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Sample records for neural computations underlying

  1. 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

  2. Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information.

    Science.gov (United States)

    Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter

    2016-12-07

    Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  3. 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.

  4. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  5. Great expectations: neural computations underlying the use of social norms in decision-making.

    Science.gov (United States)

    Chang, Luke J; Sanfey, Alan G

    2013-03-01

    Social expectations play a critical role in everyday decision-making. However, their precise neuro-computational role in the decision process remains unknown. Here we adopt a decision neuroscience framework by combining methods and theories from psychology, economics and neuroscience to outline a novel, expectation-based, computational model of social preferences. Results demonstrate that this model outperforms the standard inequity-aversion model in explaining decision behavior in a social interactive bargaining task. This is supported by fMRI findings showing that the tracking of social expectation violations is processed by anterior cingulate cortex, extending previous computational conceptualizations of this region to the social domain. This study demonstrates the usefulness of this interdisciplinary approach in better characterizing the psychological processes that underlie social interactive decision-making.

  6. 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

  7. Computation within cultured neural networks.

    Science.gov (United States)

    DeMarse, T; Cadotte, A; Douglas, P; He, P; Trinh, V

    2004-01-01

    In this paper we present three related areas of research we are pursuing to study neural computation in vitro. Rat cortical neurons cultured on 60 channel multielectrode array (MEA) allow the researcher to measure from and stimulate sixty different sites across a small population of neurons grown in vitro. Using this system we can send stimulation patterns into the network and study how these living neural networks compute by measuring its outputs. Our first series of studies uses chaotic control techniques to study the dynamics and potentially control the behavior of cortical network. At the same time, we are beginning to apply a model of computation called the liquid state machine or LSM model developed by Wolfgang Maass to provide a firm mathematical framework from which to proceed with our investigations. Each of these components is integrated into a third area investigating the role of computation and feedback using a real-time sensory-motor feedback robotic flight system.

  8. Neural computation and the computational theory of cognition.

    Science.gov (United States)

    Piccinini, Gualtiero; Bahar, Sonya

    2013-04-01

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

  9. Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

    CERN Document Server

    Siddique, Nazmul

    2013-01-01

    Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

  10. Computational chaos in massively parallel neural networks

    Science.gov (United States)

    Barhen, Jacob; Gulati, Sandeep

    1989-01-01

    A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.

  11. Turing Computation with Recurrent Artificial Neural Networks

    OpenAIRE

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

    2015-01-01

    We improve the results by Siegelmann & Sontag (1995) by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata. The architecture of the resulting R-ANNs is simple and elegant, stemming from its transparent relation with the underlying NDAs. These characteristics yield promise for developments in machine learning methods and symbolic computation with continuous time d...

  12. Cortical Neural Computation by Discrete Results Hypothesis.

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    Castejon, Carlos; Nuñez, Angel

    2016-01-01

    One of the most challenging problems we face in neuroscience is to understand how the cortex performs computations. There is increasing evidence that the power of the cortical processing is produced by populations of neurons forming dynamic neuronal ensembles. Theoretical proposals and multineuronal experimental studies have revealed that ensembles of neurons can form emergent functional units. However, how these ensembles are implicated in cortical computations is still a mystery. Although cell ensembles have been associated with brain rhythms, the functional interaction remains largely unclear. It is still unknown how spatially distributed neuronal activity can be temporally integrated to contribute to cortical computations. A theoretical explanation integrating spatial and temporal aspects of cortical processing is still lacking. In this Hypothesis and Theory article, we propose a new functional theoretical framework to explain the computational roles of these ensembles in cortical processing. We suggest that complex neural computations underlying cortical processing could be temporally discrete and that sensory information would need to be quantized to be computed by the cerebral cortex. Accordingly, we propose that cortical processing is produced by the computation of discrete spatio-temporal functional units that we have called "Discrete Results" (Discrete Results Hypothesis). This hypothesis represents a novel functional mechanism by which information processing is computed in the cortex. Furthermore, we propose that precise dynamic sequences of "Discrete Results" is the mechanism used by the cortex to extract, code, memorize and transmit neural information. The novel "Discrete Results" concept has the ability to match the spatial and temporal aspects of cortical processing. We discuss the possible neural underpinnings of these functional computational units and describe the empirical evidence supporting our hypothesis. We propose that fast-spiking (FS

  13. Neural Computation and the Computational Theory of Cognition

    Science.gov (United States)

    Piccinini, Gualtiero; Bahar, Sonya

    2013-01-01

    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism--neural processes are computations in the…

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

    Directory of Open Access Journals (Sweden)

    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.

  15. 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 ...

  16. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

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    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  17. Optimal neural computations require analog processors

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    Beiu, V.

    1998-12-31

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

  18. Symbolic functions from neural computation.

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    Smolensky, Paul

    2012-07-28

    Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.

  19. Neural dynamics underlying emotional transmissions between individuals.

    Science.gov (United States)

    Golland, Yulia; Levit-Binnun, Nava; Hendler, Talma; Lerner, Yulia

    2017-08-01

    Emotional experiences are frequently shaped by the emotional responses of co-present others. Research has shown that people constantly monitor and adapt to the incoming social-emotional signals, even without face-to-face interaction. And yet, the neural processes underlying such emotional transmissions have not been directly studied. Here, we investigated how the human brain processes emotional cues which arrive from another, co-attending individual. We presented continuous emotional feedback to participants who viewed a movie in the scanner. Participants in the social group (but not in the control group) believed that the feedback was coming from another person who was co-viewing the same movie. We found that social-emotional feedback significantly affected the neural dynamics both in the core affect and in the medial pre-frontal regions. Specifically, the response time-courses in those regions exhibited increased similarity across recipients and increased neural alignment with the timeline of the feedback in the social compared with control group. Taken in conjunction with previous research, this study suggests that emotional cues from others shape the neural dynamics across the whole neural continuum of emotional processing in the brain. Moreover, it demonstrates that interpersonal neural alignment can serve as a neural mechanism through which affective information is conveyed between individuals. © The Author (2017). Published by Oxford University Press.

  20. Computing with scale-invariant neural representations

    Science.gov (United States)

    Howard, Marc; Shankar, Karthik

    The Weber-Fechner law is perhaps the oldest quantitative relationship in psychology. Consider the problem of the brain representing a function f (x) . Different neurons have receptive fields that support different parts of the range, such that the ith neuron has a receptive field at xi. Weber-Fechner scaling refers to the finding that the width of the receptive field scales with xi as does the difference between the centers of adjacent receptive fields. Weber-Fechner scaling is exponentially resource-conserving. Neurophysiological evidence suggests that neural representations obey Weber-Fechner scaling in the visual system and perhaps other systems as well. We describe an optimality constraint that is solved by Weber-Fechner scaling, providing an information-theoretic rationale for this principle of neural coding. Weber-Fechner scaling can be generated within a mathematical framework using the Laplace transform. Within this framework, simple computations such as translation, correlation and cross-correlation can be accomplished. This framework can in principle be extended to provide a general computational language for brain-inspired cognitive computation on scale-invariant representations. Supported by NSF PHY 1444389 and the BU Initiative for the Physics and Mathematics of Neural Systems,.

  1. Recurrent Neural Network for Computing Outer Inverse.

    Science.gov (United States)

    Živković, Ivan S; Stanimirović, Predrag S; Wei, Yimin

    2016-05-01

    Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

  2. Neural Global Pattern Similarity Underlies True and False Memories.

    Science.gov (United States)

    Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

    2016-06-22

    The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

  3. Fuzzy logic, neural networks, and soft computing

    Science.gov (United States)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  4. Neural dynamics underlying emotional transmissions between individuals

    OpenAIRE

    Golland, Yulia; Levit-Binnun, Nava; Hendler, Talma; Lerner, Yulia

    2017-01-01

    Abstract Emotional experiences are frequently shaped by the emotional responses of co-present others. Research has shown that people constantly monitor and adapt to the incoming social–emotional signals, even without face-to-face interaction. And yet, the neural processes underlying such emotional transmissions have not been directly studied. Here, we investigated how the human brain processes emotional cues which arrive from another, co-attending individual. We presented continuous emotional...

  5. Statistical physics on cellular neural network computers

    Science.gov (United States)

    Ercsey-Ravasz, M.; Roska, T.; Néda, Z.

    2008-07-01

    The computational paradigm represented by Cellular Neural/nonlinear Networks (CNN) and the CNN Universal Machine (CNN-UM) as a Cellular Wave Computer, gives new perspectives also for computational statistical physics. Thousands of locally interconnected cells working in parallel, analog signals giving the possibility of generating truly random numbers, continuity in time and the optical sensors included on the chip are just a few important advantages of such computers. Although CNN computers are mainly used and designed for image processing, here we argue that they are also suitable for solving complex problems in computational statistical physics. This study presents two examples of stochastic simulations on CNN: the site-percolation problem and the two-dimensional Ising model. Promising results are obtained using an ACE16K chip with 128×128 cells. In the second part of the work we discuss the possibility of using the CNN architecture in studying problems related to spin-glasses. A CNN with locally variant parameters is used for developing an optimization algorithm on spin-glass type models. Speed of the algorithms and further trends in developing the CNN chips are discussed.

  6. New Computer Simulations of Macular Neural Functioning

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    Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

    1994-01-01

    We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

  7. Emerging trends in neuro engineering and neural computation

    CERN Document Server

    Lee, Kendall; Garmestani, Hamid; Lim, Chee

    2017-01-01

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

  8. Computational Models and Emergent Properties of Respiratory Neural Networks

    Science.gov (United States)

    Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

    2012-01-01

    Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

  9. Computation and control with neural networks

    Science.gov (United States)

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

    1990-08-01

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

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

    Science.gov (United States)

    Huk, Alexander C; Meister, Miriam L R

    2012-01-01

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

  11. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  12. Neural Population Dynamics Underlying Motor Learning Transfer.

    Science.gov (United States)

    Vyas, Saurabh; Even-Chen, Nir; Stavisky, Sergey D; Ryu, Stephen I; Nuyujukian, Paul; Shenoy, Krishna V

    2018-03-07

    Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal. Copyright © 2018 Elsevier Inc. All rights reserved.

  13. The computational power of interactive recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Siegelmann, Hava T

    2012-04-01

    In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities.

  14. Neural correlates underlying micrographia in Parkinson's disease.

    Science.gov (United States)

    Wu, Tao; Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

    2016-01-01

    Micrographia is a common symptom in Parkinson's disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  15. Two distinct neural mechanisms underlying indirect reciprocity.

    Science.gov (United States)

    Watanabe, Takamitsu; Takezawa, Masanori; Nakawake, Yo; Kunimatsu, Akira; Yamasue, Hidenori; Nakamura, Mitsuhiro; Miyashita, Yasushi; Masuda, Naoki

    2014-03-18

    Cooperation is a hallmark of human society. Humans often cooperate with strangers even if they will not meet each other again. This so-called indirect reciprocity enables large-scale cooperation among nonkin and can occur based on a reputation mechanism or as a succession of pay-it-forward behavior. Here, we provide the functional and anatomical neural evidence for two distinct mechanisms governing the two types of indirect reciprocity. Cooperation occurring as reputation-based reciprocity specifically recruited the precuneus, a region associated with self-centered cognition. During such cooperative behavior, the precuneus was functionally connected with the caudate, a region linking rewards to behavior. Furthermore, the precuneus of a cooperative subject had a strong resting-state functional connectivity (rsFC) with the caudate and a large gray matter volume. In contrast, pay-it-forward reciprocity recruited the anterior insula (AI), a brain region associated with affective empathy. The AI was functionally connected with the caudate during cooperation occurring as pay-it-forward reciprocity, and its gray matter volume and rsFC with the caudate predicted the tendency of such cooperation. The revealed difference is consistent with the existing results of evolutionary game theory: although reputation-based indirect reciprocity robustly evolves as a self-interested behavior in theory, pay-it-forward indirect reciprocity does not on its own. The present study provides neural mechanisms underlying indirect reciprocity and suggests that pay-it-forward reciprocity may not occur as myopic profit maximization but elicit emotional rewards.

  16. Brains--Computers--Machines: Neural Engineering in Science Classrooms

    Science.gov (United States)

    Chudler, Eric H.; Bergsman, Kristen Clapper

    2016-01-01

    Neural engineering is an emerging field of high relevance to students, teachers, and the general public. This feature presents online resources that educators and scientists can use to introduce students to neural engineering and to integrate core ideas from the life sciences, physical sciences, social sciences, computer science, and engineering…

  17. Connecting Neural Coding to Number Cognition: A Computational Account

    Science.gov (United States)

    Prather, Richard W.

    2012-01-01

    The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

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

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

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

  19. 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.

  20. Neural correlates underlying musical semantic memory.

    Science.gov (United States)

    Groussard, M; Viader, F; Landeau, B; Desgranges, B; Eustache, F; Platel, H

    2009-07-01

    Numerous functional imaging studies have examined the neural basis of semantic memory mainly using verbal and visuospatial materials. Musical material also allows an original way to explore semantic memory processes. We used PET imaging to determine the neural substrates that underlie musical semantic memory using different tasks and stimuli. The results of three PET studies revealed a greater involvement of the anterior part of the temporal lobe. Concerning clinical observations and our neuroimaging data, the musical lexicon (and most widely musical semantic memory) appears to be sustained by a temporo-prefrontal cerebral network involving right and left cerebral regions.

  1. A Virtual Statistical Mechanical Neural Computer.

    Science.gov (United States)

    1987-12-01

    Neural Coding Process," IEEE Transactions on Systems, Man and Cybernetics SMC is. (1985). 85 [7] tevns, . F, "he Nuro," n Th Brin ’ Scentfic merca Book, e...brain theory," J. Social Biol. Struct. 4, 211-224 (1981). [26] Haken, H., Synergetics, 3rd ed. (Springer, New York, 1983). 86 V.7• VYVV A.). .. ., [27

  2. 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.

  3. Handwritten Digits Recognition Using Neural Computing

    Directory of Open Access Journals (Sweden)

    Călin Enăchescu

    2009-12-01

    Full Text Available In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classification task is performed using a Convolutional Neural Network (CNN. CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters, and with robustness to distortions and simple geometric transformations.The main contributions of this paper are related to theoriginal methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background.By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology database.

  4. Neural Partial Differentiation for Aircraft Parameter Estimation Under Turbulent Atmospheric Conditions

    Science.gov (United States)

    Kuttieri, R. A.; Sinha, M.

    2012-07-01

    An approach based on neural partial differentiation is suggested for aircraft parameter estimation using the flight data gathered under turbulent atmospheric conditions. The classical methods such as output error and equation error methods suffer from severe convergence issues; resulting in biased, inaccurate, and inconsistent estimates. Though filter error method yields better estimates while dealing with the flight data having process noise, it has few demerits like computational overheads and it allows estimation of a single set of process noise distribution matrix. The proposed neural method does not face any such problem of the classical methods. Moreover, the neural method does not require parameter initialization and a priori knowledge of the model structure. The neural network maps the aircraft state and control variables into the output variables corresponding to aerodynamic forces and moments. The parameter estimation, pertaining to lateral-directional motion, of the research aircraft de Havilland DHC-2 with simulated process noise, is presented. The results obtained using the neural partial differentiation are compared with the nominal values given in literature and with the classical methods. The neural method yields the aerodynamic derivatives very close to the nominal values and having quite low standard deviation. The neural methodology is also validated by comparing actual output variables with the neural predicted and neural reconstructed variables.

  5. Soft computing integrating evolutionary, neural, and fuzzy systems

    CERN Document Server

    Tettamanzi, Andrea

    2001-01-01

    Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

  6. Computationally Efficient Neural Network Intrusion Security Awareness

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Milos Manic

    2009-08-01

    An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

  7. Computational aspects of feedback in neural circuits.

    Directory of Open Access Journals (Sweden)

    Wolfgang Maass

    2007-01-01

    Full Text Available It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also

  8. Review of the Neural Oscillations Underlying Meditation

    Directory of Open Access Journals (Sweden)

    Darrin J. Lee

    2018-03-01

    Full Text Available Objective: Meditation is one type of mental training that has been shown to produce many cognitive benefits. Meditation practice is associated with improvement in concentration and reduction of stress, depression, and anxiety symptoms. Furthermore, different forms of meditation training are now being used as interventions for a variety of psychological and somatic illnesses. These benefits are thought to occur as a result of neurophysiologic changes. The most commonly studied specific meditation practices are focused attention (FA, open-monitoring (OM, as well as transcendental meditation (TM, and loving-kindness (LK meditation. In this review, we compare the neural oscillatory patterns during these forms of meditation.Method: We performed a systematic review of neural oscillations during FA, OM, TM, and LK meditation practices, comparing meditators to meditation-naïve adults.Results: FA, OM, TM, and LK meditation are associated with global increases in oscillatory activity in meditators compared to meditation-naïve adults, with larger changes occurring as the length of meditation training increases. While FA and OM are related to increases in anterior theta activity, only FA is associated with changes in posterior theta oscillations. Alpha activity increases in posterior brain regions during both FA and OM. In anterior regions, FA shows a bilateral increase in alpha power, while OM shows a decrease only in left-sided power. Gamma activity in these meditation practices is similar in frontal regions, but increases are variable in parietal and occipital regions.Conclusions: The current literature suggests distinct differences in neural oscillatory activity among FA, OM, TM, and LK meditation practices. Further characterizing these oscillatory changes may better elucidate the cognitive and therapeutic effects of specific meditation practices, and potentially lead to the development of novel neuromodulation targets to take advantage of their

  9. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  10. Human aging alters the neural computation and representation of space.

    Science.gov (United States)

    Schuck, Nicolas W; Doeller, Christian F; Polk, Thad A; Lindenberger, Ulman; Li, Shu-Chen

    2015-08-15

    The hippocampus and striatum are core neural circuits involved in spatial learning and memory. Although both neural systems support spatial navigation, experimental and theoretical evidence indicate that they play different roles. In particular, whereas hippocampal place cells generate allocentric neural representations of space that are sensitive to geometric information, striatum-dependent learning is influenced by local landmarks. How human aging affects these different neural representations, however, is still not well understood. In this paper, we combined virtual reality, computational modeling, and neuroimaging to investigate the effects of age upon the neural computation and representation of space in humans. We manipulated the geometry and local landmarks of a virtual environment and examined the effects on memory performance and brain activity during spatial learning. In younger adults, both behavior and brain activity in the medial-temporal lobe were consistent with predictions of a computational model of hippocampus-dependent boundary processing. In contrast, older adults' behavior and medial-temporal lobe activity were primarily influenced by local cue information, and spatial learning was more associated with activity in the caudate nucleus rather than the hippocampus. Together these results point to altered spatial representations and information processing in the hippocampal-striatal circuitry with advancing adult age, which may contribute to spatial learning and memory deficits associated with normal and pathological aging. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. Hybrid computing using a neural network with dynamic external memory.

    Science.gov (United States)

    Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

    2016-10-27

    Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

  12. Neural mechanisms and models underlying joint action.

    Science.gov (United States)

    Chersi, Fabian

    2011-06-01

    Humans, in particular, and to a lesser extent also other species of animals, possess the impressive capability of smoothly coordinating their actions with those of others. The great amount of work done in recent years in neuroscience has provided new insights into the processes involved in joint action, intention understanding, and task sharing. In particular, the discovery of mirror neurons, which fire both when animals execute actions and when they observe the same actions done by other individuals, has shed light on the intimate relationship between perception and action elucidating the direct contribution of motor knowledge to action understanding. Up to date, however, a detailed description of the neural processes involved in these phenomena is still mostly lacking. Building upon data from single neuron recordings in monkeys observing the actions of a demonstrator and then executing the same or a complementary action, this paper describes the functioning of a biologically constraint neural network model of the motor and mirror systems during joint action. In this model, motor sequences are encoded as independent neuronal chains that represent concatenations of elementary motor acts leading to a specific goal. Action execution and recognition are achieved through the propagation of activity within specific chains. Due to the dual property of mirror neurons, the same architecture is capable of smoothly integrating and switching between observed and self-generated action sequences, thus allowing to evaluate multiple hypotheses simultaneously, understand actions done by others, and to respond in an appropriate way.

