Marc, Robert E; Tasdizen, Tolga

    2010-12-01

    Study of nervous systems via the connectome, the map of connectivities of all neurons in that system, is a challenging problem in neuroscience. Towards this goal, neurobiologists are acquiring large electron microscopy datasets. However, the shear volume of these datasets renders manual analysis infeasible. Hence, automated image analysis methods are required for reconstructing the connectome from these very large image collections. Segmentation of neurons in these images, an essential step of the reconstruction pipeline, is challenging because of noise, anisotropic shapes and brightness, and the presence of confounding structures. The method described in this paper uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context provided by the previous network to improve detection accuracy. We develop the method of serial ANNs and show that the learned context does improve detection over traditional ANNs. We also demonstrate advantages over previous membrane detection methods. The results are a significant step towards an automated system for the reconstruction of the connectome. Copyright 2010 Elsevier B.V. All rights reserved.

  1. 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.

  2. The neural architecture of age-related dual-task interferences.

    Science.gov (United States)

    Chmielewski, Witold X; Yildiz, Ali; Beste, Christian

    2014-01-01

    In daily life elderly adults exhibit deficits when dual-tasking is involved. So far these deficits have been verified on a behavioral level in dual-tasking. Yet, the neuronal architecture of these deficits in aging still remains to be explored especially when late-middle aged individuals around 60 years of age are concerned. Neuroimaging studies in young participants concerning dual-tasking were, among others, related to activity in middle frontal (MFG) and superior frontal gyrus (SFG) and the anterior insula (AI). According to the frontal lobe hypothesis of aging, alterations in these frontal regions (i.e., SFG and MFG) might be responsible for cognitive deficits. We measured brain activity using fMRI, while examining age-dependent variations in dual-tasking by utilizing the PRP (psychological refractory period) test. Behavioral data showed an increasing PRP effect in late-middle aged adults. The results suggest the age-related deteriorated performance in dual-tasking, especially in conditions of risen complexity. These effects are related to changes in networks involving the AI, the SFG and the MFG. The results suggest that different cognitive subprocesses are affected that mediate the observed dual-tasking problems in late-middle aged individuals.

  3. Role of graph architecture in controlling dynamical networks with applications to neural systems

    Science.gov (United States)

    Kim, Jason Z.; Soffer, Jonathan M.; Kahn, Ari E.; Vettel, Jean M.; Pasqualetti, Fabio; Bassett, Danielle S.

    2018-01-01

    Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviours such as synchronization. Although descriptions of these behaviours are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behaviour. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain's control performance by removing single edges in the network. Generally, our results ground the expectation of a control system's behaviour in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

  4. Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching.

    Science.gov (United States)

    Wu, Yuanyuan; Cao, Jinde; Li, Qingbo; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-01-01

    This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Shapes and geometries underlying the religious architecture in the 18th century

    Directory of Open Access Journals (Sweden)

    Sebastiano Giuliano

    2015-07-01

    of great renovation, known as the” Sicilian Baroque”.The second part of this research makes use of a very accurate graphic analysis aiming at understanding the sizing and proportioning methodologies which were used in the design phase.Sizes and proportions are essential to understand the work in its overall shape; they also make comparisons possible, regardless the sculptural and decorative apparatus and its architectural shape.The discovery  of the underlying geometrical and reference patterns allows the researchers to make some hypothesis on the proportions of the project, even if there are no drawings.; the main goal is to understand the origin of the project so as to identify the simple pattern to which all probable similarities or differences can be referred. Therefore the geometrical analysis is the way through which it is possible to study the design method of valuable works in Eastern Sicily.As altars are integral parts of churches, "actual architectures inside the architecture", expression of the complexity and the conformative dynamism of the baroque architecture, this research is based on these valuable elements, whose intermingling of shapes, functions and meanings, leads, from a figurative point of view, to some constructions which are very similar to facades.If the project design is a graphic-historical document telling about the geometrical apparatus of the architectural element in its formal and symbolic relationships, the survey carried out through advanced technologies is fundamental in the backwards survey.The comparison with the works of the age and the graphic analysis of the architectures on both small and big scales, tries to reveal and give back all those relationships which are at the origin of the design project and which have granted their conveyance to the different dimensional levels and their disclosure in  relatively distant areas.

  6. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

    Science.gov (United States)

    Cocos, Anne; Fiks, Alexander G; Masino, Aaron J

    2017-07-01

    Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets.

  7. Studies of Neuronal Gene Regulation Controlling the Molecular Mechanisms Underlying Neural Plasticity.

    Science.gov (United States)

    Fukuchi, Mamoru

    2017-01-01

    The regulation of the development and function of the nervous system is not preprogramed but responds to environmental stimuli to change neural development and function flexibly. This neural plasticity is a characteristic property of the nervous system. For example, strong synaptic activation evoked by environmental stimuli leads to changes in synaptic functions (known as synaptic plasticity). Long-lasting synaptic plasticity is one of the molecular mechanisms underlying long-term learning and memory. Since discovering the role of the transcription factor cAMP-response element-binding protein in learning and memory, it has been widely accepted that gene regulation in neurons contributes to long-lasting changes in neural functions. However, it remains unclear how synaptic activation is converted into gene regulation that results in long-lasting neural functions like long-term memory. We continue to address this question. This review introduces our recent findings on the gene regulation of brain-derived neurotrophic factor and discusses how regulation of the gene participates in long-lasting changes in neural functions.

  8. Neural mechanisms underlying the integration of situational information into attribution outcomes

    OpenAIRE

    Brosch, Tobias; Schiller, Daniela; Mojdehbakhsh, Rachel; Uleman, James S.; Phelps, Elizabeth A.

    2013-01-01

    When forming impressions and trying to figure out why other people behave the way they do, we should take into account not only dispositional factors (i.e. personality traits) but also situational constraints as potential causes for a behavior. However, in their attributions, people often ignore the importance of situational factors. To investigate the neural mechanisms underlying the integration of situational information into attributions, we decomposed the attribution process by separately...

  9. Architectural prototyping

    DEFF Research Database (Denmark)

    Bardram, Jakob Eyvind; Christensen, Henrik Bærbak; Hansen, Klaus Marius

    2004-01-01

    A major part of software architecture design is learning how specific architectural designs balance the concerns of stakeholders. We explore the notion of "architectural prototypes", correspondingly architectural prototyping, as a means of using executable prototypes to investigate stakeholders......' concerns with respect to a system under development. An architectural prototype is primarily a learning and communication vehicle used to explore and experiment with alternative architectural styles, features, and patterns in order to balance different architectural qualities. The use of architectural...... prototypes in the development process is discussed, and we argue that such prototypes can play a role throughout the entire process. The use of architectural prototypes is illustrated by three distinct cases of creating software systems. We argue that architectural prototyping can provide key insights...

  10. Architectural Prototyping

    DEFF Research Database (Denmark)

    Bardram, Jakob; Christensen, Henrik Bærbak; Hansen, Klaus Marius

    2004-01-01

    A major part of software architecture design is learning how specific architectural designs balance the concerns of stakeholders. We explore the notion of "architectural prototypes", correspondingly architectural prototyping, as a means of using executable prototypes to investigate stakeholders......' concerns with respect to a system under development. An architectural prototype is primarily a learning and communication vehicle used to explore and experiment with alternative architectural styles, features, and patterns in order to balance different architectural qualities. The use of architectural...... prototypes in the development process is discussed, and we argue that such prototypes can play a role throughout the entire process. The use of architectural prototypes is illustrated by three distinct cases of creating software systems. We argue that architectural prototyping can provide key insights...

  11. Anger under control: neural correlates of frustration as a function of trait aggression.

    Directory of Open Access Journals (Sweden)

    Christina M Pawliczek

    Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

  12. Soldiers and Marksmen Under Fire: Monitoring Performance with Neural Correlates of Small Arms Fire Localization

    Directory of Open Access Journals (Sweden)

    Jason eSherwin

    2013-03-01

    Full Text Available Important decisions in the heat of battle occur rapidly and a key aptitude of a good combat soldier is the ability to determine whether he is under fire. This rapid decision requires the soldier to make a judgment in a fraction of a second, based on a barrage of multisensory cues coming from the auditory, tactile and visual domains. The present study uses an auditory oddball paradigm to examine listener ability to differentiate shooter locations from audio recordings of small arms fire. More importantly, we address the neural correlates involved in this rapid decision process by employing single-trial analysis of electroencephalography (EEG. In particular, we examine small arms expert listeners as they differentiate the sounds of small arms firing events recorded at different observer positions relative to a shooter. Using signal detection theory, we find clear neural signatures related to shooter firing angle by identifying the times of neural discrimination on a trial-to-trial basis. Similar to previous results in oddball experiments, we find common windows relative to the response and the stimulus when neural activity discriminates between target stimuli (forward fire: observer 0° to firing angle vs. standards (off-axis fire: observer 90° to firing angle. We also find, using windows of maximum discrimination, that auditory target vs. standard discrimination yields neural sources in Brodmann Area 19 (BA 19, i.e., in the visual cortex. In summary, we show that single-trial analysis of EEG yields informative scalp distributions and source current localization of discriminating activity when the small arms experts discriminate between forward and off-axis fire observer positions. Furthermore, this perceptual decision implicates brain regions involved in visual processing, even though the task is purely auditory. Finally, we utilize these techniques to quantify the level of expertise in these subjects for the chosen task, having implications for

  13. [Men of the sugarcane fields and their hospitals: the architecture of health under the Estado Novo].

    Science.gov (United States)

    Monteiro, Marcia Rocha

    2011-12-01

    The article explores the emergence of an architectural heritage in the realm of healthcare assistance for workers in the sugarcane agroindustry in Brazil following enactment of the law known as the Estatuto da Lavoura Canavieira (1941), under the auspices of the Instituto do Açúcar e do Álcool and as part of Estado Novo policies (1937-1945). The institute proposed solutions based on surveys conducted at sugarcane mills in cane-producing states and on the medical and hospital system adopted by the institute's enlightened bureaucracy in the 1940s, which took the U.S. system as its model. Special focus is given to the central hospitals in Pernambuco and especially in Alagoas, which opposed institute guidelines.

  14. Genetic architecture of factors underlying partial resistance to Alternaria leaf blight in carrot.

    Science.gov (United States)

    Le Clerc, Valérie; Pawelec, Anna; Birolleau-Touchard, Christelle; Suel, Anita; Briard, Mathilde

    2009-05-01

    In most production areas, Alternaria leaf blight (ALB) is recognized as the most common and destructive foliage disease in carrot. To assess the genetic architecture of carrot ALB resistance, two parental coupling maps were developed with similar number of dominant markers (around 70), sizes (around 650 cM), densities (around 9.5 cM), and marker composition. The F(2:3) progenies were evaluated in field and tunnel for two scoring dates. The continuous distribution of the disease severity value indicated that ALB resistance is under polygenic control. Three QTLs regions were found on three linkage groups. Two of them were tunnel or field specific and were detected only at the second screening date suggesting that the expression of these two QTLs regions involved in resistance to Alternaria dauci might depend on environment and delay after infection.

  15. Neural mechanisms underlying cognitive control of men with lifelong antisocial behavior.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Mu Ller, Bernhard; Forsting, Michael; Gizewski, Elke; Leygraf, Norbert; Hodgins, Sheilagh

    2014-04-30

    Results of meta-analyses suggested subtle deficits in cognitive control among antisocial individuals. Because almost all studies focused on children with conduct problems or adult psychopaths, however, little is known about cognitive control mechanisms among the majority of persistent violent offenders who present an antisocial personality disorder (ASPD). The present study aimed to determine whether offenders with ASPD, relative to non-offenders, display dysfunction in the neural mechanisms underlying cognitive control and to assess the extent to which these dysfunctions are associated with psychopathic traits and trait impulsivity. Participants comprised 21 violent offenders and 23 non-offenders who underwent event-related functional magnetic resonance imaging while performing a non-verbal Stroop task. The offenders, relative to the non-offenders, exhibited reduced response time interference and a different pattern of conflict- and error-related activity in brain areas involved in cognitive control, attention, language, and emotion processing, that is, the anterior cingulate, dorsolateral prefrontal, superior temporal and postcentral cortices, putamen, thalamus, and amygdala. Moreover, between-group differences in behavioural and neural responses revealed associations with core features of psychopathy and attentional impulsivity. Thus, the results of the present study confirmed the hypothesis that offenders with ASPD display alterations in the neural mechanisms underlying cognitive control and that those alterations relate, at least in part, to personality characteristics. Copyright © 2014. Published by Elsevier Ireland Ltd.

  16. Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun [College of Medicine, Seoul National University, Seoul (Korea, Republic of)

    2004-07-01

    Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9{+-}4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7{+-} 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder.

  17. Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism.

    Science.gov (United States)

    Li, Lulu; Ho, Daniel W C; Cao, Jinde; Lu, Jianquan

    2016-04-01

    Cluster synchronization is a typical collective behavior in coupled dynamical systems, where the synchronization occurs within one group, while there is no synchronization among different groups. In this paper, under event-based mechanism, pinning cluster synchronization in an array of coupled neural networks is studied. A new event-triggered sampled-data transmission strategy, where only local and event-triggering states are utilized to update the broadcasting state of each agent, is proposed to realize cluster synchronization of the coupled neural networks. Furthermore, a self-triggered pinning cluster synchronization algorithm is proposed, and a set of iterative procedures is given to compute the event-triggered time instants. Hence, this will reduce the computational load significantly. Finally, an example is given to demonstrate the effectiveness of the theoretical results. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  18. Engagement of neural circuits underlying 2D spatial navigation in a rodent virtual reality system.

    Science.gov (United States)

    Aronov, Dmitriy; Tank, David W

    2014-10-22

    Virtual reality (VR) enables precise control of an animal's environment and otherwise impossible experimental manipulations. Neural activity in rodents has been studied on virtual 1D tracks. However, 2D navigation imposes additional requirements, such as the processing of head direction and environment boundaries, and it is unknown whether the neural circuits underlying 2D representations can be sufficiently engaged in VR. We implemented a VR setup for rats, including software and large-scale electrophysiology, that supports 2D navigation by allowing rotation and walking in any direction. The entorhinal-hippocampal circuit, including place, head direction, and grid cells, showed 2D activity patterns similar to those in the real world. Furthermore, border cells were observed, and hippocampal remapping was driven by environment shape, suggesting functional processing of virtual boundaries. These results illustrate that 2D spatial representations can be engaged by visual and rotational vestibular stimuli alone and suggest a novel VR tool for studying rat navigation.

  19. On the visual system's architecture underlying binocular rivalry and motion perception

    Science.gov (United States)

    van Boxtel, J. J. A.

    2008-01-01

    Our everyday visual perception is supported by a complicated set of interactions between different brain areas. These areas often have a specific function. A lot of communication takes place between, and also within, these areas. The total set of interactions between and within the different brain areas is the architecture of the visual system. Although a lot is known about the visual system's architecture from histological research, there is still much unknown about the functional architecture (about how the interactions actually work). In the first part of the thesis, I have investigated functional architecture underlying human perception of motion, and in particular the speed of motion. In the second part, I have researched the human perception in circumstances in which a large conflict exists between the inputs in the two eyes (a circumstance in which binocular rivalry results). Motion consists of two components: a direction and a speed. It is generally accepted that the direction-component is processed by a single system (that is, a collection of interconnected brain areas). However, for the speed of motion no such general agreement exists. It has often been proposed that speed-processing is supported by two (or more) systems: one for low speeds and one for high speeds. In this thesis I have proposed that the data know up to now also permit a simpler explanation, namely that a single system processes the entire range of visible speeds. I have tested this prediction with computational models and experiments. The results point out that a single speed-sensitive system indeed may explain the data in the literature and the newly-obtained data of this thesis. Binocular rivalry results when our two eyes receive conflicting images. In stead seeing an average of the two images, we see a continuing alternation of the images. The images fight, as is were, for dominance, hence the name binocular rivalry. It has been well-studied how spatial aspects of stimulation

  20. Metaplasticity as a Neural Substrate for Adaptive Learning and Choice under Uncertainty.

    Science.gov (United States)

    Farashahi, Shiva; Donahue, Christopher H; Khorsand, Peyman; Seo, Hyojung; Lee, Daeyeol; Soltani, Alireza

    2017-04-19

    Value-based decision making often involves integration of reward outcomes over time, but this becomes considerably more challenging if reward assignments on alternative options are probabilistic and non-stationary. Despite the existence of various models for optimally integrating reward under uncertainty, the underlying neural mechanisms are still unknown. Here we propose that reward-dependent metaplasticity (RDMP) can provide a plausible mechanism for both integration of reward under uncertainty and estimation of uncertainty itself. We show that a model based on RDMP can robustly perform the probabilistic reversal learning task via dynamic adjustment of learning based on reward feedback, while changes in its activity signal unexpected uncertainty. The model predicts time-dependent and choice-specific learning rates that strongly depend on reward history. Key predictions from this model were confirmed with behavioral data from non-human primates. Overall, our results suggest that metaplasticity can provide a neural substrate for adaptive learning and choice under uncertainty. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Neural mechanisms underlying transcranial direct current stimulation in aphasia: A feasibility study.

    Directory of Open Access Journals (Sweden)

    Lena eUlm

    2015-10-01

    Full Text Available Little is known about the neural mechanisms by which transcranial direct current stimulation (tDCS impacts on language processing in post-stroke aphasia. This was addressed in a proof-of-principle study that explored the effects of tDCS application in aphasia during simultaneous functional magnetic resonance imaging (fMRI. We employed a single subject, cross-over, sham-tDCS controlled design and the stimulation was administered to an individualized perilesional stimulation site that was identified by a baseline fMRI scan and a picture naming task. Peak activity during the baseline scan was located in the spared left inferior frontal gyrus (IFG and this area was stimulated during a subsequent cross-over phase. tDCS was successfully administered to the target region and anodal- vs. sham-tDCS resulted in selectively increased activity at the stimulation site. Our results thus demonstrate that it is feasible to precisely target an individualized stimulation site in aphasia patients during simultaneous fMRI which allows assessing the neural mechanisms underlying tDCS application. The functional imaging results of this case report highlight one possible mechanism that may have contributed to beneficial behavioural stimulation effects in previous clinical tDCS trials in aphasia. In the future, this approach will allow identifying distinct patterns of stimulation effects on neural processing in larger cohorts of patients. This may ultimately yield information about the variability of tDCS-effects on brain functions in aphasia.

  2. Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

    Directory of Open Access Journals (Sweden)

    Pei-Chen Lo

    2013-01-01

    Full Text Available This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph. Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y, the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording, in Chan meditation (stage M, and the unique Chakra-focusing practice (stage C. Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

  3. The impact of abacus training on working memory and underlying neural correlates in young adults.

    Science.gov (United States)

    Dong, Shanshan; Wang, Chunjie; Xie, Ye; Hu, Yuzheng; Weng, Jian; Chen, Feiyan

    2016-09-22

    Abacus-based mental calculation (AMC) activates the frontoparietal areas largely overlapping with the working memory (WM) network. Given the critical role of WM in cognition, how to improve WM capability has attracted intensive attention in past years. However, it is still unclear whether WM could be enhanced by AMC training. The current research thus explored the impact of AMC training on verbal and visuospatial WM, as well as the underlying neural basis. Participants were randomly assigned to an abacus group and a control group. Their verbal WM was evaluated by digit/letter memory span (DMS/LMS) tests, and visuospatial WM was assessed by a visuospatial n-back task. Neural activity during the n-back task was examined using functional MRI. Our results showed reliable improvements of both verbal and visuospatial WM in the abacus group after 20-day AMC training but not in the control. In addition, the n-back task-induced activations in the right frontoparietal circuitry and left occipitotemporal junction (OTJ) declined as a result of training. Notably, the decreases in activity were positively correlated with performance gains across trained participants. These results suggest AMC training not only improves calculating skills but also have the potential to promote individuals' WM capabilities, which is associated with the functional plasticity of the common neural substrates. Copyright © 2016 IBRO. All rights reserved.

  4. Neural Adaptive Decentralized Coordinated Control with Fault-Tolerant Capability for DFIGs under Stochastic Disturbances

    Directory of Open Access Journals (Sweden)

    Xiao-ming Li

    2017-01-01

    Full Text Available At present, most methodologies proposed to control over double fed induction generators (DFIGs are based on single machine model, where the interactions from network have been neglected. Considering this, this paper proposes a decentralized coordinated control of DFIG based on the neural interaction measurement observer. An artificial neural network is employed to approximate the nonlinear model of DFIG, and the approximation error due to neural approximation has been considered. A robust stabilization technique is also proposed to override the effect of approximation error. A H2 controller and a H∞ controller are employed to achieve specified engineering purposes, respectively. Then, the controller design is formulated as a mixed H2/H∞ optimization with constrains of regional pole placement and proportional plus integral (PI structure, which can be solved easily by using linear matrix inequality (LMI technology. The results of simulations are presented and discussed, which show the capabilities of DFIG with the proposed control strategy to fault-tolerant control of the maximum power point tracking (MPPT under slight sensor faults, low voltage ride-through (LVRT, and its contribution to power system transient stability support.

  5. Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

    Science.gov (United States)

    Chang, Chih-Hao

    2013-01-01

    This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph). Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y), the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording), in Chan meditation (stage M), and the unique Chakra-focusing practice (stage C). Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group. PMID:24489583

  6. Neural mechanisms underlying the cost of task switching: an ERP study.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: When switching from one task to a new one, reaction times are prolonged. This phenomenon is called switch cost (SC. Researchers have recently used several kinds of task-switching paradigms to uncover neural mechanisms underlying the SC. Task-set reconfiguration and passive dissipation of a previously relevant task-set have been reported to contribute to the cost of task switching. METHODOLOGY/PRINCIPAL FINDINGS: An unpredictable cued task-switching paradigm was used, during which subjects were instructed to switch between a color and an orientation discrimination task. Electroencephalography (EEG and behavioral measures were recorded in 14 subjects. Response-stimulus interval (RSI and cue-stimulus interval (CSI were manipulated with short and long intervals, respectively. Switch trials delayed reaction times (RTs and increased error rates compared with repeat trials. The SC of RTs was smaller in the long CSI condition. For cue-locked waveforms, switch trials generated a larger parietal positive event-related potential (ERP, and a larger slow parietal positivity compared with repeat trials in the short and long CSI condition. Neural SC of cue-related ERP positivity was smaller in the long RSI condition. For stimulus-locked waveforms, a larger switch-related central negative ERP component was observed, and the neural SC of the ERP negativity was smaller in the long CSI. Results of standardized low resolution electromagnetic tomography (sLORETA for both ERP positivity and negativity showed that switch trials evoked larger activation than repeat trials in dorsolateral prefrontal cortex (DLPFC and posterior parietal cortex (PPC. CONCLUSIONS/SIGNIFICANCE: The results provide evidence that both RSI and CSI modulate the neural activities in the process of task-switching, but that these have a differential role during task-set reconfiguration and passive dissipation of a previously relevant task-set.

  7. Genetic architecture of context processing in late middle age: more than one underlying mechanism.

    Science.gov (United States)

    Kremen, William S; Panizzon, Matthew S; Xian, Hong; Barch, Deanna M; Franz, Carol E; Grant, Michael D; Toomey, Rosemary; Lyons, Michael J

    2011-12-01

    Studies comparing young and older adults suggest a deficit in processing context information as a key mechanism underlying cognitive aging. However, the genetic architecture of context processing has not been examined. Consistent with previous results, we found evidence of functionally dissociable components of context processing accuracy in 1127 late middle-aged twins ages 51-60. One component emphasizes use of context cues to prepare responses (proactive cognitive control), and the other emphasizes adjustment of responses after probes are presented (reactive control). Approximately one-quarter of the variance in each component was accounted for by genes. Multivariate twin analysis indicated that genetic factors underlying two important components of context processing were independent of one another, thus implicating more than one underlying mechanism. Slower reaction time (RT) on noncontext processing trials was positively correlated with errors on the strongly proactive control component on which young adults outperform older adults, but RT was negatively correlated with errors on the strongly reactive control component on which older adults perform better. Although this RT measure was uncorrelated with chronological age in our age-homogeneous sample, slower RT was associated with performance patterns that were more like older adults. However, this did not generalize to other processing speed measures. Genetic correlations, which reflect shared genetic variance, paralleled the phenotypic correlations. There was also a positive genetic correlation between general cognitive ability and accuracy on the proactive control component, but there were still mostly distinct genetic influences underlying these measures. In contrast, the reactive control component was unrelated to general cognitive ability.

  8. Handedness is related to neural mechanisms underlying hemispheric lateralization of face processing

    Science.gov (United States)

    Frässle, Stefan; Krach, Sören; Paulus, Frieder Michel; Jansen, Andreas

    2016-06-01

    While the right-hemispheric lateralization of the face perception network is well established, recent evidence suggests that handedness affects the cerebral lateralization of face processing at the hierarchical level of the fusiform face area (FFA). However, the neural mechanisms underlying differential hemispheric lateralization of face perception in right- and left-handers are largely unknown. Using dynamic causal modeling (DCM) for fMRI, we aimed to unravel the putative processes that mediate handedness-related differences by investigating the effective connectivity in the bilateral core face perception network. Our results reveal an enhanced recruitment of the left FFA in left-handers compared to right-handers, as evidenced by more pronounced face-specific modulatory influences on both intra- and interhemispheric connections. As structural and physiological correlates of handedness-related differences in face processing, right- and left-handers varied with regard to their gray matter volume in the left fusiform gyrus and their pupil responses to face stimuli. Overall, these results describe how handedness is related to the lateralization of the core face perception network, and point to different neural mechanisms underlying face processing in right- and left-handers. In a wider context, this demonstrates the entanglement of structurally and functionally remote brain networks, suggesting a broader underlying process regulating brain lateralization.

  9. Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

    Directory of Open Access Journals (Sweden)

    Yosefu Arime

    Full Text Available Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days of either saline or PCP (10 mg/kg: (1 a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2 brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

  10. Abnormal neural activation patterns underlying working memory impairment in chronic phencyclidine-treated mice.

    Science.gov (United States)

    Arime, Yosefu; Akiyama, Kazufumi

    2017-01-01

    Working memory impairment is a hallmark feature of schizophrenia and is thought be caused by dysfunctions in the prefrontal cortex (PFC) and associated brain regions. However, the neural circuit anomalies underlying this impairment are poorly understood. The aim of this study is to assess working memory performance in the chronic phencyclidine (PCP) mouse model of schizophrenia, and to identify the neural substrates of working memory. To address this issue, we conducted the following experiments for mice after withdrawal from chronic administration (14 days) of either saline or PCP (10 mg/kg): (1) a discrete paired-trial variable-delay task in T-maze to assess working memory, and (2) brain-wide c-Fos mapping to identify activated brain regions relevant to this task performance either 90 min or 0 min after the completion of the task, with each time point examined under working memory effort and basal conditions. Correct responses in the test phase of the task were significantly reduced across delays (5, 15, and 30 s) in chronic PCP-treated mice compared with chronic saline-treated controls, suggesting delay-independent impairments in working memory in the PCP group. In layer 2-3 of the prelimbic cortex, the number of working memory effort-elicited c-Fos+ cells was significantly higher in the chronic PCP group than in the chronic saline group. The main effect of working memory effort relative to basal conditions was to induce significantly increased c-Fos+ cells in the other layers of prelimbic cortex and the anterior cingulate and infralimbic cortex regardless of the different chronic regimens. Conversely, this working memory effort had a negative effect (fewer c-Fos+ cells) in the ventral hippocampus. These results shed light on some putative neural networks relevant to working memory impairments in mice chronically treated with PCP, and emphasize the importance of the layer 2-3 of the prelimbic cortex of the PFC.

  11. Neural substrates underlying motor skill learning in chronic hemiparetic stroke patients.

    Science.gov (United States)

    Lefebvre, Stéphanie; Dricot, Laurence; Laloux, Patrice; Gradkowski, Wojciech; Desfontaines, Philippe; Evrard, Frédéric; Peeters, André; Jamart, Jacques; Vandermeeren, Yves

    2015-01-01

    Motor skill learning is critical in post-stroke motor recovery, but little is known about its underlying neural substrates. Recently, using a new visuomotor skill learning paradigm involving a speed/accuracy trade-off in healthy individuals we identified three subpopulations based on their behavioral trajectories: fitters (in whom improvement in speed or accuracy coincided with deterioration in the other parameter), shifters (in whom speed and/or accuracy improved without degradation of the other parameter), and non-learners. We aimed to identify the neural substrates underlying the first stages of motor skill learning in chronic hemiparetic stroke patients and to determine whether specific neural substrates were recruited in shifters versus fitters. During functional magnetic resonance imaging (fMRI), 23 patients learned the visuomotor skill with their paretic upper limb. In the whole-group analysis, correlation between activation and motor skill learning was restricted to the dorsal prefrontal cortex of the damaged hemisphere (DLPFCdamh: r = -0.82) and the dorsal premotor cortex (PMddamh: r = 0.70); the correlations was much lesser (-0.16 0.25) in the other regions of interest. In a subgroup analysis, significant activation was restricted to bilateral posterior parietal cortices of the fitters and did not correlate with motor skill learning. Conversely, in shifters significant activation occurred in the primary sensorimotor cortexdamh and supplementary motor areadamh and in bilateral PMd where activation changes correlated significantly with motor skill learning (r = 0.91). Finally, resting-state activity acquired before learning showed a higher functional connectivity in the salience network of shifters compared with fitters (qFDR skill learning with the paretic upper limb in chronic hemiparetic stroke patients, with a key role of bilateral PMd.

  12. Housing helpful invaders: the evolutionary and molecular architecture underlying plant root-mutualist microbe interactions.

    Science.gov (United States)

    Lagunas, B; Schäfer, P; Gifford, M L

    2015-04-01

    Plant root rhizosphere interactions with mutualistic microbes are diverse and numerous, having evolved over time in response to selective pressures on plants to attain anchorage and nutrients. These relationships can be considered to be formed through a combination of architectural connections: molecular architecture interactions that control root-microbe perception and regulate the balance between host and symbiont and developmental architecture interactions that enable the microbes to be 'housed' in the root and enable the exchange of compounds. Recent findings that help to understand the common architecture that exists between nodulation and mycorrhizal interactions, and how this architecture could be re-tuned to develop new symbioses, are discussed here. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  13. Neural Activities Underlying the Feedback Express Salience Prediction Errors for Appetitive and Aversive Stimuli.

    Science.gov (United States)

    Gu, Yan; Hu, Xueping; Pan, Weigang; Yang, Chun; Wang, Lijun; Li, Yiyuan; Chen, Antao

    2016-10-03

    Feedback information is essential for us to adapt appropriately to the environment. The feedback-related negativity (FRN), a frontocentral negative deflection after the delivery of feedback, has been found to be larger for outcomes that are worse than expected, and it reflects a reward prediction error derived from the midbrain dopaminergic projections to the anterior cingulate cortex (ACC), as stated in reinforcement learning theory. In contrast, the prediction of response-outcome (PRO) model claims that the neural activity in the mediofrontal cortex (mPFC), especially the ACC, is sensitive to the violation of expectancy, irrespective of the valence of feedback. Additionally, increasing evidence has demonstrated significant activities in the striatum, anterior insula and occipital lobe for unexpected outcomes independently of their valence. Thus, the neural mechanism of the feedback remains under dispute. Here, we investigated the feedback with monetary reward and electrical pain shock in one task via functional magnetic resonance imaging. The results revealed significant prediction-error-related activities in the bilateral fusiform gyrus, right middle frontal gyrus and left cingulate gyrus for both money and pain. This implies that some regions underlying the feedback may signal a salience prediction error rather than a reward prediction error.

  14. A comparison of neural correlates underlying social cognition in Klinefelter syndrome and autism.

    Science.gov (United States)

    Brandenburg-Goddard, Marcia N; van Rijn, Sophie; Rombouts, Serge A R B; Veer, Ilya M; Swaab, Hanna

    2014-12-01

    Klinefelter syndrome (KS) is a genetic syndrome characterized by the presence of an extra X chromosome that appears to increase the risk of psychopathology, such as autism symptoms. This study used functional magnetic resonance imaging to determine underlying mechanisms related to this risk, with the aim of gaining insight into neural mechanisms behind social-cognitive dysfunction in KS and autism, and understanding similarities and differences in social information processing deficits. Fourteen boys with KS, seventeen boys with autism spectrum disorders (ASD) and nineteen non-clinical male controls aged 10-18 years were scanned while matching and labeling facial expressions (i.e. face processing and affect labeling, respectively). No group differences in neural activation were found during face processing. However, during affect labeling, the ASD group showed increased activation in the amygdala compared with controls, while the KS group showed increased activation in frontal areas compared with both controls and the ASD group. No group differences in task performance were found. Although behavioral symptoms of social dysfunction appear similar both in boys with KS and ASD, this is the first study to demonstrate different underlying etiologies. These results may aid in identifying different pathways to autism symptoms, which may help understanding variability within the ASD spectrum. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  15. Determination of the Genetic Architecture Underlying Short Wavelength Sensitivity in Lake Malawi Cichlids.

    Science.gov (United States)

    Nandamuri, Sri Pratima; Dalton, Brian E; Carleton, Karen L

    2017-06-01

    African cichlids are an exemplary system to study organismal diversity and rapid speciation. Species differ in external morphology including jaw shape and body coloration, but also differ in sensory systems including vision. All cichlids have 7 cone opsin genes with species differing broadly in which opsins are expressed. The differential opsin expression results in closely related species with substantial differences in spectral sensitivity of their photoreceptors. In this work, we take a first step in determining the genetic basis of opsin expression in cichlids. Using a second generation cross between 2 species with different opsin expression patterns, we make a conservative estimate that short wavelength opsin expression is regulated by a few loci. Genetic mapping in 96 F2 hybrids provides clear evidence of a cis-regulatory region for SWS1 opsin that explains 34% of the variation in expression between the 2 species. Additionally, in situ hybridization has shown that SWS1 and SWS2B opsins are coexpressed in individual single cones in the retinas of F2 progeny. Results from this work will contribute to a better understanding of the genetic architecture underlying opsin expression. This knowledge will help answer long-standing questions about the evolutionary processes fundamental to opsin expression variation and how this contributes to adaptive cichlid divergence. © The American Genetic Association 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  16. Phenotyping Root System Architecture of Cotton (Gossypium barbadense L. Grown Under Salinity

    Directory of Open Access Journals (Sweden)

    Mottaleb Shady A.

    2017-12-01

    Full Text Available Soil salinity causes an annual deep negative impact to the global agricultural economy. In this study, the effects of salinity on early seedling physiology of two Egyptian cotton (Gossypium barbadense L. cultivars differing in their salinity tolerance were examined. Also the potential use of a low cost mini-rhizotron system to measure variation in root system architecture (RSA traits existing in both cultivars was assessed. Salt tolerant cotton cultivar ‘Giza 90’ produced significantly higher root and shoot biomass, accumulated lower Na+/K+ ratio through a higher Na+ exclusion from both roots and leaves as well as synthesized higher proline contents compared to salt sensitive ‘Giza 45’ cultivar. Measuring RSA in mini-rhizotrons containing solid MS nutrient medium as substrate proved to be more precise and efficient than peat moss/sand mixture. We report superior values of main root growth rate, total root system size, main root length, higher number of lateral roots and average lateral root length in ‘Giza 90’ under salinity. Higher lateral root density and length together with higher root tissue tolerance of Na+ ions in ‘Giza 90’ give it an advantage to be used as donor genotype for desirable root traits to other elite cultivars.