  13. Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

    CERN Document Server

    Keller, James M; Fogel, David B

    2016-01-01

    This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

  14. Fish species recognition using computer vision and a neural network

    NARCIS (Netherlands)

    Storbeck, F.; Daan, B.

    2001-01-01

    A system is described to recognize fish species by computer vision and a neural network program. The vision system measures a number of features of fish as seen by a camera perpendicular to a conveyor belt. The features used here are the widths and heights at various locations along the fish. First

  15. Computer simulation system of neural PID control on nuclear reactor

    International Nuclear Information System (INIS)

    Chen Yuzhong; Yang Kaijun; Shen Yongping

    2001-01-01

    Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

  16. Towards practical control design using neural computation

    Science.gov (United States)

    Troudet, Terry; Garg, Sanjay; Mattern, Duane; Merrill, Walter

    1991-01-01

    The objective is to develop neural network based control design techniques which address the issue of performance/control effort tradeoff. Additionally, the control design needs to address the important issue if achieving adequate performance in the presence of actuator nonlinearities such as position and rate limits. These issues are discussed using the example of aircraft flight control. Given a set of pilot input commands, a feedforward net is trained to control the vehicle within the constraints imposed by the actuators. This is achieved by minimizing an objective function which is the sum of the tracking errors, control input rates and control input deflections. A tradeoff between tracking performance and control smoothness is obtained by varying, adaptively, the weights of the objective function. The neurocontroller performance is evaluated in the presence of actuator dynamics using a simulation of the vehicle. Appropriate selection of the different weights in the objective function resulted in the good tracking of the pilot commands and smooth neurocontrol. An extension of the neurocontroller design approach is proposed to enhance its practicality.

  17. Computation and control with neural nets

    International Nuclear Information System (INIS)

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

    1989-01-01

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

  18. Computation and control with neural nets

    Energy Technology Data Exchange (ETDEWEB)

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

    1989-10-04

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

  19. Identification of aerodynamic coefficients using computational neural networks

    Science.gov (United States)

    Linse, Dennis J.; Stengel, Robert F.

    1992-01-01

    Precise, smooth aerodynamic models are required for implementing adaptive, nonlinear control strategies. Accurate representations of aerodynamic coefficients can be generated for the complete flight envelope by combining computational neural network models with an Estimation-Before-Modeling paradigm for on-line training information. A novel method of incorporating first-partial-derivative information is employed to estimate the weights in individual feedforward neural networks for each aerodynamic coefficient. The method is demonstrated by generating a model of the normal force coefficient of a twin-jet transport aircraft from simulated flight data, and promising results are obtained.

  20. 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

  1. 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.

  2. 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.

  3. Recurrent Neural Network for Computing the Drazin Inverse.

    Science.gov (United States)

    Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

    2015-11-01

    This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

  4. Advances in neural networks computational and theoretical issues

    CERN Document Server

    Esposito, Anna; Morabito, Francesco

    2015-01-01

    This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural Networks as well as the use of neural networks for applications, pattern recognition, signal processing, and special topics such as the detection and recognition of multimodal emotional expressions and daily cognitive functions, and  bio-inspired memristor-based networks.  Providing insights into the latest research interest from a pool of international experts coming from different research fields, the volume becomes valuable to all those with any interest in a holistic approach to implement believable, autonomous, adaptive, and context-aware Information Communication Technologies.

  5. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  6. A computationally efficient method for nonparametric modeling of neural spiking activity with point processes.

    Science.gov (United States)

    Coleman, Todd P; Sarma, Sridevi S

    2010-08-01

    Point-process models have been shown to be useful in characterizing neural spiking activity as a function of extrinsic and intrinsic factors. Most point-process models of neural activity are parametric, as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can arise. Nonparametric methods are attractive due to fewer assumptions, but computation in general grows with the size of the data. We propose a computationally efficient method for nonparametric maximum likelihood estimation when the conditional intensity function, which characterizes the point process in its entirety, is assumed to be a Lipschitz continuous function but otherwise arbitrary. We show that by exploiting much structure, the problem becomes efficiently solvable. We next demonstrate a model selection procedure to estimate the Lipshitz parameter from data, akin to the minimum description length principle and demonstrate consistency of our estimator under appropriate assumptions. Finally, we illustrate the effectiveness of our method with simulated neural spiking data, goldfish retinal ganglion neural data, and activity recorded in CA1 hippocampal neurons from an awake behaving rat. For the simulated data set, our method uncovers a more compact representation of the conditional intensity function when it exists. For the goldfish and rat neural data sets, we show that our nonparametric method gives a superior absolute goodness-of-fit measure used for point processes than the most common parametric and splines-based approaches.

  7. Preserving neural function under extreme scaling.

    Directory of Open Access Journals (Sweden)

    Hermann Cuntz

    Full Text Available Important brain functions need to be conserved throughout organisms of extremely varying sizes. Here we study the scaling properties of an essential component of computation in the brain: the single neuron. We compare morphology and signal propagation of a uniquely identifiable interneuron, the HS cell, in the blowfly (Calliphora with its exact counterpart in the fruit fly (Drosophila which is about four times smaller in each dimension. Anatomical features of the HS cell scale isometrically and minimise wiring costs but, by themselves, do not scale to preserve the electrotonic behaviour. However, the membrane properties are set to conserve dendritic as well as axonal delays and attenuation as well as dendritic integration of visual information. In conclusion, the electrotonic structure of a neuron, the HS cell in this case, is surprisingly stable over a wide range of morphological scales.

  8. A neural algorithm for a fundamental computing problem.

    Science.gov (United States)

    Dasgupta, Sanjoy; Stevens, Charles F; Navlakha, Saket

    2017-11-10

    Similarity search-for example, identifying similar images in a database or similar documents on the web-is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

  9. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

    Directory of Open Access Journals (Sweden)

    Daniel Durstewitz

    2017-06-01

    Full Text Available The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast maximum-likelihood estimation framework for PLRNNs that may enable to recover

  10. A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

    Science.gov (United States)

    Durstewitz, Daniel

    2017-06-01

    The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects

  11. Advances in neural networks computational intelligence for ICT

    CERN Document Server

    Esposito, Anna; Morabito, Francesco; Pasero, Eros

    2016-01-01

    This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...

  12. Cusps enable line attractors for neural computation

    Science.gov (United States)

    Xiao, Zhuocheng; Zhang, Jiwei; Sornborger, Andrew T.; Tao, Louis

    2017-11-01

    Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.

  13. Cusps enable line attractors for neural computation

    International Nuclear Information System (INIS)

    Xiao, Zhuocheng; Zhang, Jiwei; Sornborger, Andrew T.; Tao, Louis

    2017-01-01

    Here, line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.

  14. 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…

  15. Neural mechanisms underlying context-dependent shifts in risk preferences

    NARCIS (Netherlands)

    Losecaat Vermeer, A.B.; Boksem, M.A.S.; Sanfey, A.G.

    2014-01-01

    Studies of risky decision-making have demonstrated that humans typically prefer risky options after incurring a financial loss, while generally preferring safer options after a monetary gain. Here, we examined the neural processes underlying these inconsistent risk preferences by investigating the

  16. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  17. 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.

  18. 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.

  19. Neural Computations in a Dynamical System with Multiple Time Scales

    Directory of Open Access Journals (Sweden)

    Yuanyuan Mi

    2016-09-01

    Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

  20. 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.

  1. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Lars Buesing

    2011-11-01

    Full Text Available The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  2. Computational modeling of neural activities for statistical inference

    CERN Document Server

    Kolossa, Antonio

    2016-01-01

    This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .

  3. Neural processes underlying cultural differences in cognitive persistence.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Lin, Lynda C

    2017-08-01

    Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights

  4. Adversarial Threshold Neural Computer for Molecular de Novo Design.

    Science.gov (United States)

    Putin, Evgeny; Asadulaev, Arip; Vanhaelen, Quentin; Ivanenkov, Yan; Aladinskaya, Anastasia V; Aliper, Alex; Zhavoronkov, Alex

    2018-03-30

    In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp 3 -rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

  5. Fast computation with spikes in a recurrent neural network

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.; Seung, H. Sebastian

    2002-01-01

    Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner

  6. Parallel and interrelated neural systems underlying adaptive navigation.

    Science.gov (United States)

    Mizumori, Sheri J Y; Canfield, James G; Yeshenko, Oksana

    2005-06-01

    The ability to process in parallel multiple forms of sensory information, and link sensory-sensory associations to behavior, presumably allows for the opportunistic use of the most reliable and predictive sensory modalities in diverse behavioral contexts. Evolutionary considerations indicate that such processing may represent a fundamental operating principle underlying complex sensory associations and sensory-motor integration. Here, we suggest that animal navigation is a particularly useful model of such opportunistic use of sensory and motor information because it is possible to study directly the effects of memory on neural system functions. First, comparative evidence for parallel processing across multiple brain structures during navigation is provided from the literatures on fish and rodent navigation. Then, based on neurophysiological evidence of coordinated, multiregional processing, we provide a neurobiological explanation of learning and memory effects on neural circuitry mediating navigation.

  7. Artificial intelligence in pharmaceutical product formulation: neural computing

    Directory of Open Access Journals (Sweden)

    Svetlana Ibrić

    2009-10-01

    Full Text Available The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimazation. However, the optimization by such a method can be misleading, especially if the formulation is complex. More recently, advances in mathematics and computer science have led to the development of alternative modeling and data mining techniques which work with a wider range of data sources: neural networks (an attempt to mimic the processing of the human brain; genetic algorithms (an attempt to mimic the evolutionary process by which biological systems self-organize and adapt, and fuzzy logic (an attempt to mimic the ability of the human brain to draw conclusions and generate responses based on incomplete or imprecise information. In this review the current technology will be examined, as well as its application in pharmaceutical formulation and processing. The challenges, benefits and future possibilities of neural computing will be discussed.

  8. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    Science.gov (United States)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  9. Computing Generalized Matrix Inverse on Spiking Neural Substrate

    Directory of Open Access Journals (Sweden)

    Rohit Shukla

    2018-03-01

    Full Text Available Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines.

  10. Electricity market price forecasting by grid computing optimizing artificial neural networks

    OpenAIRE

    Niimura, T.; Ozawa, K.; Sakamoto, N.

    2007-01-01

    This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using re...

  11. Neural changes underlying early stages of L2 vocabulary acquisition.

    Science.gov (United States)

    Pu, He; Holcomb, Phillip J; Midgley, Katherine J

    2016-11-01

    Research has shown neural changes following second language (L2) acquisition after weeks or months of instruction. But are such changes detectable even earlier than previously shown? The present study examines the electrophysiological changes underlying the earliest stages of second language vocabulary acquisition by recording event-related potentials (ERPs) within the first week of learning. Adult native English speakers with no previous Spanish experience completed less than four hours of Spanish vocabulary training, with pre- and post-training ERPs recorded to a backward translation task. Results indicate that beginning L2 learners show rapid neural changes following learning, manifested in changes to the N400 - an ERP component sensitive to lexicosemantic processing and degree of L2 proficiency. Specifically, learners in early stages of L2 acquisition show growth in N400 amplitude to L2 words following learning as well as a backward translation N400 priming effect that was absent pre-training. These results were shown within days of minimal L2 training, suggesting that the neural changes captured during adult second language acquisition are more rapid than previously shown. Such findings are consistent with models of early stages of bilingualism in adult learners of L2 ( e.g. Kroll and Stewart's RHM) and reinforce the use of ERP measures to assess L2 learning.

  12. Applied Computational Electromagnetics Society Journal. Volume 18, Number 2. Special Issue on Neural Network Applications in Electromagnetics

    National Research Council Canada - National Science Library

    Christodoulou, Christos

    2003-01-01

    Neural computing and machine learning algorithms have arrived and are here to stayZ In the last ten years neural networks have experienced an unbelievable growth, both in terms of novel neural network...

  13. 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.

  14. Neural computation and particle accelerators research, technology and applications

    CERN Document Server

    D'Arras, Horace

    2010-01-01

    This book discusses neural computation, a network or circuit of biological neurons and relatedly, particle accelerators, a scientific instrument which accelerates charged particles such as protons, electrons and deuterons. Accelerators have a very broad range of applications in many industrial fields, from high energy physics to medical isotope production. Nuclear technology is one of the fields discussed in this book. The development that has been reached by particle accelerators in energy and particle intensity has opened the possibility to a wide number of new applications in nuclear technology. This book reviews the applications in the nuclear energy field and the design features of high power neutron sources are explained. Surface treatments of niobium flat samples and superconducting radio frequency cavities by a new technique called gas cluster ion beam are also studied in detail, as well as the process of electropolishing. Furthermore, magnetic devises such as solenoids, dipoles and undulators, which ...

  15. Topology and computational performance of attractor neural networks

    International Nuclear Information System (INIS)

    McGraw, Patrick N.; Menzinger, Michael

    2003-01-01

    To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies. The random configuration is the most efficient for storage and retrieval of patterns by the network as a whole. However, in the scale-free case retrieval errors are not distributed uniformly among the nodes. The portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. The implications of these findings for brain function and social dynamics are suggestive

  16. Neural Cognition and Affective Computing on Cyber Language.

    Science.gov (United States)

    Huang, Shuang; Zhou, Xuan; Xue, Ke; Wan, Xiqiong; Yang, Zhenyi; Xu, Duo; Ivanović, Mirjana; Yu, Xueer

    2015-01-01

    Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space.

  17. Neural Cognition and Affective Computing on Cyber Language

    Directory of Open Access Journals (Sweden)

    Shuang Huang

    2015-01-01

    Full Text Available Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance emotional space.

  18. Neural Cognition and Affective Computing on Cyber Language

    Science.gov (United States)

    Huang, Shuang; Zhou, Xuan; Xue, Ke; Wan, Xiqiong; Yang, Zhenyi; Xu, Duo; Ivanović, Mirjana

    2015-01-01

    Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space. PMID:26491431

  19. The behavioral and neural mechanisms underlying the tracking of expertise.

    Science.gov (United States)

    Boorman, Erie D; O'Doherty, John P; Adolphs, Ralph; Rangel, Antonio

    2013-12-18

    Evaluating the abilities of others is fundamental for successful economic and social behavior. We investigated the computational and neurobiological basis of ability tracking by designing an fMRI task that required participants to use and update estimates of both people and algorithms' expertise through observation of their predictions. Behaviorally, we find a model-based algorithm characterized subject predictions better than several alternative models. Notably, when the agent's prediction was concordant rather than discordant with the subject's own likely prediction, participants credited people more than algorithms for correct predictions and penalized them less for incorrect predictions. Neurally, many components of the mentalizing network-medial prefrontal cortex, anterior cingulate gyrus, temporoparietal junction, and precuneus-represented or updated expertise beliefs about both people and algorithms. Moreover, activity in lateral orbitofrontal and medial prefrontal cortex reflected behavioral differences in learning about people and algorithms. These findings provide basic insights into the neural basis of social learning. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Adaptive neural network motion control for aircraft under uncertainty conditions

    Science.gov (United States)

    Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.

    2018-02-01

    We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.

  1. Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study.

    Science.gov (United States)

    Hanakawa, Takashi; Honda, Manabu; Okada, Tomohisa; Fukuyama, Hidenao; Shibasaki, Hiroshi

    2003-06-01

    Experts of abacus operation demonstrate extraordinary ability in mental calculation. There is psychological evidence that abacus experts utilize a mental image of an abacus to remember and manipulate large numbers in solving problems; however, the neural correlates underlying this expertise are unknown. Using functional magnetic resonance imaging, we compared the neural correlates associated with three mental-operation tasks (numeral, spatial, verbal) among six experts in abacus operations and eight nonexperts. In general, there was more involvement of neural correlates for visuospatial processing (e.g., right premotor and parietal areas) for abacus experts during the numeral mental-operation task. Activity of these areas and the fusiform cortex was correlated with the size of numerals used in the numeral mental-operation task. Particularly, the posterior superior parietal cortex revealed significantly enhanced activity for experts compared with controls during the numeral mental-operation task. Comparison with the other mental-operation tasks indicated that activity in the posterior superior parietal cortex was relatively specific to computation in 2-dimensional space. In conclusion, mental calculation of abacus experts is likely associated with enhanced involvement of the neural resources for visuospatial information processing in 2-dimensional space.

  2. Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

    Directory of Open Access Journals (Sweden)

    Ferris Daniel P

    2010-12-01

    Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

  3. 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.

  4. Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

    Directory of Open Access Journals (Sweden)

    Taimoor Khan

    2014-01-01

    Full Text Available In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA- based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

  5. Neural correlates underlying micrographia in Parkinson’s disease

    Science.gov (United States)

    Zhang, Jiarong; Hallett, Mark; Feng, Tao; Hou, Yanan; Chan, Piu

    2016-01-01

    Micrographia is a common symptom in Parkinson’s disease, which manifests as either a consistent or progressive reduction in the size of handwriting or both. Neural correlates underlying micrographia remain unclear. We used functional magnetic resonance imaging to investigate micrographia-related neural activity and connectivity modulations. In addition, the effect of attention and dopaminergic administration on micrographia was examined. We found that consistent micrographia was associated with decreased activity and connectivity in the basal ganglia motor circuit; while progressive micrographia was related to the dysfunction of basal ganglia motor circuit together with disconnections between the rostral supplementary motor area, rostral cingulate motor area and cerebellum. Attention significantly improved both consistent and progressive micrographia, accompanied by recruitment of anterior putamen and dorsolateral prefrontal cortex. Levodopa improved consistent micrographia accompanied by increased activity and connectivity in the basal ganglia motor circuit, but had no effect on progressive micrographia. Our findings suggest that consistent micrographia is related to dysfunction of the basal ganglia motor circuit; while dysfunction of the basal ganglia motor circuit and disconnection between the rostral supplementary motor area, rostral cingulate motor area and cerebellum likely contributes to progressive micrographia. Attention improves both types of micrographia by recruiting additional brain networks. Levodopa improves consistent micrographia by restoring the function of the basal ganglia motor circuit, but does not improve progressive micrographia, probably because of failure to repair the disconnected networks. PMID:26525918

  6. Oscillations, neural computations and learning during wake and sleep.

    Science.gov (United States)

    Penagos, Hector; Varela, Carmen; Wilson, Matthew A

    2017-06-01

    Learning and memory theories consider sleep and the reactivation of waking hippocampal neural patterns to be crucial for the long-term consolidation of memories. Here we propose that precisely coordinated representations across brain regions allow the inference and evaluation of causal relationships to train an internal generative model of the world. This training starts during wakefulness and strongly benefits from sleep because its recurring nested oscillations may reflect compositional operations that facilitate a hierarchical processing of information, potentially including behavioral policy evaluations. This suggests that an important function of sleep activity is to provide conditions conducive to general inference, prediction and insight, which contribute to a more robust internal model that underlies generalization and adaptive behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

    Science.gov (United States)

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

    2018-05-01

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

  8. Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information.

    Energy Technology Data Exchange (ETDEWEB)

    Aimone, James Bradley [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Betty, Rita [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-03-01

    Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation/Completion of Episodic Information - Sandia researchers developed novel methods and metrics for studying the computational function of neurogenesis, thus generating substantial impact to the neuroscience and neural computing communities. This work could benefit applications in machine learning and other analysis activities.

  9. Honey characterization using computer vision system and artificial neural networks.

    Science.gov (United States)

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  11. Neural mechanisms underlying cognitive inflexibility in Parkinson's disease.