  17. Aspects of Information Architecture involved in process mapping in Military Organizations under the semiotic perspective

    Directory of Open Access Journals (Sweden)

    Mac Amaral Cartaxo

    2016-04-01

    Full Text Available Introduction: The description of the processes to represent the activities in an organization has important call semiotic, It is the flowcharts of uses, management reports and the various forms of representation of the strategies used. The subsequent interpretation of the organization's employees involved in learning tasks and the symbols used to translate the meanings of management practices is essential role for the organization. Objective: The objective of this study was to identify evidence of conceptual and empirical, on aspects of information architecture involved in the mapping process carried out in military organizations under the semiotic perspective. Methodology: The research is characterized as qualitative, case study and the data collection technique was the semi-structured interview, applied to management advisors. Results: The main results indicate that management practices described with the use of pictorial symbols and different layouts have greater impact to explain the relevance of management practices and indicators. Conclusion: With regard to the semiotic appeal, it was found that the impact of a management report is significant due to the use of signs and layout that stimulate further reading by simplifying complex concepts in tables, diagrams summarizing lengthy descriptions.

  18. Load forecasting using different architectures of neural networks with the assistance of the MATLAB toolboxes; Previsao de cargas eletricas utilizando diferentes arquiteturas de redes neurais artificiais com o auxilio das toolboxes do MATLAB

    Energy Technology Data Exchange (ETDEWEB)

    Nose Filho, Kenji; Araujo, Klayton A.M.; Maeda, Jorge L.Y.; Lotufo, Anna Diva P. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil)], Emails: kenjinose@yahoo.com.br, klayton_ama@hotmail.com, jorge-maeda@hotmail.com, annadiva@dee.feis.unesp.br

    2009-07-01

    This paper presents a development and implementation of a program to electrical load forecasting with data from a Brazilian electrical company, using four different architectures of neural networks of the MATLAB toolboxes: multilayer backpropagation gradient descendent with momentum, multilayer backpropagation Levenberg-Marquardt, adaptive network based fuzzy inference system and general regression neural network. The program presented a satisfactory performance, guaranteeing very good results. (author)

  19. Neural and Behavioral Correlates of Alcohol-Induced Aggression Under Provocation.

    Science.gov (United States)

    Gan, Gabriela; Sterzer, Philipp; Marxen, Michael; Zimmermann, Ulrich S; Smolka, Michael N

    2015-12-01

    Although alcohol consumption is linked to increased aggression, its neural correlates have not directly been studied in humans so far. Based on a comprehensive neurobiological model of alcohol-induced aggression, we hypothesized that alcohol-induced aggression would go along with increased amygdala and ventral striatum reactivity and impaired functioning of the prefrontal cortex (PFC) under alcohol. We measured neural and behavioral correlates of alcohol-induced aggression in a provoking vs non-provoking condition with a variant of the Taylor aggression paradigm (TAP) allowing to differentiate between reactive (provoked) and proactive (unprovoked) aggression. In a placebo-controlled cross-over design with moderate alcohol intoxication (~0.6 g/kg), 35 young healthy adults performed the TAP during functional magnetic resonance imaging (fMRI). Analyses revealed that provoking vs non-provoking conditions and alcohol vs placebo increased aggression and decreased brain responses in the anterior cingulate cortex/dorso-medial PFC (provokingalcoholalcohol specifically increased proactive (unprovoked) but not reactive (provoked) aggression (alcohol × provocation interaction). However, investigation of inter-individual differences revealed (1) that pronounced alcohol-induced proactive aggression was linked to higher levels of aggression under placebo, and (2) that pronounced alcohol-induced reactive aggression was related to increased amygdala and ventral striatum reactivity under alcohol, providing evidence for their role in human alcohol-induced reactive aggression. Our findings suggest that in healthy young adults a liability for alcohol-induced aggression in a non-provoking context might depend on overall high levels of aggression, but on alcohol-induced increased striatal and amygdala reactivity when triggered by provocation.

  20. Neural and Behavioral Correlates of Alcohol-Induced Aggression Under Provocation

    Science.gov (United States)

    Gan, Gabriela; Sterzer, Philipp; Marxen, Michael; Zimmermann, Ulrich S; Smolka, Michael N

    2015-01-01

    Although alcohol consumption is linked to increased aggression, its neural correlates have not directly been studied in humans so far. Based on a comprehensive neurobiological model of alcohol-induced aggression, we hypothesized that alcohol-induced aggression would go along with increased amygdala and ventral striatum reactivity and impaired functioning of the prefrontal cortex (PFC) under alcohol. We measured neural and behavioral correlates of alcohol-induced aggression in a provoking vs non-provoking condition with a variant of the Taylor aggression paradigm (TAP) allowing to differentiate between reactive (provoked) and proactive (unprovoked) aggression. In a placebo-controlled cross-over design with moderate alcohol intoxication (~0.6 g/kg), 35 young healthy adults performed the TAP during functional magnetic resonance imaging (fMRI). Analyses revealed that provoking vs non-provoking conditions and alcohol vs placebo increased aggression and decreased brain responses in the anterior cingulate cortex/dorso-medial PFC (provokingalcoholalcohol specifically increased proactive (unprovoked) but not reactive (provoked) aggression (alcohol × provocation interaction). However, investigation of inter-individual differences revealed (1) that pronounced alcohol-induced proactive aggression was linked to higher levels of aggression under placebo, and (2) that pronounced alcohol-induced reactive aggression was related to increased amygdala and ventral striatum reactivity under alcohol, providing evidence for their role in human alcohol-induced reactive aggression. Our findings suggest that in healthy young adults a liability for alcohol-induced aggression in a non-provoking context might depend on overall high levels of aggression, but on alcohol-induced increased striatal and amygdala reactivity when triggered by provocation. PMID:25971590

  1. A neural network underlying intentional emotional facial expression in neurodegenerative disease.

    Science.gov (United States)

    Gola, Kelly A; Shany-Ur, Tal; Pressman, Peter; Sulman, Isa; Galeana, Eduardo; Paulsen, Hillary; Nguyen, Lauren; Wu, Teresa; Adhimoolam, Babu; Poorzand, Pardis; Miller, Bruce L; Rankin, Katherine P

    2017-01-01

    Intentional facial expression of emotion is critical to healthy social interactions. Patients with neurodegenerative disease, particularly those with right temporal or prefrontal atrophy, show dramatic socioemotional impairment. This was an exploratory study examining the neural and behavioral correlates of intentional facial expression of emotion in neurodegenerative disease patients and healthy controls. One hundred and thirty three participants (45 Alzheimer's disease, 16 behavioral variant frontotemporal dementia, 8 non-fluent primary progressive aphasia, 10 progressive supranuclear palsy, 11 right-temporal frontotemporal dementia, 9 semantic variant primary progressive aphasia patients and 34 healthy controls) were video recorded while imitating static images of emotional faces and producing emotional expressions based on verbal command; the accuracy of their expression was rated by blinded raters. Participants also underwent face-to-face socioemotional testing and informants described participants' typical socioemotional behavior. Patients' performance on emotion expression tasks was correlated with gray matter volume using voxel-based morphometry (VBM) across the entire sample. We found that intentional emotional imitation scores were related to fundamental socioemotional deficits; patients with known socioemotional deficits performed worse than controls on intentional emotion imitation; and intentional emotional expression predicted caregiver ratings of empathy and interpersonal warmth. Whole brain VBMs revealed a rightward cortical atrophy pattern homologous to the left lateralized speech production network was associated with intentional emotional imitation deficits. Results point to a possible neural mechanisms underlying complex socioemotional communication deficits in neurodegenerative disease patients.

  2. A neural network underlying intentional emotional facial expression in neurodegenerative disease

    Directory of Open Access Journals (Sweden)

    Kelly A. Gola

    2017-01-01

    Full Text Available Intentional facial expression of emotion is critical to healthy social interactions. Patients with neurodegenerative disease, particularly those with right temporal or prefrontal atrophy, show dramatic socioemotional impairment. This was an exploratory study examining the neural and behavioral correlates of intentional facial expression of emotion in neurodegenerative disease patients and healthy controls. One hundred and thirty three participants (45 Alzheimer's disease, 16 behavioral variant frontotemporal dementia, 8 non-fluent primary progressive aphasia, 10 progressive supranuclear palsy, 11 right-temporal frontotemporal dementia, 9 semantic variant primary progressive aphasia patients and 34 healthy controls were video recorded while imitating static images of emotional faces and producing emotional expressions based on verbal command; the accuracy of their expression was rated by blinded raters. Participants also underwent face-to-face socioemotional testing and informants described participants' typical socioemotional behavior. Patients' performance on emotion expression tasks was correlated with gray matter volume using voxel-based morphometry (VBM across the entire sample. We found that intentional emotional imitation scores were related to fundamental socioemotional deficits; patients with known socioemotional deficits performed worse than controls on intentional emotion imitation; and intentional emotional expression predicted caregiver ratings of empathy and interpersonal warmth. Whole brain VBMs revealed a rightward cortical atrophy pattern homologous to the left lateralized speech production network was associated with intentional emotional imitation deficits. Results point to a possible neural mechanisms underlying complex socioemotional communication deficits in neurodegenerative disease patients.

  3. eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures.

    Science.gov (United States)

    Caudell, Thomas P; Xiao, Yunhai; Healy, Michael J

    2003-01-01

    eLoom is an open source graph simulation software tool, developed at the University of New Mexico (UNM), that enables users to specify and simulate neural network models. Its specification language and libraries enables users to construct and simulate arbitrary, potentially hierarchical network structures on serial and parallel processing systems. In addition, eLoom is integrated with UNM's Flatland, an open source virtual environments development tool to provide real-time visualizations of the network structure and activity. Visualization is a useful method for understanding both learning and computation in artificial neural networks. Through 3D animated pictorially representations of the state and flow of information in the network, a better understanding of network functionality is achieved. ART-1, LAPART-II, MLP, and SOM neural networks are presented to illustrate eLoom and Flatland's capabilities.

  4. Hierarchical Neural Network (HNN) for Closed Loop Decision Making: Designing the Architecture of a Hierarchical Neural Network to Model Attention, Learning and Goal Oriented Behavior

    Science.gov (United States)

    1990-12-01

    surveyed. Later control of1a robotiic system was used as the prototypical task and a HNN was designed and compared with the state of the art adaptive...Z Solution to the In’.erse kinematics efficiency foi the 6-DOF ,Lise is due to tihe Problem in Roboti s b% Neural Networks IEEE International3 trade...manipulator Desired Mechanism Actwl parameters in closed loop operation.T etr In operation, the controller is given a trajectory by a path Controller RobotI

  5. Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Omar Payán-Serrano

    2017-05-01

    Full Text Available The aim of this paper is to investigate the prediction of maximum story drift of Multi-Degree of Freedom (MDOF structures subjected to dynamics wind load using Artificial Neural Networks (ANNs through the combination of several structural and turbulent wind parameters. The maximum story drift of 1600 MDOF structures under 16 simulated wind conditions are computed with the purpose of generating the data set for the networks training with the Levenberg–Marquardt method. The Shinozuka and Newmark methods are used to simulate the turbulent wind and dynamic response, respectively. In order to optimize the computational time required for the dynamic analyses, an array format based on the Shinozuka method is presented to perform the parallel computing. Finally, it is observed that the already trained ANNs allow for predicting adequately the maximum story drift with a correlation close to 99%.

  6. Convolutional neural network architecture and input volume matrix design for ERP classifications in a tactile P300-based Brain-Computer Interface.

    Science.gov (United States)

    Kodama, Takumi; Makino, Shoji

    2017-07-01

    In the presented study we conduct the off-line ERP classification using the convolutional neural network (CNN) classifier for somatosensory ERP intervals acquired in the full- body tactile P300-based Brain-Computer Interface paradigm (fbBCI). The main objective of the study is to enhance fbBCI stimulus pattern classification accuracies by applying the CNN classifier. A 60 × 60 squared input volume transformed by one-dimensional somatosensory ERP intervals in each electrode channel is input to the convolutional architecture for a filter training. The flattened activation maps are evaluated by a multilayer perceptron with one-hidden-layer in order to calculate classification accuracy results. The proposed method reveals that the CNN classifier model can achieve a non-personal- training ERP classification with the fbBCI paradigm, scoring 100 % classification accuracy results for all the participated ten users.

  7. Mapping Common Aphasia Assessments to Underlying Cognitive Processes and Their Neural Substrates.

    Science.gov (United States)

    Lacey, Elizabeth H; Skipper-Kallal, Laura M; Xing, Shihui; Fama, Mackenzie E; Turkeltaub, Peter E

    2017-05-01

    Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments. To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests. Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores. The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing. An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.

  8. Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex.

    Science.gov (United States)

    Vaz, Alex P; Yaffe, Robert B; Wittig, John H; Inati, Sara K; Zaghloul, Kareem A

    2017-03-01

    Phase-amplitude coupling (PAC) is hypothesized to coordinate neural activity, but its role in successful memory formation in the human cortex is unknown. Measures of PAC are difficult to interpret, however. Both increases and decreases in PAC have been linked to memory encoding, and PAC may arise due to different neural mechanisms. Here, we use a waveform analysis to examine PAC in the human cortex as participants with intracranial electrodes performed a paired associates memory task. We found that successful memory formation exhibited significant decreases in left temporal lobe and prefrontal cortical PAC, and these two regions exhibited changes in PAC within different frequency bands. Two underlying neural mechanisms, nested oscillations and sharp waveforms, were responsible for the changes in these regions. Our data therefore suggest that decreases in measured cortical PAC during episodic memory reflect two distinct underlying mechanisms that are anatomically segregated in the human brain. Published by Elsevier Inc.

  9. Boll distribution and plant architecture of 14 commercial cultivars under five different irrigation regimes

    Science.gov (United States)

    There is a pressing need to identify and understand the effects of different irrigation regimes on the boll distribution, seed cotton yield, and plant architecture of commercial cultivars of cotton (Gossypium spp.). To identify the impact of different irrigation levels on the Texas High Plains 14 co...

  10. Neural plasticity of development and learning.

    Science.gov (United States)

    Galván, Adriana

    2010-06-01

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

  11. Artificial neural network model for estimating the soil respiration under different land uses

    Science.gov (United States)

    Ebrahimi, Mitra; Sarikhani, Mohammad Reza; Safari Sinegani, Ali Akbar; Ahmadi, Abbas; Keesstra, Saskia

    2017-04-01

    Soil respiration is a biological process in microbes that convert organic carbon to atmospheric CO2. This process is considered to be one of the largest global carbon fluxes and is affected by different physicochemical and biological properties of soil, land usageuse, vegetation types and climate patterns. The aim of this study was to estimate the soil basal (BR) and substrate induced respiration (SIR) of 150 data obtained from soil samples collected from depth (0-25 cm) under different land uses by Artificial Neural Network. Soil samples were chosen from three provinces of Iran, with humid subtropical and semi-arid climate patterns. In each soil sample, soil texture, pH, electrical conductivity (EC), calcium carbonate equivalent (CCE), organic carbon (OC), OC fractionation data e.g. light fraction OC (LOC), heavy fraction OC (HOC), cold water extractable OC (COC) and warm water extractable OC (WOC), population of fungi, bacteria and actinomycete, BR and SIR were measured. Our goal was to use the most efficient ANN-model to predict soil respiration with simple soil data. Our results indicated that in an ANN model containing all the measured parameters, the R2 and RMSE values for BR prediction were 0.64 and 0.047 while these statistical indicators for SIR obtained 0.58 and 0.15, respectively. The R2 and RMSE values of the BR-ANN and SIR-ANN predicted models comprising 7 variables (including OC, pH, EC, CCE and soil texture) were estimated at 0.66, 0.043 and 0.52, 0.16, respectively. It was concluded that ANN modeling is a reliable method for predicting soil respiration. KEYWORDS: Artificial neural network; Land use; Soil physicochemical properties; Soil respiration; Soil microorganism

  12. Computational Assessment of Neural Probe and Brain Tissue Interface under Transient Motion

    Directory of Open Access Journals (Sweden)

    Michael Polanco

    2016-06-01

    Full Text Available The functional longevity of a neural probe is dependent upon its ability to minimize injury risk during the insertion and recording period in vivo, which could be related to motion-related strain between the probe and surrounding tissue. A series of finite element analyses was conducted to study the extent of the strain induced within the brain in an area around a neural probe. This study focuses on the transient behavior of neural probe and brain tissue interface with a viscoelastic model. Different stages of the interface from initial insertion of neural probe to full bonding of the probe by astro-glial sheath formation are simulated utilizing analytical tools to investigate the effects of relative motion between the neural probe and the brain while friction coefficients and kinematic frequencies are varied. The analyses can provide an in-depth look at the quantitative benefits behind using soft materials for neural probes.

  13. 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

  14. Looking for underlying features in automatic and reviewed seismic bulletins through a neural network

    Science.gov (United States)

    Carluccio, R.; Console, R.; Chiappini, M.; Chiappini, S.

    2009-12-01

    Jan 2005 to Oct 2008 have been inserted in a database, together with IMS stations availability information. Part of these data have been used to create two sets of independent data (learning and verifying) used to train a "feed-forward" supervised neural network. A network supervised training algorithm using "confirmation flag" values has been used. In order to optimize network training input a significant, not redundant subset of input parameters has been looked for with the help of a genetic algorithm search tool. A suitable 12 input subset has been found and a network architecture of 12-20-1 has thus been chosen and trained on a 15094 records data set. Different runs of training sequences have been conducted, all showing CCR (Correct Classification Rate) values of the order of 75% - 80%. The trained network behavior is shown in term of ROC curve and input-out success-error matrices. The results of the analysis on our testing and validating data groups appear promising.

  15. Generalization performance of regularized neural network models

    DEFF Research Database (Denmark)

    Larsen, Jan; Hansen, Lars Kai

    1994-01-01

    Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...

  16. Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect.

    Science.gov (United States)

    Kostarakos, Konstantinos; Römer, Heiner

    2015-07-22

    produces an extremely noisy sound, yet the second species still detects its own song. Using intracellular recording techniques we identified two neural mechanisms underlying the surprising behavioral signal detection at the level of single identified interneurons. These neural mechanisms for signal detection are likely to be important for other sensory modalities as well, where noise in the communication channel creates similar problems. Also, they may be used for the development of algorithms for the filtering of specific signals in technical microphones or hearing aids. Copyright © 2015 Kostarakos and Römer.

  17. Event-based home safety problem detection under the CPS home safety architecture

    OpenAIRE

    Yang, Zhengguo; Lim, Azman Osman; Tan, Yasuo

    2013-01-01

    This paper presents a CPS(Cyber-physical System) home safety architecture for home safety problem detection and reaction and shows some example cases. In order for home safety problem detection, there are three levels of events defined: elementary event, semantic event and entire event, which representing the meaning from parameter to single safety problem, and then the whole safety status of a house. For the relationship between these events and raw data, a Finite State Machine (FSM) based m...

  18. One-day-ahead streamflow forecasting via super-ensembles of several neural network architectures based on the Multi-Level Diversity Model

    Science.gov (United States)

    Brochero, Darwin; Hajji, Islem; Pina, Jasson; Plana, Queralt; Sylvain, Jean-Daniel; Vergeynst, Jenna; Anctil, Francois

    2015-04-01

    Theories about generalization error with ensembles are mainly based on the diversity concept, which promotes resorting to many members of different properties to support mutually agreeable decisions. Kuncheva (2004) proposed the Multi Level Diversity Model (MLDM) to promote diversity in model ensembles, combining different data subsets, input subsets, models, parameters, and including a combiner level in order to optimize the final ensemble. This work tests the hypothesis about the minimisation of the generalization error with ensembles of Neural Network (NN) structures. We used the MLDM to evaluate two different scenarios: (i) ensembles from a same NN architecture, and (ii) a super-ensemble built by a combination of sub-ensembles of many NN architectures. The time series used correspond to the 12 basins of the MOdel Parameter Estimation eXperiment (MOPEX) project that were used by Duan et al. (2006) and Vos (2013) as benchmark. Six architectures are evaluated: FeedForward NN (FFNN) trained with the Levenberg Marquardt algorithm (Hagan et al., 1996), FFNN trained with SCE (Duan et al., 1993), Recurrent NN trained with a complex method (Weins et al., 2008), Dynamic NARX NN (Leontaritis and Billings, 1985), Echo State Network (ESN), and leak integrator neuron (L-ESN) (Lukosevicius and Jaeger, 2009). Each architecture performs separately an Input Variable Selection (IVS) according to a forward stepwise selection (Anctil et al., 2009) using mean square error as objective function. Post-processing by Predictor Stepwise Selection (PSS) of the super-ensemble has been done following the method proposed by Brochero et al. (2011). IVS results showed that the lagged stream flow, lagged precipitation, and Standardized Precipitation Index (SPI) (McKee et al., 1993) were the most relevant variables. They were respectively selected as one of the firsts three selected variables in 66, 45, and 28 of the 72 scenarios. A relationship between aridity index (Arora, 2002) and NN

  19. Neural correlates underlying naloxone-induced amelioration of sexual behavior deterioration due to an alarm pheromone

    Directory of Open Access Journals (Sweden)

    Tatsuya eKobayashi

    2015-02-01

    Full Text Available Sexual behavior is suppressed by various types of stressors. We previously demonstrated that an alarm pheromone released by stressed male Wistar rats is a stressor to other rats, increases the number of mounts needed for ejaculation, and decreases the hit rate (described as the number of intromissions/sum of the mounts and intromissions. This deterioration in sexual behavior was ameliorated by pretreatment with the opioid receptor antagonist naloxone. However, the neural mechanism underlying this remains to be elucidated. Here, we examined Fos expression in 31 brain regions of pheromone-exposed rats and naloxone-pretreated pheromone-exposed rats 60 min after 10 intromissions. As previously reported, the alarm pheromone increased the number of mounts and decreased the hit rate. In addition, Fos expression was increases in the anterior medial division, anterior lateral division and posterior division of the bed nucleus of the stria terminalis, parvocellular part of the paraventricular nucleus of the hypothalamus, arcuate nucleus, dorsolateral and ventrolateral periaqueductal gray, and nucleus paragigantocellularis. Fos expression decreased in the magnocellular part of the paraventricular nucleus of the hypothalamus. Pretreatment with naloxone blocked the pheromone-induced changes in Fos expression in the magnocellular part of the paraventricular nucleus of the hypothalamus, ventrolateral periaqueductal gray, and nucleus paragigantocellularis. Based on these results, we hypothesize that the alarm pheromone deteriorated sexual behavior by activating the ventrolateral periaqueductal gray-nucleus paragigantocellularis cluster and suppressing the magnocellular part of the paraventricular nucleus of the hypothalamus via the opioidergic pathway.

  20. Neural and computational processes underlying dynamic changes in self-esteem

    Science.gov (United States)

    Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

    2017-01-01

    Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an ‘interpersonal vulnerability’ dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability. PMID:29061228

  1. Stability of Neural Firing in the Trigeminal Nuclei under Mechanical Whisker Stimulation

    Directory of Open Access Journals (Sweden)

    Valeri A. Makarov

    2010-01-01

    Full Text Available Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5 and interpolaris (Sp5i show the maximal stability at the intermediate (50 ms stimulus duration, whereas Sp5o cells “prefer” shorter (10 ms stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.

  2. UTX-guided neural crest function underlies craniofacial features of Kabuki syndrome.

    Science.gov (United States)

    Shpargel, Karl B; Starmer, Joshua; Wang, Chaochen; Ge, Kai; Magnuson, Terry

    2017-10-24

    Kabuki syndrome, a congenital craniofacial disorder, manifests from mutations in an X-linked histone H3 lysine 27 demethylase (UTX/KDM6A) or a H3 lysine 4 methylase (KMT2D). However, the cellular and molecular etiology of histone-modifying enzymes in craniofacial disorders is unknown. We now establish Kabuki syndrome as a neurocristopathy, whereby the majority of clinical features are modeled in mice carrying neural crest (NC) deletion of UTX, including craniofacial dysmorphism, cardiac defects, and postnatal growth retardation. Female UTX NC knockout (FKO) demonstrates enhanced phenotypic severity over males (MKOs), due to partial redundancy with UTY, a Y-chromosome demethylase-dead homolog. Thus, NC cells may require demethylase-independent UTX activity. Consistently, Kabuki causative point mutations upstream of the JmjC domain do not disrupt UTX demethylation. We have isolated primary NC cells at a phenocritical postmigratory timepoint in both FKO and MKO mice, and genome-wide expression and histone profiling have revealed UTX molecular function in establishing appropriate chromatin structure to regulate crucial NC stem-cell signaling pathways. However, the majority of UTX regulated genes do not experience aberrations in H3K27me3 or H3K4me3, implicating alternative roles for UTX in transcriptional control. These findings are substantiated through demethylase-dead knockin mutation of UTX, which supports appropriate facial development. Published under the PNAS license.

  3. Feline Neural Progenitor Cells I: Long-Term Expansion under Defined Culture Conditions

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    Jing Yang

    2012-01-01

    Full Text Available Neural progenitor cells (NPCs of feline origin (cNPCs have demonstrated utility in transplantation experiments, yet are difficult to grow in culture beyond the 1 month time frame. Here we use an enriched, serum-free base medium (Ultraculture and report the successful long-term propagation of these cells. Primary cultures were derived from fetal brain tissue and passaged in DMEM/F12-based or Ultraculture-based proliferation media, both in the presence of EGF + bFGF. Cells in standard DMEM/F12-based medium ceased to proliferate by 1-month, whereas the cells in the Ultraculture-based medium continued to grow for at least 5 months (end of study with no evidence of senescence. The Ultraculture-based cultures expressed lower levels of progenitor and lineage-associated markers under proliferation conditions but retained multipotency as evidenced by the ability to differentiate into neurons and glia following growth factor removal in the presence of FBS. Importantly, later passage cNPCs did not develop chromosomal aberrations.

  4. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

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    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  5. Neural substrates underlying stimulation-enhanced motor skill learning after stroke.

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    Lefebvre, Stéphanie; Dricot, Laurence; Laloux, Patrice; Gradkowski, Wojciech; Desfontaines, Philippe; Evrard, Frédéric; Peeters, André; Jamart, Jacques; Vandermeeren, Yves

    2015-01-01

    Motor skill learning is one of the key components of motor function recovery after stroke, especially recovery driven by neurorehabilitation. Transcranial direct current stimulation can enhance neurorehabilitation and motor skill learning in stroke patients. However, the neural mechanisms underlying the retention of stimulation-enhanced motor skill learning involving a paretic upper limb have not been resolved. These neural substrates were explored by means of functional magnetic resonance imaging. Nineteen chronic hemiparetic stroke patients participated in a double-blind, cross-over randomized, sham-controlled experiment with two series. Each series consisted of two sessions: (i) an intervention session during which dual transcranial direct current stimulation or sham was applied during motor skill learning with the paretic upper limb; and (ii) an imaging session 1 week later, during which the patients performed the learned motor skill. The motor skill learning task, called the 'circuit game', involves a speed/accuracy trade-off and consists of moving a pointer controlled by a computer mouse along a complex circuit as quickly and accurately as possible. Relative to the sham series, dual transcranial direct current stimulation applied bilaterally over the primary motor cortex during motor skill learning with the paretic upper limb resulted in (i) enhanced online motor skill learning; (ii) enhanced 1-week retention; and (iii) superior transfer of performance improvement to an untrained task. The 1-week retention's enhancement driven by the intervention was associated with a trend towards normalization of the brain activation pattern during performance of the learned motor skill relative to the sham series. A similar trend towards normalization relative to sham was observed during performance of a simple, untrained task without a speed/accuracy constraint, despite a lack of behavioural difference between the dual transcranial direct current stimulation and sham

  6. Outcome assessment of patients with metastatic renal cell carcinoma under systemic therapy using artificial neural networks.

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    Buchner, Alexander; Kendlbacher, Martin; Nuhn, Philipp; Tüllmann, Cordula; Haseke, Nicolas; Stief, Christian G; Staehler, Michael

    2012-03-01

    The outcome of patients with advanced renal cell carcinoma (RCC) under systemic therapy shows remarkable variability, and there is a need to identify prognostic parameters that allow individual prognostic stratification and selection of optimal therapy. Artificial neural networks (ANN) are software systems that can be trained to recognize complex data patterns. In this study, we used ANNs to identify poor prognosis of patients with RCC based on common clinical parameters available at the beginning of systemic therapy. Data from patients with RCC who started systemic therapy were collected prospectively in a single center database; 175 data sets with follow-up data (median, 36 months) were available for analysis. Age, sex, body mass index, performance status, histopathologic parameters, time interval between primary tumor and detection of metastases, type of systemic therapy, number of metastases, and metastatic sites were used as input data for the ANN. The target variable was overall survival after 36 months. Logistic regression models were constructed by using the same variables. Death after 36 months occurred in 26% of the patients in the tyrosine kinase inhibitors group and in 37% of the patients in the immunotherapy group (P = .22). ANN achieved 95% overall accuracy and significantly outperformed logistic regression models (78% accuracy). Pathologic T classification, invasion of vessels, and tumor grade had the highest impact on the network's decision. ANN is a promising approach for individual risk stratification of patients with advanced RCC under systemic therapy, based on clinical parameters, and can help to optimize the therapeutic strategy. Copyright © 2012 Elsevier Inc. All rights reserved.

  7. Cuticular colour reflects underlying architecture and is affected by a limiting resource.

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    Evison, Sophie E F; Gallagher, Joe D; Thompson, John J W; Siva-Jothy, Michael T; Armitage, Sophie A O

    2017-04-01

    Central to the basis of ecological immunology are the ideas of costs and trade-offs between immunity and life history traits. As a physical barrier, the insect cuticle provides a key resistance trait, and Tenebrio molitor shows phenotypic variation in cuticular colour that correlates with resistance to the entomopathogenic fungus Metarhizium anisopliae. Here we first examined whether there is a relationship between cuticular colour variation and two aspects of cuticular architecture that we hypothesised may influence resistance to fungal invasion through the cuticle: its thickness and its porosity. Second, we tested the hypothesis that tyrosine, a semi-essential amino acid required for immune defence and cuticular melanisation and sclerotisation, can act as a limiting resource by supplementing the larval diet and subsequently examining adult cuticular colouration and thickness. We found that stock beetles and beetles artificially selected for extremes of cuticular colour had thicker less porous cuticles when they were darker, and thinner more porous cuticles when they were lighter, showing that colour co-varies with two architectural cuticular features. Experimental supplementation of the larval diet with tyrosine led to the development of darker adult cuticle and affected thickness in a sex-specific manner. However, it did not affect two immune traits. The results of this study provide a mechanism for maintenance of cuticular colour variation in this species of beetle; darker cuticles are thicker, but their production is potentially limited by resource constraints and differential investments in resistance mechanisms between the sexes. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Neural network underlying ictal pouting ("chapeau de gendarme") in frontal lobe epilepsy.

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    Souirti, Zouhayr; Landré, Elisabeth; Mellerio, Charles; Devaux, Bertrand; Chassoux, Francine

    2014-08-01

    In order to determine the anatomical neural network underlying ictal pouting (IP), with the mouth turned down like a "chapeau de gendarme", in frontal lobe epilepsy (FLE), we reviewed the video-EEG recordings of 36 patients with FLE who became seizure-free after surgery. We selected the cases presenting IP, defined as a symmetrical and sustained (>5s) lowering of labial commissures with contraction of chin, mimicking an expression of fear, disgust, or menace. Ictal pouting was identified in 11 patients (8 males; 16-48 years old). We analyzed the clinical semiology, imaging, and electrophysiological data associated with IP, including FDG-PET in 10 and SEEG in 9 cases. In 37 analyzed seizures (2-7/patient), IP was an early symptom, occurring during the first 10s in 9 cases. The main associated features consisted of fear, anguish, vegetative disturbances, behavioral disorders (sudden agitation, insults, and fighting), tonic posturing, and complex motor activities. The epileptogenic zone assessed by SEEG involved the mesial frontal areas, especially the anterior cingulate cortex (ACC) in 8 patients, whereas lateral frontal onset with an early spread to the ACC was seen in the other patient. Ictal pouting associated with emotional changes and hypermotor behavior had high localizing value for rostroventral "affective" ACC, whereas less intense facial expressions were related to the dorsal "cognitive" ACC. Fluorodeoxyglucose positron emission tomography demonstrated the involvement of both the ACC and lateral cortex including the anterior insula in all cases. We propose that IP is sustained by reciprocal mesial and lateral frontal interactions involved in emotional and cognitive processes, in which the ACC plays a pivotal role. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Neural mechanisms underlying the conditioned diminution of the unconditioned fear response.

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    Wood, Kimberly H; Ver Hoef, Lawrence W; Knight, David C

    2012-03-01

    Recognizing cues that predict an aversive event allows one to react more effectively under threatening conditions, and minimizes the reaction to the threat itself. This is demonstrated during Pavlovian fear conditioning when the unconditioned response (UCR) to a predictable unconditioned stimulus (UCS) is diminished compared to the UCR to an unpredictable UCS. The present study investigated the functional magnetic resonance imaging (fMRI) signal response associated with Pavlovian conditioned UCR diminution to better understand the relationship between individual differences in behavior and the neural mechanisms of the threat-related emotional response. Healthy volunteers participated in a fear conditioning study in which trait anxiety, skin conductance response (SCR), UCS expectancy, and the fMRI signal were assessed. During acquisition trials, a tone (CS+) was paired with a white noise UCS and a second tone (CS-) was presented without the UCS. Test trials consisted of the CS+ paired with the UCS, CS- paired with the UCS, and presentations of the UCS alone to assess conditioned UCR diminution. UCR diminution was observed within the dorsolateral PFC, dorsomedial PFC, cingulate cortex, inferior parietal lobule (IPL), anterior insula, and amygdala. The threat-related activity within the dorsolateral PFC, dorsomedial PFC, posterior cingulate cortex, and IPL varied with individual differences in trait anxiety. In addition, anticipatory (i.e. CS elicited) activity within the PFC showed an inverse relationship with threat-related (i.e. UCS elicited) activity within the PFC, IPL, and amygdala. Further, the emotional response (indexed via SCR) elicited by the threat was closely linked to amygdala activity. These findings are consistent with the view that the amygdala and PFC support learning-related processes that influence the emotional response evoked by a threat. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. Neural activity changes underlying the working memory deficit in alpha-CaMKII heterozygous knockout mice

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    Naoki Matsuo

    2009-09-01

    Full Text Available The alpha-isoform of calcium/calmodulin-dependent protein kinase II (α-CaMKII is expressed abundantly in the forebrain and is considered to have an essential role in synaptic plasticity and cognitive function. Previously, we reported that mice heterozygous for a null mutation of α-CaMKII (α-CaMKII+/- have profoundly dysregulated behaviors including a severe working memory deficit, which is an endophenotype of schizophrenia and other psychiatric disorders. In addition, we found that almost all the neurons in the dentate gyrus (DG of the mutant mice failed to mature at molecular, morphological and electrophysiological levels. In the present study, to identify the brain substrates of the working memory deficit in the mutant mice, we examined the expression of the immediate early genes (IEGs, c-Fos and Arc, in the brain after a working memory version of the eight-arm radial maze test. c-Fos expression was abolished almost completely in the DG and was reduced significantly in neurons in the CA1 and CA3 areas of the hippocampus, central amygdala, and medial prefrontal cortex (mPFC. However, c-Fos expression was intact in the entorhinal and visual cortices. Immunohistochemical studies using arc promoter driven dVenus transgenic mice demonstrated that arc gene activation after the working memory task occurred in mature, but not immature neurons in the DG of wild-type mice. These results suggest crucial insights for the neural circuits underlying spatial mnemonic processing during a working memory task and suggest the involvement of α-CaMKII in the proper maturation and integration of DG neurons into these circuits.