    Science.gov (United States)

    Lange, Florian; Seer, Caroline; Loens, Sebastian; Wegner, Florian; Schrader, Christoph; Dressler, Dirk; Dengler, Reinhard; Kopp, Bruno

    2016-12-01

    Cognitive inflexibility is a hallmark of executive dysfunction in Parkinson's disease (PD). This deficit consistently manifests itself in a PD-related increase in the number of perseverative errors committed on the Wisconsin Card Sorting Test (WCST). However, the neural processes underlying perseverative WCST performance in PD are still largely unknown. The present study is the first to investigate the event-related potential (ERP) correlates of cognitive inflexibility on the WCST in PD patients. Thirty-two PD patients and 35 matched control participants completed a computerized version of the WCST while the electroencephalogram (EEG) was recorded. Behavioral results revealed the expected increase in perseverative errors in patients with PD. ERP analysis focused on two established indicators of executive processes: the fronto-central P3a as an index of attentional orienting and the sustained parietal positivity (SPP) as an index of set-shifting processes. In comparison to controls, P3a amplitudes were significantly attenuated in PD patients. Regression analysis further revealed that P3a and SPP amplitudes interactively contributed to the prediction of perseverative errors in PD patients: The number of perseverative errors was only increased when both ERP amplitudes were attenuated. Notably, the two ERP markers of executive processes accounted for more than 40% of the variance in perseverative errors in PD patients. We conclude that cognitive inflexibility in PD occurs when the neural bases of multiple executive processes are affected by the pathophysiology of PD. The combined measurement of P3a and SPP might yield an electrophysiological marker of cognitive inflexibility in PD. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Neural processes underlying the orienting of attention without awareness.

    Science.gov (United States)

    Giattino, Charles M; Alam, Zaynah M; Woldorff, Marty G

    2017-07-22

    Despite long being of interest to both philosophers and scientists, the relationship between attention and perceptual awareness is not well understood, especially to what extent they are even dissociable. Previous studies have shown that stimuli of which we are unaware can orient spatial attention and affect behavior. Yet, relatively little is understood about the neural processes underlying such unconscious orienting of attention, and how they compare to conscious orienting. To directly compare the cascade of attentional processes with and without awareness of the orienting stimulus, we employed a spatial-cueing paradigm and used object-substitution masking to manipulate subjects' awareness of the cues. We recorded EEG during the task, from which we extracted hallmark event-related-potential (ERP) indices of attention. Behaviorally, there was a 61 ms validity effect (invalidly minus validly cued target RTs) on cue-aware trials. On cue-unaware trials, subjects also had a robust validity effect of 20 ms, despite being unaware of the cue. An N2pc to the cue, a hallmark ERP index of the lateralized orienting of attention, was observed for cue-aware but not cue-unaware trials, despite the latter showing a clear behavioral validity effect. Finally, the P1 sensory-ERP response to the targets was larger when validly versus invalidly cued, even when subjects were unaware of the preceding cue, demonstrating enhanced sensory processing of targets following subliminal cues. These results suggest that subliminal stimuli can orient attention and lead to subsequent enhancements to both stimulus sensory processing and behavior, but through different neural mechanisms (such as via a subcortical pathway) than stimuli we perceive. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Neural Mechanisms Underlying Hyperphagia in Prader-Willi Syndrome

    Science.gov (United States)

    Holsen, Laura M.; Zarcone, Jennifer R.; Brooks, William M.; Butler, Merlin G.; Thompson, Travis I.; Ahluwalia, Jasjit S.; Nollen, Nicole L.; Savage, Cary R.

    2006-01-01

    Objective Prader-Willi syndrome (PWS) is a genetic disorder associated with developmental delay, obesity, and obsessive behavior related to food consumption. The most striking symptom of PWS is hyperphagia; as such, PWS may provide important insights into factors leading to overeating and obesity in the general population. We used functional magnetic resonance imaging to study the neural mechanisms underlying responses to visual food stimuli, before and after eating, in individuals with PWS and a healthy weight control (HWC) group. Research Methods and Procedures Participants were scanned once before (pre-meal) and once after (post-meal) eating a standardized meal. Pictures of food, animals, and blurred control images were presented in a block design format during acquisition of functional magnetic resonance imaging data. Results Statistical contrasts in the HWC group showed greater activation to food pictures in the pre-meal condition compared with the post-meal condition in the amygdala, orbitofrontal cortex, medial prefrontal cortex (medial PFC), and frontal operculum. In comparison, the PWS group exhibited greater activation to food pictures in the post-meal condition compared with the pre-meal condition in the orbitofrontal cortex, medial PFC, insula, hippocampus, and parahippocampal gyrus. Between-group contrasts in the pre- and post-meal conditions confirmed group differences, with the PWS group showing greater activation than the HWC group after the meal in food motivation networks. Discussion Results point to distinct neural mechanisms associated with hyperphagia in PWS. After eating a meal, the PWS group showed hyperfunction in limbic and para-limbic regions that drive eating behavior (e.g., the amygdala) and in regions that suppress food intake (e.g., the medial PFC). PMID:16861608

  14. Neural mechanisms underlying hyperphagia in Prader-Willi syndrome.

    Science.gov (United States)

    Holsen, Laura M; Zarcone, Jennifer R; Brooks, William M; Butler, Merlin G; Thompson, Travis I; Ahluwalia, Jasjit S; Nollen, Nicole L; Savage, Cary R

    2006-06-01

    Prader-Willi syndrome (PWS) is a genetic disorder associated with developmental delay, obesity, and obsessive behavior related to food consumption. The most striking symptom of PWS is hyperphagia; as such, PWS may provide important insights into factors leading to overeating and obesity in the general population. We used functional magnetic resonance imaging to study the neural mechanisms underlying responses to visual food stimuli, before and after eating, in individuals with PWS and a healthy weight control (HWC) group. Participants were scanned once before (pre-meal) and once after (post-meal) eating a standardized meal. Pictures of food, animals, and blurred control images were presented in a block design format during acquisition of functional magnetic resonance imaging data. Statistical contrasts in the HWC group showed greater activation to food pictures in the pre-meal condition compared with the post-meal condition in the amygdala, orbitofrontal cortex, medial prefrontal cortex (medial PFC), and frontal operculum. In comparison, the PWS group exhibited greater activation to food pictures in the post-meal condition compared with the pre-meal condition in the orbitofrontal cortex, medial PFC, insula, hippocampus, and parahippocampal gyrus. Between-group contrasts in the pre- and post-meal conditions confirmed group differences, with the PWS group showing greater activation than the HWC group after the meal in food motivation networks. Results point to distinct neural mechanisms associated with hyperphagia in PWS. After eating a meal, the PWS group showed hyperfunction in limbic and paralimbic regions that drive eating behavior (e.g., the amygdala) and in regions that suppress food intake (e.g., the medial PFC).

  15. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    OpenAIRE

    Lukas Falat; Dusan Marcek; Maria Durisova

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the sug...

  16. 3-D components of a biological neural network visualized in computer generated imagery. II - Macular neural network organization

    Science.gov (United States)

    Ross, Muriel D.; Meyer, Glenn; Lam, Tony; Cutler, Lynn; Vaziri, Parshaw

    1990-01-01

    Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.

  17. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  18. Neural Computations for Biosonar Imaging in the Big Brown Bat

    Science.gov (United States)

    Saillant, Prestor Augusto

    1995-11-01

    The study of the intimate relationship between space and time has taken many forms, ranging from the Theory of Relativity down to the problem of avoiding traffic jams. However, nowhere has this relationship been more fully developed and exploited than in dolphins and bats, which have the ability to utilize biosonar. This thesis describes research on the behavioral and computational basis of echolocation carried out in order to explore the neural mechanisms which may account for the space-time constructs which are of psychological importance to the big brown bat. The SCAT (Spectrogram Correlation and Transformation) computational model was developed to provide a framework for understanding the computational requirements of FM echolocation as determined from psychophysical experiments (i.e., high resolution imaging) and neurobiological constraints (Saillant et al., 1993). The second part of the thesis consisted in developing a new behavioral paradigm for simultaneously studying acoustic behavior and flight behavior of big brown bats in pursuit of stationary or moving targets. In the third part of the thesis a complete acoustic "artificial bat" was constructed, making use of the SCAT process. The development of the artificial bat allowed us to begin experimentation with real world echoes from various targets, in order to gain a better appreciation for the additional complexities and sources of information encountered by bats in flight. Finally, the continued development of the SCAT model has allowed a deeper understanding of the phenomenon of "time expansion" and of the phenomenon of phase sensitivity in the ultrasonic range. Time expansion, first predicted through the use of the SCAT model, and later found in auditory local evoked potential recordings, opens up a new realm of information processing and representation in the brain which as of yet has not been considered. It seems possible, from the work in the auditory system, that time expansion may provide a novel

  19. Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

    International Nuclear Information System (INIS)

    Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

    2015-01-01

    This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

  20. Neural mechanisms underlying the induction and relief of perceptual curiosity

    Directory of Open Access Journals (Sweden)

    Marieke eJepma

    2012-02-01

    Full Text Available Curiosity is one of the most basic biological drives in both animals and humans, and has been identified as a key motive for learning and discovery. Despite the importance of curiosity and related behaviors, the topic has been largely neglected in human neuroscience; hence little is known about the neurobiological mechanisms underlying curiosity. We used functional magnetic resonance imaging (fMRI to investigate what happens in our brain during the induction and subsequent relief of perceptual curiosity. Our core findings were that (i the induction of perceptual curiosity, through the presentation of ambiguous visual input, activated the anterior insula and anterior cingulate cortex, brain regions sensitive to conflict and arousal; (ii the relief of perceptual curiosity, through visual disambiguation, activated regions of the striatum that have been related to reward processing; and (iii the relief of perceptual curiosity was associated with hippocampal activation and enhanced incidental memory. These findings provide the first demonstration of the neural basis of human perceptual curiosity. Our results provide neurobiological support for a classic psychological theory of curiosity, which holds that curiosity is an aversive condition of increased arousal whose termination is rewarding and facilitates memory.

  1. 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.

  2. Dissociable neural processes underlying risky decisions for self versus other

    Directory of Open Access Journals (Sweden)

    Daehyun eJung

    2013-03-01

    Full Text Available Previous neuroimaging studies on decision making have mainly focused on decisions on behalf of oneself. Considering that people often make decisions on behalf of others, it is intriguing that there is little neurobiological evidence on how decisions for others differ from those for self. Thus, the present study focused on the direct comparison between risky decisions for self and those for other using functional magnetic resonance imaging (fMRI. Participants (N = 23 were asked to perform a gambling task for themselves (decision-for-self condition or for another person (decision-for-other condition while in the scanner. Their task was to choose between a low-risk option (i.e., win or lose 10 points and a high-risk option (i.e., win or lose 90 points. The winning probabilities of each option varied from 17% to 83%. Compared to choices for others, choices for self were more risk-averse at lower winning probability and more risk-seeking at higher winning probability, perhaps due to stronger affective process during risky decision for self compared to other. The brain activation pattern changed according to the target of the decision, such that reward-related regions were more active in the decision-for-self condition than in the decision-for-other condition, whereas brain regions related to the theory of mind (ToM showed greater activation in the decision-for-other condition than in the decision-for-self condition. A parametric modulation analysis reflecting each individual’s decision model revealed that activation of the amygdala and the dorsomedial prefrontal cortex (DMPFC were associated with value computation for self and for other, respectively, during a risky financial decision. The present study suggests that decisions for self and other may recruit fundamentally distinctive neural processes, which can be mainly characterized by dominant affective/impulsive and cognitive/regulatory processes, respectively.

  3. Analytic Treatment of Deep Neural Networks Under Additive Gaussian Noise

    KAUST Repository

    Alfadly, Modar M.

    2018-04-12

    Despite the impressive performance of deep neural networks (DNNs) on numerous vision tasks, they still exhibit yet-to-understand uncouth behaviours. One puzzling behaviour is the reaction of DNNs to various noise attacks, where it has been shown that there exist small adversarial noise that can result in a severe degradation in the performance of DNNs. To rigorously treat this, we derive exact analytic expressions for the first and second moments (mean and variance) of a small piecewise linear (PL) network with a single rectified linear unit (ReLU) layer subject to general Gaussian input. We experimentally show that these expressions are tight under simple linearizations of deeper PL-DNNs, especially popular architectures in the literature (e.g. LeNet and AlexNet). Extensive experiments on image classification show that these expressions can be used to study the behaviour of the output mean of the logits for each class, the inter-class confusion and the pixel-level spatial noise sensitivity of the network. Moreover, we show how these expressions can be used to systematically construct targeted and non-targeted adversarial attacks. Then, we proposed a special estimator DNN, named mixture of linearizations (MoL), and derived the analytic expressions for its output mean and variance, as well. We employed these expressions to train the model to be particularly robust against Gaussian attacks without the need for data augmentation. Upon training this network on a loss that is consolidated with the derived output probabilistic moments, the network is not only robust under very high variance Gaussian attacks but is also as robust as networks that are trained with 20 fold data augmentation.

  4. Biological modelling of a computational spiking neural network with neuronal avalanches

    Science.gov (United States)

    Li, Xiumin; Chen, Qing; Xue, Fangzheng

    2017-05-01

    In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

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

    OpenAIRE

    Petersen, Eline B.; Wöstmann, Malte; Obleser, Jonas; Stenfelt, Stefan; Lunner, Thomas

    2015-01-01

    Degradations in external, acoustic stimulation have long been suspected to increase the load on working memory (WM). One neural signature of WM 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 WM when audibility has been ensured via amplification. Using an adapted auditory Sternberg paradigm, we varied the orthogonal factors memory load and backgrou...

  6. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  7. Neural correlates underlying true and false associative memories.

    Science.gov (United States)

    Dennis, Nancy A; Johnson, Christina E; Peterson, Kristina M

    2014-07-01

    Despite the fact that associative memory studies produce a large number of false memories, neuroimaging analyses utilizing this paradigm typically focus only on neural activity mediating successful retrieval. The current study sought to expand on this prior research by examining the neural basis of both true and false associative memories. Though associative false memories are substantially different than those found in semantic or perceptual false memory paradigms, results suggest that associative false memories are mediated by similar neural mechanisms. Specifically, we found increased frontal activity that likely represents enhanced monitoring and evaluation compared to that needed for true memories and correct rejections. Results also indicated that true, and not false associative memories, are mediated by neural activity in the MTL, specifically the hippocampus. Finally, while activity in early visual cortex distinguished true from false memories, a lack of neural differences between hits and correct rejections failed to support previous findings suggesting that activity in early visual cortex represents sensory reactivation of encoding-related processing. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Archaeological Sites Studies Based on Neural Computation Techniques

    Science.gov (United States)

    Cantero, M. C.; Martinez, P.; Perez, R. M.; Paniagua, J.; del Rio, L. M.; Cerrillo, E.; Valencia, D.; Paniagua, R.; Plaza, J.; Bejarano, A.

    2005-06-01

    Some previous archaeological studies developed by the investigation group are based in the application of Artificial Neural Networks to detect special items in aerial or remote sensing[1] scenes[2], [3]. As Artificial Neural Networks have been widely used to solve problems related with pattern recognition and classification, a good performance is expected in the detection of certain image zones susceptible to contain archaeological remains, such as buildings, ruins, roads or ways. In this paper we present the study carried in on the area of Cáparra archaeological site.

  9. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Science.gov (United States)

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    specially designed pre-list are created which perceived by software-computational neural network complex where each line the detailed description of each experiment is contained. In this case, it is possible to perform statistical studies of work ANN results. The modular architecture of application with the purpose to solve various problems to modify it is allows. The developed software complex with sets of databases in the form of arrays, plug and downloaded separately is worked. For a complex functioning at present time the number of simultaneously processable streams of events in the input data has been determined. Blocks of databases contain the numerical minute data for 30 geomagnetic storms during the period from 2000 to 2003: Solar wind parameter, an interplanetary magnetic field (from space craft "ACE") and geomagnetic indices (Dst, SYM, ASY, AU, AL). These data are received from http://cdaweb.gsfc.nasa.gov. Is it necessary, modified discrete data using the spline is provided. This complex for wide range of tasks for the general trend studies of nonlinear connection between the different parameters determining the causal relationships in the system «interplanetary space-magnetosphere-ionosphere» is used. With this the following studies are carried out: the search of nonlinear connection of auroral and mid-current systems with the parameters of the solar wind and interplanetary magnetic field, the restoration of geomagnetic activity indices, the forecast and restoration of F2 ionosphere layer critical frequencies, the forecast maximum observed frequencies on the traces of oblique HF ionospheric sounding with perturbation heliogeophysical parameters and others. Work is executed at partial support under grants of the RFBR 08-05-12051-OBR and 09-05-00495, and also program Ministry of Education and Science «Development of higher school scientific potential (2009-2010, project N 1623)».

  10. Identifying the Neural Correlates Underlying Social Pain: Implications for Developmental Processes

    Science.gov (United States)

    Eisenberger, Naomi I.

    2006-01-01

    Although the need for social connection is critical for early social development as well as for psychological well-being throughout the lifespan, relatively little is known about the neural processes involved in maintaining social connections. The following review summarizes what is known regarding the neural correlates underlying feeling of…

  11. Back propagation and Monte Carlo algorithms for neural network computations

    International Nuclear Information System (INIS)

    Junczys, R.; Wit, R.

    1996-01-01

    Results of teaching procedures for neural network for two different algorithms are presented. The first one is based on the well known back-propagation technique, the second is an adopted version of the Monte Carlo global minimum seeking method. Combination of these two, different in nature, approaches provides promising results. (author) nature, approaches provides promising results. (author)

  12. Neural correlates of tactile detection: a combined magnetoencephalography and biophysically based computational modeling study.

    Science.gov (United States)

    Jones, Stephanie R; Pritchett, Dominique L; Stufflebeam, Steven M; Hämäläinen, Matti; Moore, Christopher I

    2007-10-03

    Previous reports conflict as to the role of primary somatosensory neocortex (SI) in tactile detection. We addressed this question in normal human subjects using whole-head magnetoencephalography (MEG) recording. We found that the evoked signal (0-175 ms) showed a prominent equivalent current dipole that localized to the anterior bank of the postcentral gyrus, area 3b of SI. The magnitude and timing of peaks in the SI waveform were stimulus amplitude dependent and predicted perception beginning at approximately 70 ms after stimulus. To make a direct and principled connection between the SI waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. The SI evoked response was successfully reproduced from the intracellular currents in pyramidal neurons driven by a sequence of lamina-specific excitatory input, consisting of output from the granular layer (approximately 25 ms), exogenous input to the supragranular layers (approximately 70 ms), and a second wave of granular output (approximately 135 ms). The model also predicted that SI correlates of perception reflect stronger and shorter-latency supragranular and late granular drive during perceived trials. These findings strongly support the view that signatures of tactile detection are present in human SI and are mediated by local neural dynamics induced by lamina-specific synaptic drive. Furthermore, our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception.

  13. Emerging phenomena in neural networks with dynamic synapses and their computational implications

    Directory of Open Access Journals (Sweden)

    Joaquin J. eTorres

    2013-04-01

    Full Text Available In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilisation of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behaviour can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

  14. 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.

  15. Neural mechanisms underlying probalistic category learning in normal aging.

    NARCIS (Netherlands)

    Fera, F.; Weickert, T.W.; Goldberg, T.E.; Tessitore, A.; Hariri, A.; Das, S.; Lee, S.; Zoltick, B.; Meeter, M.; Gluck, M.A.; Weinberger, D.A.; Matta, V.S.

    2005-01-01

    Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been

  16. 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 (performance, such that the likelihood of successfully remembering relevant information is associated with limiting interference from irrelevant stimuli. The consequences of a failure to ignore distractors on recognition performance was replicated for two types of feature-based 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.

  17. 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.