  11. Neural correlates of erotic stimulation under different levels of female sexual hormones.

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    Birgit Abler

    Full Text Available Previous studies have demonstrated variable influences of sexual hormonal states on female brain activation and the necessity to control for these in neuroimaging studies. However, systematic investigations of these influences, particularly those of hormonal contraceptives as compared to the physiological menstrual cycle are scarce. In the present study, we investigated the hormonal modulation of neural correlates of erotic processing in a group of females under hormonal contraceptives (C group; N = 12, and a different group of females (nC group; N = 12 not taking contraceptives during their mid-follicular and mid-luteal phases of the cycle. We used functional magnetic resonance imaging to measure hemodynamic responses as an estimate of brain activation during three different experimental conditions of visual erotic stimulation: dynamic videos, static erotic pictures, and expectation of erotic pictures. Plasma estrogen and progesterone levels were assessed in all subjects. No strong hormonally modulating effect was detected upon more direct and explicit stimulation (viewing of videos or pictures with significant activations in cortical and subcortical brain regions previously linked to erotic stimulation consistent across hormonal levels and stimulation type. Upon less direct and less explicit stimulation (expectation, activation patterns varied between the different hormonal conditions with various, predominantly frontal brain regions showing significant within- or between-group differences. Activation in the precentral gyrus during the follicular phase in the nC group was found elevated compared to the C group and positively correlated with estrogen levels. From the results we conclude that effects of hormonal influences on brain activation during erotic stimulation are weak if stimulation is direct and explicit but that female sexual hormones may modulate more subtle aspects of sexual arousal and behaviour as involved in sexual

  12. Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

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    YuKang Jia

    2017-01-01

    Full Text Available Long Short-Term Memory (LSTM is a kind of Recurrent Neural Networks (RNN relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers. As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.

  13. Life-Long Neurogenic Activity of Individual Neural Stem Cells and Continuous Growth Establish an Outside-In Architecture in the Teleost Pallium.

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    Furlan, Giacomo; Cuccioli, Valentina; Vuillemin, Nelly; Dirian, Lara; Muntasell, Anna Janue; Coolen, Marion; Dray, Nicolas; Bedu, Sébastien; Houart, Corinne; Beaurepaire, Emmanuel; Foucher, Isabelle; Bally-Cuif, Laure

    2017-11-06

    Spatiotemporal variations of neurogenesis are thought to account for the evolution of brain shape. In the dorsal telencephalon (pallium) of vertebrates, it remains unresolved which ancestral neurogenesis mode prefigures the highly divergent cytoarchitectures that are seen in extant species. To gain insight into this question, we developed genetic tools to generate here the first 4-dimensional (3D + birthdating time) map of pallium construction in the adult teleost zebrafish. Using a Tet-On-based genetic birthdating strategy, we identify a "sequential stacking" construction mode where neurons derived from the zebrafish pallial germinal zone arrange in outside-in, age-related layers from a central core generated during embryogenesis. We obtained no evidence for overt radial or tangential neuronal migrations. Cre-lox-mediated tracing, which included following Brainbow clones, further demonstrates that this process is sustained by the persistent neurogenic activity of individual pallial neural stem cells (NSCs) from embryo to adult. Together, these data demonstrate that the spatiotemporal control of NSC activity is an important driver of the macroarchitecture of the zebrafish adult pallium. This simple mode of pallium construction shares distinct traits with pallial genesis in mammals and non-mammalian amniotes such as birds or reptiles, suggesting that it may exemplify the basal layout from which vertebrate pallial architectures were elaborated. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  14. Dynamics of BMP and Hes1/Hairy1 signaling in the dorsal neural tube underlies the transition from neural crest to definitive roof plate.

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    Nitzan, Erez; Avraham, Oshri; Kahane, Nitza; Ofek, Shai; Kumar, Deepak; Kalcheim, Chaya

    2016-03-24

    The dorsal midline region of the neural tube that results from closure of the neural folds is generally termed the roof plate (RP). However, this domain is highly dynamic and complex, and is first transiently inhabited by prospective neural crest (NC) cells that sequentially emigrate from the neuroepithelium. It only later becomes the definitive RP, the dorsal midline cells of the spinal cord. We previously showed that at the trunk level of the axis, prospective RP progenitors originate ventral to the premigratory NC and progressively reach the dorsal midline following NC emigration. However, the molecular mechanisms underlying the end of NC production and formation of the definitive RP remain virtually unknown. Based on distinctive cellular and molecular traits, we have defined an initial NC and a subsequent RP stage, allowing us to investigate the mechanisms responsible for the transition between the two phases. We demonstrate that in spite of the constant production of BMP4 in the dorsal tube at both stages, RP progenitors only transiently respond to the ligand and lose competence shortly before they arrive at their final location. In addition, exposure of dorsal tube cells at the NC stage to high levels of BMP signaling induces premature RP traits, such as Hes1/Hairy1, while concomitantly inhibiting NC production. Reciprocally, early inhibition of BMP signaling prevents Hairy1 mRNA expression at the RP stage altogether, suggesting that BMP is both necessary and sufficient for the development of this RP-specific trait. Furthermore, when Hes1/Hairy1 is misexpressed at the NC stage, it inhibits BMP signaling and downregulates BMPR1A/Alk3 mRNA expression, transcription of BMP targets such as Foxd3, cell-cycle progression, and NC emigration. Reciprocally, Foxd3 inhibits Hairy1, suggesting that repressive cross-interactions at the level of, and downstream from, BMP ensure the temporal separation between both lineages. Together, our data suggest that BMP signaling is

  15. Evolutionary divergence of the genetic architecture underlying photoperiodism in the pitcher-plant mosquito, Wyeomyia smithii.

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    Lair, K P; Bradshaw, W E; Holzapfel, C M

    1997-12-01

    We determine the contribution of composite additive, dominance, and epistatic effects to the genetic divergence of photoperiodic response along latitudinal, altitudinal, and longitudinal gradients in the pitcher-plant mosquito, Wyeomyia smithii. Joint scaling tests of crosses between populations showed widespread epistasis as well as additive and dominance differences among populations. There were differences due to epistasis between an alpine population in North Carolina and populations in Florida, lowland North Carolina, and Maine. Longitudinal displacement resulted in differences due to epistasis between Florida and Alabama populations separated by 300 km but not between Maine and Wisconsin populations separated by 2000 km. Genetic differences between New Jersey and Ontario did not involve either dominance or epistasis and we estimated the minimum number of effective factors contributing to a difference in mean critical photoperiod of 5 SD between them as nE = 5. We propose that the genetic similarity of populations within a broad northern region is due to their more recent origin since recession of the Laurentide Ice Sheet and that the unique genetic architecture of each population is the result of both mutation and repeated migration-founder-flush episodes during the dispersal of W. smithii in North America. Our results suggest that differences in composite additive and dominance effects arise early in the genetic divergence of populations while differences due to epistasis accumulate after more prolonged isolation.

  16. A Computational Analysis of Neural Mechanisms Underlying the Maturation of Multisensory Speech Integration in Neurotypical Children and Those on the Autism Spectrum.

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    Cuppini, Cristiano; Ursino, Mauro; Magosso, Elisa; Ross, Lars A; Foxe, John J; Molholm, Sophie

    2017-01-01

    Failure to appropriately develop multisensory integration (MSI) of audiovisual speech may affect a child's ability to attain optimal communication. Studies have shown protracted development of MSI into late-childhood and identified deficits in MSI in children with an autism spectrum disorder (ASD). Currently, the neural basis of acquisition of this ability is not well understood. Here, we developed a computational model informed by neurophysiology to analyze possible mechanisms underlying MSI maturation, and its delayed development in ASD. The model posits that strengthening of feedforward and cross-sensory connections, responsible for the alignment of auditory and visual speech sound representations in posterior superior temporal gyrus/sulcus, can explain behavioral data on the acquisition of MSI. This was simulated by a training phase during which the network was exposed to unisensory and multisensory stimuli, and projections were crafted by Hebbian rules of potentiation and depression. In its mature architecture, the network also reproduced the well-known multisensory McGurk speech effect. Deficits in audiovisual speech perception in ASD were well accounted for by fewer multisensory exposures, compatible with a lack of attention, but not by reduced synaptic connectivity or synaptic plasticity.

  17. A Computational Analysis of Neural Mechanisms Underlying the Maturation of Multisensory Speech Integration in Neurotypical Children and Those on the Autism Spectrum

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    Cristiano Cuppini

    2017-10-01

    Full Text Available Failure to appropriately develop multisensory integration (MSI of audiovisual speech may affect a child's ability to attain optimal communication. Studies have shown protracted development of MSI into late-childhood and identified deficits in MSI in children with an autism spectrum disorder (ASD. Currently, the neural basis of acquisition of this ability is not well understood. Here, we developed a computational model informed by neurophysiology to analyze possible mechanisms underlying MSI maturation, and its delayed development in ASD. The model posits that strengthening of feedforward and cross-sensory connections, responsible for the alignment of auditory and visual speech sound representations in posterior superior temporal gyrus/sulcus, can explain behavioral data on the acquisition of MSI. This was simulated by a training phase during which the network was exposed to unisensory and multisensory stimuli, and projections were crafted by Hebbian rules of potentiation and depression. In its mature architecture, the network also reproduced the well-known multisensory McGurk speech effect. Deficits in audiovisual speech perception in ASD were well accounted for by fewer multisensory exposures, compatible with a lack of attention, but not by reduced synaptic connectivity or synaptic plasticity.

  18. Histological Architecture Underlying Brain-Immune Cell-Cell Interactions and the Cerebral Response to Systemic Inflammation.

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    Shimada, Atsuyoshi; Hasegawa-Ishii, Sanae

    2017-01-01

    Although the brain is now known to actively interact with the immune system under non-inflammatory conditions, the site of cell-cell interactions between brain parenchymal cells and immune cells has been an open question until recently. Studies by our and other groups have indicated that brain structures such as the leptomeninges, choroid plexus stroma and epithelium, attachments of choroid plexus, vascular endothelial cells, cells of the perivascular space, circumventricular organs, and astrocytic endfeet construct the histological architecture that provides a location for intercellular interactions between bone marrow-derived myeloid lineage cells and brain parenchymal cells under non-inflammatory conditions. This architecture also functions as the interface between the brain and the immune system, through which systemic inflammation-induced molecular events can be relayed to the brain parenchyma at early stages of systemic inflammation during which the blood-brain barrier is relatively preserved. Although brain microglia are well known to be activated by systemic inflammation, the mechanism by which systemic inflammatory challenge and microglial activation are connected has not been well documented. Perturbed brain-immune interaction underlies a wide variety of neurological and psychiatric disorders including ischemic brain injury, status epilepticus, repeated social defeat, and neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. Proinflammatory status associated with cytokine imbalance is involved in autism spectrum disorders, schizophrenia, and depression. In this article, we propose a mechanism connecting systemic inflammation, brain-immune interface cells, and brain parenchymal cells and discuss the relevance of basic studies of the mechanism to neurological disorders with a special emphasis on sepsis-associated encephalopathy and preterm brain injury.

  19. Genome-Wide Association Study Dissecting the Genetic Architecture Underlying the Branch Angle Trait in Rapeseed (Brassica napus L.).

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    Sun, Chengming; Wang, Benqi; Wang, Xiaohua; Hu, Kaining; Li, Kaidi; Li, Zhanyu; Li, San; Yan, Lei; Guan, Chunyun; Zhang, Jiefu; Zhang, Zhenqian; Chen, Song; Wen, Jing; Tu, Jinxing; Shen, Jinxiong; Fu, Tingdong; Yi, Bin

    2016-09-20

    The rapeseed branch angle is an important morphological trait because an adequate branch angle enables more efficient light capture under high planting densities. Here, we report that the average angle of the five top branches provides a reliable representation of the average angle of all branches. Statistical analyses revealed a significantly positive correlation between the branch angle and multiple plant-type and yield-related traits. The 60 K Brassica Infinium(®) single nucleotide polymorphism (SNP) array was utilized to genotype an association panel with 520 diverse accessions. A genome-wide association study was performed to determine the genetic architecture of branch angle, and 56 loci were identified as being significantly associated with the branch angle trait via three models, including a robust, novel, nonparametric Anderson-Darling (A-D) test. Moreover, these loci explained 51.1% of the phenotypic variation when a simple additive model was applied. Within the linkage disequilibrium (LD) decay ranges of 53 loci, we observed plausible candidates orthologous to documented Arabidopsis genes, such as LAZY1, SGR2, SGR4, SGR8, SGR9, PIN3, PIN7, CRK5, TIR1, and APD7. These results provide insight into the genetic basis of the branch angle trait in rapeseed and might facilitate marker-based breeding for improvements in plant architecture.

  20. An auditory neural correlate suggests a mechanism underlying holistic pitch perception.

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    Daryl Wile

    Full Text Available Current theories of auditory pitch perception propose that cochlear place (spectral and activity timing pattern (temporal information are somehow combined within the brain to produce holistic pitch percepts, yet the neural mechanisms for integrating these two kinds of information remain obscure. To examine this process in more detail, stimuli made up of three pure tones whose components are individually resolved by the peripheral auditory system, but that nonetheless elicit a holistic, "missing fundamental" pitch percept, were played to human listeners. A technique was used to separate neural timing activity related to individual components of the tone complexes from timing activity related to an emergent feature of the complex (the envelope, and the region of the tonotopic map where information could originate from was simultaneously restricted by masking noise. Pitch percepts were mirrored to a very high degree by a simple combination of component-related and envelope-related neural responses with similar timing that originate within higher-frequency regions of the tonotopic map where stimulus components interact. These results suggest a coding scheme for holistic pitches whereby limited regions of the tonotopic map (spectral places carrying envelope- and component-related activity with similar timing patterns selectively provide a key source of neural pitch information. A similar mechanism of integration between local and emergent object properties may contribute to holistic percepts in a variety of sensory systems.

  1. Developmental Pathway Genes and Neural Plasticity Underlying Emotional Learning and Stress-Related Disorders

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    Maheau, Marissa E.; Ressler, Kerry J.

    2017-01-01

    The manipulation of neural plasticity as a means of intervening in the onset and progression of stress-related disorders retains its appeal for many researchers, despite our limited success in translating such interventions from the laboratory to the clinic. Given the challenges of identifying individual genetic variants that confer increased risk…

  2. Dissociable neural mechanisms underlying the modulation of pain and anxiety? An FMRI pilot study.

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    Katja Wiech

    Full Text Available The down-regulation of pain through beliefs is commonly discussed as a form of emotion regulation. In line with this interpretation, the analgesic effect has been shown to co-occur with reduced anxiety and increased activity in the ventrolateral prefrontal cortex (VLPFC, which is a key region of emotion regulation. This link between pain and anxiety modulation raises the question whether the two effects are rooted in the same neural mechanism. In this pilot fMRI study, we compared the neural basis of the analgesic and anxiolytic effect of two types of threat modulation: a "behavioral control" paradigm, which involves the ability to terminate a noxious stimulus, and a "safety signaling" paradigm, which involves visual cues that signal the threat (or absence of threat that a subsequent noxious stimulus might be of unusually high intensity. Analgesia was paralleled by VLPFC activity during behavioral control. Safety signaling engaged elements of the descending pain control system, including the rostral anterior cingulate cortex that showed increased functional connectivity with the periaqueductal gray and VLPFC. Anxiety reduction, in contrast, scaled with dorsolateral prefrontal cortex activation during behavioral control but had no distinct neural signature during safety signaling. Our pilot data therefore suggest that analgesic and anxiolytic effects are instantiated in distinguishable neural mechanisms and differ between distinct stress- and pain-modulatory approaches, supporting the recent notion of multiple pathways subserving top-down modulation of the pain experience. Additional studies in larger cohorts are needed to follow up on these preliminary findings.

  3. Infants' somatotopic neural responses to seeing human actions: I've got you under my skin.

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    Joni N Saby

    Full Text Available Human infants rapidly learn new skills and customs via imitation, but the neural linkages between action perception and production are not well understood. Neuroscience studies in adults suggest that a key component of imitation-identifying the corresponding body part used in the acts of self and other-has an organized neural signature. In adults, perceiving someone using a specific body part (e.g., hand vs. foot is associated with activation of the corresponding area of the sensory and/or motor strip in the observer's brain-a phenomenon called neural somatotopy. Here we examine whether preverbal infants also exhibit somatotopic neural responses during the observation of others' actions. 14-month-old infants were randomly assigned to watch an adult reach towards and touch an object using either her hand or her foot. The scalp electroencephalogram (EEG was recorded and event-related changes in the sensorimotor mu rhythm were analyzed. Mu rhythm desynchronization was greater over hand areas of sensorimotor cortex during observation of hand actions and was greater over the foot area for observation of foot actions. This provides the first evidence that infants' observation of someone else using a particular body part activates the corresponding areas of sensorimotor cortex. We hypothesize that this somatotopic organization in the developing brain supports imitation and cultural learning. The findings connect developmental cognitive neuroscience, adult neuroscience, action representation, and behavioral imitation.

  4. Transitions in Land Use Architecture under Multiple Human Driving Forces in a Semi-Arid Zone

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    Issa Ouedraogo

    2015-07-01

    Full Text Available The present study aimed to detect the main shifts in land-use architecture and assess the factors behind the changes in typical tropical semi-arid land in Burkina Faso. Three sets of time-series LANDSAT data over a 23-year period were used to detect land use changes and their underpinning drivers in multifunctional but vulnerable ecologies. Group discussions in selected villages were organized for mapping output interpretation and collection of essential drivers of change as perceived by local populations. Results revealed profound changes and transitions during the study period. During the last decade, shrub and wood savannahs exhibited high net changes (39% and −37% respectively with a weak net positive change for cropland (only 2%, while cropland and shrub savannah exhibited high swap (8% and 16%. This suggests that the area of cropland remained almost unchanged but was subject to relocation, wood savannah decreased drastically, and shrub savannah increased exponentially. Cropland exhibited a null net persistence while shrub and wood savannahs exhibited positive and negative net persistence (1.91 and −10.24, respectively, indicating that there is movement toward agricultural intensification and wood savannah tended to disappear to the benefit of shrub savannah. Local people are aware of the changes that have occurred and support the idea that illegal wood cutting and farming are inappropriate farming practices associated with immigration; absence of alternative cash generation sources, overgrazing and increasing demand for wood energy are driving the changes in their ecosystems. Policies that integrate restoration and conservation of natural ecosystems and promote sustainable agroforestry practices in the study zone are highly recommended.

  5. Common genetic architecture underlying young children's food fussiness and liking for vegetables and fruit

    NARCIS (Netherlands)

    Fildes, A.; Jaarsveld, C.H.M. van; Cooke, L.; Wardle, J.; Llewellyn, C.H.

    2016-01-01

    BACKGROUND: Food fussiness (FF) is common in early childhood and is often associated with the rejection of nutrient-dense foods such as vegetables and fruit. FF and liking for vegetables and fruit are likely all heritable phenotypes; the genetic influence underlying FF may explain the observed

  6. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials

    OpenAIRE

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Millán-Calenti, José Carlos; Del Percio, Claudio; Buján, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between “seen” trials and “not seen” trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expre...

  7. Neural activity underlying motor-action preparation and cognitive narrowing in approach-motivated goal states.

    Science.gov (United States)

    Gable, Philip A; Threadgill, A Hunter; Adams, David L

    2016-02-01

    High-approach-motivated (pre-goal) positive affect states encourage tenacious goal pursuit and narrow cognitive scope. As such, high approach-motivated states likely enhance the neural correlates of motor-action preparation to aid in goal acquisition. These neural correlates may also relate to the cognitive narrowing associated with high approach-motivated states. In the present study, we investigated motor-action preparation during pre-goal and post-goal states using an index of beta suppression over the motor cortex. The results revealed that beta suppression was greatest in pre-goal positive states, suggesting that higher levels of motor-action preparation occur during high approach-motivated positive states. Furthermore, beta and alpha suppression in the high approach-motivated positive states predicted greater cognitive narrowing. These results suggest that approach-motivated pre-goal states engage the neural substrates of motor-action preparation and cognitive narrowing. Individual differences in motor-action preparation relate to the degree of cognitive narrowing.

  8. Artificial language training reveals the neural substrates underlying addressed and assembled phonologies.

    Directory of Open Access Journals (Sweden)

    Leilei Mei

    Full Text Available Although behavioral and neuropsychological studies have suggested two distinct routes of phonological access, their neural substrates have not been clearly elucidated. Here, we designed an artificial language (based on Korean Hangul that can be read either through addressed (i.e., whole word mapping or assembled (i.e., grapheme-to-phoneme mapping phonology. Two matched groups of native English-speaking participants were trained in one of the two conditions, one hour per day for eight days. Behavioral results showed that both groups correctly named more than 90% of the trained words after training. At the neural level, we found a clear dissociation of the neural pathways for addressed and assembled phonologies: There was greater involvement of the anterior cingulate cortex, posterior cingulate cortex, right orbital frontal cortex, angular gyrus and middle temporal gyrus for addressed phonology, but stronger activation in the left precentral gyrus/inferior frontal gyrus and supramarginal gyrus for assembled phonology. Furthermore, we found evidence supporting the strategy-shift hypothesis, which postulates that, with practice, reading strategy shifts from assembled to addressed phonology. Specifically, compared to untrained words, trained words in the assembled phonology group showed stronger activation in the addressed phonology network and less activation in the assembled phonology network. Our results provide clear brain-imaging evidence for the dual-route models of reading.

  9. Predicting Carbonation Depth of Prestressed Concrete under Different Stress States Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Chunhua Lu

    2009-01-01

    Full Text Available Two artificial neural networks (ANN, back-propagation neural network (BPNN and the radial basis function neural network (RBFNN, are proposed to predict the carbonation depth of prestressed concrete. In order to generate the training and testing data for the ANNs, an accelerated carbonation experiment was carried out, and the influence of stress level of concrete on carbonation process was taken into account especially. Then, based on the experimental results, the BPNN and RBFNN models which all take the stress level of concrete, water-cement ratio, cement-fine aggregate, cement-coarse aggregate ratio and testing age as input parameters were built and all the training and testing work was performed in MATLAB. It can be found that the two ANN models seem to have a high prediction and generalization capability in evaluation of carbonation depth, and the largest absolute percentage errors of BPNN and RBFNN are 10.88% and 8.46%, respectively. The RBFNN model shows a better prediction precision in comparison to BPNN model.

  10. Neural mechanisms underlying sound-induced visual motion perception: An fMRI study.

    Science.gov (United States)

    Hidaka, Souta; Higuchi, Satomi; Teramoto, Wataru; Sugita, Yoichi

    2017-07-01

    Studies of crossmodal interactions in motion perception have reported activation in several brain areas, including those related to motion processing and/or sensory association, in response to multimodal (e.g., visual and auditory) stimuli that were both in motion. Recent studies have demonstrated that sounds can trigger illusory visual apparent motion to static visual stimuli (sound-induced visual motion: SIVM): A visual stimulus blinking at a fixed location is perceived to be moving laterally when an alternating left-right sound is also present. Here, we investigated brain activity related to the perception of SIVM using a 7T functional magnetic resonance imaging technique. Specifically, we focused on the patterns of neural activities in SIVM and visually induced visual apparent motion (VIVM). We observed shared activations in the middle occipital area (V5/hMT), which is thought to be involved in visual motion processing, for SIVM and VIVM. Moreover, as compared to VIVM, SIVM resulted in greater activation in the superior temporal area and dominant functional connectivity between the V5/hMT area and the areas related to auditory and crossmodal motion processing. These findings indicate that similar but partially different neural mechanisms could be involved in auditory-induced and visually-induced motion perception, and neural signals in auditory, visual, and, crossmodal motion processing areas closely and directly interact in the perception of SIVM. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials

    Science.gov (United States)

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Millán-Calenti, José Carlos; Del Percio, Claudio; Buján, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between “seen” trials and “not seen” trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expression, and written words). Results showed the typical visual ERP components in both “seen” and “not seen” trials. There was no statistical difference in the ERP peak latencies between the “seen” and “not seen” trials, suggesting a similar timing of the cortical neural synchronization regardless the primary visual consciousness. In contrast, ERP sources showed differences between “seen” and “not seen” trials. For the visuospatial stimuli, the primary consciousness was related to higher activity in dorsal occipital and parietal sources at about 400 ms post-stimulus. For the emotional face expressions, there was greater activity in parietal and frontal sources at about 180 ms post-stimulus. For the written letters, there was higher activity in occipital, parietal and temporal sources at about 230 ms post-stimulus. These results hint that primary visual consciousness is associated with an enhanced cortical neural synchronization having entirely different spatiotemporal characteristics as a function of the features of the visual stimuli and possibly, the relative qualia (i.e., visuospatial, face expression, and words). In this framework, the dorsal visual stream may be synchronized in association with the primary consciousness of visuospatial and emotional face contents. Analogously, both dorsal and ventral visual streams may be synchronized in association with the primary consciousness of linguistic contents. In this line of reasoning, the ensemble

  12. Cortical Neural Synchronization Underlies Primary Visual Consciousness of Qualia: Evidence from Event-Related Potentials.

    Science.gov (United States)

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Millán-Calenti, José Carlos; Del Percio, Claudio; Buján, Ana

    2016-01-01

    This article reviews three experiments on event-related potentials (ERPs) testing the hypothesis that primary visual consciousness (stimulus self-report) is related to enhanced cortical neural synchronization as a function of stimulus features. ERP peak latency and sources were compared between "seen" trials and "not seen" trials, respectively related and unrelated to the primary visual consciousness. Three salient features of visual stimuli were considered (visuospatial, emotional face expression, and written words). Results showed the typical visual ERP components in both "seen" and "not seen" trials. There was no statistical difference in the ERP peak latencies between the "seen" and "not seen" trials, suggesting a similar timing of the cortical neural synchronization regardless the primary visual consciousness. In contrast, ERP sources showed differences between "seen" and "not seen" trials. For the visuospatial stimuli, the primary consciousness was related to higher activity in dorsal occipital and parietal sources at about 400 ms post-stimulus. For the emotional face expressions, there was greater activity in parietal and frontal sources at about 180 ms post-stimulus. For the written letters, there was higher activity in occipital, parietal and temporal sources at about 230 ms post-stimulus. These results hint that primary visual consciousness is associated with an enhanced cortical neural synchronization having entirely different spatiotemporal characteristics as a function of the features of the visual stimuli and possibly, the relative qualia (i.e., visuospatial, face expression, and words). In this framework, the dorsal visual stream may be synchronized in association with the primary consciousness of visuospatial and emotional face contents. Analogously, both dorsal and ventral visual streams may be synchronized in association with the primary consciousness of linguistic contents. In this line of reasoning, the ensemble of the cortical neural networks

  13. Genetic architecture of delayed senescence, biomass, and grain yield under drought stress in cowpea.

    Directory of Open Access Journals (Sweden)

    Wellington Muchero

    Full Text Available The stay-green phenomenon is a key plant trait with wide usage in managing crop production under limited water conditions. This trait enhances delayed senescence, biomass, and grain yield under drought stress. In this study we sought to identify QTLs in cowpea (Vigna unguiculata consistent across experiments conducted in Burkina Faso, Nigeria, Senegal, and the United States of America under limited water conditions. A panel of 383 diverse cowpea accessions and a recombinant inbred line population (RIL were SNP genotyped using an Illumina 1536 GoldenGate assay. Phenotypic data from thirteen experiments conducted across the four countries were used to identify SNP-trait associations based on linkage disequilibrium association mapping, with bi-parental QTL mapping as a complementary strategy. We identified seven loci, five of which exhibited evidence suggesting pleiotropic effects (stay-green between delayed senescence, biomass, and grain yield. Further, we provide evidence suggesting the existence of positive pleiotropy in cowpea based on positively correlated mean phenotypic values (0.34< r <0.87 and allele effects (0.07< r <0.86 for delayed senescence and grain yield across three African environments. Three of the five putative stay-green QTLs, Dro-1, 3, and 7 were identified in both RILs and diverse germplasm with resolutions of 3.2 cM or less for each of the three loci, suggesting that these may be valuable targets for marker-assisted breeding in cowpea. Also, the co-location of early vegetative delayed senescence with biomass and grain yield QTLs suggests the possibility of using delayed senescence at the seedling stage as a rapid screening tool for post-flowering drought tolerance in cowpea breeding. BLAST analysis using EST sequences harboring SNPs with the highest associations provided a genomic context for loci identified in this study in closely related common bean (Phaseolus vulgaris and soybean (Glycine max reference genomes.

  14. Robust Finite-Time Stabilization of Fractional-Order Neural Networks With Discontinuous and Continuous Activation Functions Under Uncertainty.

    Science.gov (United States)

    Ding, Zhixia; Zeng, Zhigang; Wang, Leimin

    2017-03-10

    This paper is concerned with robust finite-time stabilization for a class of fractional-order neural networks (FNNs) with two types of activation functions (i.e., discontinuous and continuous activation function) under uncertainty. It is worth noting that there exist few results about FNNs with discontinuous activation functions, which is mainly because classical solutions and theories of differential equations cannot be applied in this case. Especially, there is no relevant finite-time stabilization research for such system, and this paper makes up for the gap. The existence of global solution under the framework of Filippov for such system is guaranteed by limiting discontinuous activation functions. According to set-valued analysis and Kakutani's fixed point theorem, we obtain the existence of equilibrium point. In particular, based on differential inclusion theory and fractional Lyapunov stability theory, several new sufficient conditions are given to ensure finite-time stabilization via a novel discontinuous controller, and the upper bound of the settling time for stabilization is estimated. In addition, we analyze the finite-time stabilization of FNNs with Lipschitz-continuous activation functions under uncertainty. The results of this paper improve corresponding ones of integer-order neural networks with discontinuous and continuous activation functions. Finally, three numerical examples are given to show the effectiveness of the theoretical results.

  15. Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar.

    Science.gov (United States)

    Lomp, Oliver; Richter, Mathis; Zibner, Stephan K U; Schöner, Gregor

    2016-01-01

    Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

  16. Developing dynamic field theory architectures for embodied cognitive systems with cedar

    Directory of Open Access Journals (Sweden)

    Oliver Lomp

    2016-11-01

    Full Text Available Embodied artificial cognitive systems such as autonomous robots or intelligent observers connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT, a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real-time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

  17. Genetic variants associated with the root system architecture of oilseed rape (Brassica napus L.) under contrasting phosphate supply

    OpenAIRE

    Wang, Xiaohua; Chen, Yanling; Thomas, Catherine L.; Ding, Guangda; Xu, Ping; Shi, Dexu; Grandke, Fabian; Jin, Kemo; Cai, Hongmei; Xu, Fangsen; Yi, Bin; Broadley, Martin R.; Shi, Lei

    2017-01-01

    Breeding crops with ideal root system architecture for efficient absorption of phosphorus is an important strategy to reduce the use of phosphate fertilizers. To investigate genetic variants leading to changes in root system architecture, 405 oilseed rape cultivars were genotyped with a 60K Brassica Infinium SNP array in low and high P environments. A total of 285 single-nucleotide polymorphisms were associated with root system architecture traits at varying phosphorus levels. Nine single-nuc...

  18. Neural systems underlying aversive conditioning in humans with primary and secondary reinforcers

    Directory of Open Access Journals (Sweden)

    Mauricio R Delgado

    2011-05-01

    Full Text Available Money is a secondary reinforcer commonly used across a range of disciplines in experimental paradigms investigating reward learning and decision-making. The effectiveness of monetary reinforcers during aversive learning and its neural basis, however, remains a topic of debate. Specifically, it is unclear if the initial acquisition of aversive representations of monetary losses depends on similar neural systems as more traditional aversive conditioning that involves primary reinforcers. This study contrasts the efficacy of a biologically defined primary reinforcer (shock and a socially defined secondary reinforcer (money during aversive learning and its associated neural circuitry. During a two-part experiment, participants first played a gambling game where wins and losses were based on performance to gain an experimental bank. Participants were then exposed to two separate aversive conditioning sessions. In one session, a primary reinforcer (mild shock served as an unconditioned stimulus (US and was paired with one of two colored squares, the conditioned stimuli (CS+ and CS-, respectively. In another session, a secondary reinforcer (loss of money served as the US and was paired with one of two different CS. Skin conductance responses were greater for CS+ compared to CS- trials irrespective of type of reinforcer. Neuroimaging results revealed that the striatum, a region typically linked with reward-related processing, was found to be involved in the acquisition of aversive conditioned response irrespective of reinforcer type. In contrast, the amygdala was involved during aversive conditioning with primary reinforcers, as suggested by both an exploratory fMRI analysis and a follow-up case study with a patient with bilateral amygdala damage. Taken together, these results suggest that learning about potential monetary losses may depend on reinforcement learning related systems, rather than on typical structures involved in more biologically based

  19. Effect of abacus training on executive function development and underlying neural correlates in Chinese children.

    Science.gov (United States)

    Wang, Chunjie; Weng, Jian; Yao, Yuan; Dong, Shanshan; Liu, Yuqiu; Chen, Feiyan

    2017-10-01

    Executive function (EF) refers to a set of cognitive abilities involved in self-regulated behavior. Given the critical role of EF in cognition, strategies for improving EF have attracted intensive attention in recent years. Previous studies have explored the effects of abacus-based mental calculation (AMC) training on several cognitive abilities. However, it remains unclear whether AMC training affects EF and its neural correlates. In this study, participants were randomly assigned to AMC or control groups upon starting primary school. The AMC group received 2 h AMC training every week, while the control group did not have any abacus experience. Neural activity during an EF task was examined using functional MRI for both groups in their 4th and 6th grades. Our results showed that the AMC group performed better and faster than the control group in both grades. They also had lower activation in the frontoparietal reigons than the control group in the 6th grade. From the 4th to the 6th grade, the AMC group showed activation decreases in the frontoparietal regions, while the control group exhibited an opposite pattern. Furthermore, voxel-wise regression analyses revealed that better performance was associated with lower task-relevant brain activity in the AMC group but associated with greater task-relevant brain activity in the control group. These results suggest that long-term AMC training, with calculation ability as its original target, may improve EF and enhance neural efficiency of the frontoparietal regions during development. Hum Brain Mapp 38:5234-5249, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  20. Neural Mechanisms Underlying Musical Pitch Perception and Clinical Applications Including Developmental Dyslexia.

    Science.gov (United States)

    Yuskaitis, Christopher J; Parviz, Mahsa; Loui, Psyche; Wan, Catherine Y; Pearl, Phillip L

    2015-08-01

    Music production and perception invoke a complex set of cognitive functions that rely on the integration of sensorimotor, cognitive, and emotional pathways. Pitch is a fundamental perceptual attribute of sound and a building block for both music and speech. Although the cerebral processing of pitch is not completely understood, recent advances in imaging and electrophysiology have provided insight into the functional and anatomical pathways of pitch processing. This review examines the current understanding of pitch processing and behavioral and neural variations that give rise to difficulties in pitch processing, and potential applications of music education for language processing disorders such as dyslexia.

  1. Neural Mechanisms Underlying Musical Pitch Perception and Clinical Applications including Developmental Dyselxia

    Science.gov (United States)

    Yuskaitis, Christopher J.; Parviz, Mahsa; Loui, Psyche; Wan, Catherine Y.; Pearl, Phillip L.

    2017-01-01

    Music production and perception invoke a complex set of cognitive functions that rely on the integration of sensory-motor, cognitive, and emotional pathways. Pitch is a fundamental perceptual attribute of sound and a building block for both music and speech. Although the cerebral processing of pitch is not completely understood, recent advances in imaging and electrophysiology have provided insight into the functional and anatomical pathways of pitch processing. This review examines the current understanding of pitch processing, behavioral and neural variations that give rise to difficulties in pitch processing, and potential applications of music education for language processing disorders such as dyslexia. PMID:26092314

  2. Identifying temporal and causal contributions of neural processes underlying the Implicit Association Test (IAT

    Directory of Open Access Journals (Sweden)

    Chad Edward Forbes

    2012-11-01

    Full Text Available The Implicit Association Test (IAT is a popular behavioral measure that assesses the associative strength between outgroup members and stereotypical and counterstereotypical traits. Less is known, however, about the degree to which the IAT reflects automatic processing. Two studies examined automatic processing contributions to a gender-IAT using a data driven, social neuroscience approach. Performance on congruent (e.g., categorizing male names with synonyms of strength and incongruent (e.g., categorizing female names with synonyms of strength IAT blocks were separately analyzed using EEG (event-related potentials, or ERPs, and coherence; Study 1 and lesion (Study 2 methodologies. Compared to incongruent blocks, performance on congruent IAT blocks was associated with more positive ERPs that manifested in frontal and occipital regions at automatic processing speeds, occipital regions at more controlled processing speeds and was compromised by volume loss in the anterior temporal lobe, insula and medial PFC. Performance on incongruent blocks was associated with volume loss in supplementary motor areas, cingulate gyrus and a region in medial PFC similar to that found for congruent blocks. Greater coherence was found between frontal and occipital regions to the extent individuals exhibited more bias. This suggests there are separable neural contributions to congruent and incongruent blocks of the IAT but there is also a surprising amount of overlap. Given the temporal and regional neural distinctions, these results provide converging evidence that stereotypic associative strength assessed by the IAT indexes automatic processing to a degree.

  3. Neural emotion regulation circuitry underlying anxiolytic effects of perceived control over pain.

    Science.gov (United States)

    Salomons, Tim V; Nusslock, Robin; Detloff, Allison; Johnstone, Tom; Davidson, Richard J

    2015-02-01

    Anxiolytic effects of perceived control have been observed across species. In humans, neuroimaging studies have suggested that perceived control and cognitive reappraisal reduce negative affect through similar mechanisms. An important limitation of extant neuroimaging studies of perceived control in terms of directly testing this hypothesis, however, is the use of within-subject designs, which confound participants' affective response to controllable and uncontrollable stress. To compare neural and affective responses when participants were exposed to either uncontrollable or controllable stress, two groups of participants received an identical series of stressors (thermal pain stimuli). One group ("controllable") was led to believe they had behavioral control over the pain stimuli, whereas another ("uncontrollable") believed they had no control. Controllable pain was associated with decreased state anxiety, decreased activation in amygdala, and increased activation in nucleus accumbens. In participants who perceived control over the pain, reduced state anxiety was associated with increased functional connectivity between each of these regions and ventral lateral/ventral medial pFC. The location of pFC findings is consistent with regions found to be critical for the anxiolytic effects of perceived control in rodents. Furthermore, interactions observed between pFC and both amygdala and nucleus accumbens are remarkably similar to neural mechanisms of emotion regulation through reappraisal in humans. These results suggest that perceived control reduces negative affect through a general mechanism involved in the cognitive regulation of emotion.

  4. Neural mechanisms underlying contextual dependency of subjective values: converging evidence from monkeys and humans.

    Science.gov (United States)

    Abitbol, Raphaëlle; Lebreton, Maël; Hollard, Guillaume; Richmond, Barry J; Bouret, Sébastien; Pessiglione, Mathias

    2015-02-04

    A major challenge for decision theory is to account for the instability of expressed preferences across time and context. Such variability could arise from specific properties of the brain system used to assign subjective values. Growing evidence has identified the ventromedial prefrontal cortex (VMPFC) as a key node of the human brain valuation system. Here, we first replicate this observation with an fMRI study in humans showing that subjective values of painting pictures, as expressed in explicit pleasantness ratings, are specifically encoded in the VMPFC. We then establish a bridge with monkey electrophysiology, by comparing single-unit activity evoked by visual cues between the VMPFC and the orbitofrontal cortex. At the neural population level, expected reward magnitude was only encoded in the VMPFC, which also reflected subjective cue values, as expressed in Pavlovian appetitive responses. In addition, we demonstrate in both species that the additive effect of prestimulus activity on evoked activity has a significant impact on subjective values. In monkeys, the factor dominating prestimulus VMPFC activity was trial number, which likely indexed variations in internal dispositions related to fatigue or satiety. In humans, prestimulus VMPFC activity was externally manipulated through changes in the musical context, which induced a systematic bias in subjective values. Thus, the apparent stochasticity of preferences might relate to the VMPFC automatically aggregating the values of contextual features, which would bias subsequent valuation because of temporal autocorrelation in neural activity. Copyright © 2015 the authors 0270-6474/15/352308-13$15.00/0.

  5. Neural circuit dynamics underlying accumulation of time-varying evidence during perceptual decision making

    Directory of Open Access Journals (Sweden)

    Kong-Fatt Wong

    2007-11-01

    Full Text Available How do neurons in a decision circuit integrate time-varying signals, in favor of or against alternative choice options? To address this question, we used a recurrent neural circuit model to simulate an experiment in which monkeys performed a direction-discrimination task on a visual motion stimulus. In a recent study, it was found that brief pulses of motion perturbed neural activity in the lateral intraparietal area (LIP, and exerted corresponding effects on the monkey's choices and response times. Our model reproduces the behavioral observations and replicates LIP activity which, depending on whether the direction of the pulse is the same or opposite to that of a preferred motion stimulus, increases or decreases persistently over a few hundred milliseconds. Furthermore, our model accounts for the observation that the pulse exerts a weaker influence on LIP neuronal responses when the pulse is late relative to motion stimulus onset. We show that this violation of time-shift invariance (TSI is consistent with a recurrent circuit mechanism of time integration. We further examine time integration using two consecutive pulses of the same or opposite motion directions. The induced changes in the performance are not additive, and the second of the paired pulses is less effective than its standalone impact, a prediction that is experimentally testable. Taken together, these findings lend further support for an attractor network model of time integration in perceptual decision making.

  6. Neural network configuration and efficiency underlies individual differences in spatial orientation ability.

    Science.gov (United States)

    Arnold, Aiden E G F; Protzner, Andrea B; Bray, Signe; Levy, Richard M; Iaria, Giuseppe

    2014-02-01

    Spatial orientation is a complex cognitive process requiring the integration of information processed in a distributed system of brain regions. Current models on the neural basis of spatial orientation are based primarily on the functional role of single brain regions, with limited understanding of how interaction among these brain regions relates to behavior. In this study, we investigated two sources of variability in the neural networks that support spatial orientation--network configuration and efficiency--and assessed whether variability in these topological properties relates to individual differences in orientation accuracy. Participants with higher accuracy were shown to express greater activity in the right supramarginal gyrus, the right precentral cortex, and the left hippocampus, over and above a core network engaged by the whole group. Additionally, high-performing individuals had increased levels of global efficiency within a resting-state network composed of brain regions engaged during orientation and increased levels of node centrality in the right supramarginal gyrus, the right primary motor cortex, and the left hippocampus. These results indicate that individual differences in the configuration of task-related networks and their efficiency measured at rest relate to the ability to spatially orient. Our findings advance systems neuroscience models of orientation and navigation by providing insight into the role of functional integration in shaping orientation behavior.

  7. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

  8. Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

    Science.gov (United States)

    Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

    2017-01-01

    Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

  9. Assessing Optimal Neural Network Architecture for Identifying Disease-associated Multi-marker Genotypes using a Permutation Test, and Application to Calpain 10 Polymorphisms Associated with Diabetes

    National Research Council Canada - National Science Library

    North B.V; Curtis D; Cassell P.G; Hitman G.A; Sham P.C

    2003-01-01

    .... An artificial neural network allows investigation of association between disease phenotype and tightly linked markers without requiring haplotype phase and without modelling any evolutionary history...

  10. Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

    Directory of Open Access Journals (Sweden)

    Ali T. Hasan

    2012-01-01

    Full Text Available This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.

  11. Estimation of lost circulation amount occurs during under balanced drilling using drilling data and neural network

    Directory of Open Access Journals (Sweden)

    Pouria Behnoud far

    2017-09-01

    Full Text Available Lost circulation can cause an increase in time and cost of operation. Pipe sticking, formation damage and uncontrolled flow of oil and gas may be consequences of lost circulation. Dealing with this problem is a key factor to conduct a successful drilling operation. Estimation of lost circulation amount is necessary to find a solution. Lost circulation is influenced by different parameters such as mud weight, pump pressure, depth etc. Mud weight, pump pressure and flow rate of mud should be designed to prevent induced fractures and have the least amount of lost circulation. Artificial neural network is useful to find the relations of parameters with lost circulation. Genetic algorithm is applied on the achieved relations to determine the optimum mud weight, pump pressure, and flow rate. In an Iranian oil field, daily drilling reports of wells which are drilled using UBD technique are studied. Asmari formation is the most important oil reservoir of the studied field and UBD is used only in this interval. Three wells with the most, moderate and without lost circulation are chosen. In this article, the effect of mud weight, depth, pump pressure and flow rate of pump on lost circulation in UBD of Asmari formation in one of the Southwest Iranian fields is studied using drilling data and artificial neural network. In addition, the amount of lost circulation is predicted precisely with respect to two of the studied parameters using the presented correlations and the optimum mud weight, pump pressure and flow rate are calculated to minimize the lost circulation amount.

  12. Neural Mechanisms Underlying Affective Theory of Mind in Violent Antisocial Personality Disorder and/or Schizophrenia.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Müller, Bernhard W; Wiltfang, Jens; Brüne, Martin; Forsting, Michael; Gizewski, Elke R; Leygraf, Norbert; Hodgins, Sheilagh

    2017-10-21

    Among violent offenders with schizophrenia, there are 2 sub-groups, one with and one without, conduct disorder (CD) and antisocial personality disorder (ASPD), who differ as to treatment response and alterations of brain structure. The present study aimed to determine whether the 2 groups also differ in Theory of Mind and neural activations subsuming this task. Five groups of men were compared: 3 groups of violent offenders-schizophrenia plus CD/ASPD, schizophrenia with no history of antisocial behavior prior to illness onset, and CD/ASPD with no severe mental illness-and 2 groups of non-offenders, one with schizophrenia and one without (H). Participants completed diagnostic interviews, the Psychopathy Checklist Screening Version Interview, the Interpersonal Reactivity Index, authorized access to clinical and criminal files, and underwent functional magnetic resonance imaging while completing an adapted version of the Reading-the-Mind-in-the-Eyes Task (RMET). Relative to H, nonviolent and violent men with schizophrenia and not CD/ASPD performed more poorly on the RMET, while violent offenders with CD/ASPD, both those with and without schizophrenia, performed similarly. The 2 groups of violent offenders with CD/ASPD, both those with and without schizophrenia, relative to the other groups, displayed higher levels of activation in a network of prefrontal and temporal-parietal regions and reduced activation in the amygdala. Relative to men without CD/ASPD, both groups of violent offenders with CD/ASPD displayed a distinct pattern of neural responses during emotional/mental state attribution pointing to distinct and comparatively successful processing of social information. © The Author 2017. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  13. Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation

    Science.gov (United States)

    Hayashibe, Mitsuhiro; Zhang, Qin; Guiraud, David; Fattal, Charles

    2011-10-01

    In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.

  14. Genome-wide association analyses reveal complex genetic architecture underlying natural variation for flowering time in canola.

    Science.gov (United States)

    Raman, H; Raman, R; Coombes, N; Song, J; Prangnell, R; Bandaranayake, C; Tahira, R; Sundaramoorthi, V; Killian, A; Meng, J; Dennis, E S; Balasubramanian, S

    2016-06-01

    Optimum flowering time is the key to maximize canola production in order to meet global demand of vegetable oil, biodiesel and canola-meal. We reveal extensive variation in flowering time across diverse genotypes of canola under field, glasshouse and controlled environmental conditions. We conduct a genome-wide association study and identify 69 single nucleotide polymorphism (SNP) markers associated with flowering time, which are repeatedly detected across experiments. Several associated SNPs occur in clusters across the canola genome; seven of them were detected within 20 Kb regions of a priori candidate genes; FLOWERING LOCUS T, FRUITFUL, FLOWERING LOCUS C, CONSTANS, FRIGIDA, PHYTOCHROME B and an additional five SNPs were localized within 14 Kb of a previously identified quantitative trait loci for flowering time. Expression analyses showed that among FLC paralogs, BnFLC.A2 accounts for ~23% of natural variation in diverse accessions. Genome-wide association analysis for FLC expression levels mapped not only BnFLC.C2 but also other loci that contribute to variation in FLC expression. In addition to revealing the complex genetic architecture of flowering time variation, we demonstrate that the identified SNPs can be modelled to predict flowering time in diverse canola germplasm accurately and hence are suitable for genomic selection of adaptative traits in canola improvement programmes. ©2015 The Authors. Plant, Cell & Environment published by JohnWiley & Sons Ltd.

  15. Neural sensitivity to statistical regularities as a fundamental biological process that underlies auditory learning: the role of musical practice.

    Science.gov (United States)

    François, Clément; Schön, Daniele

    2014-02-01

    There is increasing evidence that humans and other nonhuman mammals are sensitive to the statistical structure of auditory input. Indeed, neural sensitivity to statistical regularities seems to be a fundamental biological property underlying auditory learning. In the case of speech, statistical regularities play a crucial role in the acquisition of several linguistic features, from phonotactic to more complex rules such as morphosyntactic rules. Interestingly, a similar sensitivity has been shown with non-speech streams: sequences of sounds changing in frequency or timbre can be segmented on the sole basis of conditional probabilities between adjacent sounds. We recently ran a set of cross-sectional and longitudinal experiments showing that merging music and speech information in song facilitates stream segmentation and, further, that musical practice enhances sensitivity to statistical regularities in speech at both neural and behavioral levels. Based on recent findings showing the involvement of a fronto-temporal network in speech segmentation, we defend the idea that enhanced auditory learning observed in musicians originates via at least three distinct pathways: enhanced low-level auditory processing, enhanced phono-articulatory mapping via the left Inferior Frontal Gyrus and Pre-Motor cortex and increased functional connectivity within the audio-motor network. Finally, we discuss how these data predict a beneficial use of music for optimizing speech acquisition in both normal and impaired populations. Copyright © 2013 Elsevier B.V. All rights reserved.

  16. Neural mechanisms underlying stop-and-restart difficulties: involvement of the motor and perceptual systems.

    Directory of Open Access Journals (Sweden)

    Kentaro Yamanaka

    Full Text Available The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (~100 ms stop-to-restart intervals (SRSI, and an increased probability of difficulties after longer (>200 ms SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms, the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM excitability. Finally, we recorded electroencephalogram (EEG activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms, weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150-200 ms, because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results

  17. Neural mechanisms underlying stop-and-restart difficulties: involvement of the motor and perceptual systems.

    Science.gov (United States)

    Yamanaka, Kentaro; Nozaki, Daichi

    2013-01-01

    The ability to suddenly stop a planned movement or a movement being performed and restart it after a short interval is an important mechanism that allows appropriate behavior in response to contextual or environmental changes. However, performing such stop-and-restart movements smoothly is difficult at times. We investigated performance (response time) of stop-and-restart movements using a go/stop/re-go task and found consistent stop-and-restart difficulties after short (~100 ms) stop-to-restart intervals (SRSI), and an increased probability of difficulties after longer (>200 ms) SRSIs, suggesting that two different mechanisms underlie stop-and-restart difficulties. Next, we investigated motor evoked potentials (MEPs) in a moving muscle induced by transcranial magnetic stimulation during a go/stop/re-go task. In re-go trials with a short SRSI (100 ms), the MEP amplitude continued to decrease after the re-go-signal onset, indicating that stop-and-restart difficulties with short SRSIs might be associated with a neural mechanism in the human motor system, namely, stop-related suppression of corticomotor (CM) excitability. Finally, we recorded electroencephalogram (EEG) activity during a go/stop/re-go task and performed a single-trial-based EEG power and phase time-frequency analysis. Alpha-band EEG phase locking to re-go-signal, which was only observed in re-go trials with long SRSI (250 ms), weakened in the delayed re-go response trials. These EEG phase dynamics indicate an association between stop-and-restart difficulties with long SRSIs and a neural mechanism in the human perception system, namely, decreased probability of EEG phase locking to visual stimuli. In contrast, smooth stop-and-restart human movement can be achieved in re-go trials with sufficient SRSI (150-200 ms), because release of stop-related suppression and simultaneous counter-activation of CM excitability may occur as a single task without second re-go-signal perception. These results suggest that

  18. Neural oscillatory mechanisms during novel grammar learning underlying language analytical abilities.

    Science.gov (United States)

    Kepinska, Olga; Pereda, Ernesto; Caspers, Johanneke; Schiller, Niels O

    2017-12-01

    The goal of the present study was to investigate the initial phases of novel grammar learning on a neural level, concentrating on mechanisms responsible for individual variability between learners. Two groups of participants, one with high and one with average language analytical abilities, performed an Artificial Grammar Learning (AGL) task consisting of learning and test phases. During the task, EEG signals from 32 cap-mounted electrodes were recorded and epochs corresponding to the learning phases were analysed. We investigated spectral power modulations over time, and functional connectivity patterns by means of a bivariate, frequency-specific index of phase synchronization termed Phase Locking Value (PLV). Behavioural data showed learning effects in both groups, with a steeper learning curve and higher ultimate attainment for the highly skilled learners. Moreover, we established that cortical connectivity patterns and profiles of spectral power modulations over time differentiated L2 learners with various levels of language analytical abilities. Over the course of the task, the learning process seemed to be driven by whole-brain functional connectivity between neuronal assemblies achieved by means of communication in the beta band frequency. On a shorter time-scale, increasing proficiency on the AGL task appeared to be supported by stronger local synchronisation within the right hemisphere regions. Finally, we observed that the highly skilled learners might have exerted less mental effort, or reduced attention for the task at hand once the learning was achieved, as evidenced by the higher alpha band power. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Neural mechanisms underlying changes in stress-sensitivity across the menstrual cycle.

    Science.gov (United States)

    Ossewaarde, Lindsey; Hermans, Erno J; van Wingen, Guido A; Kooijman, Sabine C; Johansson, Inga-Maj; Bäckström, Torbjörn; Fernández, Guillén

    2010-01-01

    Hormonal fluctuations across the menstrual cycle are thought to play a central role in premenstrual mood symptoms. In agreement, fluctuations in gonadal hormone levels affect brain processes in regions involved in emotion regulation. Recent findings, however, implicate psychological stress as a potential mediating factor and thus, we investigated whether effects of moderate psychological stress on relevant brain regions interact with menstrual cycle phase. Twenty-eight healthy women were tested in a crossover design with menstrual cycle phase (late luteal versus late follicular) and stress (stress induction versus control) as within-subject factors. After stress induction (or control), we probed neural responses to facial expressions using fMRI. During the late luteal phase, negative affect was highest and the stress-induced increase in heart rate was mildly augmented. fMRI data of the control condition replicate previous findings of elevated amygdala and medial prefrontal cortex responses when comparing the late luteal with the late follicular phase. Importantly, stress induction had opposite effects in the two cycle phases, with unexpected lower response magnitudes in the late luteal phase. Moreover, the larger the increase in allopregnanolone concentration across the menstrual cycle was, the smaller the amygdala and medial prefrontal cortex responses were after stress induction in the late luteal phase. Our findings show that moderate psychological stress influences menstrual cycle effects on activity in the emotion regulation circuitry. These results provide potential insights into how fluctuations in allopregnanolone that naturally occur during the menstrual cycle may change stress vulnerability.

  20. Reduced Fidelity of Neural Representation Underlies Episodic Memory Decline in Normal Aging.

    Science.gov (United States)

    Zheng, Li; Gao, Zhiyao; Xiao, Xiaoqian; Ye, Zhifang; Chen, Chuansheng; Xue, Gui

    2017-06-07

    Emerging studies have emphasized the importance of the fidelity of cortical representation in forming enduring episodic memory. No study, however, has examined whether there are age-related reductions in representation fidelity that can explain memory declines in normal aging. Using functional MRI and multivariate pattern analysis, we found that older adults showed reduced representation fidelity in the visual cortex, which accounted for their decreased memory performance even after controlling for the contribution of reduced activation level. This reduced fidelity was specifically due to older adults' poorer item-specific representation, not due to their lower activation level and variance, greater variability in neuro-vascular coupling, or decreased selectivity of categorical representation (i.e., dedifferentiation). Older adults also showed an enhanced subsequent memory effect in the prefrontal cortex based on activation level, and their prefrontal activation was associated with greater fidelity of representation in the visual cortex and better memory performance. The fidelity of cortical representation thus may serve as a promising neural index for better mechanistic understanding of the memory declines and its compensation in normal aging. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  1. Artificial neural networks based estimation of optical parameters by diffuse reflectance imaging under in vitro conditions

    Directory of Open Access Journals (Sweden)

    Mahmut Ozan Gökkan

    2017-01-01

    Full Text Available Optical parameters (properties of tissue-mimicking phantoms are determined through noninvasive optical imaging. Objective of this study is to decompose obtained diffuse reflectance into these optical properties such as absorption and scattering coefficients. To do so, transmission spectroscopy is firstly used to measure the coefficients via an experimental setup. Next, the optical properties of each characterized phantom are input for Monte Carlo (MC simulations to get diffuse reflectance. Also, a surface image for each single phantom with its known optical properties is obliquely captured due to reflectance-based geometrical setup using CMOS camera that is positioned at 5∘ angle to the phantoms. For the illumination of light, a laser light source at 633nm wavelength is preferred, because optical properties of different components in a biological tissue on that wavelength are nonoverlapped. During in vitro measurements, we prepared 30 different mixture samples adding clinoleic intravenous lipid emulsion (CILE and evans blue (EB dye into a distilled water. Finally, all obtained diffuse reflectance values are used to estimate the optical coefficients by artificial neural networks (ANNs in inverse modeling. For a biological tissue it is found that the simulated and measured values in our results are in good agreement.

  2. Neural computations underlying arbitration between model-based and model-free learning

    Science.gov (United States)

    Lee, Sang Wan; Shimojo, Shinsuke; O’Doherty, John P.

    2014-01-01

    SUMMARY There is accumulating neural evidence to support the existence of two distinct systems for guiding action-selection in the brain, a deliberative “model-based” and a reflexive “model-free” system. However, little is known about how the brain determines which of these systems controls behavior at one moment in time. We provide evidence for an arbitration mechanism that allocates the degree of control over behavior by model-based and model-free systems as a function of the reliability of their respective predictions. We show that inferior lateral prefrontal and frontopolar cortex encode both reliability signals and the output of a comparison between those signals, implicating these regions in the arbitration process. Moreover, connectivity between these regions and model-free valuation areas is negatively modulated by the degree of model-based control in the arbitrator, suggesting that arbitration may work through modulation of the model-free valuation system when the arbitrator deems that the model-based system should drive behavior. PMID:24507199

  3. What ethologically based models have taught us about the neural systems underlying fear and anxiety

    Directory of Open Access Journals (Sweden)

    N.S. Canteras

    2012-04-01

    Full Text Available Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.

  4. Neural Responses to Truth Telling and Risk Propensity under Asymmetric Information.

    Science.gov (United States)

    Suzuki, Hideo; Misaki, Masaya; Krueger, Frank; Bodurka, Jerzy

    2015-01-01

    Trust is multi-dimensional because it can be characterized by subjective trust, trust antecedent, and behavioral trust. Previous research has investigated functional brain responses to subjective trust (e.g., a judgment of trustworthiness) or behavioral trust (e.g., decisions to trust) in perfect information, where all relevant information is available to all participants. In contrast, we conducted a novel examination of the patterns of functional brain activity to a trust antecedent, specifically truth telling, in asymmetric information, where one individual has more information than others, with the effect of varying risk propensity. We used functional magnetic resonance imaging (fMRI) and recruited 13 adults, who played the Communication Game, where they served as the "Sender" and chose either truth telling (true advice) or lie telling (false advice) regarding the best payment allocation for their partner. Our behavioral results revealed that subjects with recreational high risk tended to choose true advice. Moreover, fMRI results yielded that the choices of true advice were associated with increased cortical activation in the anterior rostral medial and frontopolar prefrontal cortices, middle frontal cortex, temporoparietal junction, and precuneus. Furthermore, when we specifically evaluated a role of the bilateral amygdala as the region of interest (ROI), decreased amygdala response was associated with high risk propensity, regardless of truth telling or lying. In conclusion, our results have implications for how differential functions of the cortical areas may contribute to the neural processing of truth telling.

  5. Preservation of neuronal functions by exosomes derived from different human neural cell types under ischemic conditions.

    Science.gov (United States)

    Deng, Mingyang; Xiao, Han; Peng, Hongling; Yuan, Huan; Xu, Yunxiao; Zhang, Guangsen; Tang, Jianguang; Hu, Zhiping

    2017-11-27

    Stem cell-based therapies have been reported in protecting cerebral infarction-induced neuronal dysfunction and death. However, most studies used rat/mouse neuron as model cell when treated with stem cell or exosomes. Whether these findings can be translated from rodent to humans has been in doubt. Here, we used human embryonic stem cell-derived neurons to detect the protective potential of exosomes against ischemia. Neurons were treated with in vitro oxygen-glucose deprivation (OGD) for 1 h. For treatment group, different exosomes were derived from neuron, embryonic stem cell, neural progenitor cell and astrocyte differentiated from H9 human embryonic stem cell and added to culture medium 30 min after OGD (100 μg/mL). Western blotting was performed 12 h after OGD, while cell counting and electrophysiological recording were performed 48 h after OGD. We found that these exosomes attenuated OGD-induced neuronal death, Mammalian target of rapamycin (mTOR), pro-inflammatory and apoptotic signaling pathway changes, as well as basal spontaneous synaptic transmission inhibition in varying degrees. The results implicate the protective effect of exosomes on OGD-induced neuronal death and dysfunction in human embryonic stem cell-derived neurons, potentially through their modulation on mTOR, pro-inflammatory and apoptotic signaling pathways. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  6. Neural correlates of exemplar novelty processing under different spatial attention conditions.

    Science.gov (United States)

    Stoppel, Christian Michael; Boehler, Carsten Nicolas; Strumpf, Hendrik; Heinze, Hans-Jochen; Hopf, Jens Max; Düzel, Emrah; Schoenfeld, Mircea Ariel

    2009-11-01

    The detection of novel events and their identification is a basic prerequisite in a rapidly changing environment. Recently, the processing of novelty has been shown to rely on the hippocampus and to be associated with activity in reward-related areas. The present study investigated the influence of spatial attention on neural processing of novel relative to frequently presented standard and target stimuli. Never-before-seen Mandelbrot-fractals absent of semantic content were employed as stimulus material. Consistent with current theories, novelty activated a widespread network of brain areas including the hippocampus. No activity, however, could be observed in reward-related areas with the novel stimuli absent of a semantic meaning employed here. In the perceptual part of the novelty-processing network a region in the lingual gyrus was found to specifically process novel events when they occurred outside the focus of spatial attention. These findings indicate that the initial detection of unexpected novel events generally occurs in specialized perceptual areas within the ventral visual stream, whereas activation of reward-related areas appears to be restricted to events that do possess a semantic content indicative of the biological relevance of the stimulus.

  7. More cells, bigger cells or simply reorganization? Alternative mechanisms leading to changed internode architecture under contrasting stress regimes

    NARCIS (Netherlands)

    Huber, Heidrun; de Brouwer, Jan; von Wettberg, Eric J.; During, Heinjo J.|info:eu-repo/dai/nl/068848625; Anten, Niels P. R.|info:eu-repo/dai/nl/138797862

    Shading and mechanical stress (MS) modulate plant architecture by inducing different developmental pathways. Shading results in increased stem elongation, often reducing whole-plant mechanical stability, while MS inhibits elongation, with a concomitant increase in stability. Here, we examined how

  8. More cells, bigger cells or simply reorganization? Alternative mechanisms leading to changed internode architecture under contrasting stress regimes

    NARCIS (Netherlands)

    Huber, H.; Brouwer, de J.H.F.; Wettberg, von E.J.; During, H.J.; Anten, N.P.R.

    2014-01-01

    Shading and mechanical stress (MS) modulate plant architecture by inducing different developmental pathways. Shading results in increased stem elongation, often reducing whole-plant mechanical stability, while MS inhibits elongation, with a concomitant increase in stability. Here, we examined how

  9. Forecasting of Market Clearing Price by Using GA Based Neural Network

    Science.gov (United States)

    Yang, Bo; Chen, Yun-Ping; Zhao, Zun-Lian; Han, Qi-Ye

    Forecasting of Market Clearing Price (MCP) is important to economic benefits of electricity market participants. To accurately forecast MCP, a novel two-stage GA-based neural network model (GA-NN) is proposed. In the first stage, GA chromosome is designed into two parts: boolean coding part for neural network topology and real coding part for connection weights. By hybrid genetic operation of selection, crossover and mutation under the criterion of error minimization between the actual output and the desired output, optimal architecture of neural network is obtained. In the second stage, gradient learning algorithm with momentum rate is imposed on neural network with optimal architecture. After learning process, optimal connection weights are obtained. The proposed model is tested on MCP forecasting in California electricity market. The test results show that GA-NN has self-adaptive ability in its topology and connection weights and can obtain more accurate MCP forecasting values than BP neural network.

  10. Modeling Root Length Density of Field Grown Potatoes under Different Irrigation Strategies and Soil Textures Using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Ahmadi, Seyed Hamid; Sepaskhah, A R; Andersen, Mathias Neumann

    2014-01-01

    Root length density (RLD) is a highly wanted parameter for use in crop growth modeling but difficult to measure under field conditions. Therefore, artificial neural networks (ANNs) were implemented to predict the RLD of field grown potatoes that were subject to three irrigation strategies and three...... soil textures with different soil water status and soil densities. The objectives of the study were to test whether soil textural information, soil water status, and soil density might be used by ANN to simulate RLD at harvest. In the study 63 data pairs were divided into data sets of training (80......% of the data) and testing (20% of the data). A feed forward three-layer perceptron network and the sigmoid, hyperbolic tangent, and linear transfer functions were used for the ANN modeling. The RLDs (target variable) in different soil layers were predicted by nine ANNs representing combinations (models...

  11. Exploring the developmental mechanisms underlying Wolf-Hirschhorn Syndrome: Evidence for defects in neural crest cell migration.

    Science.gov (United States)

    Rutherford, Erin L; Lowery, Laura Anne

    2016-12-01

    Wolf-Hirschhorn Syndrome (WHS) is a neurodevelopmental disorder characterized by mental retardation, craniofacial malformation, and defects in skeletal and heart development. The syndrome is associated with irregularities on the short arm of chromosome 4, including deletions of varying sizes and microduplications. Many of these genotypic aberrations in humans have been correlated with the classic WHS phenotype, and animal models have provided a context for mapping these genetic irregularities to specific phenotypes; however, there remains a significant knowledge gap concerning the cell biological mechanisms underlying these phenotypes. This review summarizes literature that has made recent contributions to this topic, drawing from the vast body of knowledge detailing the genetic particularities of the disorder and the more limited pool of information on its cell biology. Finally, we propose a novel characterization for WHS as a pathophysiology owing in part to defects in neural crest cell motility and migration during development. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  12. Plant Growth Models Using Artificial Neural Networks

    Science.gov (United States)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  13. Colour or shape: examination of neural processes underlying mental flexibility in posttraumatic stress disorder.

    Science.gov (United States)

    Pang, E W; Sedge, P; Grodecki, R; Robertson, A; MacDonald, M J; Jetly, R; Shek, P N; Taylor, M J

    2014-08-05

    Posttraumatic stress disorder (PTSD) is a mental disorder that stems from exposure to one or more traumatic events. While PTSD is thought to result from a dysregulation of emotional neurocircuitry, neurocognitive difficulties are frequently reported. Mental flexibility is a core executive function that involves the ability to shift and adapt to new information. It is essential for appropriate social-cognitive behaviours. Magnetoencephalography (MEG), a neuroimaging modality with high spatial and temporal resolution, has been used to track the progression of brain activation during tasks of mental flexibility called set-shifting. We hypothesized that the sensitivity of MEG would be able to capture the abnormal neurocircuitry implicated in PTSD and this would negatively impact brain regions involved in set-shifting. Twenty-two soldiers with PTSD and 24 matched control soldiers completed a colour-shape set-shifting task. MEG data were recorded and source localized to identify significant brain regions involved in the task. Activation latencies were obtained by analysing the time course of activation in each region. The control group showed a sequence of activity that involved dorsolateral frontal cortex, insula and posterior parietal cortices. The soldiers with PTSD showed these activations but they were interrupted by activations in paralimbic regions. This is consistent with models of PTSD that suggest dysfunctional neurocircuitry is driven by hyper-reactive limbic areas that are not appropriately modulated by prefrontal cortical control regions. This is the first study identifying the timing and location of atypical neural responses in PTSD with set-shifting and supports the model that hyperactive limbic structures negatively impact cognitive function.

  14. Neural substrates of cognitive control under the belief of getting neurofeedback training

    Directory of Open Access Journals (Sweden)

    Manuel eNinaus

    2013-12-01

    Full Text Available Learning to modulate one’s own brain activity is the fundament of neurofeedback (NF applications. Besides the neural networks directly involved in the generation and modulation of the neurophysiological parameter being specifically trained, more general determinants of NF efficacy such as self-referential processes and cognitive control have been frequently disregarded. Nonetheless, deeper insight into these cognitive mechanisms and their neuronal underpinnings sheds light on various open NF related questions concerning individual differences, brain-computer interface (BCI illiteracy as well as a more general model of NF learning. In this context, we investigated the neuronal substrate of these more general regulatory mechanisms that are engaged when participants believe that they are receiving NF. Twenty healthy participants (40-63 years, 10 female performed a sham NF paradigm during fMRI scanning. All participants were novices to NF-experiments and were instructed to voluntarily modulate their own brain activity based on a visual display of moving color bars. However, the bar depicted a recording and not the actual brain activity of participants. Reports collected at the end of the experiment indicate that participants were unaware of the sham feedback. In comparison to a passive watching condition, bilateral insula, anterior cingulate cortex and supplementary motor and dorsomedial and lateral prefrontal area were activated when participants actively tried to control the bar. In contrast, when merely watching moving bars, increased activation in the left angular gyrus was observed. These results show that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control. The results of the present study indicate that tasks such as those generally employed in NF training recruit the neuronal correlates of cognitive control even when only sham NF is presented.

  15. Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

    Science.gov (United States)

    Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

    2016-12-01

    Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

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

    Directory of Open Access Journals (Sweden)

    Aleksi J. Sihvonen

    2017-07-01

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

  17. Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain.