  18. Neural mechanisms underlying neurooptometric rehabilitation following traumatic brain injury

    Directory of Open Access Journals (Sweden)

    Hudac CM

    2012-01-01

    Full Text Available Caitlin M Hudac1, Srinivas Kota1, James L Nedrow2, Dennis L Molfese1,31Department of Psychology, University of Nebraska-Lincoln, 2Oculi Vision Rehabilitation, 3Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NEAbstract: Mild to severe traumatic brain injuries have lasting effects on everyday functioning. Issues relating to sensory problems are often overlooked or not addressed until well after the onset of the injury. In particular, vision problems related to ambient vision and the magnocellular pathway often result in posttrauma vision syndrome or visual midline shift syndrome. Symptoms from these syndromes are not restricted to the visual domain. Patients commonly experience proprioceptive, kinesthetic, vestibular, cognitive, and language problems. Neurooptometric rehabilitation often entails the use of corrective lenses, prisms, and binasal occlusion to accommodate the unstable magnocellular system. However, little is known regarding the neural mechanisms engaged during neurooptometric rehabilitation, nor how these mechanisms impact other domains. Event-related potentials from noninvasive electrophysiological recordings can be used to assess rehabilitation progress in patients. In this case report, high-density visual event-related potentials were recorded from one patient with posttrauma vision syndrome and secondary visual midline shift syndrome during a pattern reversal task, both with and without prisms. Results indicate that two factors occurring during the end portion of the P148 component (168–256 milliseconds poststimulus onset map onto two separate neural systems that were engaged with and without neurooptometric rehabilitation. Without prisms, neural sources within somatosensory, language, and executive brain regions engage inefficient magnocellular system processing. However, when corrective prisms were worn, primary visual areas were appropriately engaged. The impact of using early

  19. Measuring human emotions with modular neural networks and computer vision based applications

    Directory of Open Access Journals (Sweden)

    Veaceslav Albu

    2015-05-01

    Full Text Available This paper describes a neural network architecture for emotion recognition for human-computer interfaces and applied systems. In the current research, we propose a combination of the most recent biometric techniques with the neural networks (NN approach for real-time emotion and behavioral analysis. The system will be tested in real-time applications of customers' behavior for distributed on-land systems, such as kiosks and ATMs.

  20. Efficiently outsourcing multiparty computation under multiple keys

    NARCIS (Netherlands)

    Peter, Andreas; Tews, Erik; Tews, Erik; Katzenbeisser, Stefan

    2013-01-01

    Secure multiparty computation enables a set of users to evaluate certain functionalities on their respective inputs while keeping these inputs encrypted throughout the computation. In many applications, however, outsourcing these computations to an untrusted server is desirable, so that the server

  1. A Neural Information Field Approach to Computational Cognition

    Science.gov (United States)

    2016-11-18

    followed combines three recent methods developed in Dr. Eliasmith’s group. The first method is called the Neural Engineering Framework (NEF), and...architecture called the Semantic Pointer Architecture (SPA; Eliasmith, 2013). The SPA suggests specific functional components and methods for...Polanyi won the 1986 Nobel Prize in Chemistry. NSERC is proud to offer a prestigious research prize in his name. Created in 2006, the NSERC John C

  2. Age-related neural correlates of cognitive task performance under increased postural load

    NARCIS (Netherlands)

    Van Impe, A; Bruijn, S M; Coxon, J P; Wenderoth, N; Sunaert, S; Duysens, J; Swinnen, S P

    2013-01-01

    Behavioral studies suggest that postural control requires increased cognitive control and visuospatial processing with aging. Consequently, performance can decline when concurrently performing a postural and a demanding cognitive task. We aimed to identify the neural substrate underlying this

  3. The neural sociometer: brain mechanisms underlying state self-esteem.

    Science.gov (United States)

    Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R

    2011-11-01

    On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

  4. The super-Turing computational power of plastic recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Siegelmann, Hava T

    2014-12-01

    We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

  5. On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review.

    Science.gov (United States)

    Laudani, Antonino; Lozito, Gabriele Maria; Riganti Fulginei, Francesco; Salvini, Alessandro

    2015-01-01

    A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented.

  6. A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing.

    Energy Technology Data Exchange (ETDEWEB)

    Vineyard, Craig Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-09-01

    As high performance computing architectures pursue more computational power there is a need for increased memory capacity and bandwidth as well. A multi-level memory (MLM) architecture addresses this need by combining multiple memory types with different characteristics as varying levels of the same architecture. How to efficiently utilize this memory infrastructure is an unknown challenge, and in this research we sought to investigate whether neural inspired approaches can meaningfully help with memory management. In particular we explored neurogenesis inspired re- source allocation, and were able to show a neural inspired mixed controller policy can beneficially impact how MLM architectures utilize memory.

  7. Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

    International Nuclear Information System (INIS)

    Stefaniak, B.; Cholewinski, W.; Tarkowska, A.

    2005-01-01

    Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

  8. Computational Certification under Limited Experiments (Preprint)

    Science.gov (United States)

    2017-04-26

    periodic microstructures," Computers & amp ; Structures, 82(7), pp. 593-606. [53] Smit, R., Brekelmans, W., and Meijer, H., 1998, "Prediction of the...55(11), pp. 1285-1322. [58] Zohdi, T. I., and Wriggers, P., 2008, An introduction to computational micromechanics, Springer Science & amp

  9. 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

  10. Neural-network-based depth computation for blind navigation

    Science.gov (United States)

    Wong, Farrah; Nagarajan, Ramachandran R.; Yaacob, Sazali

    2004-12-01

    A research undertaken to help blind people to navigate autonomously or with minimum assistance is termed as "Blind Navigation". In this research, an aid that could help blind people in their navigation is proposed. Distance serves as an important clue during our navigation. A stereovision navigation aid implemented with two digital video cameras that are spaced apart and fixed on a headgear to obtain the distance information is presented. In this paper, a neural network methodology is used to obtain the required parameters of the camera which is known as camera calibration. These parameters are not known but obtained by adjusting the weights in the network. The inputs to the network consist of the matching features in the stereo pair images. A back propagation network with 16-input neurons, 3 hidden neurons and 1 output neuron, which gives depth, is created. The distance information is incorporated into the final processed image as four gray levels such as white, light gray, dark gray and black. Preliminary results have shown that the percentage errors fall below 10%. It is envisaged that the distance provided by neural network shall enable blind individuals to go near and pick up an object of interest.

  11. 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.

  12. Neural networks and neuroscience-inspired computer vision.

    Science.gov (United States)

    Cox, David Daniel; Dean, Thomas

    2014-09-22

    Brains are, at a fundamental level, biological computing machines. They transform a torrent of complex and ambiguous sensory information into coherent thought and action, allowing an organism to perceive and model its environment, synthesize and make decisions from disparate streams of information, and adapt to a changing environment. Against this backdrop, it is perhaps not surprising that computer science, the science of building artificial computational systems, has long looked to biology for inspiration. However, while the opportunities for cross-pollination between neuroscience and computer science are great, the road to achieving brain-like algorithms has been long and rocky. Here, we review the historical connections between neuroscience and computer science, and we look forward to a new era of potential collaboration, enabled by recent rapid advances in both biologically-inspired computer vision and in experimental neuroscience methods. In particular, we explore where neuroscience-inspired algorithms have succeeded, where they still fail, and we identify areas where deeper connections are likely to be fruitful. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Computation of optimal transport and related hedging problems via penalization and neural networks

    OpenAIRE

    Eckstein, Stephan; Kupper, Michael

    2018-01-01

    This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks. The core idea is to penalize the optimization problem in its dual formulation and reduce it to a finite dimensional one which corresponds to optimizing a neural network with smooth objective function. We present numerical examples from optimal transport, martingale optimal transport, portfolio optimization under uncertainty and generative adversa...

  14. Propagation of Computer Virus under Human Intervention: A Dynamical Model

    OpenAIRE

    Chenquan Gan; Xiaofan Yang; Wanping Liu; Qingyi Zhu; Xulong Zhang

    2012-01-01

    This paper examines the propagation behavior of computer virus under human intervention. A dynamical model describing the spread of computer virus, under which a susceptible computer can become recovered directly and an infected computer can become susceptible directly, is proposed. Through a qualitative analysis of this model, it is found that the virus-free equilibrium is globally asymptotically stable when the basic reproduction number R0≤1, whereas the viral equilibrium is globally asympt...

  15. A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human population.

    Directory of Open Access Journals (Sweden)

    Roman Bauer

    Full Text Available Glioma is the most common form of primary brain tumor. Demographically, the risk of occurrence increases until old age. Here we present a novel computational model to reproduce the probability of glioma incidence across the lifespan. Previous mathematical models explaining glioma incidence are framed in a rather abstract way, and do not directly relate to empirical findings. To decrease this gap between theory and experimental observations, we incorporate recent data on cellular and molecular factors underlying gliomagenesis. Since evidence implicates the adult neural stem cell as the likely cell-of-origin of glioma, we have incorporated empirically-determined estimates of neural stem cell number, cell division rate, mutation rate and oncogenic potential into our model. We demonstrate that our model yields results which match actual demographic data in the human population. In particular, this model accounts for the observed peak incidence of glioma at approximately 80 years of age, without the need to assert differential susceptibility throughout the population. Overall, our model supports the hypothesis that glioma is caused by randomly-occurring oncogenic mutations within the neural stem cell population. Based on this model, we assess the influence of the (experimentally indicated decrease in the number of neural stem cells and increase of cell division rate during aging. Our model provides multiple testable predictions, and suggests that different temporal sequences of oncogenic mutations can lead to tumorigenesis. Finally, we conclude that four or five oncogenic mutations are sufficient for the formation of glioma.

  16. Group Membership Modulates the Neural Circuitry Underlying Third Party Punishment.

    Science.gov (United States)

    Morese, Rosalba; Rabellino, Daniela; Sambataro, Fabio; Perussia, Felice; Valentini, Maria Consuelo; Bara, Bruno G; Bosco, Francesca M

    2016-01-01

    This research aims to explore the neural correlates involved in altruistic punishment, parochial altruism and anti-social punishment, using the Third-Party Punishment (TPP) game. In particular, this study considered these punishment behaviors in in-group vs. out-group game settings, to compare how people behave with members of their own national group and with members of another national group. The results showed that participants act altruistically to protect in-group members. This study indicates that norm violation in in-group (but not in out-group) settings results in increased activity in the medial prefrontal cortex and temporo-parietal junction, brain regions involved in the mentalizing network, as the third-party attempts to understand or justify in-group members' behavior. Finally, exploratory analysis during anti-social punishment behavior showed brain activation recruitment of the ventromedial prefrontal cortex, an area associated with altered regulation of emotions.

  17. Evidence for Neural Computations of Temporal Coherence in an Auditory Scene and Their Enhancement during Active Listening.

    Science.gov (United States)

    O'Sullivan, James A; Shamma, Shihab A; Lalor, Edmund C

    2015-05-06

    The human brain has evolved to operate effectively in highly complex acoustic environments, segregating multiple sound sources into perceptually distinct auditory objects. A recent theory seeks to explain this ability by arguing that stream segregation occurs primarily due to the temporal coherence of the neural populations that encode the various features of an individual acoustic source. This theory has received support from both psychoacoustic and functional magnetic resonance imaging (fMRI) studies that use stimuli which model complex acoustic environments. Termed stochastic figure-ground (SFG) stimuli, they are composed of a "figure" and background that overlap in spectrotemporal space, such that the only way to segregate the figure is by computing the coherence of its frequency components over time. Here, we extend these psychoacoustic and fMRI findings by using the greater temporal resolution of electroencephalography to investigate the neural computation of temporal coherence. We present subjects with modified SFG stimuli wherein the temporal coherence of the figure is modulated stochastically over time, which allows us to use linear regression methods to extract a signature of the neural processing of this temporal coherence. We do this under both active and passive listening conditions. Our findings show an early effect of coherence during passive listening, lasting from ∼115 to 185 ms post-stimulus. When subjects are actively listening to the stimuli, these responses are larger and last longer, up to ∼265 ms. These findings provide evidence for early and preattentive neural computations of temporal coherence that are enhanced by active analysis of an auditory scene. Copyright © 2015 the authors 0270-6474/15/357256-08$15.00/0.

  18. Neural computation of visual imaging based on Kronecker product in the primary visual cortex

    Directory of Open Access Journals (Sweden)

    Guozheng Yao

    2010-03-01

    Full Text Available Abstract Background What kind of neural computation is actually performed by the primary visual cortex and how is this represented mathematically at the system level? It is an important problem in the visual information processing, but has not been well answered. In this paper, according to our understanding of retinal organization and parallel multi-channel topographical mapping between retina and primary visual cortex V1, we divide an image into orthogonal and orderly array of image primitives (or patches, in which each patch will evoke activities of simple cells in V1. From viewpoint of information processing, this activated process, essentially, involves optimal detection and optimal matching of receptive fields of simple cells with features contained in image patches. For the reconstruction of the visual image in the visual cortex V1 based on the principle of minimum mean squares error, it is natural to use the inner product expression in neural computation, which then is transformed into matrix form. Results The inner product is carried out by using Kronecker product between patches and function architecture (or functional column in localized and oriented neural computing. Compared with Fourier Transform, the mathematical description of Kronecker product is simple and intuitive, so is the algorithm more suitable for neural computation of visual cortex V1. Results of computer simulation based on two-dimensional Gabor pyramid wavelets show that the theoretical analysis and the proposed model are reasonable. Conclusions Our results are: 1. The neural computation of the retinal image in cortex V1 can be expressed to Kronecker product operation and its matrix form, this algorithm is implemented by the inner operation between retinal image primitives and primary visual cortex's column. It has simple, efficient and robust features, which is, therefore, such a neural algorithm, which can be completed by biological vision. 2. It is more suitable

  19. A computational framework for ultrastructural mapping of neural circuitry.

    Directory of Open Access Journals (Sweden)

    James R Anderson

    2009-03-01

    Full Text Available Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM, mosaicking and registration (ir-tools, and large slice viewers (MosaicBuilder, Viking can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina, terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally

  20. Modulating conscious movement intention by noninvasive brain stimulation and the underlying neural mechanisms.

    Science.gov (United States)

    Douglas, Zachary H; Maniscalco, Brian; Hallett, Mark; Wassermann, Eric M; He, Biyu J

    2015-05-06

    Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be ∼60-70 ms earlier. Slow brain waves recorded ∼2-3 s before movement onset, as well as hundreds of milliseconds after movement onset, independently correlated with the modulation of conscious intention by brain stimulation. These brain activities together accounted for 81% of interindividual variability in the modulation of movement intention by brain stimulation. A computational model using coupled leaky integrator units with biophysically plausible assumptions about the effect of tDCS captured the effects of stimulation on both neural activity and behavior. These results reveal a temporally extended brain process underlying conscious movement intention that spans seconds around movement commencement. Copyright © 2015 Douglas et al.

  1. Impaired Tuning of Neural Ensembles and the Pathophysiology of Schizophrenia: A Translational and Computational Neuroscience Perspective.

    Science.gov (United States)

    Krystal, John H; Anticevic, Alan; Yang, Genevieve J; Dragoi, George; Driesen, Naomi R; Wang, Xiao-Jing; Murray, John D

    2017-05-15

    The functional optimization of neural ensembles is central to human higher cognitive functions. When the functions through which neural activity is tuned fail to develop or break down, symptoms and cognitive impairments arise. This review considers ways in which disturbances in the balance of excitation and inhibition might develop and be expressed in cortical networks in association with schizophrenia. This presentation is framed within a developmental perspective that begins with disturbances in glutamate synaptic development in utero. It considers developmental correlates and consequences, including compensatory mechanisms that increase intrinsic excitability or reduce inhibitory tone. It also considers the possibility that these homeostatic increases in excitability have potential negative functional and structural consequences. These negative functional consequences of disinhibition may include reduced working memory-related cortical activity associated with the downslope of the "inverted-U" input-output curve, impaired spatial tuning of neural activity and impaired sparse coding of information, and deficits in the temporal tuning of neural activity and its implication for neural codes. The review concludes by considering the functional significance of noisy activity for neural network function. The presentation draws on computational neuroscience and pharmacologic and genetic studies in animals and humans, particularly those involving N-methyl-D-aspartate glutamate receptor antagonists, to illustrate principles of network regulation that give rise to features of neural dysfunction associated with schizophrenia. While this presentation focuses on schizophrenia, the general principles outlined in the review may have broad implications for considering disturbances in the regulation of neural ensembles in psychiatric disorders. Published by Elsevier Inc.

  2. The neural underpinnings of music listening under different attention conditions.

    Science.gov (United States)

    Jäncke, Lutz; Leipold, Simon; Burkhard, Anja

    2018-05-02

    Most studies examining the neural underpinnings of music listening have no specific instruction on how to process the presented musical pieces. In this study, we explicitly manipulated the participants' focus of attention while they listened to the musical pieces. We used an ecologically valid experimental setting by presenting the musical stimuli simultaneously with naturalistic film sequences. In one condition, the participants were instructed to focus their attention on the musical piece (attentive listening), whereas in the second condition, the participants directed their attention to the film sequence (passive listening). We used two instrumental musical pieces: an electronic pop song, which was a major hit at the time of testing, and a classical musical piece. During music presentation, we measured electroencephalographic oscillations and responses from the autonomic nervous system (heart rate and high-frequency heart rate variability). During passive listening to the pop song, we found strong event-related synchronizations in all analyzed frequency bands (theta, lower alpha, upper alpha, lower beta, and upper beta). The neurophysiological responses during attentive listening to the pop song were similar to those of the classical musical piece during both listening conditions. Thus, the focus of attention had a strong influence on the neurophysiological responses to the pop song, but not on the responses to the classical musical piece. The electroencephalographic responses during passive listening to the pop song are interpreted as a neurophysiological and psychological state typically observed when the participants are 'drawn into the music'.

  3. Neural correlates underlying change in state self-esteem.

    Science.gov (United States)

    Kawamichi, Hiroaki; Sugawara, Sho K; Hamano, Yuki H; Kitada, Ryo; Nakagawa, Eri; Kochiyama, Takanori; Sadato, Norihiro

    2018-01-29

    State self-esteem, the momentary feeling of self-worth, functions as a sociometer involved in maintenance of interpersonal relations. How others' appraisal is subjectively interpreted to change state self-esteem is unknown, and the neural underpinnings of this process remain to be elucidated. We hypothesized that changes in state self-esteem are represented by the mentalizing network, which is modulated by interactions with regions involved in the subjective interpretation of others' appraisal. To test this hypothesis, we conducted task-based and resting-state fMRI. Participants were repeatedly presented with their reputations, and then rated their pleasantness and reported their state self-esteem. To evaluate the individual sensitivity of the change in state self-esteem based on pleasantness (i.e., the subjective interpretation of reputation), we calculated evaluation sensitivity as the rate of change in state self-esteem per unit pleasantness. Evaluation sensitivity varied across participants, and was positively correlated with precuneus activity evoked by reputation rating. Resting-state fMRI revealed that evaluation sensitivity was positively correlated with functional connectivity of the precuneus with areas activated by negative reputation, but negatively correlated with areas activated by positive reputation. Thus, the precuneus, as the part of the mentalizing system, serves as a gateway for translating the subjective interpretation of reputation into state self-esteem.

  4. Depth perception in frogs and toads a study in neural computing

    CERN Document Server

    House, Donald

    1989-01-01

    Depth Perception in Frogs and Toads provides a comprehensive exploration of the phenomenon of depth perception in frogs and toads, as seen from a neuro-computational point of view. Perhaps the most important feature of the book is the development and presentation of two neurally realizable depth perception algorithms that utilize both monocular and binocular depth cues in a cooperative fashion. One of these algorithms is specialized for computation of depth maps for navigation, and the other for the selection and localization of a single prey for prey catching. The book is also unique in that it thoroughly reviews the known neuroanatomical, neurophysiological and behavioral data, and then synthesizes, organizes and interprets that information to explain a complex sensory-motor task. The book will be of special interest to that segment of the neural computing community interested in understanding natural neurocomputational structures, particularly to those working in perception and sensory-motor coordination. ...