    Science.gov (United States)

    Spreng, R Nathan; Sepulcre, Jorge; Turner, Gary R; Stevens, W Dale; Schacter, Daniel L

    2013-01-01

    Human cognition is increasingly characterized as an emergent property of interactions among distributed, functionally specialized brain networks. We recently demonstrated that the antagonistic "default" and "dorsal attention" networks--subserving internally and externally directed cognition, respectively--are modulated by a third "frontoparietal control" network that flexibly couples with either network depending on task domain. However, little is known about the intrinsic functional architecture underlying this relationship. We used graph theory to analyze network properties of intrinsic functional connectivity within and between these three large-scale networks. Task-based activation from three independent studies were used to identify reliable brain regions ("nodes") of each network. We then examined pairwise connections ("edges") between nodes, as defined by resting-state functional connectivity MRI. Importantly, we used a novel bootstrap resampling procedure to determine the reliability of graph edges. Furthermore, we examined both full and partial correlations. As predicted, there was a higher degree of integration within each network than between networks. Critically, whereas the default and dorsal attention networks shared little positive connectivity with one another, the frontoparietal control network showed a high degree of between-network interconnectivity with each of these networks. Furthermore, we identified nodes within the frontoparietal control network of three different types--default-aligned, dorsal attention-aligned, and dual-aligned--that we propose play dissociable roles in mediating internetwork communication. The results provide evidence consistent with the idea that the frontoparietal control network plays a pivotal gate-keeping role in goal-directed cognition, mediating the dynamic balance between default and dorsal attention networks.

  18. Exploiting gene deletion fitness effects in yeast to understand the modular architecture of protein complexes under different growth conditions

    Directory of Open Access Journals (Sweden)

    Babu M Madan

    2009-07-01

    Full Text Available Abstract Background Understanding how individual genes contribute towards the fitness of an organism is a fundamental problem in biology. Although recent genome-wide screens have generated abundant data on quantitative fitness for single gene knockouts, very few studies have systematically integrated other types of biological information to understand how and why deletion of specific genes give rise to a particular fitness effect. In this study, we combine quantitative fitness data for single gene knock-outs in yeast with large-scale interaction discovery experiments to understand the effect of gene deletion on the modular architecture of protein complexes, under different growth conditions. Results Our analysis reveals that genes in complexes show more severe fitness effects upon deletion than other genes but, in contrast to what has been observed in binary protein-protein interaction networks, we find that this is not related to the number of complexes in which they are present. We also find that, in general, the core and attachment components of protein complexes are equally important for the complex machinery to function. However, when quantifying the importance of core and attachments in single complex variations, or isoforms, we observe that this global trend originates from either the core or the attachment components being more important for strain fitness, both being equally important or both being dispensable. Finally, our study reveals that different isoforms of a complex can exhibit distinct fitness patterns across growth conditions. Conclusion This study presents a powerful approach to unveil the molecular basis for various complex phenotypic profiles observed in gene deletion experiments. It also highlights some interesting cases of potential functional compensation between protein paralogues and suggests a new piece to fit into the histone-code puzzle.

  19. Architecture on Architecture

    DEFF Research Database (Denmark)

    Olesen, Karen

    2016-01-01

    This paper will discuss the challenges faced by architectural education today. It takes as its starting point the double commitment of any school of architecture: on the one hand the task of preserving the particular knowledge that belongs to the discipline of architecture, and on the other hand...... the obligation to prepare students to perform in a profession that is largely defined by forces outside that discipline. It will be proposed that the autonomy of architecture can be understood as a unique kind of information: as architecture’s self-reliance or knowledge-about itself. A knowledge...... that is not scientific or academic but is more like a latent body of data that we find embedded in existing works of architecture. This information, it is argued, is not limited by the historical context of the work. It can be thought of as a virtual capacity – a reservoir of spatial configurations that can...

  20. Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm.

    Science.gov (United States)

    Tra, Viet; Kim, Jaeyoung; Khan, Sheraz Ali; Kim, Jong-Myon

    2017-12-06

    This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing's speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds.

  1. Memory trace in feeding neural circuitry underlying conditioned taste aversion in Lymnaea.

    Directory of Open Access Journals (Sweden)

    Etsuro Ito

    Full Text Available BACKGROUND: The pond snail Lymnaea stagnalis can maintain a conditioned taste aversion (CTA as a long-term memory. Previous studies have shown that the inhibitory postsynaptic potential (IPSP evoked in the neuron 1 medial (N1M cell by activation of the cerebral giant cell (CGC in taste aversion-trained snails was larger and lasted longer than that in control snails. The N1M cell is one of the interneurons in the feeding central pattern generator (CPG, and the CGC is a key regulatory neuron for the feeding CPG. METHODOLOGY/PRINCIPLE FINDINGS: Previous studies have suggested that the neural circuit between the CGC and the N1M cell consists of two synaptic connections: (1 the excitatory connection from the CGC to the neuron 3 tonic (N3t cell and (2 the inhibitory connection from the N3t cell to the N1M cell. However, because the N3t cell is too small to access consistently by electrophysiological methods, in the present study the synaptic inputs from the CGC to the N3t cell and those from the N3t cell to the N1M cell were monitored as the monosynaptic excitatory postsynaptic potential (EPSP recorded in the large B1 and B3 motor neurons, respectively. The evoked monosynaptic EPSPs of the B1 motor neurons in the brains isolated from the taste aversion-trained snails were identical to those in the control snails, whereas the spontaneous monosynaptic EPSPs of the B3 motor neurons were significantly enlarged. CONCLUSION/SIGNIFICANCE: These results suggest that, after taste aversion training, the monosynaptic inputs from the N3t cell to the following neurons including the N1M cell are specifically facilitated. That is, one of the memory traces for taste aversion remains as an increase in neurotransmitter released from the N3t cell. We thus conclude that the N3t cell suppresses the N1M cell in the feeding CPG, in response to the conditioned stimulus in Lymnaea CTA.

  2. Genetic variants associated with the root system architecture of oilseed rape (Brassica napus L.) under contrasting phosphate supply.

    Science.gov (United States)

    Wang, Xiaohua; Chen, Yanling; Thomas, Catherine L; Ding, Guangda; Xu, Ping; Shi, Dexu; Grandke, Fabian; Jin, Kemo; Cai, Hongmei; Xu, Fangsen; Yi, Bin; Broadley, Martin R; Shi, Lei

    2017-08-01

    Breeding crops with ideal root system architecture for efficient absorption of phosphorus is an important strategy to reduce the use of phosphate fertilizers. To investigate genetic variants leading to changes in root system architecture, 405 oilseed rape cultivars were genotyped with a 60K Brassica Infinium SNP array in low and high P environments. A total of 285 single-nucleotide polymorphisms were associated with root system architecture traits at varying phosphorus levels. Nine single-nucleotide polymorphisms corroborate a previous linkage analysis of root system architecture quantitative trait loci in the BnaTNDH population. One peak single-nucleotide polymorphism region on A3 was associated with all root system architecture traits and co-localized with a quantitative trait locus for primary root length at low phosphorus. Two more single-nucleotide polymorphism peaks on A5 for root dry weight at low phosphorus were detected in both growth systems and co-localized with a quantitative trait locus for the same trait. The candidate genes identified on A3 form a haplotype 'BnA3Hap', that will be important for understanding the phosphorus/root system interaction and for the incorporation into Brassica napus breeding programs. © The Author 2017. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

  3. Decision making under uncertainty in a spiking neural network model of the basal ganglia

    NARCIS (Netherlands)

    Héricé, C.; Khalil, R.; Moftah, M.; Boraud, T.; Guthrie, M.J.; Garenne, A.

    2016-01-01

    The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used

  4. Neural Mechanisms Underlying Social Intelligence and Their Relationship with the Performance of Sales Managers

    NARCIS (Netherlands)

    R.C. Dietvorst (Roeland)

    2010-01-01

    textabstractIdentifying the drivers of salespeople’s performance, strategies and moral behavior have been under the scrutiny of marketing scholars for many years. The functioning of the drivers of salespeople’s behaviors rests on processes going on in the minds of salespeople. However, research to

  5. A neuro-fuzzy architecture for real-time applications

    Science.gov (United States)

    Ramamoorthy, P. A.; Huang, Song

    1992-01-01

    Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

  6. 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

  7. Artificial neural network for prediction of the area under the disease progress curve of tomato late blight

    Directory of Open Access Journals (Sweden)

    Daniel Pedrosa Alves

    Full Text Available ABSTRACT: Artificial neural networks (ANN are computational models inspired by the neural systems of living beings capable of learning from examples and using them to solve problems such as non-linear prediction, and pattern recognition, in addition to several other applications. In this study, ANN were used to predict the value of the area under the disease progress curve (AUDPC for the tomato late blight pathosystem. The AUDPC is widely used by epidemiologic studies of polycyclic diseases, especially those regarding quantitative resistance of genotypes. However, a series of six evaluations over time is necessary to obtain the final area value for this pathosystem. This study aimed to investigate the utilization of ANN to construct an AUDPC in the tomato late blight pathosystem, using a reduced number of severity evaluations. For this, four independent experiments were performed giving a total of 1836 plants infected with Phytophthora infestans pathogen. They were assessed every three days, comprised six opportunities and AUDPC calculations were performed by the conventional method. After the ANN were created it was possible to predict the AUDPC with correlations of 0.97 and 0.84 when compared to conventional methods, using 50 % and 67 % of the genotype evaluations, respectively. When using the ANN created in an experiment to predict the AUDPC of the other experiments the average correlation was 0.94, with two evaluations, 0.96, with three evaluations, between the predicted values of the ANN and they were observed in six evaluations. We present in this study a new paradigm for the use of AUDPC information in tomato experiments faced with P. infestans. This new proposed paradigm might be adapted to different pathosystems.

  8. Changes in Neurocognitive Architecture in Patients with Obstructive Sleep Apnea Treated with Continuous Positive Airway Pressure

    Directory of Open Access Journals (Sweden)

    Ivana Rosenzweig

    2016-05-01

    Interpretation: One month of CPAP treatment can lead to adaptive alterations in the neurocognitive architecture that underlies the reduced sleepiness, and improved verbal episodic memory in patients with OSA. We propose that partial neural recovery occurs during short periods of treatment with CPAP.

  9. Architectural slicing

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Hansen, Klaus Marius

    2013-01-01

    Architectural prototyping is a widely used practice, con- cerned with taking architectural decisions through experiments with light- weight implementations. However, many architectural decisions are only taken when systems are already (partially) implemented. This is prob- lematic in the context...... a system and a slicing criterion, architectural slicing produces an architectural prototype that contain the elements in the architecture that are dependent on the ele- ments in the slicing criterion. Furthermore, we present an initial design and implementation of an architectural slicer for Java....

  10. High-throughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities.

    Science.gov (United States)

    Shi, Lei; Shi, Taoxiong; Broadley, Martin R; White, Philip J; Long, Yan; Meng, Jinling; Xu, Fangsen; Hammond, John P

    2013-07-01

    Phosphate (Pi) deficiency in soils is a major limiting factor for crop growth worldwide. Plant growth under low Pi conditions correlates with root architectural traits and it may therefore be possible to select these traits for crop improvement. The aim of this study was to characterize root architectural traits, and to test quantitative trait loci (QTL) associated with these traits, under low Pi (LP) and high Pi (HP) availability in Brassica napus. Root architectural traits were characterized in seedlings of a double haploid (DH) mapping population (n = 190) of B. napus ['Tapidor' × 'Ningyou 7' (TNDH)] using high-throughput phenotyping methods. Primary root length (PRL), lateral root length (LRL), lateral root number (LRN), lateral root density (LRD) and biomass traits were measured 12 d post-germination in agar at LP and HP. In general, root and biomass traits were highly correlated under LP and HP conditions. 'Ningyou 7' had greater LRL, LRN and LRD than 'Tapidor', at both LP and HP availability, but smaller PRL. A cluster of highly significant QTL for LRN, LRD and biomass traits at LP availability were identified on chromosome A03; QTL for PRL were identified on chromosomes A07 and C06. High-throughput phenotyping of Brassica can be used to identify root architectural traits which correlate with shoot biomass. It is feasible that these traits could be used in crop improvement strategies. The identification of QTL linked to root traits under LP and HP conditions provides further insights on the genetic basis of plant tolerance to P deficiency, and these QTL warrant further dissection.

  11. 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.

  12. Decision making under uncertainty in a spiking neural network model of the basal ganglia.

    Science.gov (United States)

    Héricé, Charlotte; Khalil, Radwa; Moftah, Marie; Boraud, Thomas; Guthrie, Martin; Garenne, André

    2016-12-01

    The mechanisms of decision-making and action selection are generally thought to be under the control of parallel cortico-subcortical loops connecting back to distinct areas of cortex through the basal ganglia and processing motor, cognitive and limbic modalities of decision-making. We have used these properties to develop and extend a connectionist model at a spiking neuron level based on a previous rate model approach. This model is demonstrated on decision-making tasks that have been studied in primates and the electrophysiology interpreted to show that the decision is made in two steps. To model this, we have used two parallel loops, each of which performs decision-making based on interactions between positive and negative feedback pathways. This model is able to perform two-level decision-making as in primates. We show here that, before learning, synaptic noise is sufficient to drive the decision-making process and that, after learning, the decision is based on the choice that has proven most likely to be rewarded. The model is then submitted to lesion tests, reversal learning and extinction protocols. We show that, under these conditions, it behaves in a consistent manner and provides predictions in accordance with observed experimental data.

  13. Neural and psychophysiological correlates of human performance under stress and high mental workload.

    Science.gov (United States)

    Mandrick, Kevin; Peysakhovich, Vsevolod; Rémy, Florence; Lepron, Evelyne; Causse, Mickaël

    2016-12-01

    In our anxiogenic and stressful world, the maintenance of an optimal cognitive performance is a constant challenge. It is particularly true in complex working environments (e.g. flight deck, air traffic control tower), where individuals have sometimes to cope with a high mental workload and stressful situations. Several models (i.e. processing efficiency theory, cognitive-energetical framework) have attempted to provide a conceptual basis on how human performance is modulated by high workload and stress/anxiety. These models predict that stress can reduce human cognitive efficiency, even in the absence of a visible impact on the task performance. Performance may be protected under stress thanks to compensatory effort, but only at the expense of a cognitive cost. Yet, the psychophysiological cost of this regulation remains unclear. We designed two experiments involving pupil diameter, cardiovascular and prefrontal oxygenation measurements. Participants performed the Toulouse N-back Task that intensively engaged both working memory and mental calculation processes under the threat (or not) of unpredictable aversive sounds. The results revealed that higher task difficulty (higher n level) degraded the performance and induced an increased tonic pupil diameter, heart rate and activity in the lateral prefrontal cortex, and a decreased phasic pupil response and heart rate variability. Importantly, the condition of stress did not impact the performance, but at the expense of a psychophysiological cost as demonstrated by lower phasic pupil response, and greater heart rate and prefrontal activity. Prefrontal cortex seems to be a central region for mitigating the influence of stress because it subserves crucial functions (e.g. inhibition, working memory) that can promote the engagement of coping strategies. Overall, findings confirmed the psychophysiological cost of both mental effort and stress. Stress likely triggered increased motivation and the recruitment of additional

  14. A View of the Neural Representation of Second Language Syntax through Artificial Language Learning under Implicit Contexts of Exposure

    Science.gov (United States)

    Morgan-Short, Kara; Deng, ZhiZhou; Brill-Schuetz, Katherine A.; Faretta- Stutenberg, Mandy; Wong, Patrick C. M.; Wong, Francis C. K.

    2015-01-01

    The current study aims to make an initial neuroimaging contribution to central implicit-explicit issues in second language (L2) acquisition by considering how implicit and explicit contexts mediate the neural representation of L2. Focusing on implicit contexts, the study employs a longitudinal design to examine the neural representation of L2…

  15. Do horizontal saccadic eye movements increase interhemispheric coherence? Investigation of a hypothesized neural mechanism underlying EMDR

    Directory of Open Access Journals (Sweden)

    Zoe eSamara

    2011-03-01

    Full Text Available Series of horizontal saccadic eye movements (EMs are known to improve episodic memory retrieval in healthy adults and to facilitate the processing of traumatic memories in eye-movement desensitization and reprocessing (EMDR therapy. Several authors have proposed that EMs achieve these effects by increasing the functional connectivity of the two brain hemispheres, but direct evidence for this proposal is lacking. The aim of this study was to investigate whether memory enhancement following bilateral EMs is associated with increased interhemispheric coherence in the electroencephalogram (EEG. Fourteen healthy young adults were asked to freely recall lists of studied neutral and emotional words after a series of bilateral EMs and a control procedure. Baseline EEG activity was recorded before and after the EM and control procedures. Phase and amplitude coherence between bilaterally homologous brain areas were calculated for six frequency bands and electrode pairs across the entire scalp. Behavioral analyses showed that participants recalled more emotional (but not neutral words following the EM procedure than following the control procedure. However, the EEG analyses indicated no evidence that the EMs altered participants’ interhemispheric coherence or that improvements in recall were correlated with such changes in coherence. These findings cast doubt on the interhemispheric interaction hypothesis, and therefore may have important implications for future research on the neurobiological mechanism underlying EMDR.

  16. Finding the self by losing the self: Neural correlates of ego-dissolution under psilocybin.

    Science.gov (United States)

    Lebedev, Alexander V; Lövdén, Martin; Rosenthal, Gidon; Feilding, Amanda; Nutt, David J; Carhart-Harris, Robin L

    2015-08-01

    Ego-disturbances have been a topic in schizophrenia research since the earliest clinical descriptions of the disorder. Manifesting as a feeling that one's "self," "ego," or "I" is disintegrating or that the border between one's self and the external world is dissolving, "ego-disintegration" or "dissolution" is also an important feature of the psychedelic experience, such as is produced by psilocybin (a compound found in "magic mushrooms"). Fifteen healthy subjects took part in this placebo-controlled study. Twelve-minute functional MRI scans were acquired on two occasions: subjects received an intravenous infusion of saline on one occasion (placebo) and 2 mg psilocybin on the other. Twenty-two visual analogue scale ratings were completed soon after scanning and the first principal component of these, dominated by items referring to "ego-dissolution", was used as a primary measure of interest in subsequent analyses. Employing methods of connectivity analysis and graph theory, an association was found between psilocybin-induced ego-dissolution and decreased functional connectivity between the medial temporal lobe and high-level cortical regions. Ego-dissolution was also associated with a "disintegration" of the salience network and reduced interhemispheric communication. Addressing baseline brain dynamics as a predictor of drug-response, individuals with lower diversity of executive network nodes were more likely to experience ego-dissolution under psilocybin. These results implicate MTL-cortical decoupling, decreased salience network integrity, and reduced inter-hemispheric communication in psilocybin-induced ego disturbance and suggest that the maintenance of "self"or "ego," as a perceptual phenomenon, may rest on the normal functioning of these systems. © 2015 Wiley Periodicals, Inc.

  17. Changes in Neural Activity Underlying Working Memory after Computerized Cognitive Training in Older Adults.

    Science.gov (United States)

    Tusch, Erich S; Alperin, Brittany R; Ryan, Eliza; Holcomb, Phillip J; Mohammed, Abdul H; Daffner, Kirk R

    2016-01-01

    Computerized cognitive training (CCT) may counter the impact of aging on cognition, but both the efficacy and neurocognitive mechanisms underlying CCT remain controversial. In this study, 35 older individuals were randomly assigned to Cogmed adaptive working memory (WM) CCT or an active control CCT, featuring five weeks of five ∼40 min sessions per week. Before and after the 5-week intervention, event-related potentials were measured while subjects completed a visual n-back task with three levels of demand (0-back, 1-back, 2-back). The anterior P3a served as an index of directing attention and the posterior P3b as an index of categorization/WM updating. We hypothesized that adaptive CCT would be associated with decreased P3 amplitude at low WM demand and increased P3 amplitude at high WM demand. The adaptive CCT group exhibited a training-related increase in the amplitude of the anterior P3a and posterior P3b in response to target stimuli across n-back tasks, while subjects in the active control CCT group demonstrated a post-training decrease in the anterior P3a. Performance did not differ between groups or sessions. Larger overall P3 amplitudes were strongly associated with better task performance. Increased post-CCT P3 amplitude correlated with improved task performance; this relationship was especially robust at high task load. Our findings suggest that adaptive WM training was associated with increased orienting of attention, as indexed by the P3a, and the enhancement of categorization/WM updating processes, as indexed by the P3b. Increased P3 amplitude was linked to improved performance; however. there was no direct association between adaptive training and improved performance.

  18. Changes in neural activity underlying working memory after computerized cognitive training in older adults

    Directory of Open Access Journals (Sweden)

    Erich Tusch

    2016-11-01

    Full Text Available Computerized cognitive training (CCT may counter the impact of aging on cognition, but both the efficacy and neurocognitive mechanisms underlying CCT remain controversial. In this study, 35 older individuals were randomly assigned to Cogmed adaptive working memory (WM CCT or an active control CCT, featuring five weeks of five ~40 minute sessions per week. Before and after the 5-week intervention, ERPs were measured while subjects completed a visual n-back task with 3 levels of demand (0-back, 1-back, 2-back. The anterior P3a served as an index of directing attention and the posterior P3b as an index of categorization/WM updating. We hypothesized that adaptive CCT would be associated with decreased P3 amplitude at low WM demand and increased P3 amplitude at high WM demand. The adaptive CCT group exhibited a training-related increase in the amplitude of the anterior P3a and posterior P3b in response to target stimuli across n-back tasks, while subjects in the active control CCT group demonstrated a post-training decrease in the anterior P3a. Performance did not differ between groups or sessions. Larger overall P3 amplitudes were strongly associated with better task performance. Increased post-CCT P3 amplitude correlated with improved task performance; this relationship was especially robust at high task load. Our findings suggest that adaptive WM training was associated with increased orienting of attention, as indexed by the P3a, and the enhancement of categorization/WM updating processes, as indexed by the P3b. Increased P3 amplitude was linked to improved performance; however there was no direct association between adaptive training and improved performance.

  19. Exporting Humanist Architecture

    DEFF Research Database (Denmark)

    Nielsen, Tom

    2016-01-01

    The article is a chapter in the catalogue for the Danish exhibition at the 2016 Architecture Biennale in Venice. The catalogue is conceived at an independent book exploring the theme Art of Many - The Right to Space. The chapter is an essay in this anthology tracing and discussing the different...... values and ethical stands involved in the export of Danish Architecture. Abstract: Danish architecture has, in a sense, been driven by an unwritten contract between the architects and the democratic state and its institutions. This contract may be viewed as an ethos – an architectural tradition...... with inherent aesthetic and moral values. Today, however, Danish architecture is also an export commodity. That raises questions, which should be debated as openly as possible. What does it mean for architecture and architects to practice in cultures and under political systems that do not use architecture...

  20. S1-2: The Temporal Aspect of Neural Activities Underlying the Perception of Biological Motion in Infants, Children, Adults, and Patients with Developmental Disorders

    Directory of Open Access Journals (Sweden)

    Masahiro Hirai

    2012-10-01

    Full Text Available It has been demonstrated that our visual system can extract rich visual information from point-light motion. Despite the fact that we can perceive human actions from point-light motion with a brief exposure, the temporal aspect of the neural activities underlying the perception of biological motion has not been well explored. In this talk, I'll introduce a series of behavioral, electroencephalography (EEG, and magnetoencephalography (MEG studies on biological motion perception and propose a hierarchical model for its processing based on these findings. I'll then show the developmental changes of the neural responses to biological motion in infants and children and how developmental disorders such as Williams Syndrome and pervasive development disorder (PDD alter its neural responses.

  1. Pansharpening by Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Masi, Giuseppe; Cozzolino, Davide; Verdoliva, Luisa; Scarpa, Giuseppe

    2016-01-01

    A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem...

  2. Stochastic Spiking Neural Networks Enabled by Magnetic Tunnel Junctions: From Nontelegraphic to Telegraphic Switching Regimes

    Science.gov (United States)

    Liyanagedera, Chamika M.; Sengupta, Abhronil; Jaiswal, Akhilesh; Roy, Kaushik

    2017-12-01

    Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-efficient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.

  3. Neural mechanisms underlying the effects of face-based affective signals on memory for faces: a tentative model.

    Science.gov (United States)

    Tsukiura, Takashi

    2012-01-01

    In our daily lives, we form some impressions of other people. Although those impressions are affected by many factors, face-based affective signals such as facial expression, facial attractiveness, or trustworthiness are important. Previous psychological studies have demonstrated the impact of facial impressions on remembering other people, but little is known about the neural mechanisms underlying this psychological process. The purpose of this article is to review recent functional MRI (fMRI) studies to investigate the effects of face-based affective signals including facial expression, facial attractiveness, and trustworthiness on memory for faces, and to propose a tentative concept for understanding this affective-cognitive interaction. On the basis of the aforementioned research, three brain regions are potentially involved in the processing of face-based affective signals. The first candidate is the amygdala, where activity is generally modulated by both affectively positive and negative signals from faces. Activity in the orbitofrontal cortex (OFC), as the second candidate, increases as a function of perceived positive signals from faces; whereas activity in the insular cortex, as the third candidate, reflects a function of face-based negative signals. In addition, neuroscientific studies have reported that the three regions are functionally connected to the memory-related hippocampal regions. These findings suggest that the effects of face-based affective signals on memory for faces could be modulated by interactions between the regions associated with the processing of face-based affective signals and the hippocampus as a memory-related region.

  4. Mixed Stimulus-Induced Mode Selection in Neural Activity Driven by High and Low Frequency Current under Electromagnetic Radiation

    Directory of Open Access Journals (Sweden)

    Lulu Lu

    2017-01-01

    Full Text Available The electrical activities of neurons are dependent on the complex electrophysiological condition in neuronal system, the three-variable Hindmarsh-Rose (HR neuron model is improved to describe the dynamical behaviors of neuronal activities with electromagnetic induction being considered, and the mode transition of electrical activities in neuron is detected when external electromagnetic radiation is imposed on the neuron. In this paper, different types of electrical stimulus impended with a high-low frequency current are imposed on new HR neuron model, and mixed stimulus-induced mode selection in neural activity is discussed in detail. It is found that mode selection of electrical activities stimulated by high-low frequency current, which also changes the excitability of neuron, can be triggered owing to adding the Gaussian white noise. Meanwhile, the mode selection of the neuron electrical activity is much dependent on the amplitude B of the high frequency current under the same noise intensity, and the high frequency response is selected preferentially by applying appropriate parameters and noise intensity. Our results provide insights into the transmission of complex signals in nerve system, which is valuable in engineering prospective applications such as information encoding.

  5. Mechanisms Underlying the Antiproliferative and Prodifferentiative Effects of Psoralen on Adult Neural Stem Cells via DNA Microarray

    Directory of Open Access Journals (Sweden)

    You Ning

    2013-01-01

    Full Text Available Adult neural stem cells (NSCs persist throughout life to replace mature cells that are lost during turnover, disease, or injury. The investigation of NSC creates novel treatments for central nervous system (CNS injuries and neurodegenerative disorders. The plasticity and reparative potential of NSC are regulated by different factors, which are critical for neurological regenerative medicine research. We investigated the effects of Psoralen, which is the mature fruit of Psoralea corylifolia L., on NSC behaviors and the underlying mechanisms. The self-renewal and proliferation of NSC were examined. We detected neuron- and/or astrocyte-specific markers using immunofluorescence and Western blotting, which could evaluate NSC differentiation. Psoralen treatment significantly inhibited neurosphere formation in a dose-dependent manner. Psoralen treatment increased the expression of the astrocyte-specific marker but decreased neuron-specific marker expression. These results suggested that Psoralen was a differentiation inducer in astrocyte. Differential gene expression following Psoralen treatment was screened using DNA microarray and confirmed by quantitative real-time PCR. Our microarray study demonstrated that Psoralen could effectively regulate the specific gene expression profile of NSC. The genes involved in the classification of cellular differentiation, proliferation, and metabolism, the transcription factors belonging to Ets family, and the hedgehog pathway may be closely related to the regulation.

  6. Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

    Directory of Open Access Journals (Sweden)

    Jonas Kalderstam

    Full Text Available We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart, which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.

  7. Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory.

    Science.gov (United States)

    Thaut, Michael H; Peterson, David A; McIntosh, Gerald C

    2005-12-01

    In a series of experiments, we have begun to investigate the effect of music as a mnemonic device on learning and memory and the underlying plasticity of oscillatory neural networks. We used verbal learning and memory tests (standardized word lists, AVLT) in conjunction with electroencephalographic analysis to determine differences between verbal learning in either a spoken or musical (verbal materials as song lyrics) modality. In healthy adults, learning in both the spoken and music condition was associated with significant increases in oscillatory synchrony across all frequency bands. A significant difference between the spoken and music condition emerged in the cortical topography of the learning-related synchronization. When using EEG measures as predictors during learning for subsequent successful memory recall, significantly increased coherence (phase-locked synchronization) within and between oscillatory brain networks emerged for music in alpha and gamma bands. In a similar study with multiple sclerosis patients, superior learning and memory was shown in the music condition when controlled for word order recall, and subjects were instructed to sing back the word lists. Also, the music condition was associated with a significant power increase in the low-alpha band in bilateral frontal networks, indicating increased neuronal synchronization. Musical learning may access compensatory pathways for memory functions during compromised PFC functions associated with learning and recall. Music learning may also confer a neurophysiological advantage through the stronger synchronization of the neuronal cell assemblies underlying verbal learning and memory. Collectively our data provide evidence that melodic-rhythmic templates as temporal structures in music may drive internal rhythm formation in recurrent cortical networks involved in learning and memory.

  8. Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

    Science.gov (United States)

    Kalderstam, Jonas; Edén, Patrik; Ohlsson, Mattias

    2015-01-01

    We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart's predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do.

  9. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.

  10. Protein kinase C substrate phosphorylation in relation to neural growth and synaptic plasticity: a common molecular mechanism underlying multiple neural functions

    Energy Technology Data Exchange (ETDEWEB)

    Nelson, R.B.

    1987-01-01

    In these studies, we addressed the issues of: (1) whether neural protein kinase C (PKC) substrates might be altered in phosphorylation following induction of long-term potentiation (LTP); (2) whether PKC substrate phosphorylation might be specifically related to a model of neural plasticity other than LTP; and (3) whether the PKC substrates implicated in adult synaptic plasticity might be present in axonal growth cones given reports that high concentrations of PKC are found in these structures. Using quantitative analysis of multiple two-dimensional gels, we found that the two major substrates of exogenous purified PKC in adult hippocampal homogenate are both directly correlated to persistence of LTP. In rhesus monkey cerebral cortex, the proteins corresponding to protein F1 and 80k displayed topographical gradients in /sup 32/P-incorporation along the occipitotemporal visual processing pathway. The phosphorylation of both proteins was 11- and 14-fold higher, respectively, in temporal regions of this pathway implicated in the storage of visual representations, than in occipital regions, which do not appear to directly participate in visual memory functions.

  11. Improvement of Wear Performance of Nano-Multilayer PVD Coatings under Dry Hard End Milling Conditions Based on Their Architectural Development

    Directory of Open Access Journals (Sweden)

    Shahereen Chowdhury

    2018-02-01

    Full Text Available The TiAlCrSiYN-based family of PVD (physical vapor deposition hard coatings was specially designed for extreme conditions involving the dry ultra-performance machining of hardened tool steels. However, there is a strong potential for further advances in the wear performance of the coatings through improvements in their architecture. A few different coating architectures (monolayer, multilayer, bi-multilayer, bi-multilayer with increased number of alternating nano-layers were studied in relation to cutting-tool life. Comprehensive characterization of the structure and properties of the coatings has been performed using XRD, SEM, TEM, micro-mechanical studies and tool-life evaluation. The wear performance was then related to the ability of the coating layer to exhibit minimal surface damage under operation, which is directly associated with the various micro-mechanical characteristics (such as hardness, elastic modulus and related characteristics; nano-impact; scratch test-based characteristics. The results presented exhibited that a substantial increase in tool life as well as improvement of the mechanical properties could be achieved through the architectural development of the coatings.

  12. More cells, bigger cells or simply reorganization? Alternative mechanisms leading to changed internode architecture under contrasting stress regimes.

    Science.gov (United States)

    Huber, Heidrun; de Brouwer, Jan; von Wettberg, Eric J; During, Heinjo J; Anten, Niels P R

    2014-01-01

    Shading and mechanical stress (MS) modulate plant architecture by inducing different developmental pathways. Shading results in increased stem elongation, often reducing whole-plant mechanical stability, while MS inhibits elongation, with a concomitant increase in stability. Here, we examined how these organ-level responses are related to patterns and processes at the cellular level by exposing Impatiens capensis to shading and MS. Shading led to the production of narrower cells along the vertical axis. By contrast, MS led to the production of fewer, smaller and broader cells. These responses to treatments were largely in line with genetic differences found among plants from open and closed canopy sites. Shading- and MS-induced plastic responses in cellular characteristics were negatively correlated: genotypes that were more responsive to shading were less responsive to MS and vice versa. This negative correlation, however, did not scale to mechanical and architectural traits. Our data show how environmental conditions elicit distinctly different associations between characteristics at the cellular level, plant morphology and biomechanics. The evolution of optimal response to different environmental cues may be limited by negative correlations of stress-induced responses at the cellular level. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

  13. Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

    Directory of Open Access Journals (Sweden)

    Yadollah Abdollahi

    2014-01-01

    Full Text Available The artificial neural network (ANN modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP, Incremental Back Propagation (IBP, Batch Back Propagation (BBP, and Levenberg-Marquardt (LM algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

  14. Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration.

    Science.gov (United States)

    Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L; Deadwyler, Sam A; Hampson, Robert E; Kraft, Robert A

    2015-04-15

    Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Grid Architecture 2

    Energy Technology Data Exchange (ETDEWEB)

    Taft, Jeffrey D. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-01-01

    The report describes work done on Grid Architecture under the auspices of the Department of Electricity Office of Electricity Delivery and Reliability in 2015. As described in the first Grid Architecture report, the primary purpose of this work is to provide stakeholder insight about grid issues so as to enable superior decision making on their part. Doing this requires the creation of various work products, including oft-times complex diagrams, analyses, and explanations. This report provides architectural insights into several important grid topics and also describes work done to advance the science of Grid Architecture as well.

  16. Neural substrates underlying reconcentration for the preparation of an appropriate cognitive state to prevent future mistakes: A functional magnetic resonance imaging study

    Directory of Open Access Journals (Sweden)

    Naoki eMiura

    2015-11-01

    Full Text Available The ability to reconcentrate on the present situation by recognizing one’s own recent errors is a cognitive mechanism that is crucial for safe and appropriate behavior in a particular situation. However, an individual may not be able to adequately perform a subsequent task even if he/she recognize his/her own error; thus, it is hypothesized that the neural mechanisms underlying the reconcentration process are different from the neural substrates supporting error recognition. The present study performed a functional magnetic resonance imaging (fMRI analysis to explore the neural substrates associated with reconcentration related to achieving an appropriate cognitive state, and to dissociate these brain regions from the neural substrates involved in recognizing one’s own mistake. This study included 44 healthy volunteers who completed an experimental procedure that was based on the Eriksen flanker task and included feedback regarding the results of the current trial. The hemodynamic response induced by each instance of feedback was modeled using a combination of the successes and failures of the current and subsequent trials in order to identify the neural substrates underlying the ability to reconcentrate for the next situation and to dissociate them from those involved in recognizing current errors. The fMRI findings revealed significant and specific activation in the dorsal aspect of the medial prefrontal cortex (MFC when participants successfully reconcentrated on the task after recognizing their own error based on feedback. Additionally, this specific activation was clearly dissociated from the activation foci that occurred during error recognition. These findings indicate that the dorsal aspect of the MFC may be a distinct functional region that specifically supports the reconcentration process and that is associated with the prevention of successive errors when a human subject recognizes his/her own mistake. Furthermore, it is likely

  17. Neural substrates underlying reconcentration for the preparation of an appropriate cognitive state to prevent future mistakes: a functional magnetic resonance imaging study

    Science.gov (United States)

    Miura, Naoki; Nozawa, Takayuki; Takahashi, Makoto; Yokoyama, Ryoichi; Sasaki, Yukako; Sakaki, Kohei; Kawashima, Ryuta

    2015-01-01

    The ability to reconcentrate on the present situation by recognizing one’s own recent errors is a cognitive mechanism that is crucial for safe and appropriate behavior in a particular situation. However, an individual may not be able to adequately perform a subsequent task even if he/she recognize his/her own error; thus, it is hypothesized that the neural mechanisms underlying the reconcentration process are different from the neural substrates supporting error recognition. The present study performed a functional magnetic resonance imaging (fMRI) analysis to explore the neural substrates associated with reconcentration related to achieving an appropriate cognitive state, and to dissociate these brain regions from the neural substrates involved in recognizing one’s own mistake. This study included 44 healthy volunteers who completed an experimental procedure that was based on the Eriksen flanker task and included feedback regarding the results of the current trial. The hemodynamic response induced by each instance of feedback was modeled using a combination of the successes and failures of the current and subsequent trials in order to identify the neural substrates underlying the ability to reconcentrate for the next situation and to dissociate them from those involved in recognizing current errors. The fMRI findings revealed significant and specific activation in the dorsal aspect of the medial prefrontal cortex (MFC) when participants successfully reconcentrated on the task after recognizing their own error based on feedback. Additionally, this specific activation was clearly dissociated from the activation foci that occurred during error recognition. These findings indicate that the dorsal aspect of the MFC may be a distinct functional region that specifically supports the reconcentration process and that is associated with the prevention of successive errors when a human subject recognizes his/her own mistake. Furthermore, it is likely that this

  18. Dissociable neural modulation underlying lasting first impressions, changing your mind for the better, and changing it for the worse.

    Science.gov (United States)

    Bhanji, Jamil P; Beer, Jennifer S

    2013-05-29

    Unattractive job candidates face a disadvantage when interviewing for a job. Employers' evaluations are colored by the candidate's physical attractiveness even when they take job interview performance into account. This example illustrates unexplored questions about the neural basis of social evaluation in humans. What neural regions support the lasting effects of initial impressions (even after getting to know someone)? How does the brain process information that changes our minds about someone? Job candidates' competence was evaluated from photographs and again after seeing snippets of job interviews. Left lateral orbitofrontal cortex modulation serves as a warning signal for initial reactions that ultimately undermine evaluations even when additional information is taken into account. The neural basis of changing one's mind about a candidate is not a simple matter of computing the amount of competence-affirming information in their job interview. Instead, seeing a candidate for the better is somewhat distinguishable at the neural level from seeing a candidate for the worse. Whereas amygdala modulation marks the extremity of evaluation change, favorable impression change additionally draws on parametric modulation of lateral prefrontal cortex and unfavorable impression change additionally draws on parametric modulation of medial prefrontal cortex, temporal cortex, and striatum. Investigating social evaluation as a dynamic process (rather than a one-time impression) paints a new picture of its neural basis and highlights the partially dissociable processes that contribute to changing your mind about someone for the better or the worse.

  19. Recyclable magnetic photocatalysts of Fe{sup 2+}/TiO{sub 2} hierarchical architecture with effective removal of Cr(VI) under UV light from water

    Energy Technology Data Exchange (ETDEWEB)

    Xu, S.C.; Zhang, Y.X.; Pan, S.S.; Ding, H.L. [Key Laboratory of Materials Physics, Anhui Key Lab of Nanomaterials and Nanostructure, Institute of Solid State Physics, Chinese Academy of Sciences, Hefei 230031 (China); Li, G.H., E-mail: ghli@issp.ac.cn [Key Laboratory of Materials Physics, Anhui Key Lab of Nanomaterials and Nanostructure, Institute of Solid State Physics, Chinese Academy of Sciences, Hefei 230031 (China)

    2011-11-30

    Highlights: Black-Right-Pointing-Pointer Fe{sup 2+}/TiO{sub 2} catalyst has a three-level hierarchical architecture. Black-Right-Pointing-Pointer With a removal effectiveness of 99.3% at Cr(VI) concentration of 10 mg L{sup -1}. Black-Right-Pointing-Pointer Two-step reduction: TiO{sub 2} photoreduces Fe{sup 2+} to Fe and Fe reduces Cr(VI) to Cr(III). Black-Right-Pointing-Pointer Hierarchical architecture serves as both photocatalytic reactor and absorbent. Black-Right-Pointing-Pointer Fe{sup 2+}/TiO{sub 2} catalyst can be magnetically separated from wastewater and recycled. - Abstract: We report the synthesis and photocatalytic removal of Cr(VI) from water of hierarchical micro/nanostructured Fe{sup 2+}/TiO{sub 2} tubes. The TiO{sub 2} tubes fabricated by a facile solvothermal approach show a three-level hierarchical architecture assembled from dense nanosheets nearly vertically standing on the surface of TiO{sub 2} microtube. The nanosheets with a thickness of about 20 nm are composed of numerous TiO{sub 2} nanocrystals with size in the range of 15-20 nm. Ferrous ions are doped into the hierarchical architecture by a reduction route. The Fe{sup 2+}/TiO{sub 2} catalyst demonstrates an effective removal of Cr(VI) from water under UV light and the removal effectiveness reaches 99.3% at the initial Cr(VI) concentration of 10 mg L{sup -1}. The ferrous ion in the catalyst serves not as the photo-electron trap but as an intermedium of a two-step reduction. The TiO{sub 2} photoreduces the Fe{sup 2+} ions to Fe atoms firstly, then the Fe atoms reduce the Cr(VI) to Cr(III), and the later is removed by adsorption. The hierarchical architecture of the catalyst serves as a reactor for the photocatalytic reaction of Cr(VI) ions and an effective absorbent for the removal of Cr(III) ions. The catalyst can be easily magnetically separated from the wastewater after photocatalytic reaction and recycled after acid treatment.

  20. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  1. Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes.

    Science.gov (United States)

    North, B V; Curtis, D; Cassell, P G; Hitman, G A; Sham, P C

    2003-07-01

    Biallelic markers, such as single nucleotide polymorphisms (SNPs), provide greater information for localising disease loci when treated as multilocus haplotypes, but often haplotypes are not immediately available from multilocus genotypes in case-control studies. An artificial neural network allows investigation of association between disease phenotype and tightly linked markers without requiring haplotype phase and without modelling any evolutionary history for the disease-related haplotypes. The network assesses whether marker haplotypes differ between cases and controls to the extent that classification of disease status based on multi-marker genotypes is achievable. The network is "trained" to "recognise" affection status based on supplied marker genotypes, and then for each multi-marker genotype it produces outputs which aim to approximate the associated affection status. Next, the genotypes are permuted relative to affection status to produce many random datasets and the process of training and recording of outputs is repeated. The extent to which the ability to predict affection for the real dataset exceeds that for the random datasets measures the statistical significance of the association between multi-marker genotype and affection. This permutation test performs well with simulated case-control datasets, particularly when major gene effects are present. We have explored the effects of systematically varying different network parameters in order to identify their optimal values. We have applied the permutation test to 4 SNPs of the calpain 10 (CAPN10) gene typed in a case-control sample of subjects with type 2 diabetes, impaired glucose tolerance, and controls. We show that the neural network produces more highly significant evidence for association than do single marker tests corrected for the number of markers genotyped. The use of a permutation test could potentially allow conditional analyses which could incorporate known risk factors alongside marker

  2. Communication of IT-Architecture

    NARCIS (Netherlands)

    Koning, H.

    2008-01-01

    This PhD thesis contains the results of various research activities that fall under the topic ‘communication of IT-architecture’. The term IT-architecture defines the various types of architecture that can be found in the domain of Information Technology (software architecture, enterprise

  3. Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

    DEFF Research Database (Denmark)

    Gunnink, J.L.; Bosch, A.; Siemon, B.

    2012-01-01

    Airborne electromagnetic (AEM) methods supply data over large areas in a cost-effective way. We used ArtificialNeural Networks (ANN) to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case...

  4. Untangling the neurobiology of coping styles in rodents : Towards neural mechanisms underlying individual differences in disease susceptibility

    NARCIS (Netherlands)

    de Boer, Sietse F; Buwalda, Bauke; Koolhaas, Jaap M.

    Considerable individual differences exist in trait-like patterns of behavioral and physiological responses to salient environmental challenges. This individual variation in stress coping styles has an important functional role in terms of health and fitness. Hence, understanding the neural embedding

  5. Robotic architectures

    CSIR Research Space (South Africa)

    Mtshali, M

    2010-01-01

    Full Text Available In the development of mobile robotic systems, a robotic architecture plays a crucial role in interconnecting all the sub-systems and controlling the system. The design of robotic architectures for mobile autonomous robots is a challenging...

  6. Shell architecture and its relation to shell occupation by the hermit crab Clibanarius antillensis under different wave action conditions

    Directory of Open Access Journals (Sweden)

    Araceli Argüelles

    2009-12-01

    Full Text Available We studied the intertidal hermit crab Clibanarius antillensis at Montepio Beach, Veracruz, Mexico, to determine whether architecture and weight of occupied shells varied with the degree of exposure to wave action. Data on shell use were obtained from 30-m transects perpendicular to the shoreline. The gastropod shells species used by C. antillensis were classified into four groups according to their morphology: neritiform, conical, turriculate, and turbinate. Neither the size nor the weight of hermit crabs varied along transects. A pattern showing differential use of shell type according to water velocity was detected. Neritiform and turriculate shells were the least occupied, and their abundance decreased with increasing water velocities. Conical and turbinate shells were the most used and their presence increased with increasing water velocities. Turbinate and conical shells are heavier and have a higher weight/exposed-area ratio than neritiform and turriculate shells, so using them at higher energy sites seems to be more advantageous than using turriculate shells. The pattern that emerges is one in which C. antillensis occupy different shells along the intertidal transect, probably due to the advantages that different shells may bring, such as minimising drag and the risk of dislodgement.

  7. Architectural Theatricality

    DEFF Research Database (Denmark)

    Tvedebrink, Tenna Doktor Olsen; Fisker, Anna Marie; Kirkegaard, Poul Henning

    2013-01-01

    and recovery through the architecture framing eating experiences, this article examines, from a theoretical perspective, two less debated concepts relating to hospitality called food design and architectural theatricality. In architectural theory the nineteenth century German architect Gottfried Semper...... is known for his writings on theatricality, understood as a holistic design approach emphasizing the contextual, cultural, ritual and social meanings rooted in architecture. Relative hereto, the International Food Design Society recently argued, in a similar holistic manner, that the methodology used...

  8. Architecture & Environment

    Science.gov (United States)

    Erickson, Mary; Delahunt, Michael

    2010-01-01

    Most art teachers would agree that architecture is an important form of visual art, but they do not always include it in their curriculums. In this article, the authors share core ideas from "Architecture and Environment," a teaching resource that they developed out of a long-term interest in teaching architecture and their fascination with the…

  9. Linking neural and symbolic representation and processing of conceptual structures

    NARCIS (Netherlands)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual

  10. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Project Integration Architecture: Application Architecture

    Science.gov (United States)

    Jones, William Henry

    2005-01-01

    The Project Integration Architecture (PIA) implements a flexible, object-oriented, wrapping architecture which encapsulates all of the information associated with engineering applications. The architecture allows the progress of a project to be tracked and documented in its entirety. Additionally, by bringing all of the information sources and sinks of a project into a single architectural space, the ability to transport information between those applications is enabled.

  12. Glaucoma detection based on deep convolutional neural network.

    Science.gov (United States)

    Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

    2015-08-01

    Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.

  13. 1D-FALCON: Accelerating Deep Convolutional Neural Network Inference by Co-optimization of Models and Underlying Arithmetic Implementation

    OpenAIRE

    Maji, PP; Mullins, R.

    2017-01-01

    Deep convolutional neural networks (CNNs), which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks, at the expense of high computational complexity, limiting their deployability. In modern CNNs, convolutional layers mostly consume 90% of the processing time during a forward inference and acceleration of these layers are of great research and commercial interest. In this paper, we examine the effects of co-optimizing intern...

  14. Generation of Regionally Specified Neural Progenitors and Functional Neurons from Human Embryonic Stem Cells under Defined Conditions

    Directory of Open Access Journals (Sweden)

    Agnete Kirkeby

    2012-06-01

    Full Text Available To model human neural-cell-fate specification and to provide cells for regenerative therapies, we have developed a method to generate human neural progenitors and neurons from human embryonic stem cells, which recapitulates human fetal brain development. Through the addition of a small molecule that activates canonical WNT signaling, we induced rapid and efficient dose-dependent specification of regionally defined neural progenitors ranging from telencephalic forebrain to posterior hindbrain fates. Ten days after initiation of differentiation, the progenitors could be transplanted to the adult rat striatum, where they formed neuron-rich and tumor-free grafts with maintained regional specification. Cells patterned toward a ventral midbrain (VM identity generated a high proportion of authentic dopaminergic neurons after transplantation. The dopamine neurons showed morphology, projection pattern, and protein expression identical to that of human fetal VM cells grafted in parallel. VM-patterned but not forebrain-patterned neurons released dopamine and reversed motor deficits in an animal model of Parkinson's disease.

  15. Serotonin 2A Receptor Signaling Underlies LSD-induced Alteration of the Neural Response to Dynamic Changes in Music.

    Science.gov (United States)

    Barrett, Frederick S; Preller, Katrin H; Herdener, Marcus; Janata, Petr; Vollenweider, Franz X

    2017-09-28

    Classic psychedelic drugs (serotonin 2A, or 5HT2A, receptor agonists) have notable effects on music listening. In the current report, blood oxygen level-dependent (BOLD) signal was collected during music listening in 25 healthy adults after administration of placebo, lysergic acid diethylamide (LSD), and LSD pretreated with the 5HT2A antagonist ketanserin, to investigate the role of 5HT2A receptor signaling in the neural response to the time-varying tonal structure of music. Tonality-tracking analysis of BOLD data revealed that 5HT2A receptor signaling alters the neural response to music in brain regions supporting basic and higher-level musical and auditory processing, and areas involved in memory, emotion, and self-referential processing. This suggests a critical role of 5HT2A receptor signaling in supporting the neural tracking of dynamic tonal structure in music, as well as in supporting the associated increases in emotionality, connectedness, and meaningfulness in response to music that are commonly observed after the administration of LSD and other psychedelics. Together, these findings inform the neuropsychopharmacology of music perception and cognition, meaningful music listening experiences, and altered perception of music during psychedelic experiences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. The U.S. Personnel Recovery Architecture under Chief of Mission Responsibility: Department of State and Department of Defense Coordination

    Science.gov (United States)

    2013-05-22

    In order to meet the requirements set forth in Annex G (draft, 12 FAM-1 at the tactical and operational levels, the PR Officer ( PRO ) under the SDO...Printing Office, July 31. Zaid, Mark S. 1997. Military might versus sovereign right: The kidnapping of Dr. Humberto Alvarez-Machain and the resulting

  17. Computer architecture technology trends

    CERN Document Server

    1991-01-01

    Please note this is a Short Discount publication. This year's edition of Computer Architecture Technology Trends analyses the trends which are taking place in the architecture of computing systems today. Due to the sheer number of different applications to which computers are being applied, there seems no end to the different adoptions which proliferate. There are, however, some underlying trends which appear. Decision makers should be aware of these trends when specifying architectures, particularly for future applications. This report is fully revised and updated and provides insight in

  18. Distinct genetic architecture underlies the emergence of sleep loss and prey-seeking behavior in the Mexican cavefish.

    Science.gov (United States)

    Yoshizawa, Masato; Robinson, Beatriz G; Duboué, Erik R; Masek, Pavel; Jaggard, James B; O'Quin, Kelly E; Borowsky, Richard L; Jeffery, William R; Keene, Alex C

    2015-02-20

    Sleep is characterized by extended periods of quiescence and reduced responsiveness to sensory stimuli. Animals ranging from insects to mammals adapt to environments with limited food by suppressing sleep and enhancing their response to food cues, yet little is known about the genetic and evolutionary relationship between these processes. The blind Mexican cavefish, Astyanax mexicanus is a powerful model for elucidating the genetic mechanisms underlying behavioral evolution. A. mexicanus comprises an extant ancestral-type surface dwelling morph and at least five independently evolved cave populations. Evolutionary convergence on sleep loss and vibration attraction behavior, which is involved in prey seeking, have been documented in cavefish raising the possibility that enhanced sensory responsiveness underlies changes in sleep. We established a system to study sleep and vibration attraction behavior in adult A. mexicanus and used high coverage quantitative trait loci (QTL) mapping to investigate the functional and evolutionary relationship between these traits. Analysis of surface-cave F2 hybrid fish and an outbred cave population indicates that independent genetic factors underlie changes in sleep/locomotor activity and vibration attraction behavior. High-coverage QTL mapping with genotyping-by-sequencing technology identify two novel QTL intervals that associate with locomotor activity and include the narcolepsy-associated tp53 regulating kinase. These QTLs represent the first genomic localization of locomotor activity in cavefish and are distinct from two QTLs previously identified as associating with vibration attraction behavior. Taken together, these results localize genomic regions underlying sleep/locomotor and sensory changes in cavefish populations and provide evidence that sleep loss evolved independently from enhanced sensory responsiveness.

  19. Genetic Architecture of Natural Variation Underlying Adult Foraging Behavior That Is Essential for Survival of Drosophila melanogaster.

    Science.gov (United States)

    Lee, Yuh Chwen G; Yang, Qian; Chi, Wanhao; Turkson, Susie A; Du, Wei A; Kemkemer, Claus; Zeng, Zhao-Bang; Long, Manyuan; Zhuang, Xiaoxi

    2017-05-01

    Foraging behavior is critical for the fitness of individuals. However, the genetic basis of variation in foraging behavior and the evolutionary forces underlying such natural variation have rarely been investigated. We developed a systematic approach to assay the variation in survival rate in a foraging environment for adult flies derived from a wild Drosophila melanogaster population. Despite being such an essential trait, there is substantial variation of foraging behavior among D. melanogaster strains. Importantly, we provided the first evaluation of the potential caveats of using inbred Drosophila strains to perform genome-wide association studies on life-history traits, and concluded that inbreeding depression is unlikely a major contributor for the observed large variation in adult foraging behavior. We found that adult foraging behavior has a strong genetic component and, unlike larval foraging behavior, depends on multiple loci. Identified candidate genes are enriched in those with high expression in adult heads and, demonstrated by expression knock down assay, are involved in maintaining normal functions of the nervous system. Our study not only identified candidate genes for foraging behavior that is relevant to individual fitness, but also shed light on the initial stage underlying the evolution of the behavior. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  20. Effect on Bone Architecture of Marginal Grooves in Dental Implants Under Occlusal Loaded Conditions in Beagle Dogs.

    Science.gov (United States)

    Kato, Hatsumi; Kuroshima, Shinichiro; Inaba, Nao; Uto, Yusuke; Sawase, Takashi

    2018-02-01

    The aim of this study was to clarify whether marginal grooves on dental implants affect osseointegration, bone structure, and the alignment of collagen fibers to determine bone quality under loaded conditions. Anodized Ti-6Al-4V alloy dental implants, with and without marginal grooves (test and control implants, respectively), were used (3.7 × 8.0 mm). Fourth premolars and first molars of 6 beagle mandibles were extracted. Two control and test implants were placed in randomly selected healed sites at 12 weeks after tooth extraction. Screw-retained single crowns for first molars were fabricated. Euthanasia was performed at 8 weeks after the application of occlusal forces. Implant marginal bone level, bone to implant contact (BIC), bone structure around dental implants, and the alignment of collagen fibers determining bone quality were analyzed. The marginal bone level in test implants was significantly higher than that in control implants. Occlusal forces significantly increased BIC in test implants ( P = .007), whereas BIC did not change in control implants, irrespective of occlusal forces ( P = .303). Moreover, occlusal forces significantly increased BIC in test implants compared with control implants ( P = .032). Additionally, occlusal forces preferentially aligned collagen fibers in test implants, but not control implants. Hence, marginal grooves on dental implants have positive effects on increased osseointegration and adapted bone quality based on the preferential alignment of collagen fibers around dental implants under loaded conditions.

  1. Architectural Anthropology

    DEFF Research Database (Denmark)

    Stender, Marie

    , while recent material and spatial turns in anthropology have also brought an increasing interest in design, architecture and the built environment. Understanding the relationship between the social and the physical is at the heart of both disciplines, and they can obviously benefit from further...... collaboration: How can qualitative anthropological approaches contribute to contemporary architecture? And just as importantly: What can anthropologists learn from architects’ understanding of spatial and material surroundings? Recent theoretical developments in anthropology stress the role of materials...... and maybe also contribute to architectural design and the shaping of built environments? Through various empirical examples this paper explores the challenges and potentials of architectural anthropology....

  2. Catalyst Architecture

    DEFF Research Database (Denmark)

    Kiib, Hans; Marling, Gitte; Hansen, Peter Mandal

    2014-01-01

    How can architecture promote the enriching experiences of the tolerant, the democratic, and the learning city - a city worth living in, worth supporting and worth investing in? Catalyst Architecture comprises architectural projects, which, by virtue of their location, context and their combination...... of programs, have a role in mediating positive social and/or cultural development. In this sense, we talk about architecture as a catalyst for: sustainable adaptation of the city’s infrastructure appropriate renovation of dilapidated urban districts strengthening of social cohesiveness in the city development...

  3. Architectural Narratives

    DEFF Research Database (Denmark)

    Kiib, Hans

    2010-01-01

    a functional framework for these concepts, but tries increasingly to endow the main idea of the cultural project with a spatially aesthetic expression - a shift towards “experience architecture.” A great number of these projects typically recycle and reinterpret narratives related to historical buildings...... and architectural heritage; another group tries to embed new performative technologies in expressive architectural representation. Finally, this essay provides a theoretical framework for the analysis of the political rationales of these projects and for the architectural representation bridges the gap between...

  4. Classifying epilepsy diseases using artificial neural networks and genetic algorithm.

    Science.gov (United States)

    Koçer, Sabri; Canal, M Rahmi

    2011-08-01

    In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.

  5. Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Geon Pil; Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun, E-mail: magyna@chosun.ac.kr

    2016-04-15

    Highlights: • We present a hydrogen-concentration prediction method in an NPP containment. • The cascaded fuzzy neural network (CFNN) is used in this prediction model. • The CFNN model is much better than the existing FNN model. • This prediction can help prevent severe accidents in NPP due to hydrogen explosion. - Abstract: Recently, severe accidents in nuclear power plants (NPPs) have attracted worldwide interest since the Fukushima accident. If the hydrogen concentration in an NPP containment is increased above 4% in atmospheric pressure, hydrogen combustion will likely occur. Therefore, the hydrogen concentration must be kept below 4%. This study presents the prediction of hydrogen concentration using cascaded fuzzy neural network (CFNN). The CFNN model repeatedly applies FNN modules that are serially connected. The CFNN model was developed using data on severe accidents in NPPs. The data were obtained by numerically simulating the accident scenarios using the MAAP4 code for optimized power reactor 1000 (OPR1000) because real severe accident data cannot be obtained from actual NPP accidents. The root-mean-square error level predicted by the CFNN model is below approximately 5%. It was confirmed that the CFNN model could accurately predict the hydrogen concentration in the containment. If NPP operators can predict the hydrogen concentration in the containment using the CFNN model, this prediction can assist them in preventing a hydrogen explosion.

  6. Social pain and social gain in the adolescent brain: A common neural circuitry underlying both positive and negative social evaluation

    Science.gov (United States)

    Dalgleish, Tim; Walsh, Nicholas D.; Mobbs, Dean; Schweizer, Susanne; van Harmelen, Anne-Laura; Dunn, Barnaby; Dunn, Valerie; Goodyer, Ian; Stretton, Jason

    2017-01-01

    Social interaction inherently involves the subjective evaluation of cues salient to social inclusion and exclusion. Testifying to the importance of such social cues, parts of the neural system dedicated to the detection of physical pain, the dorsal anterior cingulate cortex (dACC) and anterior insula (AI), have been shown to be equally sensitive to the detection of social pain experienced after social exclusion. However, recent work suggests that this dACC-AI matrix may index any socially pertinent information. We directly tested the hypothesis that the dACC-AI would respond to cues of both inclusion and exclusion, using a novel social feedback fMRI paradigm in a population-derived sample of adolescents. We show that the dACC and left AI are commonly activated by feedback cues of inclusion and exclusion. Our findings suggest that theoretical accounts of the dACC-AI network as a neural alarm system restricted within the social domain to the processing of signals of exclusion require significant revision. PMID:28169323

  7. Untangling the neurobiology of coping styles in rodents: Towards neural mechanisms underlying individual differences in disease susceptibility.

    Science.gov (United States)

    de Boer, Sietse F; Buwalda, Bauke; Koolhaas, Jaap M

    2017-03-01

    Considerable individual differences exist in trait-like patterns of behavioral and physiological responses to salient environmental challenges. This individual variation in stress coping styles has an important functional role in terms of health and fitness. Hence, understanding the neural embedding of coping style variation is fundamental for biobehavioral neurosciences in probing individual disease susceptibility. This review outlines individual differences in trait-aggressiveness as an adaptive component of the natural sociobiology of rats and mice, and highlights that these reflect the general style of coping that varies from proactive (aggressive) to reactive (docile). We propose that this qualitative coping style can be disentangled into multiple quantitative behavioral domains, e.g., flexibility/impulse control, emotional reactivity and harm avoidance/reward processing, that each are encoded into selective neural circuitries. Since functioning of all these brain circuitries rely on fine-tuned serotonin signaling, autoinhibitory control mechanisms of serotonergic neuron (re)activity are crucial in orchestrating general coping style. Untangling the precise neuromolecular mechanisms of different coping styles will provide a roadmap for developing better therapeutic strategies of stress-related diseases. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Architectural freedom and industrialised architecture

    DEFF Research Database (Denmark)

    Vestergaard, Inge

    2012-01-01

    strategies which architects can use - to give the individual project attitudes and designs with architectural quality. Through the customized component production it is possible to choose many different proportions, to organize the process at site choosing either one room components or several rooms......Architectural freedom and industrialized architecture. Inge Vestergaard, Associate Professor, Cand. Arch. Aarhus School of Architecture, Denmark Noerreport 20, 8000 Aarhus C Telephone +45 89 36 0000 E-mai l inge.vestergaard@aarch.dk Based on the repetitive architecture from the "building boom" 1960...... to 1973 it is discussed how architects can handle these Danish element and montage buildings through the transformation to upgraded aesthetical, functional and energy efficient architecture. The method used is analysis of cases, parallels to literature studies and producer interviews. This analysis...

  9. RAD-QTL Mapping Reveals Both Genome-Level Parallelism and Different Genetic Architecture Underlying the Evolution of Body Shape in Lake Whitefish (Coregonus clupeaformis) Species Pairs.

    Science.gov (United States)

    Laporte, Martin; Rogers, Sean M; Dion-Côté, Anne-Marie; Normandeau, Eric; Gagnaire, Pierre-Alexandre; Dalziel, Anne C; Chebib, Jobran; Bernatchez, Louis

    2015-05-21

    Parallel changes in body shape may evolve in response to similar environmental conditions, but whether such parallel phenotypic changes share a common genetic basis is still debated. The goal of this study was to assess whether parallel phenotypic changes could be explained by genetic parallelism, multiple genetic routes, or both. We first provide evidence for parallelism in fish shape by using geometric morphometrics among 300 fish representing five species pairs of Lake Whitefish. Using a genetic map comprising 3438 restriction site-associated DNA sequencing single-nucleotide polymorphisms, we then identified quantitative trait loci underlying body shape traits in a backcross family reared in the laboratory. A total of 138 body shape quantitative trait loci were identified in this cross, thus revealing a highly polygenic architecture of body shape in Lake Whitefish. Third, we tested for evidence of genetic parallelism among independent wild populations using both a single-locus method (outlier analysis) and a polygenic approach (analysis of covariation among markers). The single-locus approach provided limited evidence for genetic parallelism. However, the polygenic analysis revealed genetic parallelism for three of the five lakes, which differed from the two other lakes. These results provide evidence for both genetic parallelism and multiple genetic routes underlying parallel phenotypic evolution in fish shape among populations occupying similar ecological niches. Copyright © 2015 Laporte et al.

  10. Temporal evolution of brain reorganization under cross-modal training: insights into the functional architecture of encoding and retrieval networks

    Science.gov (United States)

    Likova, Lora T.

    2015-03-01

    This study is based on the recent discovery of massive and well-structured cross-modal memory activation generated in the primary visual cortex (V1) of totally blind people as a result of novel training in drawing without any vision (Likova, 2012). This unexpected functional reorganization of primary visual cortex was obtained after undergoing only a week of training by the novel Cognitive-Kinesthetic Method, and was consistent across pilot groups of different categories of visual deprivation: congenitally blind, late-onset blind and blindfolded (Likova, 2014). These findings led us to implicate V1 as the implementation of the theoretical visuo-spatial 'sketchpad' for working memory in the human brain. Since neither the source nor the subsequent 'recipient' of this non-visual memory information in V1 is known, these results raise a number of important questions about the underlying functional organization of the respective encoding and retrieval networks in the brain. To address these questions, an individual totally blind from birth was given a week of Cognitive-Kinesthetic training, accompanied by functional magnetic resonance imaging (fMRI) both before and just after training, and again after a two-month consolidation period. The results revealed a remarkable temporal sequence of training-based response reorganization in both the hippocampal complex and the temporal-lobe object processing hierarchy over the prolonged consolidation period. In particular, a pattern of profound learning-based transformations in the hippocampus was strongly reflected in V1, with the retrieval function showing massive growth as result of the Cognitive-Kinesthetic memory training and consolidation, while the initially strong hippocampal response during tactile exploration and encoding became non-existent. Furthermore, after training, an alternating patch structure in the form of a cascade of discrete ventral regions underwent radical transformations to reach complete functional

  11. Architectural geometry

    NARCIS (Netherlands)

    Pottmann, Helmut; Eigensatz, Michael; Vaxman, A.; Wallner, Johannes

    2015-01-01

    Around 2005 it became apparent in the geometry processing community that freeform architecture contains many problems of a geometric nature to be solved, and many opportunities for optimization which however require geometric understanding. This area of research, which has been called architectural

  12. Architectural Contestation

    NARCIS (Netherlands)

    Merle, J.

    2012-01-01

    This dissertation addresses the reductive reading of Georges Bataille's work done within the field of architectural criticism and theory which tends to set aside the fundamental ‘broken’ totality of Bataille's oeuvre and also to narrowly interpret it as a mere critique of architectural form,

  13. Architecture Sustainability

    NARCIS (Netherlands)

    Avgeriou, Paris; Stal, Michael; Hilliard, Rich

    2013-01-01

    Software architecture is the foundation of software system development, encompassing a system's architects' and stakeholders' strategic decisions. A special issue of IEEE Software is intended to raise awareness of architecture sustainability issues and increase interest and work in the area. The

  14. Architectural Creation of Light

    DEFF Research Database (Denmark)

    Bülow, Katja

    2015-01-01

    Bidraget "Architectural Creation of Light" indgår sammen med 108 andre bidrag i bogen "You Say Light, I Think Shadow". Bogens indhold undersøger: "Hvad er lys". I dette bidrag besvares spørgsmålet gennem iagttagelser af arkitektstuderendes undersøgelser af lyset i deres arbejdsmodeller i...

  15. Systemic Architecture

    DEFF Research Database (Denmark)

    Poletto, Marco; Pasquero, Claudia

    This is a manual investigating the subject of urban ecology and systemic development from the perspective of architectural design. It sets out to explore two main goals: to discuss the contemporary relevance of a systemic practice to architectural design, and to share a toolbox of informational...... design protocols developed to describe the city as a territory of self-organization. Collecting together nearly a decade of design experiments by the authors and their practice, ecoLogicStudio, the book discusses key disciplinary definitions such as ecologic urbanism, algorithmic architecture, bottom......-up or tactical design, behavioural space and the boundary of the natural and the artificial realms within the city and architecture. A new kind of "real-time world-city" is illustrated in the form of an operational design manual for the assemblage of proto-architectures, the incubation of proto...

  16. Architectural Anthropology

    DEFF Research Database (Denmark)

    Stender, Marie

    anthropology. On the one hand, there are obviously good reasons for developing architecture based on anthropological insights in local contexts and anthropologically inspired techniques for ‘collaborative formation of issues’. Houses and built environments are huge investments, their life expectancy......-anthropology. Within the field of architecture, however, there has not yet been quite the same eagerness to include anthropological approaches in design processes. This paper discusses why this is so and how and whether architectural anthropology has different conditions and objectives than other types of design...... and other spaces that architects are preoccupied with. On the other hand, the distinction between architecture and design is not merely one of scale. Design and architecture represent – at least in Denmark – also quite different disciplinary traditions and methods. Where designers develop prototypes...

  17. States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.

    Science.gov (United States)

    Gläscher, Jan; Daw, Nathaniel; Dayan, Peter; O'Doherty, John P

    2010-05-27

    Reinforcement learning (RL) uses sequential experience with situations ("states") and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior. Copyright 2010 Elsevier Inc. All rights reserved.

  18. Melanoma Spheroids Grown Under Neural Crest Cell Conditions Are Highly Plastic Migratory/Invasive Tumor Cells Endowed with Immunomodulator Function

    Science.gov (United States)

    Lalou, Claude; Lauden, Laura; Michel, Laurence; de la Grange, Pierre; Khatib, Abdel-Majid; Aoudjit, Fawzi; Charron, Dominique; Alcaide-Loridan, Catherine; Al-Daccak, Reem

    2011-01-01

    Background The aggressiveness of melanoma tumors is likely to rely on their well-recognized heterogeneity and plasticity. Melanoma comprises multi-subpopulations of cancer cells some of which may possess stem cell-like properties. Although useful, the sphere-formation assay to identify stem cell-like or tumor initiating cell subpopulations in melanoma has been challenged, and it is unclear if this model can predict a functional phenotype associated with aggressive tumor cells. Methodology/Principal Findings We analyzed the molecular and functional phenotypes of melanoma spheroids formed in neural crest cell medium. Whether from metastatic or advanced primary tumors, spheroid cells expressed melanoma-associated markers. They displayed higher capacity to differentiate along mesenchymal lineages and enhanced expression of SOX2, NANOG, KLF4, and/or OCT4 transcription factors, but not enhanced self-renewal or tumorigenicity when compared to their adherent counterparts. Gene expression profiling attributed a neural crest cell signature to these spheroids and indicated that a migratory/invasive and immune-function modulating program could be associated with these cells. In vitro assays confirmed that spheroids display enhanced migratory/invasive capacities. In immune activation assays, spheroid cells elicited a poorer allogenic response from immune cells and inhibited mitogen-dependent T cells activation and proliferation more efficiently than their adherent counterparts. Our findings reveal a novel immune-modulator function of melanoma spheroids and suggest specific roles for spheroids in invasion and in evasion of antitumor immunity. Conclusion/Significance The association of a more plastic, invasive and evasive, thus a more aggressive tumor phenotype with melanoma spheroids reveals a previously unrecognized aspect of tumor cells expanded as spheroid cultures. While of limited efficiency for melanoma initiating cell identification, our melanoma spheroid model predicted

  19. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermore......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  20. DSP Architecture Design Essentials

    CERN Document Server

    Marković, Dejan

    2012-01-01

    In DSP Architecture Design Essentials, authors Dejan Marković and Robert W. Brodersen cover a key subject for the successful realization of DSP algorithms for communications, multimedia, and healthcare applications. The book addresses the need for DSP architecture design that maps advanced DSP algorithms to hardware in the most power- and area-efficient way. The key feature of this text is a design methodology based on a high-level design model that leads to hardware implementation with minimum power and area. The methodology includes algorithm-level considerations such as automated word-length reduction and intrinsic data properties that can be leveraged to reduce hardware complexity. From a high-level data-flow graph model, an architecture exploration methodology based on linear programming is used to create an array of architectural solutions tailored to the underlying hardware technology. The book is supplemented with online material: bibliography, design examples, CAD tutorials and custom software.