  5. 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

  6. Computation and dissipative dynamical systems in neural networks for classification

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank

    2015-01-01

    Foundational issues related to learning, processing and representation underlying pattern recognition have been discussed in history and in recent times. The scientific approach to pattern recognition could provide new tools to investigate these foundational issues, which in turn could inform the

  7. Neural Network Computed Bootstrap Current for Real Time Control in DIII-D

    Science.gov (United States)

    Tema Biwole, Arsene; Smith, Sterling P.; Meneghini, Orso; Belli, Emily; Candy, Jeff

    2017-10-01

    In an effort to provide a fast and accurate calculation of the bootstrap current density for use as a constraint in real-time equilibrium reconstructions, we have developed a neural network (NN) non-linear regression of the NEO code calculated bootstrap current jBS. A new formulation for jBS in NEO allows for a determination of the coefficients on the density and temperature scale lengths. The new formulation reduces the number of inputs to the NN, and the number of output coefficients is 2 times the number of species (including electrons). The NN can reproduce the NEO and Sauter coefficients to a high degree of accuracy (bootstrap current density calculated in NEO has been used as a constraint in an offline equilibrium reconstruction for comparison to the NN calculation. The computational time of this method (μs) makes it ideal for real time calculation in DIII-D. Work supported by US DOE under DE-FC02-04ER54698, DE-FG2-95ER-54309, DE-SC 0012656, DE-FC02-06ER54873.

  8. Behavioral, Neural, and Computational Principles of Bodily Self-Consciousness.

    Science.gov (United States)

    Blanke, Olaf; Slater, Mel; Serino, Andrea

    2015-10-07

    Recent work in human cognitive neuroscience has linked self-consciousness to the processing of multisensory bodily signals (bodily self-consciousness [BSC]) in fronto-parietal cortex and more posterior temporo-parietal regions. We highlight the behavioral, neurophysiological, neuroimaging, and computational laws that subtend BSC in humans and non-human primates. We propose that BSC includes body-centered perception (hand, face, and trunk), based on the integration of proprioceptive, vestibular, and visual bodily inputs, and involves spatio-temporal mechanisms integrating multisensory bodily stimuli within peripersonal space (PPS). We develop four major constraints of BSC (proprioception, body-related visual information, PPS, and embodiment) and argue that the fronto-parietal and temporo-parietal processing of trunk-centered multisensory signals in PPS is of particular relevance for theoretical models and simulations of BSC and eventually of self-consciousness. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

    Science.gov (United States)

    Falat, Lukas; Marcek, Dusan; Durisova, Maria

    2016-01-01

    This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  10. Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

    Directory of Open Access Journals (Sweden)

    Lukas Falat

    2016-01-01

    Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

  11. Computing single step operators of logic programming in radial basis function neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

    2014-07-10

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  12. Computing single step operators of logic programming in radial basis function neural networks

    Science.gov (United States)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

    2014-07-01

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

  13. Computing single step operators of logic programming in radial basis function neural networks

    International Nuclear Information System (INIS)

    Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

    2014-01-01

    Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks

  14. Building bridges between perceptual and economic decision-making: neural and computational mechanisms

    Directory of Open Access Journals (Sweden)

    Christopher eSummerfield

    2012-05-01

    Full Text Available Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision making (PDM is concerned with how observers detect, discriminate and categorise noisy sensory information. Economic decision making (EDM explores how options are selected on the basis of their reinforcement history. Traditionally, the subfields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both domains, under which an agent has to combine sensory information (what is the stimulus with value information (what is it worth. We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex – to perceptual and economic decision-making, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasised the role of the striatum and orbitofrontal cortex in value-guided choices, they may play an important role in categorisation of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move towards a general framework for understanding decision-making in humans and other primates.

  15. Low-cost autonomous perceptron neural network inspired by quantum computation

    Science.gov (United States)

    Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

    2017-11-01

    Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

  16. Potential neural mechanisms underlying the effectiveness of early intervention for children with autism spectrum disorder

    Science.gov (United States)

    Sullivan, Katherine; Stone, Wendy L.; Dawson, Geraldine

    2014-01-01

    Although evidence supports the efficacy of early intervention for improving outcomes for children with autism spectrum disorder (ASD), the mechanisms underlying their effectiveness remain poorly understood. This paper reviews the research literature on the neural bases of the early core deficits in ASD and proposes three key features of early intervention related to the neural mechanisms that may contribute to its effectiveness in improving deficit areas. These features include (1) the early onset of intensive intervention which capitalizes on the experience-expectant plasticity of the immature brain, (2) the use of treatment strategies that address core deficits in social motivation through an emphasis on positive social engagement and arousal modulation, and (3) promotion of complex neural networks and connectivity through thematic, multi-sensory and multi-domain teaching approaches. Understanding the mechanisms of effective early intervention will enable us to identify common or foundational active ingredients for promoting optimal outcomes in children with ASD. PMID:25108609

  17. 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.

  18. Neural network model for evaluation of seedling vigour under clinostated conditions

    Science.gov (United States)

    Zaidi, M.; Murase, H.

    A hierarchical neural net can be applied to simulate nonlinear phenomena found in biological systems. The learning process of the hierarchical neural net can be used as an algorithm for nonlinear multivariate analysis. The non- invasive technique for monitoring the plant's growth stage is one part of the required technology of the bio-response feedback control system. The stage of a plant's growth can be identified or quantified by measuring physical indices. Automated monitoring is also necessary in the clinostat experiment and neural networks are used for the calibration of lettuce plant growth. A back propagation neural network was trained to evaluate the plant growth in terms of plant growth characteristics, with a network consisting of 4, 8 and 1 processing units in the input, hidden and output layers, respectively. Sixteen sets of training data were used. The training was terminated after 800 times of iterative calculations at the RMS error value equal to 3.35x10-3 . Four sets of validation data were used to calculate the prediction error. The ability of the neural network models to predict the required information is very accurate. As a result, there is potential for the present technique to be applied to seedling vigour evaluating system under the clinostated conditions.

  19. Current focussing in cochlear implants: an analysis of neural recruitment in a computational model.

    Science.gov (United States)

    Kalkman, Randy K; Briaire, Jeroen J; Frijns, Johan H M

    2015-04-01

    Several multipolar current focussing strategies are examined in a computational model of the implanted human cochlea. The model includes a realistic spatial distribution of cell bodies of the auditory neurons throughout Rosenthal's canal. Simulations are performed of monopolar, (partial) tripolar and phased array stimulation. Excitation patterns, estimated thresholds, electrical dynamic range, excitation density and neural recruitment curves are determined and compared. The main findings are: (I) Current focussing requires electrical field interaction to induce spatially restricted excitation patterns. For perimodiolar electrodes the distance to the neurons is too small to have sufficient electrical field interaction, which results in neural excitation near non-centre contacts. (II) Current focussing only produces spatially restricted excitation patterns when there is little or no excitation occurring in the peripheral processes, either because of geometrical factors or due to neural degeneration. (III) The model predicts that neural recruitment with electrical stimulation is a three-dimensional process; regions of excitation not only expand in apical and basal directions, but also by penetrating deeper into the spiral ganglion. (IV) At equal loudness certain differences between the spatial excitation patterns of various multipoles cannot be simulated in a model containing linearly aligned neurons of identical morphology. Introducing a form of variability in the neurons, such as the spatial distribution of cell bodies in the spiral ganglion used in this study, is therefore essential in the modelling of spread of excitation. This article is part of a Special Issue entitled . Copyright © 2014 Elsevier B.V. All rights reserved.

  20. Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing.

    Science.gov (United States)

    van der Velde, Frank

    2016-01-01

    In situ concept-based computing is based on the notion that conceptual representations in the human brain are "in situ." In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain areas. In situ concepts representations cannot be copied or duplicated because that will disrupt their connection structure, and thus the meaning of these concepts. Higher-level cognitive processes, as found in language and reasoning, can be performed with in situ concepts by embedding them in specialized neurally inspired "blackboards." The interactions between the in situ concepts and the blackboards form the basis for in situ concept computing architectures. In these architectures, memory (concepts) and processing are interwoven, in contrast with the separation between memory and processing found in Von Neumann architectures. Because the further development of Von Neumann computing (more, faster, yet power limited) is questionable, in situ concept computing might be an alternative for concept-based computing. In situ concept computing will be illustrated with a recently developed BABI reasoning task. Neurorobotics can play an important role in the development of in situ concept computing because of the development of in situ concept representations derived in scenarios as needed for reasoning tasks. Neurorobotics would also benefit from power limited and in situ concept computing.

  1. The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety

    Directory of Open Access Journals (Sweden)

    Sylvia A. Morelli

    2013-05-01

    Full Text Available Although many studies have examined the neural basis of experiencing empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, while instructed to empathize, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; septal area, SA. Two key regions – the ventral AI and SA – were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching versus empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others’ emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy (DMPFC, MPFC, TPJ, amygdala and social cognition. The current results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions.

  2. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

    Science.gov (United States)

    Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

    2016-01-01

    Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

  3. Biomaterials and computation: a strategic alliance to investigate emergent responses of neural cells.

    Science.gov (United States)

    Sergi, Pier Nicola; Cavalcanti-Adam, Elisabetta Ada

    2017-03-28

    Topographical and chemical cues drive migration, outgrowth and regeneration of neurons in different and crucial biological conditions. In the natural extracellular matrix, their influences are so closely coupled that they result in complex cellular responses. As a consequence, engineered biomaterials are widely used to simplify in vitro conditions, disentangling intricate in vivo behaviours, and narrowing the investigation on particular emergent responses. Nevertheless, how topographical and chemical cues affect the emergent response of neural cells is still unclear, thus in silico models are used as additional tools to reproduce and investigate the interactions between cells and engineered biomaterials. This work aims at presenting the synergistic use of biomaterials-based experiments and computation as a strategic way to promote the discovering of complex neural responses as well as to allow the interactions between cells and biomaterials to be quantitatively investigated, fostering a rational design of experiments.

  4. Neural disparity computation from IKONOS stereo imagery in the presence of occlusions

    Science.gov (United States)

    Binaghi, E.; Gallo, I.; Baraldi, A.; Gerhardinger, A.

    2006-09-01

    In computer vision, stereoscopic image analysis is a well-known technique capable of extracting the third (vertical) dimension. Starting from this knowledge, the Remote Sensing (RS) community has spent increasing efforts on the exploitation of Ikonos one-meter resolution stereo imagery for high accuracy 3D surface modelling and elevation data extraction. In previous works our team investigated the potential of neural adaptive learning to solve the correspondence problem in the presence of occlusions. In this paper we present an experimental evaluation of an improved version of the neural based stereo matching method when applied to Ikonos one-meter resolution stereo images affected by occlusion problems. Disparity maps generated with the proposed approach are compared with those obtained by an alternative stereo matching algorithm implemented in a (non-)commercial image processing software toolbox. To compare competing disparity maps, quality metrics recommended by the evaluation methodology proposed by Scharstein and Szelinski (2002, IJCV, 47, 7-42) are adopted.

  5. The prediction in computer color matching of dentistry based on GA+BP neural network.

    Science.gov (United States)

    Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang

    2015-01-01

    Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

  6. Computation of reactor control rod drop time under accident conditions

    International Nuclear Information System (INIS)

    Dou Yikang; Yao Weida; Yang Renan; Jiang Nanyan

    1998-01-01

    The computational method of reactor control rod drop time under accident conditions lies mainly in establishing forced vibration equations for the components under action of outside forces on control rod driven line and motion equation for the control rod moving in vertical direction. The above two kinds of equations are connected by considering the impact effects between control rod and its outside components. Finite difference method is adopted to make discretization of the vibration equations and Wilson-θ method is applied to deal with the time history problem. The non-linearity caused by impact is iteratively treated with modified Newton method. Some experimental results are used to validate the validity and reliability of the computational method. Theoretical and experimental testing problems show that the computer program based on the computational method is applicable and reliable. The program can act as an effective tool of design by analysis and safety analysis for the relevant components

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

    Science.gov (United States)

    Pawliczek, Christina M; Derntl, Birgit; Kellermann, Thilo; Gur, Ruben C; Schneider, Frank; Habel, Ute

    2013-01-01

    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.

  8. 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.

  9. Anger under Control: Neural Correlates of Frustration as a Function of Trait Aggression

    Science.gov (United States)

    Pawliczek, Christina M.; Derntl, Birgit; Kellermann, Thilo; Gur, Ruben C.; Schneider, Frank; Habel, Ute

    2013-01-01

    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. PMID:24205247

  10. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Black, Michael J.

    2008-12-01

    Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

  11. Complexity, chaos and human physiology: the justification for non-linear neural computational analysis.

    Science.gov (United States)

    Baxt, W G

    1994-03-15

    Background is presented to suggest that a great many biologic processes are chaotic. It is well known that chaotic processes can be accurately characterized by non-linear technologies. Evidence is presented that an artificial neural network, which is a known method for the application of non-linear statistics, is able to perform more accurately in identifying patients with and without myocardial infarction than either physicians or other computer paradigms. It is suggested that the improved performance may be due to the network's better ability to characterize what is a chaotic process imbedded in the problem of the clinical diagnosis of this entity.

  12. Binary Factorization in Hopfield-Like Neural Networks: Single-Step Approximation and Computer Simulations

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Sirota, A.M.; Húsek, Dušan; Muraviev, I. P.

    2004-01-01

    Roč. 14, č. 2 (2004), s. 139-152 ISSN 1210-0552 R&D Projects: GA ČR GA201/01/1192 Grant - others:BARRANDE(EU) 99010-2/99053; Intellectual computer Systems(EU) Grant 2.45 Institutional research plan: CEZ:AV0Z1030915 Keywords : nonlinear binary factor analysis * feature extraction * recurrent neural network * Single-Step approximation * neurodynamics simulation * attraction basins * Hebbian learning * unsupervised learning * neuroscience * brain function modeling Subject RIV: BA - General Mathematics

  13. Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe-Based Neural Network Computing

    Science.gov (United States)

    Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko

    In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.

  14. Dimensions of childhood adversity have distinct associations with neural systems underlying executive functioning.

    Science.gov (United States)

    Sheridan, Margaret A; Peverill, Matthew; Finn, Amy S; McLaughlin, Katie A

    2017-12-01

    Childhood adversity is associated with increased risk for psychopathology. Neurodevelopmental pathways underlying this risk remain poorly understood. A recent conceptual model posits that childhood adversity can be deconstructed into at least two underlying dimensions, deprivation and threat, that are associated with distinct neurocognitive consequences. This model argues that deprivation (i.e., a lack of cognitive stimulation and learning opportunities) is associated with poor executive function (EF), whereas threat is not. We examine this hypothesis in two studies measuring EF at multiple levels: performance on EF tasks, neural recruitment during EF, and problems with EF in daily life. In Study 1, deprivation (low parental education and child neglect) was associated with greater parent-reported problems with EF in adolescents (N = 169; 13-17 years) after adjustment for levels of threat (community violence and abuse), which were unrelated to EF. In Study 2, low parental education was associated with poor working memory (WM) performance and inefficient neural recruitment in the parietal and prefrontal cortex during high WM load among adolescents (N = 51, 13-20 years) after adjusting for abuse, which was unrelated to WM task performance and neural recruitment during WM. These findings constitute strong preliminary evidence for a novel model of the neurodevelopmental consequences of childhood adversity.

  15. 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.

  16. Neural Computation via Neural Geometry: A Place Code for Inter-whisker Timing in the Barrel Cortex?

    Science.gov (United States)

    Wilson, Stuart P.; Bednar, James A.; Prescott, Tony J.; Mitchinson, Ben

    2011-01-01

    The place theory proposed by Jeffress (1948) is still the dominant model of how the brain represents the movement of sensory stimuli between sensory receptors. According to the place theory, delays in signalling between neurons, dependent on the distances between them, compensate for time differences in the stimulation of sensory receptors. Hence the location of neurons, activated by the coincident arrival of multiple signals, reports the stimulus movement velocity. Despite its generality, most evidence for the place theory has been provided by studies of the auditory system of auditory specialists like the barn owl, but in the study of mammalian auditory systems the evidence is inconclusive. We ask to what extent the somatosensory systems of tactile specialists like rats and mice use distance dependent delays between neurons to compute the motion of tactile stimuli between the facial whiskers (or ‘vibrissae’). We present a model in which synaptic inputs evoked by whisker deflections arrive at neurons in layer 2/3 (L2/3) somatosensory ‘barrel’ cortex at different times. The timing of synaptic inputs to each neuron depends on its location relative to sources of input in layer 4 (L4) that represent stimulation of each whisker. Constrained by the geometry and timing of projections from L4 to L2/3, the model can account for a range of experimentally measured responses to two-whisker stimuli. Consistent with that data, responses of model neurons located between the barrels to paired stimulation of two whiskers are greater than the sum of the responses to either whisker input alone. The model predicts that for neurons located closer to either barrel these supralinear responses are tuned for longer inter-whisker stimulation intervals, yielding a topographic map for the inter-whisker deflection interval across the surface of L2/3. This map constitutes a neural place code for the relative timing of sensory stimuli. PMID:22022245

  17. Neural computation via neural geometry: a place code for inter-whisker timing in the barrel cortex?

    Directory of Open Access Journals (Sweden)

    Stuart P Wilson

    2011-10-01

    Full Text Available The place theory proposed by Jeffress (1948 is still the dominant model of how the brain represents the movement of sensory stimuli between sensory receptors. According to the place theory, delays in signalling between neurons, dependent on the distances between them, compensate for time differences in the stimulation of sensory receptors. Hence the location of neurons, activated by the coincident arrival of multiple signals, reports the stimulus movement velocity. Despite its generality, most evidence for the place theory has been provided by studies of the auditory system of auditory specialists like the barn owl, but in the study of mammalian auditory systems the evidence is inconclusive. We ask to what extent the somatosensory systems of tactile specialists like rats and mice use distance dependent delays between neurons to compute the motion of tactile stimuli between the facial whiskers (or 'vibrissae'. We present a model in which synaptic inputs evoked by whisker deflections arrive at neurons in layer 2/3 (L2/3 somatosensory 'barrel' cortex at different times. The timing of synaptic inputs to each neuron depends on its location relative to sources of input in layer 4 (L4 that represent stimulation of each whisker. Constrained by the geometry and timing of projections from L4 to L2/3, the model can account for a range of experimentally measured responses to two-whisker stimuli. Consistent with that data, responses of model neurons located between the barrels to paired stimulation of two whiskers are greater than the sum of the responses to either whisker input alone. The model predicts that for neurons located closer to either barrel these supralinear responses are tuned for longer inter-whisker stimulation intervals, yielding a topographic map for the inter-whisker deflection interval across the surface of L2/3. This map constitutes a neural place code for the relative timing of sensory stimuli.

  18. Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

    Science.gov (United States)

    Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

    2017-11-01

    Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

  19. Computer vision system for egg volume prediction using backpropagation neural network

    Science.gov (United States)

    Siswantoro, J.; Hilman, M. Y.; Widiasri, M.

    2017-11-01

    Volume is one of considered aspects in egg sorting process. A rapid and accurate volume measurement method is needed to develop an egg sorting system. Computer vision system (CVS) provides a promising solution for volume measurement problem. Artificial neural network (ANN) has been used to predict the volume of egg in several CVSs. However, volume prediction from ANN could have less accuracy due to inappropriate input features or inappropriate ANN structure. This paper proposes a CVS for predicting the volume of egg using ANN. The CVS acquired an image of egg from top view and then processed the image to extract its 1D and 2 D size features. The features were used as input for ANN in predicting the volume of egg. The experiment results show that the proposed CSV can predict the volume of egg with a good accuracy and less computation time.