  1. BUILDING SUSTAINABLE ARCHITECTURAL DESIGN: A RENOVATION PROJECT

    OpenAIRE

    Hakan ÜNALAN,; Leyla Y. TOKMAN

    2011-01-01

    Today, the conservation of energy and respect for the natural environment appears to be the most important phenomena in all areas. In this regard, "sustainability" concept emerged and the architectural platform "Sustainable Architecture" is composed of a research subject to the new and permanent. Architecture underlying the "design" as including also the new concept of "sustainable architectural design" has revealed that field. Sustainable architecture "building in-house", "building envelop...

  2. Project Integration Architecture: Architectural Overview

    Science.gov (United States)

    Jones, William Henry

    2001-01-01

    The Project Integration Architecture (PIA) implements a flexible, object-oriented, wrapping architecture which encapsulates all of the information associated with engineering applications. The architecture allows the progress of a project to be tracked and documented in its entirety. By being a single, self-revealing architecture, the ability to develop single tools, for example a single graphical user interface, to span all applications is enabled. Additionally, by bringing all of the information sources and sinks of a project into a single architectural space, the ability to transport information between those applications becomes possible, Object-encapsulation further allows information to become in a sense self-aware, knowing things such as its own dimensionality and providing functionality appropriate to its kind.

  3. Architectural technology

    DEFF Research Database (Denmark)

    2005-01-01

    The booklet offers an overall introduction to the Institute of Architectural Technology and its projects and activities, and an invitation to the reader to contact the institute or the individual researcher for further information. The research, which takes place at the Institute of Architectural...... Technology at the Roayl Danish Academy of Fine Arts, School of Architecture, reflects a spread between strategic, goal-oriented pilot projects, commissioned by a ministry, a fund or a private company, and on the other hand projects which originate from strong personal interests and enthusiasm of individual...

  4. Humanizing Architecture

    DEFF Research Database (Denmark)

    Toft, Tanya Søndergaard

    2015-01-01

    The article proposes the urban digital gallery as an opportunity to explore the relationship between ‘human’ and ‘technology,’ through the programming of media architecture. It takes a curatorial perspective when proposing an ontological shift from considering media facades as visual spectacles...... agency and a sense of being by way of dematerializing architecture. This is achieved by way of programming the symbolic to provide new emotional realizations and situations of enlightenment in the public audience. This reflects a greater potential to humanize the digital in media architecture....

  5. Healing Architecture

    DEFF Research Database (Denmark)

    Folmer, Mette Blicher; Mullins, Michael; Frandsen, Anne Kathrine

    2012-01-01

    The project examines how architecture and design of space in the intensive unit promotes or hinders interaction between relatives and patients. The primary starting point is the relatives. Relatives’ support and interaction with their loved ones is important in order to promote the patients healing...... process. Therefore knowledge on how space can support interaction is fundamental for the architect, in order to make the best design solutions. Several scientific studies document that the hospital's architecture and design are important for human healing processes, including how the physical environment...... architectural and design solutions in order to improve quality of interaction between relative and patient in the hospital's intensive unit....

  6. Self-assembled 3D flowerlike hierarchical Fe3O4@Bi2O3 core-shell architectures and their enhanced photocatalytic activity under visible light.

    Science.gov (United States)

    Wang, Yang; Li, Shikuo; Xing, Xianran; Huang, Fangzhi; Shen, Yuhua; Xie, Anjian; Wang, Xiufang; Zhang, Jian

    2011-04-18

    Three-dimensional (3D) flowerlike hierarchical Fe(3)O(4)@Bi(2)O(3) core-shell architectures were synthesized by a simple and direct solvothermal route without any linker shell. The results indicated that the size of the 3D flowerlike hierarchical microspheres was about 420 nm and the shell was composed of several nanosheets with a thickness of 4-10 nm and a width of 100-140 nm. The saturation magnetization of the superparamagnetic composite microspheres was about 41 emu g(-1) at room temperature. Moreover, the Fe(3)O(4)@Bi(2)O(3) composite microspheres showed much higher (7-10 times) photocatalytic activity than commercial Bi(2)O(3) particles under visible-light irradiation. The possible formation mechanism was proposed for Ostwald ripening and the self-assembled process. This novel composite material may have potential applications in water treatment, degradation of dye pollutants, and environmental cleaning, for example. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Risk-taking and social exclusion in adolescence: neural mechanisms underlying peer influences on decision-making.

    Science.gov (United States)

    Peake, Shannon J; Dishion, Thomas J; Stormshak, Elizabeth A; Moore, William E; Pfeifer, Jennifer H

    2013-11-15

    Social exclusion and risk-taking are both common experiences of concern in adolescence, yet little is known about how the two may be related at behavioral or neural levels. In this fMRI study, adolescents (N=27, 14 male, 14-17years-old) completed a series of tasks in the scanner assessing risky decision-making before and after an episode of social exclusion. In this particular context, exclusion was associated with greater behavioral risk-taking among adolescents with low self-reported resistance to peer influence (RPI). When making risky decisions after social exclusion, adolescents who had lower RPI exhibited higher levels of activity in the right temporoparietal junction (rTPJ), and this response in rTPJ was a significant mediator of the relationship between RPI and greater risk-taking after social exclusion. Lower RPI was also associated with lower levels of activity in lPFC during crashes following social exclusion, but unlike rTPJ this response in lPFC was not a significant mediator of the relationship between RPI and greater risk-taking after social exclusion. The results suggest that mentalizing and/or attentional mechanisms have a unique direct effect on adolescents' vulnerability to peer influence on risk-taking. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Reduced functional integration and segregation of distributed neural systems underlying social and emotional information processing in autism spectrum disorders.

    Science.gov (United States)

    Rudie, Jeffrey D; Shehzad, Zarrar; Hernandez, Leanna M; Colich, Natalie L; Bookheimer, Susan Y; Iacoboni, Marco; Dapretto, Mirella

    2012-05-01

    A growing body of evidence suggests that autism spectrum disorders (ASDs) are related to altered communication between brain regions. Here, we present findings showing that ASD is characterized by a pattern of reduced functional integration as well as reduced segregation of large-scale brain networks. Twenty-three children with ASD and 25 typically developing matched controls underwent functional magnetic resonance imaging while passively viewing emotional face expressions. We examined whole-brain functional connectivity of two brain structures previously implicated in emotional face processing in autism: the amygdala bilaterally and the right pars opercularis of the inferior frontal gyrus (rIFGpo). In the ASD group, we observed reduced functional integration (i.e., less long-range connectivity) between amygdala and secondary visual areas, as well as reduced segregation between amygdala and dorsolateral prefrontal cortex. For the rIFGpo seed, we observed reduced functional integration with parietal cortex and increased integration with right frontal cortex as well as right nucleus accumbens. Finally, we observed reduced segregation between rIFGpo and the ventromedial prefrontal cortex. We propose that a systems-level approach-whereby the integration and segregation of large-scale brain networks in ASD is examined in relation to typical development-may provide a more detailed characterization of the neural basis of ASD.

  9. Combining ground-based and airborne EM through Artificial Neural Networks for modelling glacial till under saline groundwater conditions

    Directory of Open Access Journals (Sweden)

    J. L. Gunnink

    2012-08-01

    Full Text Available Airborne electromagnetic (AEM methods supply data over large areas in a cost-effective way. We used Artificial Neural Networks (ANN to classify the geophysical signal into a meaningful geological parameter. By using examples of known relations between ground-based geophysical data (in this case electrical conductivity, EC, from electrical cone penetration tests and geological parameters (presence of glacial till, we extracted learning rules that could be applied to map the presence of a glacial till using the EC profiles from the airborne EM data. The saline groundwater in the area was obscuring the EC signal from the till but by using ANN we were able to extract subtle and often non-linear, relations in EC that were representative of the presence of the till. The ANN results were interpreted as the probability of having till and showed a good agreement with drilling data. The glacial till is acting as a layer that inhibits groundwater flow, due to its high clay-content, and is therefore an important layer in hydrogeological modelling and for predicting the effects of climate change on groundwater quantity and quality.

  10. Architectural Mealscapes

    DEFF Research Database (Denmark)

    Tvedebrink, Tenna Doktor Olsen; Fisker, Anna Marie; Kirkegaard, Poul Henning

    2012-01-01

    the German architect Gottfried Semper developed a theory on the “four elements of Architecture” tracing the origin of architecture back to the rise of the early human settlement and the creation of fire. With the notion ‘hearth’ as the first motive in architecture and the definition of three enclosing...... motives; mounding, enclosure and roof, Semper linked the cultural and social values of the primordial fireplace with the order and shape of architecture. He claimed that any building ever made was nothing but a variation of the first primitive shelters erected around the fireplace, and that the three...... enclosing motives existed only as defenders of the “sacred flame”. In that way Semper developed the idea that any architectural scenery can be described, analyzed and explained by understanding the contextual, symbolic and social values of how the four basic motives of hearth, mounding, enclosure, and roof...

  11. Architectured Nanomembranes

    Energy Technology Data Exchange (ETDEWEB)

    Sturgeon, Matthew R. [Former ORNL postdoc; Hu, Michael Z. [ORNL

    2017-07-01

    This paper has reviewed the frontier field of “architectured membranes” that contains anisotropic oriented porous nanostructures of inorganic materials. Three example types of architectured membranes were discussed with some relevant results from our own research: (1) anodized thin-layer titania membranes on porous anodized aluminum oxide (AAO) substrates of different pore sizes, (2) porous glass membranes on alumina substrate, and (3) guest-host membranes based on infiltration of yttrium-stabilized zirconia inside the pore channels of AAO matrices.

  12. Textile Architecture

    OpenAIRE

    Maurin, Bernard; Motro, René

    2013-01-01

    The basic idea for a textile architecture project originates during early meetings between the architect and the engineer. The morphologic richness of such projects is provided by the varying curvatures of shapes, in contradiction with a classical straight line and orthogonal architecture. However the rules of construction are quite different in terms of realisation and of mechanical behaviour: textile membranes are subjected to a pre-stress conferring them their rigidity, and a major objecti...

  13. Parametric and nonparametric statistical methods for genomic selection of traits with additive and epistatic genetic architectures.

    Science.gov (United States)

    Howard, Réka; Carriquiry, Alicia L; Beavis, William D

    2014-04-11

    Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE. Copyright © 2014 Howard et al.

  14. Meta Review of Systematic and Meta Analytic Reviews on Movement Differences, Effect of Movement Based Interventions, and the Underlying Neural Mechanisms in Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Motohide eMiyahara

    2013-03-01

    Full Text Available Purposes: The aims of this paper are three-folds: first, to identify and appraise evidence from published systematic and meta-analytic reviews on 1 movement characteristics of individuals with autism spectrum disorders (ASD; 2 the effects of movement based interventions for ASD; 3 hypothesized underlying neural mechanisms for the movement characteristics. Methods: A meta review of published systematic and meta-analytic reviews on movement characteristics, structural and functional brain anomalies in ASD and the effects of movement based interventions for individuals with ASD between 1806 and October 2012. The methodological quality of the identified systematic and meta-analytic reviews was independently assessed by two assessors with the assessment of multiple systematic reviews (AMSTAR.Results: The search yielded a total of twelve reviews on the movement differences or the movement based interventions. The methodological quality of the reviews varied, but the review conclusions were similar. Although individuals with ASD generally perform less well than age-matched controls in developmental movement tasks, there are few exceptions whose movement abilities are intact. Most movement based interventions report their efficacies. However, all existing studies employ the research design that is inherently incapable of providing strong evidence, and they often fail to report the extent of psychosocial interactions within the movement interventions. The hypothesized neural mechanisms are still under development and speculative in nature.Conclusions: It is premature to designate movement disturbance as a core symptom of ASD. The effects of movement based interventions on ASD core symptoms need to be further validated by stronger evidence based on verified theoretical mechanisms linking ASD with movement disorders.

  15. Interactive actions of Bdnf methylation and cell metabolism for building neural resilience under the influence of diet.

    Science.gov (United States)

    Tyagi, Ethika; Zhuang, Yumei; Agrawal, Rahul; Ying, Zhe; Gomez-Pinilla, Fernando

    2015-01-01

    Quality nutrition during the period of brain formation is a predictor of brain functional capacity and plasticity during adulthood; however it is not clear how this conferred plasticity imparts long-term neural resilience. Here we report that early exposure to dietary omega-3 fatty acids orchestrates key interactions between metabolic signals and Bdnf methylation creating a reservoir of neuroplasticity that can protect the brain against the deleterious effects of switching to a Western diet (WD). We observed that the switch to a WD increased Bdnf methylation specific to exon IV, in proportion to anxiety-like behavior, in Sprague Dawley rats reared in low omega-3 fatty acid diet, and these effects were abolished by the DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine. Blocking methylation also counteracted the reducing action of WD on the transcription regulator CTCF binding to Bdnf promoter IV. In vitro studies confirmed that CTCF binding to Bdnf promoter IV is essential for the action of DHA on BDNF regulation. Diet is also intrinsically associated to cell metabolism, and here we show that the switch to WD downregulated cell metabolism (NAD/NADH ratio and SIRT1). The fact that DNA methyltransferase inhibitor did not alter these parameters suggests they occur upstream to methylation. In turn, the methylation inhibitor counteracted the action of WD on PGC-1α, a mitochondrial transcription co-activator and BDNF regulator, suggesting that PGC-1α is an effector of Bdnf methylation. Results support a model in which diet can build an "epigenetic memory" during brain formation that confers resilience to metabolic perturbations occurring in adulthood. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Cardiac, mandibular and thymic phenotypical association indicates that cranial neural crest underlies bicuspid aortic valve formation in hamsters.

    Directory of Open Access Journals (Sweden)

    Jessica Martínez-Vargas

    Full Text Available Bicuspid aortic valve (BAV is the most prevalent human congenital cardiac malformation. It may appear isolated, associated with other cardiovascular malformations, or forming part of syndromes. Cranial neural crest (NC defects are supposed to be the cause of the spectrum of disorders associated with syndromic BAV. Experimental studies with an inbred hamster model of isolated BAV showed that alterations in the migration or differentiation of the cardiac NC cells in the embryonic cardiac outflow tract are most probably responsible for the development of this congenital valvular defect. We hypothesize that isolated BAV is not the result of local, but of early alterations in the behavior of the NC cells, thus also affecting other cranial NC-derived structures. Therefore, we tested whether morphological variation of the aortic valve is linked to phenotypic variation of the mandible and the thymus in the hamster model of isolated BAV, compared to a control strain. Our results show significant differences in the size and shape of the mandible as well as in the cellular composition of the thymus between the two strains, and in mandible shape regarding the morphology of the aortic valve. Given that both the mandible and the thymus are cranial NC derivatives, and that the cardiac NC belongs to the cephalic domain, we propose that the causal defect leading to isolated BAV during embryonic development is not restricted to local alterations of the cardiac NC cells in the cardiac outflow tract, but it is of pleiotropic or polytopic nature. Our results suggest that isolated BAV may be the forme fruste of a polytopic syndrome involving the cranial NC in the hamster model and in a proportion of affected patients.

  17. 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.

  18. Architectural freedom and industrialized architecture

    DEFF Research Database (Denmark)

    Vestergaard, Inge

    2012-01-01

    proportions, to organize the process on site choosing either one room wall components or several rooms wall components – either horizontally or vertically. Combined with the seamless joint the playing with these possibilities the new industrialized architecture can deliver variations in choice of solutions......Based on the repetitive architecture from the “building boom” from 1960 to 1973, it is discussed how architects can handle these Danish element and montage buildings through the transformation to upgraded aesthetical, functional and energy efficient architecture. The method used is analysis...... of cases, parallels to literature studies and client and producer interviews. The analysis compares best practice in Denmark and best practice in Austria. Modern architects accepted the fact that industrialized architecture told the storey of repetition and monotony as basic condition. This article aims...

  19. Neural Network Architectures for General Image Recognition.

    Science.gov (United States)

    1992-07-21

    Adomian and Adomian [43], the first two terms of an operator decomposition solution, X0 and X 1 , are given by: X0 = Bf(I) (A.24) XI = A(Xo) + Xo (A.25) 91... Adomian and Adomian [43] for a description.] A.5 Examples Consider designing an 0/0 NN that is sensitive to horizontal binary patterns, to a resolution of...Nonlinear Functional Analysis, New York, NY: Springer-Verlag (1985), pp. 186- 216. 43. G. Adomian and G.E. Adomian ,"A global method for solution of complex

  20. PICNIC Architecture.

    Science.gov (United States)

    Saranummi, Niilo

    2005-01-01

    The PICNIC architecture aims at supporting inter-enterprise integration and the facilitation of collaboration between healthcare organisations. The concept of a Regional Health Economy (RHE) is introduced to illustrate the varying nature of inter-enterprise collaboration between healthcare organisations collaborating in providing health services to citizens and patients in a regional setting. The PICNIC architecture comprises a number of PICNIC IT Services, the interfaces between them and presents a way to assemble these into a functioning Regional Health Care Network meeting the needs and concerns of its stakeholders. The PICNIC architecture is presented through a number of views relevant to different stakeholder groups. The stakeholders of the first view are national and regional health authorities and policy makers. The view describes how the architecture enables the implementation of national and regional health policies, strategies and organisational structures. The stakeholders of the second view, the service viewpoint, are the care providers, health professionals, patients and citizens. The view describes how the architecture supports and enables regional care delivery and process management including continuity of care (shared care) and citizen-centred health services. The stakeholders of the third view, the engineering view, are those that design, build and implement the RHCN. The view comprises four sub views: software engineering, IT services engineering, security and data. The proposed architecture is founded into the main stream of how distributed computing environments are evolving. The architecture is realised using the web services approach. A number of well established technology platforms and generic standards exist that can be used to implement the software components. The software components that are specified in PICNIC are implemented in Open Source.

  1. The architecture of functional interaction networks in the retina.

    Science.gov (United States)

    Ganmor, Elad; Segev, Ronen; Schneidman, Elad

    2011-02-23

    Sensory information is represented in the brain by the joint activity of large groups of neurons. Recent studies have shown that, although the number of possible activity patterns and underlying interactions is exponentially large, pairwise-based models give a surprisingly accurate description of neural population activity patterns. We explored the architecture of maximum entropy models of the functional interaction networks underlying the response of large populations of retinal ganglion cells, in adult tiger salamander retina, responding to natural and artificial stimuli. We found that we can further simplify these pairwise models by neglecting weak interaction terms or by relying on a small set of interaction strengths. Comparing network interactions under different visual stimuli, we show the existence of local network motifs in the interaction map of the retina. Our results demonstrate that the underlying interaction map of the retina is sparse and dominated by local overlapping interaction modules.

  2. Architectural geometry

    KAUST Repository

    Pottmann, Helmut

    2014-11-26

    Around 2005 it became apparent in the geometry processing community that freeform architecture contains many problems of a geometric nature to be solved, and many opportunities for optimization which however require geometric understanding. This area of research, which has been called architectural geometry, meanwhile contains a great wealth of individual contributions which are relevant in various fields. For mathematicians, the relation to discrete differential geometry is significant, in particular the integrable system viewpoint. Besides, new application contexts have become available for quite some old-established concepts. Regarding graphics and geometry processing, architectural geometry yields interesting new questions but also new objects, e.g. replacing meshes by other combinatorial arrangements. Numerical optimization plays a major role but in itself would be powerless without geometric understanding. Summing up, architectural geometry has become a rewarding field of study. We here survey the main directions which have been pursued, we show real projects where geometric considerations have played a role, and we outline open problems which we think are significant for the future development of both theory and practice of architectural geometry.

  3. Neural Correlates of Antidepressant-Related Sexual Dysfunction: A Placebo-Controlled fMRI Study on Healthy Males Under Subchronic Paroxetine and Bupropion

    Science.gov (United States)

    Abler, Birgit; Seeringer, Angela; Hartmann, Antonie; Grön, Georg; Metzger, Coraline; Walter, Martin; Stingl, Julia

    2011-01-01

    Sexual dysfunction is a common side effect of selective serotonin reuptake inhibitors (SSRIs) like paroxetine in the treatment of depression, imposing a considerable risk on medication adherence and hence therapeutic success. Bupropion, a norepinephrine and dopamine reuptake inhibitor, is recommended as an alternative treatment without adverse effects concerning sexual arousal and libido. We investigated the neural bases of paroxetine-related subjective sexual dysfunction when compared with bupropion and placebo. We scanned 18 healthy, heterosexual males in a randomized, double-blind, within-subject design while watching video clips of erotic and nonerotic content under steady-state conditions after taking 20 mg of paroxetine, 150 mg of bupropion, and placebo for 7 days each. Under paroxetine, ratings of subjective sexual dysfunction increased compared with placebo or bupropion. Activation along the anterior cingulate cortex (ACC), including subgenual, pregenual, and midcingulate cortices, in the ventral striatum and midbrain was decreased when compared with placebo. In contrast, bupropion let subjective ratings and ACC activations unchanged and increased activity of brain regions including posterior midcingulate cortex, mediodorsal thalamus, and extended amygdala relative to placebo and paroxetine. Brain regions that have been related to the processing of motivational (ventral striatum), emotional, and autonomic components of erotic stimulation (anterior cingulate) in previous studies showed reduced responsiveness under paroxetine in our study. Drug effects on these regions may be part of the mechanism underlying SSRI-related sexual dysfunction. Increased activation under bupropion may point to an opposite effect that may relate to the lack of impaired sexual functioning. PMID:21544071

  4. 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)

  5. Architectural Theatricality

    DEFF Research Database (Denmark)

    Tvedebrink, Tenna Doktor Olsen

    This PhD thesis is motived by a personal interest in the theoretical, practical and creative qualities of architecture. But also a wonder and curiosity about the cultural and social relations architecture represents through its occupation with both the sciences and the arts. Inspired by present...... and well-being, as well as outline a set of basic design principles ‘predicting’ the future interior architectural qualities of patient eating environments. Methodologically the thesis is based on an explorative study employing an abductive approach and hermeneutic-interpretative strategy utilizing tactics...... such as a literature review, timeline and historical outline to create a “knowledge map”, which in an eclectic manner merges the positive, normative and polemical knowledge rooted in research, objects and writings. The results of these investigations show that sparse researchbased knowledge exist directly taking...

  6. Architectural Engineers

    DEFF Research Database (Denmark)

    Petersen, Rikke Premer

    engineering is addresses from two perspectives – as an educational response and an occupational constellation. Architecture and engineering are two of the traditional design professions and they frequently meet in the occupational setting, but at educational institutions they remain largely estranged....... The paper builds on a multi-sited study of an architectural engineering program at the Technical University of Denmark and an architectural engineering team within an international engineering consultancy based on Denmark. They are both responding to new tendencies within the building industry where...... the role of engineers and architects increasingly overlap during the design process, but their approaches reflect different perceptions of the consequences. The paper discusses some of the challenges that design education, not only within engineering, is facing today: young designers must be equipped...

  7. YB-1 gene expression is kept constant during myocyte differentiation through replacement of different transcription factors and then falls gradually under the control of neural activity.

    Science.gov (United States)

    Kobayashi, Shunsuke; Tanaka, Toru; Moue, Masamitsu; Ohashi, Sachiyo; Nishikawa, Taishi

    2015-11-01

    We have previously reported that translation of acetylcholine receptor α-subunit (AChR α) mRNA in skeletal muscle cells is regulated by Y-box binding protein 1 (YB-1) in response to neural activity, and that in the postnatal mouse developmental changes in the amount of YB-1 mRNA are similar to those of AChR α mRNA, which is known to be regulated by myogenic transcription factors. Here, we examined transcriptional regulation of the YB-1 gene in mouse skeletal muscle and differentiating C2C12 myocytes. Although neither YB-1 nor AChR α was detected at either the mRNA or protein level in adult hind limb muscle, YB-1 expression was transiently activated in response to denervation of the sciatic nerve and completely paralleled that of AChR α, suggesting that these genes are regulated by the same transcription factors. However, during differentiation of C2C12 cells to myotubes, the level of YB-1 remained constant even though the level of AChR α increased markedly. Reporter gene, gel mobility shift and ChIP assays revealed that in the initial stage of myocyte differentiation, transcription of the YB-1 gene was regulated by E2F1 and Sp1, and was then gradually replaced under the control of both MyoD and myogenin through an E-box sequence in the proximal region of the YB-1 gene promoter. These results suggest that transcription factors for the YB-1 gene are exchanged during skeletal muscle cell differentiation, perhaps playing a role in translational control of mRNAs by YB-1 in both myotube formation and the response of skeletal muscle tissues to neural stimulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Reframing Architecture

    DEFF Research Database (Denmark)

    Riis, Søren

    2013-01-01

    I would like to thank Prof. Stephen Read (2011) and Prof. Andrew Benjamin (2011) for both giving inspiring and elaborate comments on my article “Dwelling in-between walls: the architectural surround”. As I will try to demonstrate below, their two different responses not only supplement my article...... focuses on how the absence of an initial distinction might threaten the endeavour of my paper. In my reply to Read and Benjamin, I will discuss their suggestions and arguments, while at the same time hopefully clarifying the postphenomenological approach to architecture....

  9. Multithreading architecture

    CERN Document Server

    Nemirovsky, Mario

    2013-01-01

    Multithreaded architectures now appear across the entire range of computing devices, from the highest-performing general purpose devices to low-end embedded processors. Multithreading enables a processor core to more effectively utilize its computational resources, as a stall in one thread need not cause execution resources to be idle. This enables the computer architect to maximize performance within area constraints, power constraints, or energy constraints. However, the architectural options for the processor designer or architect looking to implement multithreading are quite extensive and

  10. From green architecture to architectural green

    DEFF Research Database (Denmark)

    Earon, Ofri

    2011-01-01

    that describes the architectural exclusivity of this particular architecture genre. The adjective green expresses architectural qualities differentiating green architecture from none-green architecture. Currently, adding trees and vegetation to the building’s facade is the main architectural characteristics......The paper investigates the topic of green architecture from an architectural point of view and not an energy point of view. The purpose of the paper is to establish a debate about the architectural language and spatial characteristics of green architecture. In this light, green becomes an adjective...... of green architecture. The paper argues that this greenification of facades is insufficient. The green is only a skin cladding the exterior envelope without having a spatial significance. Through the paper it is proposed to flip the order of words from green architecture to architectural green...

  11. New Energy Architecture. Myanmar

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-06-15

    A global transition towards a new energy architecture is under way, driven by countries' need to respond to the changing dynamics of economic growth, environmental sustainability and energy security. The World Economic Forum, in collaboration with Accenture, has created the New Energy Architecture Initiative to address and accelerate this transition. The Initiative supports the development of national strategies and policy frameworks as countries seek to achieve the combined goals of energy security and access, sustainability, and economic growth and development. The World Economic Forum has formed a partnership with the Ministry of Energy of Myanmar to help apply the Initiative's approach to this developing and resource-rich nation. The Asian Development Bank and the World Economic Forum's Project Adviser, Accenture, have collaborated with the Forum on this consultation process, and have been supported by relevant government, industry and civil society stakeholders. The consultation process aims to understand the nation's current energy architecture challenges and provide an overview of a path to a New Energy Architecture through a series of insights. These insights could form the basis for a long-term multistakeholder roadmap to build Myanmar's energy sector in a way that is secure and sustainable, and promotes economic growth as the country makes its democratic transition. While not all recommendations can be implemented in the near term, they do provide options for creating a prioritized roadmap for Myanmar's energy transition. This report is the culmination of a nine-month multistakeholder process investigating Myanmar's energy architecture. Over the course of many visits to the country, the team has conducted numerous interviews, multistakeholder workshops, and learning and data-gathering exercises to ensure a comprehensive range of information and views. The team has also engaged with a variety of stakeholders to better

  12. 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...

  13. Co-combustion of sewage sludge and coffee grounds under increased O2/CO2 atmospheres: Thermodynamic characteristics, kinetics and artificial neural network modeling.

    Science.gov (United States)

    Chen, Jiacong; Xie, Candie; Liu, Jingyong; He, Yao; Xie, Wuming; Zhang, Xiaochun; Chang, Kenlin; Kuo, Jiahong; Sun, Jian; Zheng, Li; Sun, Shuiyu; Buyukada, Musa; Evrendilek, Fatih

    2017-11-13

    (Co-)combustion characteristics of sewage sludge (SS), coffee grounds (CG) and their blends were quantified under increased O2/CO2 atmosphere (21, 30, 40 and 60%) using a thermogravimetric analysis. Observed percentages of CG mass loss and its maximum were higher than those of SS. Under the same atmospheric O2 concentration, both higher ignition and lower burnout temperatures occurred with the increased CG content. Results showed that ignition temperature and comprehensive combustion index for the blend of 60%SS-40%CG increased, whereas burnout temperature and co-combustion time decreased with the increased O2 concentration. Artificial neural network was applied to predict mass loss percent as a function of gas mixing ratio, heating rate, and temperature, with a good agreement between the experimental and ANN-predicted values. Activation energy in response to the increased O2 concentration was found to increase from 218.91 to 347.32 kJ·mol-1 and from 218.34 to 340.08 kJ·mol-1 according to the Kissinger-Akahira-Sunose and Flynn-Wall-Ozawa methods, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Textile Architecture

    DEFF Research Database (Denmark)

    Heimdal, Elisabeth Jacobsen

    2010-01-01

    Textiles can be used as building skins, adding new aesthetic and functional qualities to architecture. Just like we as humans can put on a coat, buildings can also get dressed. Depending on our mood, or on the weather, we can change coat, and so can the building. But the idea of using textiles...

  15. Software Architecture

    NARCIS (Netherlands)

    Tekinerdogan, B.; Zdun, Uwe; Babar, Ali

    2016-01-01

    This book constitutes the proceedings of the 10th European Conference on Software Architecture, ECSA 2016, held in Copenhagen, Denmark, in November/December 2016.

    The 13 full papers presented together with 12 short papers were carefully reviewed and selected from 84 submissions. They are

  16. 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.

  17. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  18. Manipulations of Totalitarian Nazi Architecture

    Science.gov (United States)

    Antoszczyszyn, Marek

    2017-10-01

    The paper takes under considerations controversies surrounding German architecture designed during Nazi period between 1933-45. This architecture is commonly criticized for being out of innovation, taste & elementary sense of beauty. Moreover, it has been consequently wiped out from architectural manuals, probably for its undoubted associations with the totalitarian system considered as the most maleficent in the whole history. But in the meantime the architecture of another totalitarian system which appeared to be not less sinister than Nazi one is not stigmatized with such verve. It is Socrealism architecture, developed especially in East Europe & reportedly containing lots of similarities with Nazi architecture. Socrealism totalitarian architecture was never condemned like Nazi one, probably due to politically manipulated propaganda that influenced postwar public opinion. This observation leads to reflection that maybe in the same propaganda way some values of Nazi architecture are still consciously dissembled in order to hide the fact that some rules used by Nazi German architects have been also consciously used after the war. Those are especially manipulations that allegedly Nazi architecture consisted of. The paper provides some definitions around totalitarian manipulations as well as ideological assumptions for their implementation. Finally, the register of confirmed manipulations is provided with use of photo case study.

  19. TUTORIAL: The dynamic neural field approach to cognitive robotics

    Science.gov (United States)

    Erlhagen, Wolfram; Bicho, Estela

    2006-09-01

    This tutorial presents an architecture for autonomous robots to generate behavior in joint action tasks. To efficiently interact with another agent in solving a mutual task, a robot should be endowed with cognitive skills such as memory, decision making, action understanding and prediction. The proposed architecture is strongly inspired by our current understanding of the processing principles and the neuronal circuitry underlying these functionalities in the primate brain. As a mathematical framework, we use a coupled system of dynamic neural fields, each representing the basic functionality of neuronal populations in different brain areas. It implements goal-directed behavior in joint action as a continuous process that builds on the interpretation of observed movements in terms of the partner's action goal. We validate the architecture in two experimental paradigms: (1) a joint search task; (2) a reproduction of an observed or inferred end state of a grasping-placing sequence. We also review some of the mathematical results about dynamic neural fields that are important for the implementation work. .

  20. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

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

  1. MUF architecture /art London

    DEFF Research Database (Denmark)

    Svenningsen Kajita, Heidi

    2009-01-01

    Om MUF architecture samt interview med Liza Fior og Katherine Clarke, partnere i muf architecture/art......Om MUF architecture samt interview med Liza Fior og Katherine Clarke, partnere i muf architecture/art...

  2. 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.

  3. Architectural fragments

    DEFF Research Database (Denmark)

    Bang, Jacob Sebastian

    2018-01-01

    . I try to invent the ways of drawing the models - that decode and unfold them into architectural fragments- into future buildings or constructions in the landscape. [1] Luigi Moretti: Italian architect, 1907 - 1973 [2] Man Ray: American artist, 1890 - 1976. in 2015, I saw the wonderful exhibition......I have created a large collection of plaster models: a collection of Obstructions, errors and opportunities that may develop into architecture. The models are fragments of different complex shapes as well as more simple circular models with different profiling and diameters. In this contect I have...... been studying Luigi Moretti's [1] plastermodel - "the Model of the inner spaces of the Saint Maria of the Divine Providence" - in which context I see my own models. In 1934, Man Ray [2] photographed mathematical rmodels (in plaster) at the Henri Poincaré Institute in Paris and later used...

  4. AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model

    Directory of Open Access Journals (Sweden)

    Jianzhu Ma

    2015-01-01

    Full Text Available Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

  5. AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.

    Science.gov (United States)

    Ma, Jianzhu; Wang, Sheng

    2015-01-01

    The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.

  6. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  7. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

    Science.gov (United States)

    Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

  8. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  9. Architectures for intelligent machines

    Science.gov (United States)

    Saridis, George N.

    1991-01-01

    The theory of intelligent machines has been recently reformulated to incorporate new architectures that are using neural and Petri nets. The analytic functions of an intelligent machine are implemented by intelligent controls, using entropy as a measure. The resulting hierarchical control structure is based on the principle of increasing precision with decreasing intelligence. Each of the three levels of the intelligent control is using different architectures, in order to satisfy the requirements of the principle: the organization level is moduled after a Boltzmann machine for abstract reasoning, task planning and decision making; the coordination level is composed of a number of Petri net transducers supervised, for command exchange, by a dispatcher, which also serves as an interface to the organization level; the execution level, include the sensory, planning for navigation and control hardware which interacts one-to-one with the appropriate coordinators, while a VME bus provides a channel for database exchange among the several devices. This system is currently implemented on a robotic transporter, designed for space construction at the CIRSSE laboratories at the Rensselaer Polytechnic Institute. The progress of its development is reported.

  10. Elements of neurogeometry functional architectures of vision

    CERN Document Server

    Petitot, Jean

    2017-01-01

    This book describes several mathematical models of the primary visual cortex, referring them to a vast ensemble of experimental data and putting forward an original geometrical model for its functional architecture, that is, the highly specific organization of its neural connections. The book spells out the geometrical algorithms implemented by this functional architecture, or put another way, the “neurogeometry” immanent in visual perception. Focusing on the neural origins of our spatial representations, it demonstrates three things: firstly, the way the visual neurons filter the optical signal is closely related to a wavelet analysis; secondly, the contact structure of the 1-jets of the curves in the plane (the retinal plane here) is implemented by the cortical functional architecture; and lastly, the visual algorithms for integrating contours from what may be rather incomplete sensory data can be modelled by the sub-Riemannian geometry associated with this contact structure. As such, it provides rea...

  11. Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

    Science.gov (United States)

    Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

    2002-01-01

    A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

  12. Architectural Drawing

    DEFF Research Database (Denmark)

    Steinø, Nicolai

    2018-01-01

    is that computers can represent graphic ideas both faster and better than most medium-skilled draftsmen, drawing in design is not only about representing final designs. In fact, several steps involving the capacity to draw lie before the representation of a final design. Not only is drawing skills an important...... it is developed from empirical and partly introspective observations of the design process – from analysis over design development to the presentation of final designs – it seeks to corroborate these observations through literature on architectural drawing....

  13. Ultra-low-power and robust digital-signal-processing hardware for implantable neural interface microsystems.

    Science.gov (United States)

    Narasimhan, S; Chiel, H J; Bhunia, S

    2011-04-01

    Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

  14. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  15. A neurocomputer based on an analog-digital hybrid architecture

    Science.gov (United States)

    Moopenn, A.; Thakoor, A. P.; Duong, T.; Khanna, S. K.

    1987-01-01

    A novel analog-digital hybrid architecture based on the utilization of high density digital random access memories for the storage of the synaptic weights of a neural network, and high speed analog hardware to perform neural computation is described. An electronic neurocomputer based on such an architecture is ideally suited for investigating the dynamics, associative recall properties, and computational capabilities of neural networks and provides significant speed improvement in comparison to conventional software based neural network simulations. As a demonstration of the feasibility of the hybrid architectural concept, a prototype breadboard hybrid neurocomputer system with 32 neurons has been designed and fabricated with off-the-shelf hardware components. The performance of the breadboard system has been tested for variety of applications including associative memory and combinatorial problem solving such as Graph Coloring, and is discussed in this paper.

  16. Predictive and Neural Predictive Control of Uncertain Systems

    Science.gov (United States)

    Kelkar, Atul G.

    2000-01-01

    Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.

  17. Invariant recognition drives neural representations of action sequences.

    Directory of Open Access Journals (Sweden)

    Andrea Tacchetti

    2017-12-01

    Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  18. 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.

  19. Semantic segmentation of bioimages using convolutional neural networks

    CSIR Research Space (South Africa)

    Wiehman, S

    2016-07-01

    Full Text Available Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live...

  20. Artificial neural networks a practical course

    CERN Document Server

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

    2017-01-01

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

  1. 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.

  2. Mapping of the Underlying Neural Mechanisms of Maintenance and Manipulation in Visuo-Spatial Working Memory Using An n-back Mental Rotation Task: A Functional Magnetic Resonance Imaging Study

    OpenAIRE

    Lamp, Gemma; Alexander, Bonnie; Laycock, Robin; Crewther, David P.; Crewther, Sheila G.

    2016-01-01

    Mapping of the underlying neural mechanisms of visuo-spatial working memory (WM) has been shown to consistently elicit activity in right hemisphere dominant fronto-parietal networks. However to date, the bulk of neuroimaging literature has focused largely on the maintenance aspect of visuo-spatial WM, with a scarcity of research into the aspects of WM involving manipulation of information. Thus, this study aimed to compare maintenance-only with maintenance and manipulation of visuo-spatial st...

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

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2017-08-01

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

  4. Drift chamber tracking with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Lindsey, C.S.; Denby, B.; Haggerty, H.

    1992-10-01

    We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed.

  5. Parallelization of Neural Network Training for NLP with Hogwild!

    Directory of Open Access Journals (Sweden)

    Deyringer Valentin

    2017-10-01

    Full Text Available Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

  6. Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

    Science.gov (United States)

    Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

    2017-01-01

    Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

  7. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

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

  8. A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and roadmap for future research

    Science.gov (United States)

    Phillips, Mary L; Swartz, Holly A.

    2014-01-01

    Objective This critical review appraises neuroimaging findings in bipolar disorder in emotion processing, emotion regulation, and reward processing neural circuitry, to synthesize current knowledge of the neural underpinnings of bipolar disorder, and provide a neuroimaging research “roadmap” for future studies. Method We examined findings from all major studies in bipolar disorder that used fMRI, volumetric analyses, diffusion imaging, and resting state techniques, to inform current conceptual models of larger-scale neural circuitry abnormalities in bipolar disorder Results Bipolar disorder can be conceptualized in neural circuitry terms as parallel dysfunction in bilateral prefrontal cortical (especially ventrolateral prefrontal cortical)-hippocampal-amygdala emotion processing and emotion regulation neural circuitries, together with an “overactive” left-sided ventral striatal-ventrolateral and orbitofrontal cortical reward processing circuitry, that result in characteristic behavioral abnormalities associated with bipolar disorder: emotional lability, emotional dysregulation and heightened reward sensitivity. A potential structural basis for these functional abnormalities are gray matter decreases in prefrontal and temporal cortices, amygdala and hippocampus, and fractional anisotropy decreases in white matter tracts connecting prefrontal and subcortical regions. Conclusion Neuroimaging studies of bipolar disorder clearly demonstrate abnormalities in neural circuitries supporting emotion processing, emotion regulation and reward processing, although there are several limitations to these studies. Future neuroimaging research in bipolar disorder should include studies adopting dimensional approaches; larger studies examining neurodevelopmental trajectories in bipolar disorder and at-risk youth; multimodal neuroimaging studies using integrated systems approaches; and studies using pattern recognition approaches to provide clinically useful, individual

  9. The signs of life in architecture.

    Science.gov (United States)

    Gruber, Petra

    2008-06-01

    Engineers, designers and architects often look to nature for inspiration. The research on 'natural constructions' is aiming at innovation and the improvement of architectural quality. The introduction of life sciences terminology in the context of architecture delivers new perspectives towards innovation in architecture and design. The investigation is focused on the analogies between nature and architecture. Apart from other principles that are found in living nature, an interpretation of the so-called 'signs of life', which characterize living systems, in architecture is presented. Selected architectural projects that have applied specific characteristics of life, whether on purpose or not, will show the state of development in this field and open up future challenges. The survey will include famous built architecture as well as students' design programs, which were carried out under supervision of the author at the Department of Design and Building Construction at the Vienna University of Technology.

  10. A neural circuit for angular velocity computation

    Directory of Open Access Journals (Sweden)

    Samuel B Snider

    2010-12-01

    Full Text Available In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly-tunable wing-steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuro-mechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  11. A neural circuit for angular velocity computation.

    Science.gov (United States)

    Snider, Samuel B; Yuste, Rafael; Packer, Adam M

    2010-01-01

    In one of the most remarkable feats of motor control in the animal world, some Diptera, such as the housefly, can accurately execute corrective flight maneuvers in tens of milliseconds. These reflexive movements are achieved by the halteres, gyroscopic force sensors, in conjunction with rapidly tunable wing steering muscles. Specifically, the mechanosensory campaniform sensilla located at the base of the halteres transduce and transform rotation-induced gyroscopic forces into information about the angular velocity of the fly's body. But how exactly does the fly's neural architecture generate the angular velocity from the lateral strain forces on the left and right halteres? To explore potential algorithms, we built a neuromechanical model of the rotation detection circuit. We propose a neurobiologically plausible method by which the fly could accurately separate and measure the three-dimensional components of an imposed angular velocity. Our model assumes a single sign-inverting synapse and formally resembles some models of directional selectivity by the retina. Using multidimensional error analysis, we demonstrate the robustness of our model under a variety of input conditions. Our analysis reveals the maximum information available to the fly given its physical architecture and the mathematics governing the rotation-induced forces at the haltere's end knob.

  12. Network architecture as internet governance

    Directory of Open Access Journals (Sweden)

    Francesca Musiani

    2013-10-01

    Full Text Available The architecture of a networked system is its underlying technical and logical structure, including transmission equipment, communication protocols, infrastructure, and connectivity between its components or nodes. This article introduces the idea of network architecture as internet governance, and more specifically, it outlines the dialectic between centralised and distributed architectures, institutions and practices, and how they mutually affect each other. The article argues that network architecture is internet governance in the sense that, by changing the design of the networks subtending internet-based services and the global internet itself, its politics are affected – the balance of rights between users and providers, the capacity of online communities to engage in open and direct interaction, the fair competition between actors of the internet market.

  13. Software Architecture Simulation

    OpenAIRE

    Mårtensson, Frans; Jönsson, Per

    2002-01-01

    A software architecture is one of the first steps towards a software system. A software architecture can be designed in different ways. During the design phase, it is important to select the most suitable design of the architecture, in order to create a good foundation for the system. The selection process is performed by evaluating architecture alternatives against each other. We investigate the use of continuous simulation of a software architecture as a support tool for architecture evalua...

  14. SUSTAINABLE ARCHITECTURE : WHAT ARCHITECTURE STUDENTS THINK

    OpenAIRE

    Satwiko, Prasasto

    2013-01-01

    Sustainable architecture has become a hot issue lately as the impacts of climate change become more intense. Architecture educations have responded by integrating knowledge of sustainable design in their curriculum. However, in the real life, new buildings keep coming with designs that completely ignore sustainable principles. This paper discusses the results of two national competitions on sustainable architecture targeted for architecture students (conducted in 2012 and 2013). The results a...

  15. On the sequence-directed nature of human gene mutation: the role of genomic architecture and the local DNA sequence environment in mediating gene mutations underlying human inherited disease.

    Science.gov (United States)

    Cooper, David N; Bacolla, Albino; Férec, Claude; Vasquez, Karen M; Kehrer-Sawatzki, Hildegard; Chen, Jian-Min

    2011-10-01

    Different types of human gene mutation may vary in size, from structural variants (SVs) to single base-pair substitutions, but what they all have in common is that their nature, size and location are often determined either by specific characteristics of the local DNA sequence environment or by higher order features of the genomic architecture. The human genome is now recognized to contain "pervasive architectural flaws" in that certain DNA sequences are inherently mutation prone by virtue of their base composition, sequence repetitivity and/or epigenetic modification. Here, we explore how the nature, location and frequency of different types of mutation causing inherited disease are shaped in large part, and often in remarkably predictable ways, by the local DNA sequence environment. The mutability of a given gene or genomic region may also be influenced indirectly by a variety of noncanonical (non-B) secondary structures whose formation is facilitated by the underlying DNA sequence. Since these non-B DNA structures can interfere with subsequent DNA replication and repair and may serve to increase mutation frequencies in generalized fashion (i.e., both in the context of subtle mutations and SVs), they have the potential to serve as a unifying concept in studies of mutational mechanisms underlying human inherited disease. © 2011 Wiley-Liss, Inc.

  16. Lightweight enterprise architectures

    CERN Document Server

    Theuerkorn, Fenix

    2004-01-01

    STATE OF ARCHITECTUREArchitectural ChaosRelation of Technology and Architecture The Many Faces of Architecture The Scope of Enterprise Architecture The Need for Enterprise ArchitectureThe History of Architecture The Current Environment Standardization Barriers The Need for Lightweight Architecture in the EnterpriseThe Cost of TechnologyThe Benefits of Enterprise Architecture The Domains of Architecture The Gap between Business and ITWhere Does LEA Fit? LEA's FrameworkFrameworks, Methodologies, and Approaches The Framework of LEATypes of Methodologies Types of ApproachesActual System Environmen

  17. Software architecture 1

    CERN Document Server

    Oussalah , Mourad Chabane

    2014-01-01

    Over the past 20 years, software architectures have significantly contributed to the development of complex and distributed systems. Nowadays, it is recognized that one of the critical problems in the design and development of any complex software system is its architecture, i.e. the organization of its architectural elements. Software Architecture presents the software architecture paradigms based on objects, components, services and models, as well as the various architectural techniques and methods, the analysis of architectural qualities, models of representation of architectural template

  18. Software architecture 2

    CERN Document Server

    Oussalah, Mourad Chabanne

    2014-01-01

    Over the past 20 years, software architectures have significantly contributed to the development of complex and distributed systems. Nowadays, it is recognized that one of the critical problems in the design and development of any complex software system is its architecture, i.e. the organization of its architectural elements. Software Architecture presents the software architecture paradigms based on objects, components, services and models, as well as the various architectural techniques and methods, the analysis of architectural qualities, models of representation of architectural templa

  19. The neural basis of emotions varies over time: different regions go with onset- and offset-bound processes underlying emotion intensity.

    Science.gov (United States)

    Résibois, Maxime; Verduyn, Philippe; Delaveau, Pauline; Rotgé, Jean-Yves; Kuppens, Peter; Van Mechelen, Iven; Fossati, Philippe

    2017-08-01

    According to theories of emotion dynamics, emotions unfold across two phases in which different types of processes come to the fore: emotion onset and emotion offset. Differences in onset-bound processes are reflected by the degree of explosiveness or steepness of the response at onset, and differences in offset-bound processes by the degree of accumulation or intensification of the subsequent response. Whether onset- and offset-bound processes have distinctive neural correlates and, hence, whether the neural basis of emotions varies over time, still remains unknown. In the present fMRI study, we address this question using a recently developed paradigm that allows to disentangle explosiveness and accumulation. Thirty-one participants were exposed to neutral and negative social feedback, and asked to reflect on its contents. Emotional intensity while reading and thinking about the feedback was measured with an intensity profile tracking approach. Using non-negative matrix factorization, the resulting profile data were decomposed in explosiveness and accumulation components, which were subsequently entered as continuous regressors of the BOLD response. It was found that the neural basis of emotion intensity shifts as emotions unfold over time with emotion explosiveness and accumulation having distinctive neural correlates. © The Author (2017). Published by Oxford University Press.

  20. Functional Comparison of Neuronal Cells Differentiated from Human Induced Pluripotent Stem Cell-Derived Neural Stem Cells under Different Oxygen and Medium Conditions.

    Science.gov (United States)

    Yamazaki, Kazuto; Fukushima, Kazuyuki; Sugawara, Michiko; Tabata, Yoshikuni; Imaizumi, Yoichi; Ishihara, Yasuharu; Ito, Masashi; Tsukahara, Kappei; Kohyama, Jun; Okano, Hideyuki

    2016-12-01

    Because neurons are difficult to obtain from humans, generating functional neurons from human induced pluripotent stem cells (hiPSCs) is important for establishing physiological or disease-relevant screening systems for drug discovery. To examine the culture conditions leading to efficient differentiation of functional neural cells, we investigated the effects of oxygen stress (2% or 20% O2) and differentiation medium (DMEM/F12:Neurobasal-based [DN] or commercial [PhoenixSongs Biologicals; PS]) on the expression of genes related to neural differentiation, glutamate receptor function, and the formation of networks of neurons differentiated from hiPSCs (201B7) via long-term self-renewing neuroepithelial-like stem (lt-NES) cells. Expression of genes related to neural differentiation occurred more quickly in PS and/or 2% O2 than in DN and/or 20% O2, resulting in high responsiveness of neural cells to glutamate, N-methyl-d-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA), and ( S)-3,5-dihydroxyphenylglycine (an agonist for mGluR1/5), as revealed by calcium imaging assays. NMDA receptors, AMPA receptors, mGluR1, and mGluR5 were functionally validated by using the specific antagonists MK-801, NBQX, JNJ16259685, and 2-methyl-6-(phenylethynyl)-pyridine, respectively. Multielectrode array analysis showed that spontaneous firing occurred earlier in cells cultured in 2% O2 than in 20% O2. Optimization of O2 tension and culture medium for neural differentiation of hiPSCs can efficiently generate physiologically relevant cells for screening systems.

  1. 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.

  2. The architecture of the BubR1 tetratricopeptide tandem repeat defines a protein motif underlying mitotic checkpoint-kinetochore communication.

    Science.gov (United States)

    Bolanos-Garcia, Victor M; Nilsson, Jakob; Blundell, Tom L

    2012-01-01

    The accurate and timely transmission of the genetic material to progeny during successive rounds of cell division is sine qua non for the maintenance of genome stability. Eukaryotic cells have evolved a surveillance mechanism, the mitotic spindle assembly checkpoint (SAC), to prevent premature advance to anaphase before every chromosome is properly attached to microtubules of the mitotic spindle. The architecture of the KNL1-BubR1 complex reveals important features of the molecular recognition between SAC components and the kinetochore. The interaction is important for a functional SAC as substitution of BubR1 residues engaged in KNL1 binding impaired the SAC and BubR1 recruitment into checkpoint complexes in stable cell lines. Here we discuss the implications of the disorder-to-order transition of KNL1 upon BubR1 binding for SAC signaling and propose a mechanistic model of how BUBs binding may affect the recognition of KNL1 by its other interacting partners.

  3. Integration of hydro-climatic data and land use in neural networks for ...

    African Journals Online (AJOL)

    BEEMG

    neural networks for modeling river flows: Case of Lobo river in the .... inevitable case of confusion, other landscape units were ignored as individual ... Koffi et al. 785. Table 1. Artificial neural networks models. Neural network model. Architecture Input. Output. Unguided model with input rain. [1 10 1]. Rain (t). Flow rate (t).

  4. Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?

    OpenAIRE

    Baltacıoğlu, A. K.; Öztürk, B.; Civalek, Ö.; Akgöz, B.

    2010-01-01

    In the present study, an artificial neural network (ANN) application is introduced for estimation of damage level of reinforced concrete structures. Back-propagation learning algorithm is adopted. A typical neural network architecture is proposed and some conclusions are presented. Applicability of artificial neural network (ANN) for the assessment of earthquake related damage is investigated

  5. Game engine architecture

    CERN Document Server

    Gregory, Jason

    2014-01-01

    ""… this book is the best of its kind, and you're lucky to have found it. It covers the huge field of game engine architecture in a succinct, clear way, and expertly balances the breadth and depth of its coverage, offering enough detail that even a beginner can easily understand the concepts it presents. The author, Jason Gregory, is not only a world expert in his field; he's a working programmer with production-quality knowledge and many shipped game projects under his belt. … Jason is also an experienced educator who has taught in the top-ranked university game program in North America. …

  6. Optimization of the architecture of a neural network in neutron spectrometry to reduce the number of Bonner spheres; Optimizacion de la arquitectura de una red neuronal en espectrometria de neutrones para reducer el numero de esferas Bonner

    Energy Technology Data Exchange (ETDEWEB)

    Leon P, A. A.; Martinez B, M. R.; Hernandez P, C. F.; Espinoza G, J. G.; Castaneda M, V. H.; Solis S, L. O.; Castaneda M, R.; Ortiz R, J. M.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico); Mendez V, R. [Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas, Laboratorio de Patrones Neutronicos, Av. Complutense 22, 28040 Madrid (Spain); Gallego, E. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, Jose Gutierrez Abascal 2, 28006 Madrid (Spain); De Sousa L, M. A. [Centro de Desenvolvimento da Tecnologia Nuclear / CNEN, Av. Pte. Antonio Carlos 6627, 31270-901 Pampulha, Belo Horizonte, Minas Gerais (Brazil)

    2016-10-15

    The neutron spectrometry is an experimental process for determining the energy distribution called the Spectrum. Among the methods available for neutron spectrometry, one can mention the Bonner Sphere Spectrometric System as one of the most used, consisting of a detector placed in the center of a set of polyethylene spheres whose diameters range from 2 to 18 inches, however has some disadvantages such as the long periods of time to perform the measurements, the weight and the spheres number that vary according to the system. From this, alternative methods such as artificial neural networks are proposed. For this project neural networks of reverse propagation were used with the methodology of robust design of artificial neural networks, with the aid of a computational tool that maximizes the performance, making the time used for the training s of the network is the smallest possible and thus gets the orthogonal fixes quickly to determine the best network topology. The counting rates of a spectrometric system with 7 spheres, 2 spheres and one sphere of 5 and 8 inches were used. This methodology seeks to reduce the work used as in the spectrometric system formed by a greater number of spheres, since to enter less data in the counting rates to obtain the spectra with 60 energy levels saves time and space, because at having a smaller number of spheres its portability is easier to move from one place to another, for this we performed several experiments with different errors until we reached the optimal error so that the topology of the network was appropriate and find the best design parameters. A statistical software JMP was also used to obtain the best topologies and thus to retrain obtaining its best and worst spectra, in order to determine if the reduction is possible. (Author)

  7. Parameter estimation using compensatory neural networks

    Indian Academy of Sciences (India)

    Proposed here is a new neuron model, a basis for Compensatory Neural Network Architecture (CNNA), which not only reduces the total number of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron model as well as the higher ...

  8. Neural Classifier Construction using Regularization, Pruning

    DEFF Research Database (Denmark)

    Hintz-Madsen, Mads; Hansen, Lars Kai; Larsen, Jan

    1998-01-01

    In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction...

  9. 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

  10. Architecture as Design Study.

    Science.gov (United States)

    Kauppinen, Heta

    1989-01-01

    Explores the use of analogies in architectural design, the importance of Gestalt theory and aesthetic cannons in understanding and being sensitive to architecture. Emphasizes the variation between public and professional appreciation of architecture. Notes that an understanding of architectural process enables students to improve the aesthetic…

  11. Fragments of Architecture

    DEFF Research Database (Denmark)

    Bang, Jacob Sebastian

    2016-01-01

    Topic 3: “Case studies dealing with the artistic and architectural work of architects worldwide, and the ties between specific artistic and architectural projects, methodologies and products”......Topic 3: “Case studies dealing with the artistic and architectural work of architects worldwide, and the ties between specific artistic and architectural projects, methodologies and products”...

  12. A computational architecture for social agents

    Energy Technology Data Exchange (ETDEWEB)

    Bond, A.H. [California Institute of Technology, Pasadena, CA (United States)

    1996-12-31

    This article describes a new class of information-processing models for social agents. They axe derived from primate brain architecture, the processing in brain regions, the interactions among brain regions, and the social behavior of primates. In another paper, we have reviewed the neuroanatomical connections and functional involvements of cortical regions. We reviewed the evidence for a hierarchical architecture in the primate brain. By examining neuroanatomical evidence for connections among neural areas, we were able to establish anatomical regions and connections. We then examined evidence for specific functional involvements of the different neural axeas and found some support for hierarchical functioning, not only for the perception hierarchies but also for the planning and action hierarchy in the frontal lobes.

  13. Improving prediction of neural networks: a study of tow financial prediction tasks

    Directory of Open Access Journals (Sweden)

    Tarun K. Sen

    2004-01-01

    Full Text Available Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.

  14. Biological neural networks as model systems for designing future parallel processing computers

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

  15. Creating product line architectures

    OpenAIRE

    Bayer, J.; Flege, O.; Gacek, C.

    2000-01-01

    The creation and validation of product line software architectures are inherently more complex than those of software architectures for single systems. This paper compares a process for creating and evaluating a traditional, one-of-a- kind software architecture with one for a reference software architecture. The comparison is done in the context of PuLSE-DSSA, a customizable process that integrates both product line architecture creation and evaluation.

  16. Data architecture from zen to reality

    CERN Document Server

    Tupper, Charles D

    2011-01-01

    Data Architecture: From Zen to Reality explains the principles underlying data architecture, how data evolves with organizations, and the challenges organizations face in structuring and managing their data. It also discusses proven methods and technologies to solve the complex issues dealing with data. The book uses a holistic approach to the field of data architecture by covering the various applied areas of data, including data modelling and data model management, data quality , data governance, enterprise information management, database design, data warehousing, and warehouse design. This book is a core resource for anyone emplacing, customizing or aligning data management systems, taking the Zen-like idea of data architecture to an attainable reality.

  17. Exploiting context in mammograms: a hierarchical neural network for detecting microcalcifications

    Science.gov (United States)

    Sajda, Paul; Spence, Clay D.; Pearson, John C.; Nishikawa, Robert M.

    1996-04-01

    Microcalcifications are important cues used by radiologists for early detection in breast cancer. Individually, microcalcifications are difficult to detect, and often contextual information (e.g. clustering, location relative to ducts) can be exploited to aid in their detection. We have developed an algorithm for constructing a hierarchical pyramid/neural network (HPNN) architecture to automatically learn context information for detection. To test the HPNN we first examined if the hierarchical architecture improves detection of individual microcalcifications and if context is in fact extracted by the network hierarchy. We compared the performance of our hierarchical architecture versus a single neural network receiving input from all resolutions of a feature pyramid. Receiver operator characteristic (ROC) analysis shows that the hierarchical architecture reduces false positives by a factor of two. We examined hidden units at various levels of the processing hierarchy and found what appears to be representations of ductal location. We next investigated the utility of the HPNN if integrated as part of a complete computer-aided diagnosis (CAD) system for microcalcification detection, such as that being developed at the University of Chicago. Using ROC analysis, we tested the HPNN's ability to eliminate false positive regions of interest generated by the computer, comparing its performance to the neural network currently used in the Chicago system. The HPNN achieves an area under the ROC curve of Az equal to .94 and a false positive fraction of FPF equal to .21 at TPF equals 1.0. This is in comparison to the results reported for the Chicago network; Az equal to .91, FPF equal to .43 at TPF equal to 1.0. These differences are statistically significant. We conclude that the HPNN algorithm is able to utilize contextual information for improving microcalcifications detection and potentially reduce the false positive rates in CAD systems.

  18. Architecture in the network society

    DEFF Research Database (Denmark)

    2004-01-01

    Under the theme Architecture in the Network Society, participants were invited to focus on the dialog and sharing of knowledge between architects and other disciplines and to reflect on, and propose, new methods in the design process, to enhance and improve the impact of information technology...

  19. An Empirical Investigation of Architectural Prototyping

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Hansen, Klaus Marius

    2010-01-01

    Architectural prototyping is the process of using executable code to investigate stakeholders’ software architecture concerns with respect to a system under development. Previous work has established this as a useful and cost-effective way of exploration and learning of the design space of a system...... and in addressing issues regarding quality attributes, architectural risks, and the problem of knowledge transfer and conformance. However, the actual industrial use of architectural prototyping has not been thoroughly researched so far. In this article, we report from three studies of architectural prototyping...... in practice. First, we report findings from an ethnographic study of practicing software architects. Secondly, we report from a focus group on architectural prototyping involving architects from four companies. And, thirdly, we report from a survey study of 20 practicing software architects and software...

  20. 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

  1. Cooperating attackers in neural cryptography.

    Science.gov (United States)

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

    2004-06-01

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

  2. Cooperating attackers in neural cryptography

    Science.gov (United States)

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

    2004-06-01

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

  3. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

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

  4. Neural components of altruistic punishment.

    Science.gov (United States)

    Du, Emily; Chang, Steve W C

    2015-01-01

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

  5. A Cooperative Unsupervised Architecture to Develop a Business Management Model.

    OpenAIRE

    Corchado Rodríguez, Emilio; Herrero Cosio, Álvaro; Baruque, Bruno; R., Redondo; Sáiz Bárcena, Lourdes; Fyfe Colin

    2004-01-01

    his paper describes ongoing multidisciplinary research which aims to analyse an to apply neural networks architectures to the emerging field of Knowledge Management. In this case, we illustrate the particular use of a novel connectionist architecture, characterised for its ability to capture some type of topological ordering. It has been used to build a part of a Global and Integral Model of Business Management, which provides a global improvement in the firm, in terms of flexibility, value a...

  6. Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity.

    Science.gov (United States)

    Zhou, Yuan; Wang, Yun; Rao, Li-Lin; Liang, Zhu-Yuan; Chen, Xiao-Ping; Zheng, Dang; Tan, Cheng; Tian, Zhi-Qiang; Wang, Chun-Hui; Bai, Yan-Qiang; Chen, Shan-Guang; Li, Shu

    2014-01-01

    Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI) to study whether the functional architecture of the brain is altered after 45 days of -6° head-down tilt (HDT) bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of -6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC), to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC) analysis. We found decreased DC in two regions, the left anterior insula (aINS) and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies), in the male volunteers after 45 days of -6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS-MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function, and central neural activity.

  7. Disrutpted resting-state functional architecture of the brain after 45-day simulated microgravity

    Directory of Open Access Journals (Sweden)

    Yuan eZhou

    2014-06-01

    Full Text Available Long-term spaceflight induces both physiological and psychological changes in astronauts. To understand the neural mechanisms underlying these physiological and psychological changes, it is critical to investigate the effects of microgravity on the functional architecture of the brain. In this study, we used resting-state functional MRI (rs-fMRI to study whether the functional architecture of the brain is altered after 45 days of -6° head-down tilt (HDT bed rest, which is a reliable model for the simulation of microgravity. Sixteen healthy male volunteers underwent rs-fMRI scans before and after 45 days of -6° HDT bed rest. Specifically, we used a commonly employed graph-based measure of network organization, i.e., degree centrality (DC, to perform a full-brain exploration of the regions that were influenced by simulated microgravity. We subsequently examined the functional connectivities of these regions using a seed-based resting-state functional connectivity (RSFC analysis. We found decreased DC in two regions, the left anterior insula (aINS and the anterior part of the middle cingulate cortex (MCC; also called the dorsal anterior cingulate cortex in many studies, in the male volunteers after 45 days of -6° HDT bed rest. Furthermore, seed-based RSFC analyses revealed that a functional network anchored in the aINS and MCC was particularly influenced by simulated microgravity. These results provide evidence that simulated microgravity alters the resting-state functional architecture of the brains of males and suggest that the processing of salience information, which is primarily subserved by the aINS–MCC functional network, is particularly influenced by spaceflight. The current findings provide a new perspective for understanding the relationships between microgravity, cognitive function, autonomic neural function and central neural activity.

  8. The architecture of enterprise hospital information system.

    Science.gov (United States)

    Lu, Xudong; Duan, Huilong; Li, Haomin; Zhao, Chenhui; An, Jiye

    2005-01-01

    Because of the complexity of the hospital environment, there exist a lot of medical information systems from different vendors with incompatible structures. In order to establish an enterprise hospital information system, the integration among these heterogeneous systems must be considered. Complete integration should cover three aspects: data integration, function integration and workflow integration. However most of the previous design of architecture did not accomplish such a complete integration. This article offers an architecture design of the enterprise hospital information system based on the concept of digital neural network system in hospital. It covers all three aspects of integration, and eventually achieves the target of one virtual data center with Enterprise Viewer for users of different roles. The initial implementation of the architecture in the 5-year Digital Hospital Project in Huzhou Central hospital of Zhejiang Province is also described.

  9. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    Science.gov (United States)

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  10. Neural-Network Quantum States, String-Bond States, and Chiral Topological States

    Science.gov (United States)

    Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio

    2018-01-01

    Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

  11. Neural-Network Quantum States, String-Bond States, and Chiral Topological States

    Directory of Open Access Journals (Sweden)

    Ivan Glasser

    2018-01-01

    Full Text Available Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

  12. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  13. Catalyst Architecture

    DEFF Research Database (Denmark)

    qualities, which we find interesting to study and to learn from. The projects have in common that they all connect, transform and set a new agenda in the given context. They all have public access and in most cases, they are also under public administration. Thus, we do not deal with shopping malls, office...... in Rio de Janeiro and Nørrebro in Copenhagen, but we do not examine the topic further. Case Studies in Transition Zones In the selection as well as the analysis of the projects, particular emphasis has been placed on their localization. There has been particular focus on transition zones between one...... on the analysis of the strategic cases, drawing conclusions by comparison across the cases is not immediately possible. The thematic summary in chapter 7 should therefore be read concurrently with the presentation of the individual cases. In this way, the empirical richness, which the analyses make available, can...

  14. A Hexapod Walker Using a Heterarchical Architecture for Action Selection

    Directory of Open Access Journals (Sweden)

    Malte eSchilling

    2013-09-01

    Full Text Available Moving in a cluttered environment with a six-legged walking machine that has additional body actuators, therefore controlling 22 DoFs, is not a trivial task. Already simple forward walking on a flat plane requires the system to select between different internal states. The orchestration of these states depends on walking velocity and on external disturbances. Such disturbances occur continuously, for example due to irregular up-and-down movements of the body or slipping of the legs, even on flat surfaces, in particular when negotiating tight curves. The number of possible states is further increased when the system is allowed to walk backward or when front legs are used as grippers and cannot contribute to walking. Further states are necessary for expansion that allow for navigation. Here we demonstrate a solution for the selection and sequencing of different (attractor states required to control different behaviors as are forward walking at different speeds, backward walking, as well as negotiation of tight curves. This selection is made by a recurrent neural network of motivation units, controlling a bank of decentralized memory elements in combination with the feedback through the environment. The underlying heterarchical architecture of the network allows to select various combinations of these elements. This modular approach representing an example of neural reuse of a limited number of procedures allows for adaptation to different internal and external conditions. A way is sketched as to how this approach may be expanded to form a cognitive system being able to plan ahead. This architecture is characterized by different types of modules being arranged in layers and columns, but the complete network can also be considered as a holistic system showing emergent properties which cannot be attributed to a specific module.

  15. Modeling Architectural Patterns' Behavior Using Architectural Primitives

    NARCIS (Netherlands)

    Kamal, Ahmad Waqas; Avgeriou, Paris; Morrison, R; Balasubramaniam, D; Falkner, K

    2008-01-01

    Architectural patterns have an impact on both the structure and the behavior of a system at the architecture design level. However, it is challenging to model patterns' behavior in a systematic way because modeling languages do not provide the appropriate abstractions and because each pattern

  16. National positioning, navigation, and timing architecture study

    Science.gov (United States)

    2008-09-01

    The Assistant Secretary of Defense for Networks and Information Integration (ASD/NII) : and the Under Secretary of Transportation for Policy (UST/P) sponsored a National : Positioning, Navigation, and Timing (PNT) Architecture Study to provide mor...

  17. A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

    Science.gov (United States)

    Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

    2015-07-01

    Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

  18. 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.

  19. Religious architecture: anthropological perspectives

    NARCIS (Netherlands)

    Verkaaik, O.

    2013-01-01

    Religious Architecture: Anthropological Perspectives develops an anthropological perspective on modern religious architecture, including mosques, churches and synagogues. Borrowing from a range of theoretical perspectives on space-making and material religion, this volume looks at how religious

  20. Software architecture evolution

    DEFF Research Database (Denmark)

    Barais, Olivier; Le Meur, Anne-Francoise; Duchien, Laurence

    2008-01-01

    architecture. Following the early aspect paradigm, Tran SAT allows the software architect to design a software architecture stepwise in terms of aspects at the design stage. It realises the evolution as the weaving of new architectural aspects into an existing software architecture.......Software architectures must frequently evolve to cope with changing requirements, and this evolution often implies integrating new concerns. Unfortunately, when the new concerns are crosscutting, existing architecture description languages provide little or no support for this kind of evolution....... The software architect must modify multiple elements of the architecture manually, which risks introducing inconsistencies. This chapter provides an overview, comparison and detailed treatment of the various state-of-the-art approaches to describing and evolving software architectures. Furthermore, we discuss...