  20. Efficient shortest-path-tree computation in network routing based on pulse-coupled neural networks.

    Science.gov (United States)

    Qu, Hong; Yi, Zhang; Yang, Simon X

    2013-06-01

    Shortest path tree (SPT) computation is a critical issue for routers using link-state routing protocols, such as the most commonly used open shortest path first and intermediate system to intermediate system. Each router needs to recompute a new SPT rooted from itself whenever a change happens in the link state. Most commercial routers do this computation by deleting the current SPT and building a new one using static algorithms such as the Dijkstra algorithm at the beginning. Such recomputation of an entire SPT is inefficient, which may consume a considerable amount of CPU time and result in a time delay in the network. Some dynamic updating methods using the information in the updated SPT have been proposed in recent years. However, there are still many limitations in those dynamic algorithms. In this paper, a new modified model of pulse-coupled neural networks (M-PCNNs) is proposed for the SPT computation. It is rigorously proved that the proposed model is capable of solving some optimization problems, such as the SPT. A static algorithm is proposed based on the M-PCNNs to compute the SPT efficiently for large-scale problems. In addition, a dynamic algorithm that makes use of the structure of the previously computed SPT is proposed, which significantly improves the efficiency of the algorithm. Simulation results demonstrate the effective and efficient performance of the proposed approach.

  1. Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion

    Science.gov (United States)

    Shimoda, Shingo; Kimura, Hidenori

    The growing need for controlling complex behaviors of versatile robots working in unpredictable environment has revealed the fundamental limitation of model-based control strategy that requires precise models of robots and environments before their operations. This difficulty is fundamental and has the same root with the well-known frame problem in artificial intelligence. It has been a central long standing issue in advanced robotics, as well as machine intelligence, to find a prospective clue to attack this fundamental difficulty. The general consensus shared by many leading researchers in the related field is that the body plays an important role in acquiring intelligence that can conquer unknowns. In particular, purposeful behaviors emerge during body-environment interactions with the help of an appropriately organized neural computational scheme that can exploit what the environment can afford. Along this line, we propose a new scheme of neural computation based on compound control which represents a typical feature of biological controls. This scheme is based on classical neuron models with local rules that can create macroscopic purposeful behaviors. This scheme is applied to a bipedal robot and generates the rhythm of walking without any model of robot dynamics and environments.

  2. Quantum neural network-based EEG filtering for a brain-computer interface.

    Science.gov (United States)

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  3. Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

    Science.gov (United States)

    Dai, Yonghui; Han, Dongmei; Dai, Weihui

    2014-01-01

    The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659

  4. Efficient computation of root mean square deviations under rigid transformations.

    Science.gov (United States)

    Hildebrandt, Anna K; Dietzen, Matthias; Lengauer, Thomas; Lenhof, Hans-Peter; Althaus, Ernst; Hildebrandt, Andreas

    2014-04-15

    The computation of root mean square deviations (RMSD) is an important step in many bioinformatics applications. If approached naively, each RMSD computation takes time linear in the number of atoms. In addition, a careful implementation is required to achieve numerical stability, which further increases runtimes. In practice, the structural variations under consideration are often induced by rigid transformations of the protein, or are at least dominated by a rigid component. In this work, we show how RMSD values resulting from rigid transformations can be computed in constant time from the protein's covariance matrix, which can be precomputed in linear time. As a typical application scenario is protein clustering, we will also show how the Ward-distance which is popular in this field can be reduced to RMSD evaluations, yielding a constant time approach for their computation. Copyright © 2014 Wiley Periodicals, Inc.

  5. 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.

  6. 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.

  7. 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.

  8. Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing

    Science.gov (United States)

    Sengupta, Abhronil; Roy, Kaushik

    2017-12-01

    Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform every day. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.

  9. Neural network remodeling underlying motor map reorganization induced by rehabilitative training after ischemic stroke.

    Science.gov (United States)

    Okabe, Naohiko; Shiromoto, Takashi; Himi, Naoyuki; Lu, Feng; Maruyama-Nakamura, Emi; Narita, Kazuhiko; Iwachidou, Nobuhisa; Yagita, Yoshiki; Miyamoto, Osamu

    2016-12-17

    Motor map reorganization is believed to be one mechanism underlying rehabilitation-induced functional recovery. Although the ipsilesional secondary motor area has been known to reorganize motor maps and contribute to rehabilitation-induced functional recovery, it is unknown how the secondary motor area is reorganized by rehabilitative training. In the present study, using skilled forelimb reaching tasks, we investigated neural network remodeling in the rat rostral forelimb area (RFA) of the secondary motor area during 4weeks of rehabilitative training. Following photothrombotic stroke in the caudal forelimb area (CFA), rehabilitative training led to task-specific recovery and motor map reorganization in the RFA. A second injury to the RFA resulted in reappearance of motor deficits. Further, when both the CFA and RFA were destroyed simultaneously, rehabilitative training no longer improved task-specific recovery. In neural tracer studies, although rehabilitative training did not alter neural projection to the RFA from other brain areas, rehabilitative training increased neural projection from the RFA to the lower spinal cord, which innervates the muscles in the forelimb. Double retrograde tracer studies revealed that rehabilitative training increased the neurons projecting from the RFA to both the upper cervical cord, which innervates the muscles in the neck, trunk, and part of the proximal forelimb, and the lower cervical cord. These results suggest that neurons projecting to the upper cervical cord provide new connections to the denervated forelimb area of the spinal cord, and these new connections may contribute to rehabilitation-induced task-specific recovery and motor map reorganization in the secondary motor area. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Stephanie eLefebvre

    2015-06-01

    Full Text Available 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 (-0.160.25 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<0.05. These data suggest a neuroplastic compensatory reorganization of brain activity underlying the first stages of motor skill learning with the paretic upper limb in chronic hemiparetic stroke patients, with a key role of

  11. At-risk for pathological gambling: imaging neural reward processing under chronic dopamine agonists.

    Science.gov (United States)

    Abler, Birgit; Hahlbrock, Roman; Unrath, Alexander; Grön, Georg; Kassubek, Jan

    2009-09-01

    Treatment with dopamine receptor agonists has been associated with impulse control disorders and pathological gambling (PG) secondary to medication in previously unaffected patients with Parkinson's disease or restless legs syndrome (RLS). In a within-subjects design, we investigated the underlying neurobiology in RLS patients using functional magnetic resonance imaging. We scanned 12 female RLS patients without a history of PG. All patients were scanned twice: once whilst taking their regular medication with low dose dopamine receptor agonists and once after a washout phase interval. They performed an established gambling game task involving expectation and receipt or omission of monetary rewards at different levels of probabilities. Upon expectation of rewards, reliable ventral striatal activation was detected only when patients were on, but not when patients were off medication. Upon receipt or omission of rewards, the observed ventral striatal signal under medication differed markedly from its predicted pattern which by contrast was apparent when patients were off medication. Orbitofrontal activation was not affected by medication. Chronic dopamine receptor agonist medication changed the neural signalling of reward expectation predisposing the dopaminergic reward system to mediate an increased appetitive drive. Even without manifest PG, chronic medication with dopamine receptor agonists led to markedly changed neural processing of negative consequences probably mediating dysfunctional learning of contingencies. Intact orbitofrontal functioning, potentially moderating impulse control, may explain why none of the patients actually developed PG. Our results support the notion of a general medication effect in patients under dopamine receptor agonists in terms of a sensitization towards impulse control disorders.

  12. 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, TiO 2 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 TiO 2 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.

  13. Design of a computation tool for neutron spectrometry and dosimetry through evolutionary neural networks

    International Nuclear Information System (INIS)

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

    2009-10-01

    The neutron dosimetry is one of the most complicated tasks of radiation protection, due to it is a complex technique and highly dependent of neutron energy. One of the first devices used to perform neutron spectrometry is the system known as spectrometric system of Bonner spheres, that continuous being one of spectrometers most commonly used. This system has disadvantages such as: the components weight, the low resolution of spectrum, long and drawn out procedure for the spectra reconstruction, which require an expert user in system management, the need of use a reconstruction code as BUNKIE, SAND, etc., which are based on an iterative reconstruction algorithm and whose greatest inconvenience is that for the spectrum reconstruction, are needed to provide to system and initial spectrum as close as possible to the desired spectrum get. Consequently, researchers have mentioned the need to developed alternative measurement techniques to improve existing monitoring systems for workers. Among these alternative techniques have been reported several reconstruction procedures based on artificial intelligence techniques such as genetic algorithms, artificial neural networks and hybrid systems of evolutionary artificial neural networks using genetic algorithms. However, the use of these techniques in the nuclear science area is not free of problems, so it has been suggested that more research is conducted in such a way as to solve these disadvantages. Because they are emerging technologies, there are no tools for the results analysis, so in this paper we present first the design of a computation tool that allow to analyze the neutron spectra and equivalent doses, obtained through the hybrid technology of neural networks and genetic algorithms. This tool provides an user graphical environment, friendly, intuitive and easy of operate. The speed of program operation is high, executing the analysis in a few seconds, so it may storage and or print the obtained information for

  14. Algorithmic mechanisms for reliable crowdsourcing computation under collusion.

    Science.gov (United States)

    Fernández Anta, Antonio; Georgiou, Chryssis; Mosteiro, Miguel A; Pareja, Daniel

    2015-01-01

    We consider a computing system where a master processor assigns a task for execution to worker processors that may collude. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a game among workers. That is, we assume that workers are rational in a game-theoretic sense. We identify analytically the parameter conditions for a unique Nash Equilibrium where the master obtains the correct result. We also evaluate experimentally mixed equilibria aiming to attain better reliability-profit trade-offs. For a wide range of parameter values that may be used in practice, our simulations show that, in fact, both master and workers are better off using a pure equilibrium where no worker cheats, even under collusion, and even for colluding behaviors that involve deviating from the game.

  15. Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors

    Science.gov (United States)

    McCullough, KM; Morrison, FG; Ressler, KJ

    2016-01-01

    Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

  16. 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.

  17. Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making

    Science.gov (United States)

    Daniels, Bryan C.; Flack, Jessica C.; Krakauer, David C.

    2017-01-01

    A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a “coding duality” in which there are accumulation and consensus formation processes distinguished by different timescales. PMID:28634436

  18. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

    Science.gov (United States)

    Barton, Alan J; Valdés, Julio J; Orchard, Robert

    2009-01-01

    Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

  19. 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.

  20. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  1. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography.

    Science.gov (United States)

    Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

    2017-03-01

    This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

  2. Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network.

    Science.gov (United States)

    Ghaderi, Forouzan; Ghaderi, Amir H; Ghaderi, Noushin; Najafi, Bijan

    2017-01-01

    Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose.

  3. The Study of Learners' Preference for Visual Complexity on Small Screens of Mobile Computers Using Neural Networks

    Science.gov (United States)

    Wang, Lan-Ting; Lee, Kun-Chou

    2014-01-01

    The vision plays an important role in educational technologies because it can produce and communicate quite important functions in teaching and learning. In this paper, learners' preference for the visual complexity on small screens of mobile computers is studied by neural networks. The visual complexity in this study is divided into five…

  4. Data systems and computer science: Neural networks base R/T program overview

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

    The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

  5. Classification of dried vegetables using computer image analysis and artificial neural networks

    Science.gov (United States)

    Koszela, K.; Łukomski, M.; Mueller, W.; Górna, K.; Okoń, P.; Boniecki, P.; Zaborowicz, M.; Wojcieszak, D.

    2017-07-01

    In the recent years, there has been a continuously increasing demand for vegetables and dried vegetables. This trend affects the growth of the dehydration industry in Poland helping to exploit excess production. More and more often dried vegetables are used in various sectors of the food industry, both due to their high nutritional qualities and changes in consumers' food preferences. As we observe an increase in consumer awareness regarding a healthy lifestyle and a boom in health food, there is also an increase in the consumption of such food, which means that the production and crop area can increase further. Among the dried vegetables, dried carrots play a strategic role due to their wide application range and high nutritional value. They contain high concentrations of carotene and sugar which is present in the form of crystals. Carrots are also the vegetables which are most often subjected to a wide range of dehydration processes; this makes it difficult to perform a reliable qualitative assessment and classification of this dried product. The many qualitative properties of dried carrots determining their positive or negative quality assessment include colour and shape. The aim of the research was to develop and implement the model of a computer system for the recognition and classification of freeze-dried, convection-dried and microwave vacuum dried products using the methods of computer image analysis and artificial neural networks.

  6. Computer vision-based method for classification of wheat grains using artificial neural network.

    Science.gov (United States)

    Sabanci, Kadir; Kayabasi, Ahmet; Toktas, Abdurrahim

    2017-06-01

    A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains from a total of 200 wheat grains. Seven input parameters that are most effective on the classifying results are determined using the correlation-based CfsSubsetEval algorithm to simplify the ANN model. The results of the ANN model are compared in terms of accuracy rate. The best result is achieved with a mean absolute error (MAE) of 9.8 × 10 -6 by the simplified ANN model. This shows that the proposed classifier based on computer vision can be successfully exploited to automatically classify a variety of grains. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

  7. The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking.

    Directory of Open Access Journals (Sweden)

    Jelmer P Borst

    Full Text Available BACKGROUND: It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state bottleneck. METHODOLOGY: In the current study we use the complimentary methodologies of computational cognitive modeling and neuroimaging to investigate the neural correlates of this problem state bottleneck. In particular, an existing computational cognitive model was used to generate a priori fMRI predictions for a multitasking experiment in which the problem state bottleneck plays a major role. Hemodynamic responses were predicted for five brain regions, corresponding to five cognitive resources in the model. Most importantly, we predicted the intraparietal sulcus to show a strong effect of the problem state manipulations. CONCLUSIONS: Some of the predictions were confirmed by a subsequent fMRI experiment, while others were not matched by the data. The experiment supported the hypothesis that the problem state bottleneck is a plausible cause of the interference in the experiment and that it could be located in the intraparietal sulcus.

  8. 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.

  9. 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.

  10. 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.

  11. Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children.

    Science.gov (United States)

    Skeide, Michael A; Kirsten, Holger; Kraft, Indra; Schaadt, Gesa; Müller, Bent; Neef, Nicole; Brauer, Jens; Wilcke, Arndt; Emmrich, Frank; Boltze, Johannes; Friederici, Angela D

    2015-09-01

    Phonological awareness is the best-validated predictor of reading and spelling skill and therefore highly relevant for developmental dyslexia. Prior imaging genetics studies link several dyslexia risk genes to either brain-functional or brain-structural factors of phonological deficits. However, coherent evidence for genetic associations with both functional and structural neural phenotypes underlying variation in phonological awareness has not yet been provided. Here we demonstrate that rs11100040, a reported modifier of SLC2A3, is related to the functional connectivity of left fronto-temporal phonological processing areas at resting state in a sample of 9- to 12-year-old children. Furthermore, we provide evidence that rs11100040 is related to the fractional anisotropy of the arcuate fasciculus, which forms the structural connection between these areas. This structural connectivity phenotype is associated with phonological awareness, which is in turn associated with the individual retrospective risk scores in an early dyslexia screening as well as to spelling. These results suggest a link between a dyslexia risk genotype and a functional as well as a structural neural phenotype, which is associated with a phonological awareness phenotype. The present study goes beyond previous work by integrating genetic, brain-functional and brain-structural aspects of phonological awareness within a single approach. These combined findings might be another step towards a multimodal biomarker for developmental dyslexia. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. 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.

  13. 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.

  14. Limits to high-speed simulations of spiking neural networks using general-purpose computers

    Directory of Open Access Journals (Sweden)

    Friedemann eZenke

    2014-09-01

    Full Text Available To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed towards synaptic plasticity. In particular spike-timing-dependent plasticity (STDP creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

  15. 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

  16. 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

  17. 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.

  18. The manipulative skill: Cognitive devices and their neural correlates underlying Machiavellian's decision making.

    Science.gov (United States)

    Bereczkei, Tamas

    2015-10-01

    Until now, Machiavellianism has mainly been studied in personality and social psychological framework, and little attention has been paid to the underlying cognitive and neural equipment. In light of recent findings, Machiavellian social skills are not limited to emotion regulation and "cold-mindedness" as many authors have recently stated, but linked to specific cognitive abilities. Although Machiavellians appear to have a relatively poor mindreading ability and emotional intelligence, they can efficiently exploit others which is likely to come from their flexible problem solving processes in changing environmental circumstances. The author proposed that Machiavellians have specialized cognitive domains of decision making, such as monitoring others' behavior, task orientation, reward seeking, inhibition of cooperative feelings, and choosing victims. He related the relevant aspects of cognitive functions to their neurological substrates, and argued why they make Machiavellians so successful in interpersonal relationships. Copyright © 2015 Elsevier Inc. All rights reserved.

  19. Neural mechanisms underlying paradoxical performance for monetary incentives are driven by loss aversion.

    Science.gov (United States)

    Chib, Vikram S; De Martino, Benedetto; Shimojo, Shinsuke; O'Doherty, John P

    2012-05-10

    Employers often make payment contingent on performance in order to motivate workers. We used fMRI with a novel incentivized skill task to examine the neural processes underlying behavioral responses to performance-based pay. We found that individuals' performance increased with increasing incentives; however, very high incentive levels led to the paradoxical consequence of worse performance. Between initial incentive presentation and task execution, striatal activity rapidly switched between activation and deactivation in response to increasing incentives. Critically, decrements in performance and striatal deactivations were directly predicted by an independent measure of behavioral loss aversion. These results suggest that incentives associated with successful task performance are initially encoded as a potential gain; however, when actually performing a task, individuals encode the potential loss that would arise from failure. Copyright © 2012 Elsevier Inc. All rights reserved.

  20. PREDICTION OF SITE RESPONSE SPECTRUM UNDER EARTHQUAKE VIBRATION USING AN OPTIMIZED DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    Reza Esmaeilabadi

    2016-06-01

    Full Text Available Site response spectrum is one of the key factors to determine the maximum acceleration and displacement, as well as structure behavior analysis during earthquake vibrations. The main objective of this paper is to develop an optimized model based on artificial neural network (ANN using five different training algorithms to predict nonlinear site response spectrum subjected to Silakhor earthquake vibrations is. The model output was tested for a specified area in west of Iran. The performance and quality of optimized model under all training algorithms have been examined by various statistical, analytical and graph analyses criteria as well as a comparison with numerical methods. The observed adaptabilities in results indicate a feasible and satisfactory engineering alternative method for predicting the analysis of nonlinear site response.

  1. Neural connectivity patterns underlying symbolic number processing indicate mathematical achievement in children.

    Science.gov (United States)

    Park, Joonkoo; Li, Rosa; Brannon, Elizabeth M

    2014-03-01

    In early childhood, humans learn culturally specific symbols for number that allow them entry into the world of complex numerical thinking. Yet little is known about how the brain supports the development of the uniquely human symbolic number system. Here, we use functional magnetic resonance imaging along with an effective connectivity analysis to investigate the neural substrates for symbolic number processing in young children. We hypothesized that, as children solidify the mapping between symbols and underlying magnitudes, important developmental changes occur in the neural communication between the right parietal region, important for the representation of non-symbolic numerical magnitudes, and other brain regions known to be critical for processing numerical symbols. To test this hypothesis, we scanned children between 4 and 6 years of age while they performed a magnitude comparison task with Arabic numerals (numerical, symbolic), dot arrays (numerical, non-symbolic), and lines (non-numerical). We then identified the right parietal seed region that showed greater blood-oxygen-level-dependent signal in the numerical versus the non-numerical conditions. A psychophysiological interaction method was used to find patterns of effective connectivity arising from this parietal seed region specific to symbolic compared to non-symbolic number processing. Two brain regions, the left supramarginal gyrus and the right precentral gyrus, showed significant effective connectivity from the right parietal cortex. Moreover, the degree of this effective connectivity to the left supramarginal gyrus was correlated with age, and the degree of the connectivity to the right precentral gyrus predicted performance on a standardized symbolic math test. These findings suggest that effective connectivity underlying symbolic number processing may be critical as children master the associations between numerical symbols and magnitudes, and that these connectivity patterns may serve as an

  2. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

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

    International Nuclear Information System (INIS)

    Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

    2007-01-01

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

  4. Computation of thermodynamic equilibrium in systems under stress

    Science.gov (United States)

    Vrijmoed, Johannes C.; Podladchikov, Yuri Y.

    2016-04-01

    pressure. To compute a case of high and low pressure around a stressed inclusion we first did a Finite Element Method calculation of a rigid inclusion in a viscous matrix under simple shear. From the computed stress distribution we took the local pressure (mean stress) in each grid point of the FEM calculation. This was used as input thermodynamic pressure in the Gibbs minimization and the result showed it is possible to have an equilibrium situation in which chlorite-amphibole is stable in the low pressure domain and kyanite in the high pressure domain of the stress field around the inclusion. Interestingly, the calculation predicts the redistribution of fluid from an average content of fluid in the system. The fluid in equilibrium tends to accumulate in the low pressure areas whereas it leaves the high pressure areas dry. Transport of fluid components occurs not necessarily by fluid flow, but may happen for example by diffusion. We conclude that an apparent disequilibrium texture may be explained by equilibrium under pressure variations, and apparent fluid addition by redistribution of fluid controlled by the local stress distribution. [1] Mukai et al. (2014), Journal of Petrology, 55 (8), p. 1457-1477. [2] Holland and Powell (1998), Journal of Metamorphic Geology, 16, p. 309-343 [3] Johnson et al. (1992), Computers & Geosciences, 18 (7), p. 899-947 [4] Connolly (2005), Earth and Planetary Science Letters, 236, p. 524-541

  5. Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach.

    Science.gov (United States)

    Dheeba, J; Albert Singh, N; Tamil Selvi, S

    2014-06-01

    Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%. Copyright © 2014. Published by Elsevier Inc.

  6. 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.

  7. Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

    Directory of Open Access Journals (Sweden)

    Nafise Erfanian Saeedi

    2016-04-01

    Full Text Available Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

  8. Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

    Science.gov (United States)

    Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

    2018-03-15

    Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

  9. Abstract Computation in Schizophrenia Detection through Artificial Neural Network Based Systems

    Directory of Open Access Journals (Sweden)

    L. Cardoso

    2015-01-01

    Full Text Available Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason of defective information.

  10. Abstract computation in schizophrenia detection through artificial neural network based systems.

    Science.gov (United States)

    Cardoso, L; Marins, F; Magalhães, R; Marins, N; Oliveira, T; Vicente, H; Abelha, A; Machado, J; Neves, J

    2015-01-01

    Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

  11. Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis

    Science.gov (United States)

    Lowe, Robert; Almér, Alexander; Lindblad, Gustaf; Gander, Pierre; Michael, John; Vesper, Cordula

    2016-01-01

    Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process (ATP) theory that entails the classification of external stimuli according to outcome expectancies. This approach has been used to describe animal and human action that concerns differential outcome expectancies. Until now it has not been applied to social interaction. We describe our Affective ATP model as applied to social learning consistent with an “extended common currency” perspective in the social neuroscience literature. We contrast this to an alternative mechanism that provides an example implementation of the so-called social-specific value perspective. In brief, our Social-Affective ATP mechanism builds upon established formalisms for reinforcement learning (temporal difference learning models) nuanced to accommodate expectations (consistent with ATP theory) and extended to integrate non-social and social cues for use in Joint Action. PMID:27601989

  12. A multitarget training method for artificial neural network with application to computer-aided diagnosis.

    Science.gov (United States)

    Liu, Bei; Jiang, Yulei

    2013-01-01

    The authors propose a new training method for artificial neural networks (ANNs) in two-class classification tasks such as classifying breast lesions on a mammogram as malignant or benign. Whereas the conventional binary training method uses binary training target values based on the diagnostic truth of a lesion being malignant or benign, the authors use multiple training target values based on more detailed histological diagnosis that presumably are related to the posterior probability of a lesion being malignant. The authors performed Monte Carlo simulation studies in which training target values were assigned based on posterior probability, and they also performed a mammography study in which training target values were assigned according to histological subtypes. These studies showed that the multitarget training method produced less variability in the ANN outputs than the binary training method. The simulation studies also showed that except for when the number of training cases was extremely large, the multitarget training method produced improved overall classification performance over the binary training method. Therefore, the multitarget ANN training method is potentially useful for ANN applications in computer-aided diagnosis of breast cancer.

  13. A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

    2017-01-24

    This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

  14. Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform

    Science.gov (United States)

    Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B.

    2016-01-01

    Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks. PMID:26909015

  15. Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders.

    Science.gov (United States)

    Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer

    2017-07-28

    The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.

  16. Computational modeling for hexcan failure under core distruptive accidental conditions

    Energy Technology Data Exchange (ETDEWEB)

    Sawada, T.; Ninokata, H.; Shimizu, A. [Tokyo Institute of Technology (Japan)

    1995-09-01

    This paper describes the development of computational modeling for hexcan wall failures under core disruptive accident conditions of fast breeder reactors. A series of out-of-pile experiments named SIMBATH has been analyzed by using the SIMMER-II code. The SIMBATH experiments were performed at KfK in Germany. The experiments used a thermite mixture to simulate fuel. The test geometry of SIMBATH ranged from single pin to 37-pin bundles. In this study, phenomena of hexcan wall failure found in a SIMBATH test were analyzed by SIMMER-II. Although the original model of SIMMER-II did not calculate any hexcan failure, several simple modifications made it possible to reproduce the hexcan wall melt-through observed in the experiment. In this paper the modifications and their significance are discussed for further modeling improvements.

  17. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

    Science.gov (United States)

    Liu, Jiamin; Wang, David; Lu, Le; Wei, Zhuoshi; Kim, Lauren; Turkbey, Evrim B; Sahiner, Berkman; Petrick, Nicholas A; Summers, Ronald M

    2017-09-01

    Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and a support vector machine (SVM) classifier for patient-level colitis diagnosis on routine abdominal CT scans. The recently developed Faster Region-based Convolutional Neural Network (Faster RCNN) is utilized for lesion-level colitis detection. For each 2D slice, rectangular region proposals are generated by region proposal networks (RPN). Then, each region proposal is jointly classified and refined by a softmax classifier and bounding-box regressor. Two convolutional neural networks, eight layers of ZF net and 16 layers of VGG net are compared for colitis detection. Finally, for each patient, the detections on all 2D slices are collected and a SVM classifier is applied to develop a patient-level diagnosis. We trained and evaluated our method with 80 colitis patients and 80 normal cases using 4 × 4-fold cross validation. For lesion-level colitis detection, with ZF net, the mean of average precisions (mAP) were 48.7% and 50.9% for RCNN and Faster RCNN, respectively. The detection system achieved sensitivities of 51.4% and 54.0% at two false positives per patient for RCNN and Faster RCNN, respectively. With VGG net, Faster RCNN increased the mAP to 56.9% and increased the sensitivity to 58.4% at two false positive per patient. For patient-level colitis diagnosis, with ZF net, the average areas under the ROC curve (AUC) were 0.978 ± 0.009 and 0.984 ± 0.008 for RCNN and Faster RCNN method, respectively. The difference was not statistically significant with P = 0.18. At the optimal operating point, the RCNN method correctly identified 90.4% (72.3/80) of the colitis patients and 94.0% (75.2/80) of normal cases. The sensitivity improved to 91.6% (73.3/80) and the specificity improved to 95.0% (76.0/80) for the Faster RCNN

  18. Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

    DEFF Research Database (Denmark)

    Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

    2004-01-01

    to a battery of parametric and non-parametric test statistics to measure their performance in one- and four-step ahead forecasts of quarterly data. Using genetic-neural fuzzy systems we find the computational approach superior to some degree and show how to combine both techniques successfully.......This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

  19. Selection of an optimal neural network architecture for computer-aided detection of microcalcifications - Comparison of automated optimization techniques

    International Nuclear Information System (INIS)

    Gurcan, Metin N.; Sahiner, Berkman; Chan Heangping; Hadjiiski, Lubomir; Petrick, Nicholas

    2001-01-01

    Many computer-aided diagnosis (CAD) systems use neural networks (NNs) for either detection or classification of abnormalities. Currently, most NNs are 'optimized' by manual search in a very limited parameter space. In this work, we evaluated the use of automated optimization methods for selecting an optimal convolution neural network (CNN) architecture. Three automated methods, the steepest descent (SD), the simulated annealing (SA), and the genetic algorithm (GA), were compared. We used as an example the CNN that classifies true and false microcalcifications detected on digitized mammograms by a prescreening algorithm. Four parameters of the CNN architecture were considered for optimization, the numbers of node groups and the filter kernel sizes in the first and second hidden layers, resulting in a search space of 432 possible architectures. The area A z under the receiver operating characteristic (ROC) curve was used to design a cost function. The SA experiments were conducted with four different annealing schedules. Three different parent selection methods were compared for the GA experiments. An available data set was split into two groups with approximately equal number of samples. By using the two groups alternately for training and testing, two different cost surfaces were evaluated. For the first cost surface, the SD method was trapped in a local minimum 91% (392/432) of the time. The SA using the Boltzman schedule selected the best architecture after evaluating, on average, 167 architectures. The GA achieved its best performance with linearly scaled roulette-wheel parent selection; however, it evaluated 391 different architectures, on average, to find the best one. The second cost surface contained no local minimum. For this surface, a simple SD algorithm could quickly find the global minimum, but the SA with the very fast reannealing schedule was still the most efficient. The same SA scheme, however, was trapped in a local minimum on the first cost

  20. Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice.

    Directory of Open Access Journals (Sweden)

    Sebastian Gluth

    2013-10-01

    Full Text Available Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose. Sequential sampling models (SSMs have greatly advanced the decision sciences by assuming decisions to emerge from a bounded evidence accumulation process so that response times (RTs become predictable. Here, we demonstrate a difficulty of SSMs that occurs when people are not forced to respond at once but are allowed to sample information sequentially: The decision maker might decide to delay the choice and terminate the accumulation process temporarily, a scenario not accounted for by the standard SSM approach. We developed several SSMs for predicting RTs from two independent samples of an electroencephalography (EEG and a functional magnetic resonance imaging (fMRI study. In these studies, participants bought or rejected fictitious stocks based on sequentially presented cues and were free to respond at any time. Standard SSM implementations did not describe RT distributions adequately. However, by adding a mechanism for postponing decisions to the model we obtained an accurate fit to the data. Time-frequency analysis of EEG data revealed alternating states of de- and increasing oscillatory power in beta-band frequencies (14-30 Hz, indicating that responses were repeatedly prepared and inhibited and thus lending further support for the existence of a decision not to decide. Finally, the extended model accounted for the results of an adapted version of our paradigm in which participants had to press a button for sampling more information. Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.

  1. The insula: a critical neural substrate for craving and drug seeking under conflict and risk

    Science.gov (United States)

    Naqvi, Nasir H.; Gaznick, Natassia; Tranel, Daniel; Bechara, Antoine

    2014-01-01

    Drug addiction is characterized by the inability to control drug use when it results in negative consequences or conflicts with more adaptive goals. Our previous work showed that damage to the insula disrupted addiction to cigarette smoking—the first time that the insula was shown to be a critical neural substrate for addiction. Here, we review those findings, as well as more recent studies that corroborate and extend them, demonstrating the role of the insula in (1) incentive motivational processes that drive addictive behavior, (2) control processes that moderate or inhibit addictive behavior, and (3) interoceptive processes that represent bodily states associated with drug use. We then describe a theoretical framework that attempts to integrate these seemingly disparate findings. In this framework, the insula functions in the recall of interoceptive drug effects during craving and drug seeking under specific conditions where drug taking is perceived as risky and/or where there is conflict between drug taking and more adaptive goals. We describe this framework in an evolutionary context and discuss its implications for understanding the mechanisms of behavior change in addiction treatments. PMID:24690001

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    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

    Directory of Open Access Journals (Sweden)

    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. The insula: a critical neural substrate for craving and drug seeking under conflict and risk.

    Science.gov (United States)

    Naqvi, Nasir H; Gaznick, Natassia; Tranel, Daniel; Bechara, Antoine

    2014-05-01

    Drug addiction is characterized by the inability to control drug use when it results in negative consequences or conflicts with more adaptive goals. Our previous work showed that damage to the insula disrupted addiction to cigarette smoking-the first time that the insula was shown to be a critical neural substrate for addiction. Here, we review those findings, as well as more recent studies that corroborate and extend them, demonstrating the role of the insula in (1) incentive motivational processes that drive addictive behavior, (2) control processes that moderate or inhibit addictive behavior, and (3) interoceptive processes that represent bodily states associated with drug use. We then describe a theoretical framework that attempts to integrate these seemingly disparate findings. In this framework, the insula functions in the recall of interoceptive drug effects during craving and drug seeking under specific conditions where drug taking is perceived as risky and/or where there is conflict between drug taking and more adaptive goals. We describe this framework in an evolutionary context and discuss its implications for understanding the mechanisms of behavior change in addiction treatments. © 2014 New York Academy of Sciences.

  6. Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

    Science.gov (United States)

    Song, Chen; Zhong-Cheng, Wu; Hong, Lv

    2018-03-01

    Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

  7. Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

    International Nuclear Information System (INIS)

    Jerebko, Anna K.; Summers, Ronald M.; Malley, James D.; Franaszek, Marek; Johnson, C. Daniel

    2003-01-01

    Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study

  8. 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.

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

    Science.gov (United States)

    Ninaus, Manuel; Kober, Silvia E; Witte, Matthias; Koschutnig, Karl; Stangl, Matthias; Neuper, Christa; Wood, Guilherme

    2013-01-01

    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 areas 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.

  10. Unified neural field theory of brain dynamics underlying oscillations in Parkinson's disease and generalized epilepsies.

    Science.gov (United States)

    Müller, E J; van Albada, S J; Kim, J W; Robinson, P A

    2017-09-07

    The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are explored in parallel via a physiologically-based neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in the modulation of oscillatory dynamics of the corticothalamic (CT) system and vice versa are analyzed. In addition to normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with the characteristic frequencies observed in PD. These rhythms result from resonances in loops formed between the BG and CT populations, analogous to those that underlie epileptic oscillations in a previous CT model, and which are still present in the combined CTBG system. Dopamine depletion is argued to weaken the dampening of these loop resonances in PD, and network connections then explain the significant coherence observed between BG, thalamic, and cortical population activity around 4-8 Hz and 20 Hz. Parallels between the afferent and efferent connection sites of the thalamic reticular nucleus (TRN) and BG predict low dopamine to correspond to a reduced likelihood of tonic-clonic (grand mal) seizures, which agrees with experimental findings. Furthermore, the model predicts an increased likelihood of absence (petit mal) seizure resulting from pathologically low dopamine levels in accordance with experimental observations. Suppression of absence seizure activity is demonstrated when afferent and efferent BG connections to the CT system are strengthened, which is consistent with other CTBG modeling studies. The BG are demonstrated to have a suppressive effect on activity of the CTBG system near tonic-clonic seizure states, which provides insight into the reported efficacy of current treatments in BG circuits. Sleep states of the TRN are also found to suppress pathological PD activity in accordance with

  11. A thesis on the application of neural network computing to the constrained flight control allocation problem

    OpenAIRE

    Grogan, Robert L.

    1994-01-01

    The feasibility of utilizing a neural network to solve the constrained flight control allocation problem is investigated for the purposes of developing guidelines for the selection of a neural network structure as a function of the control allocation problem parameters. The control allocation problem of finding the combination of several flight controls that generate a desired body axis moment without violating any control constraint is considered. Since the number of controls,...

  12. Engineering Applications of Neural Computing: A State-of-the-Art Survey

    Science.gov (United States)

    1991-05-01

    results of the neural networks and compare with doctor’s knowledge. In veterinary medicine, a system for diag- nosis of mastitis in dairy cows is...takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency as one of the cells firing B...construction of a hybrid system for diagnosing mastitis in COWS . Perhaps the most noteworthy application is the neural network-based explosive

  13. A neutron spectrum unfolding computer code based on artificial neural networks

    International Nuclear Information System (INIS)

    Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

    2014-01-01

    The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, 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 Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(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, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

  14. A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Jim Harkin

    2009-01-01

    Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

  15. Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

    Science.gov (United States)

    Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

    1998-01-01

    The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

  16. Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

    Science.gov (United States)

    Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

    2012-01-01

    The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

  17. Under-reporting of osteoporotic vertebral fractures on computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Williams, Alexandra L. [Department of Radiology, Manchester Royal Infirmary, Oxford Road, Manchester (United Kingdom)], E-mail: alexandra.firth@virgin.net; Al-Busaidi, Aisha [Department of Radiology, Manchester Royal Infirmary, Oxford Road, Manchester (United Kingdom)], E-mail: albusaidi@doctors.org.uk; Sparrow, Patrick J. [Department of Radiology, Manchester Royal Infirmary, Oxford Road, Manchester (United Kingdom)], E-mail: patsparrow@doctors.org.uk; Adams, Judith E. [Department of Radiology, Manchester Royal Infirmary, Oxford Road, Manchester (United Kingdom); Department of Clinical Radiology, Imaging Science and Biomedical Engineering, Stopford Building, , University of Manchester, Oxford Road, Manchester M13 9PT (United Kingdom)], E-mail: judith.adams@manchester.ac.uk; Whitehouse, Richard W. [Department of Radiology, Manchester Royal Infirmary, Oxford Road, Manchester (United Kingdom)], E-mail: Richard.Whitehouse@cmmc.nhs.uk

    2009-01-15

    Purpose: Osteoporotic vertebral fractures are frequently asymptomatic. They are often not diagnosed clinically or radiologically. Despite this, prevalent osteoporotic vertebral fractures predict future osteoporotic fractures and are associated with increased mortality and morbidity. Appropriate management of osteoporosis can reduce future fracture risk. Fractures on lateral chest radiographs taken for other conditions are frequently overlooked by radiologists. Our aim was to assess the value of computed tomography (CT) in the diagnosis of vertebral fracture and identify the frequency with which significant fractures are missed. Materials and methods: The thoracic CT scans of 100 consecutive male and 100 consecutive female patients over 55 years were reviewed. CT images were acquired on General Electric Lightspeed multi-detector (MD) CT scanners (16 or 32 row) using 1.25 mm slice thickness. Midline sagittal images were reconstructed from the 3D volume images. The presence of moderate (25-40% height loss) or severe (>40% height loss) vertebral fractures between T1 and L1 was determined using an established semi-quantitative method and confirmed by morphological measurement. Results were compared with the formal CT report. Results: Scans of 192 patients were analysed (95 female; 97 male); mean age 70.1 years. Thirty-eight (19.8%) patients had one or more moderate to severe vertebral fractures. Only 5 (13%) were correctly reported as having osteoporotic fractures in the official report. The sensitivity of axial CT images to vertebral fracture was 0.35. Conclusion: Incidental osteoporotic vertebral fractures are under-reported on CT. The sensitivity of axial images in detecting these fractures is poor. Sagittal reformations are strongly recommended to improve the detection rate.

  18. Computational chemical product design problems under property uncertainties

    DEFF Research Database (Denmark)

    Frutiger, Jerome; Cignitti, Stefano; Abildskov, Jens

    2017-01-01

    Three different strategies of how to combine computational chemical product design with Monte Carlo based methods for uncertainty analysis of chemical properties are outlined. One method consists of a computer-aided molecular design (CAMD) solution and a post-processing property uncertainty...

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

    Science.gov (United States)

    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.

  20. Neural mechanisms underlying migrating motor complex formation in mouse isolated colon

    Science.gov (United States)

    Brierley, Stuart M; Nichols, Kim; Grasby, Dallas J; Waterman, Sally A

    2001-01-01

    Little is known about the intrinsic enteric reflex pathways associated with migrating motor complex (MMC) formation. Acetylcholine (ACh) mediates the rapid component of the MMC, however a non-cholinergic component also exists. The present study investigated the possible role of endogenous tachykinins (TKs) in the formation of colonic MMCs and the relative roles of excitatory and inhibitory pathways.MMCs were recorded from the circular muscle at four sites (proximal, proximal-mid, mid-distal and distal) along the mouse colon using force transducers.The tachykinin (NK1 and NK2) receptor antagonists SR-140 333 (250 nM) and SR-48 968 (250 nM) reduced the amplitude of MMCs at all recording sites, preferentially abolishing the long duration contraction. Residual MMCs were abolished by the subsequent addition of atropine (1 μM).The neuronal nitric oxide synthase inhibitor, Nωnitro-L-arginine (L-NOARG, 100 μM), increased MMC amplitude in the distal region, whilst reducing the amplitude in the proximal region. In preparations where MMCs did not migrate to the distal colon, addition of L-NOARG resulted in the formation of MMCs. Subsequent addition of apamin (250 nM) or suramin (100 μM) further increased MMC amplitude in the distal region, whilst suramin increased MMC amplitude in the mid-distal region. Apamin but not suramin reduced MMC amplitude in the proximal region. Subsequent addition of SR-140 333 and SR-48 968 reduced MMC amplitude at all sites. Residual MMCs were abolished by atropine (1 μM).In conclusion, TKs, ACh, nitric oxide (NO) and ATP are involved in the neural mechanisms underlying the formation of MMCs in the mouse colon. Tachykinins mediate the long duration component of the MMC via NK1 and NK2 receptors. Inhibitory pathways may be involved in determining whether MMCs are formed. PMID:11159701

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

    Directory of Open Access Journals (Sweden)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  3. Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images

    Directory of Open Access Journals (Sweden)

    Kuo Men

    2017-12-01

    Full Text Available BackgroundRadiotherapy is one of the main treatment methods for nasopharyngeal carcinoma (NPC. It requires exact delineation of the nasopharynx gross tumor volume (GTVnx, the metastatic lymph node gross tumor volume (GTVnd, the clinical target volume (CTV, and organs at risk in the planning computed tomography images. However, this task is time-consuming and operator dependent. In the present study, we developed an end-to-end deep deconvolutional neural network (DDNN for segmentation of these targets.MethodsThe proposed DDNN is an end-to-end architecture enabling fast training and testing. It consists of two important components: an encoder network and a decoder network. The encoder network was used to extract the visual features of a medical image and the decoder network was used to recover the original resolution by deploying deconvolution. A total of 230 patients diagnosed with NPC stage I or stage II were included in this study. Data from 184 patients were chosen randomly as a training set to adjust the parameters of DDNN, and the remaining 46 patients were the test set to assess the performance of the model. The Dice similarity coefficient (DSC was used to quantify the segmentation results of the GTVnx, GTVnd, and CTV. In addition, the performance of DDNN was compared with the VGG-16 model.ResultsThe proposed DDNN method outperformed the VGG-16 in all the segmentation. The mean DSC values of DDNN were 80.9% for GTVnx, 62.3% for the GTVnd, and 82.6% for CTV, whereas VGG-16 obtained 72.3, 33.7, and 73.7% for the DSC values, respectively.ConclusionDDNN can be used to segment the GTVnx and CTV accurately. The accuracy for the GTVnd segmentation was relatively low due to the considerable differences in its shape, volume, and location among patients. The accuracy is expected to increase with more training data and combination of MR images. In conclusion, DDNN has the potential to improve the consistency of contouring and streamline radiotherapy

  4. Neural network screening of electromyographic signals as the first phase to design novel human-computer interaction.

    Science.gov (United States)

    Niemenlehto, Pekka-Henrik; Juhola, Martti; Surakka, Veikko

    2005-01-01

    The present aim was to describe the first phase attempts to recognise voluntarily produced changes in electromyographic signals measured from two facial muscles. Thirty subjects voluntarily activated two facial muscles, corrugator supercilii and zygomaticus major. We designed a neural network based recognition system that screened out muscle activations from the electromyographic signals. When several subjects were tested according to the same test protocol, the neural network system was able to correctly recognise more than 95 % of all muscle activations. This is a promising result and we shall next proceed to modify the system for real-time functioning and then design its utilisation for various multimodal human-computer interaction techniques. The subsequent phase in the future will be the interaction backwards: when a computer program first recognised the use of the facial muscles, it will then follow the instructions given by the user. For instance, by using the facial muscles the subject could select or activate objects on the computer screen. This would be one of the opportunities that we develop to help, e.g., disabled persons, who are unable to use their hands.

  5. Neural mechanisms underlying subsequent memory for personal beliefs:An fMRI study.

    Science.gov (United States)

    Wing, Erik A; Iyengar, Vijeth; Hess, Thomas M; LaBar, Kevin S; Huettel, Scott A; Cabeza, Roberto

    2018-04-01

    Many fMRI studies have examined the neural mechanisms supporting emotional memory for stimuli that generate emotion rather automatically (e.g., a picture of a dangerous animal or of appetizing food). However, far fewer studies have examined how memory is influenced by emotion related to social and political issues (e.g., a proposal for large changes in taxation policy), which clearly vary across individuals. In order to investigate the neural substrates of affective and mnemonic processes associated with personal opinions, we employed an fMRI task wherein participants rated the intensity of agreement/disagreement to sociopolitical belief statements paired with neural face pictures. Following the rating phase, participants performed an associative recognition test in which they distinguished identical versus recombined face-statement pairs. The study yielded three main findings: behaviorally, the intensity of agreement ratings was linked to greater subjective emotional arousal as well as enhanced high-confidence subsequent memory. Neurally, statements that elicited strong (vs. weak) agreement or disagreement were associated with greater activation of the amygdala. Finally, a subsequent memory analysis showed that the behavioral memory advantage for statements generating stronger ratings was dependent on the medial prefrontal cortex (mPFC). Together, these results both underscore consistencies in neural systems supporting emotional arousal and suggest a modulation of arousal-related encoding mechanisms when emotion is contingent on referencing personal beliefs.

  6. A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller

    Directory of Open Access Journals (Sweden)

    Lei Cao

    2017-05-01

    Full Text Available Brain Computer Interface (BCI speller is a typical BCI-based application to help paralyzed patients express their thoughts. This paper proposed a novel motor imagery based BCI speller with Oct-o-spell paradigm for word input. Furthermore, an intelligent input method was used for improving the performance of the BCI speller. For the English word spelling experiment, we compared synchronous control with previous asynchronous control under the same experimental condition. There were no significant differences between these two control methods in the classification accuracy, information transmission rate (ITR or letters per minute (LPM. And the accuracy rates of over 70% validated the feasibility for these two control strategies. It was indicated that MI-based synchronous control protocol was feasible for BCI speller. And the efficiency of the predictive text entry (PTE mode was superior to that of the Non-PTE mode.

  7. A Novel Computer Virus Propagation Model under Security Classification

    Directory of Open Access Journals (Sweden)

    Qingyi Zhu

    2017-01-01

    Full Text Available In reality, some computers have specific security classification. For the sake of safety and cost, the security level of computers will be upgraded with increasing of threats in networks. Here we assume that there exists a threshold value which determines when countermeasures should be taken to level up the security of a fraction of computers with low security level. And in some specific realistic environments the propagation network can be regarded as fully interconnected. Inspired by these facts, this paper presents a novel computer virus dynamics model considering the impact brought by security classification in full interconnection network. By using the theory of dynamic stability, the existence of equilibria and stability conditions is analysed and proved. And the above optimal threshold value is given analytically. Then, some numerical experiments are made to justify the model. Besides, some discussions and antivirus measures are given.

  8. 3-D components of a biological neural network visualized in computer generated imagery. I - Macular receptive field organization

    Science.gov (United States)

    Ross, Muriel D.; Cutler, Lynn; Meyer, Glenn; Lam, Tony; Vaziri, Parshaw

    1990-01-01

    Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.

  9. The neural basis of loss aversion in decision-making under risk.

    Science.gov (United States)

    Tom, Sabrina M; Fox, Craig R; Trepel, Christopher; Poldrack, Russell A

    2007-01-26

    People typically exhibit greater sensitivity to losses than to equivalent gains when making decisions. We investigated neural correlates of loss aversion while individuals decided whether to accept or reject gambles that offered a 50/50 chance of gaining or losing money. A broad set of areas (including midbrain dopaminergic regions and their targets) showed increasing activity as potential gains increased. Potential losses were represented by decreasing activity in several of these same gain-sensitive areas. Finally, individual differences in behavioral loss aversion were predicted by a measure of neural loss aversion in several regions, including the ventral striatum and prefrontal cortex.

  10. Abnormal neural connectivity in schizophrenia and fMRI-brain computer interface as a potential therapeutic approach

    Directory of Open Access Journals (Sweden)

    Sergio eRuiz

    2013-03-01

    Full Text Available Considering that single locations of structural and functional abnormalities are insufficient to explain the diverse psychopathology of schizophrenia, new models have postulated that the impairments associated with the disease arise from a failure to integrate the activity of local and distributed neural circuits: the abnormal neural connectivity hypothesis. In the last years, new evidence coming from neuroimaging have supported and expanded this theory. However, despite the increasing evidence that schizophrenia is a disorder of neural connectivity, so far there are no treatments that have shown to produce a significant change in brain connectivity, or that have been specifically designed to alleviate this problem. Brain-Computer Interfaces based on real-time functional Magnetic Resonance Imaging (fMRI-BCI are novel techniques that have allowed subjects to achieve self-regulation of circumscribed brain regions. In recent studies, experiments with this technology have resulted in new findings suggesting that this methodology could be used to train subjects to enhance brain connectivity, and therefore could potentially be used as a therapeutic tool in mental disorders including schizophrenia.The present article summarizes the findings coming from hemodynamics-based neuroimaging that support the abnormal connectivity hypothesis in schizophrenia, and discusses a new approach that could address this problem.

  11. Neural networks and wavelet analysis in the computer interpretation of pulse oximetry data

    Energy Technology Data Exchange (ETDEWEB)

    Dowla, F.U.; Skokowski, P.G.; Leach, R.R. Jr.

    1996-03-01

    Pulse oximeters determine the oxygen saturation level of blood by measuring the light absorption of arterial blood. The sensor consists of red and infrared light sources and photodetectors. A method based on neural networks and wavelet analysis is developed for improved saturation estimation in the presence of sensor motion. Spectral and correlation functions of the dual channel oximetry data are used by a backpropagation neural network to characterize the type of motion. Amplitude ratios of red to infrared signals as a function of time scale are obtained from the multiresolution wavelet decomposition of the two-channel data. Motion class and amplitude ratios are then combined to obtain a short-time estimate of the oxygen saturation level. A final estimate of oxygen saturation is obtained by applying a 15 s smoothing filter on the short-time measurements based on 3.5 s windows sampled every 1.75 s. The design employs two backpropagation neural networks. The first neural network determines the motion characteristics and the second network determines the saturation estimate. Our approach utilizes waveform analysis in contrast to the standard algorithms that are based on the successful detection of peaks and troughs in the signal. The proposed algorithm is numerically efficient and has stable characteristics with a reduced false alarm rate with a small loss in detection. The method can be rapidly developed on a digital signal processing platform.

  12. Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

    Directory of Open Access Journals (Sweden)

    Ravindranadh Bobbili

    2014-12-01

    Full Text Available An artificial neural network (ANN constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C, strains (0.05–0.2 and strain rates (1000–5500/s are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R and average absolute relative error (AARE. R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.

  13. Neural Network Prediction of Failure of Damaged Composite Pressure Vessels from Strain Field Data Acquired by a Computer Vision Method

    Science.gov (United States)

    Russell, Samuel S.; Lansing, Matthew D.

    1997-01-01

    This effort used a new and novel method of acquiring strains called Sub-pixel Digital Video Image Correlation (SDVIC) on impact damaged Kevlar/epoxy filament wound pressure vessels during a proof test. To predict the burst pressure, the hoop strain field distribution around the impact location from three vessels was used to train a neural network. The network was then tested on additional pressure vessels. Several variations on the network were tried. The best results were obtained using a single hidden layer. SDVIC is a fill-field non-contact computer vision technique which provides in-plane deformation and strain data over a load differential. This method was used to determine hoop and axial displacements, hoop and axial linear strains, the in-plane shear strains and rotations in the regions surrounding impact sites in filament wound pressure vessels (FWPV) during proof loading by internal pressurization. The relationship between these deformation measurement values and the remaining life of the pressure vessels, however, requires a complex theoretical model or numerical simulation. Both of these techniques are time consuming and complicated. Previous results using neural network methods had been successful in predicting the burst pressure for graphite/epoxy pressure vessels based upon acoustic emission (AE) measurements in similar tests. The neural network associates the character of the AE amplitude distribution, which depends upon the extent of impact damage, with the burst pressure. Similarly, higher amounts of impact damage are theorized to cause a higher amount of strain concentration in the damage effected zone at a given pressure and result in lower burst pressures. This relationship suggests that a neural network might be able to find an empirical relationship between the SDVIC strain field data and the burst pressure, analogous to the AE method, with greater speed and simplicity than theoretical or finite element modeling. The process of testing SDVIC

  14. DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Timur Kartbayev

    2017-03-01

    Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

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

    Science.gov (United States)

    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…

  16. Optogenetic dissection of neural circuit underlying locomotory decision-making in Caenorhabditis Elegans

    Science.gov (United States)

    Kocabas, Askin; Guo, Zengcai; Ramanathan, Sharad

    2011-03-01

    Despite the knowledge of the physical connectivity of the entire nervous system of C.elegans, we know little about how neuronal dynamics results in decision-making. Detailed understanding of functional and dynamic relations of the neural circuitry requires spatiotemporal control of the neuronal activity. Recent discoveries of light gated ion channels have allowed temporal optical control of neural activity. However, excitation of a specific neuron from among many expressing the channel has been a challenge. By combining optogenetic tools, micro mirror array technology and fast real time image processing, we have developed a technique to activate specific single or multiple neurons in an intact crawling animal while tracking its behavior. Using this setup we traced the neural pathway controlling the gradual turning of the animal during the locomotion. We found that the activity of a specific neuronal circuit that receives inputs from sensory neurons is coordinated with head movement. This coordination allows the animal to turn left or right based on the variation of sensory stimulus during head movement. By directly modulating the activity of the neural circuit, we can force the animal to turn in a specific direction independent of sensory stimuli. Human Frontier Science Program.

  17. Imaging Neuronal Populations in Behaving Rodents: Paradigms for Studying Neural Circuits Underlying Behavior in the Mammalian Cortex

    Science.gov (United States)

    Andermann, Mark L.; Keck, Tara; Xu, Ning-Long; Ziv, Yaniv

    2013-01-01

    Understanding the neural correlates of behavior in the mammalian cortex requires measurements of activity in awake, behaving animals. Rodents have emerged as a powerful model for dissecting the cortical circuits underlying behavior attributable to the convergence of several methods. Genetically encoded calcium indicators combined with viral-mediated or transgenic tools enable chronic monitoring of calcium signals in neuronal populations and subcellular structures of identified cell types. Stable one- and two-photon imaging of neuronal activity in awake, behaving animals is now possible using new behavioral paradigms in head-fixed animals, or using novel miniature head-mounted microscopes in freely moving animals. This mini-symposium will highlight recent applications of these methods for studying sensorimotor integration, decision making, learning, and memory in cortical and subcortical brain areas. We will outline future prospects and challenges for identifying the neural underpinnings of task-dependent behavior using cellular imaging in rodents. PMID:24198355

  18. Combined use of BP neural network and computational integral imaging reconstruction for optical multiple-image security

    Science.gov (United States)

    Li, Xiao Wei; Cho, Sung Jin; Kim, Seok Tae

    2014-03-01

    Integral imaging can provide a feasible and efficient technique for multiple-image encoding system. The computational integral imaging reconstruction (CIIR) technique reconstructs a set of plane images along the output plane, whereas the resolution of the reconstructed images will degrade due to the partial occlusion of other reconstructed images. Meanwhile, CIIR is a pixel-overlapping reconstruction method, in which the superimposition causes the undesirable interference. To overcome these problems, we first utilize the block matching algorithm to eliminate the occlusion-disturbance and introduce the back-propagation neural network algorithm to compensate for the low-resolution image. In the encryption, a computational integral imaging pickup technique is employed to record the multiple-image simultaneously to form an elemental image array (EIA). The EIA is then encrypted by combining the use of maximum length cellular automata (CA) and the double random phase encoding algorithm. Some numerical simulations have been made to demonstrate the performance of this encryption algorithm.

  19. Brain-computer interfacing under distraction: an evaluation study

    DEFF Research Database (Denmark)

    Brandl, Stephanie; Frølich, Laura; Höhne, Johannes

    2016-01-01

    Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach...

  20. Computed leaching of pesticides from soil under field conditions

    NARCIS (Netherlands)

    Leistra, M.; Dekkers, W.A.

    1976-01-01

    With a computer model, pesticide behavior in soil after spring application to a sandy loam field with a potato crop, was simulated. Special attention was paid to the risk of leaching through the upper meter of soil in catchment areas. Unsaturated water flow resulting from rainfall was modeled in

  1. General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří; Orponen, P.

    2003-01-01

    Roč. 15, č. 12 (2003), s. 2727-2778 ISSN 0899-7667 R&D Projects: GA AV ČR IAB2030007; GA ČR GA201/02/1456 Institutional research plan: AV0Z1030915 Keywords : computational power * computational complexity * perceptrons * radial basis functions * spiking neurons * feedforward network s * reccurent network s * probabilistic computation * analog computation Subject RIV: BA - General Mathematics Impact factor: 2.747, year: 2003

  2. Neural computing for numeric-to-symbolic conversion in control systems

    Science.gov (United States)

    Passino, Kevin M.; Sartori, Michael A.; Antsaklis, Panos J.

    1989-01-01

    A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

  3. Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

    OpenAIRE

    Sumit Goyal; Gyanendra Kumar Goyal

    2012-01-01

    This paper presents the capability of Time–delay artificial neural network models for predicting shelf life of processed cheese. Datasets were divided into two subsets (30 for training and 6 for validation). Models with single and multi layers were developed and compared with each other. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -
    Sutcliffo Coefficient were used as performance evaluators, Time- delay model predicted the shelf life of...

  4. Methods for the development of large computer codes under LTSS

    International Nuclear Information System (INIS)

    Sicilian, J.M.

    1977-06-01

    TRAC is a large computer code being developed by Group Q-6 for the analysis of the transient thermal hydraulic behavior of light-water nuclear reactors. A system designed to assist the development of TRAC is described. The system consists of a central HYDRA dataset, R6LIB, containing files used in the development of TRAC, and a file maintenance program, HORSE, which facilitates the use of this dataset

  5. 20 CFR 404.232 - Computing your average monthly wage under the guaranteed alternative.

    Science.gov (United States)

    2010-04-01

    ... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Computing your average monthly wage under the... OLD-AGE, SURVIVORS AND DISABILITY INSURANCE (1950- ) Computing Primary Insurance Amounts Guaranteed Alternative for People Reaching Age 62 After 1978 But Before 1984 § 404.232 Computing your average monthly...

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

    OpenAIRE

    Babiloni, Claudio; Marzano, Nicola; Soricelli, Andrea; Cordone, Susanna; Mill?n-Calenti, Jos? Carlos; Del Percio, Claudio; Buj?n, 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. Anger under Control: Neural Correlates of Frustration as a Function of Trait Aggression

    OpenAIRE

    Pawliczek, Christina M.; Derntl, Birgit; Kellermann, Thilo; Gur, Ruben C.; Schneider, Frank; Habel, Ute

    2013-01-01

    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 tw...

  8. Strong geomagnetic activity forecast by neural networks under dominant southern orientation of the interplanetary magnetic field

    Czech Academy of Sciences Publication Activity Database

    Valach, F.; Bochníček, Josef; Hejda, Pavel; Revallo, M.

    2014-01-01

    Roč. 53, č. 4 (2014), s. 589-598 ISSN 0273-1177 R&D Projects: GA AV ČR(CZ) IAA300120608; GA MŠk OC09070 Institutional support: RVO:67985530 Keywords : geomagnetic activity * interplanetary magnetic field * artificial neural network * ejection of coronal mass * X-ray flares Subject RIV: DE - Earth Magnetism, Geodesy, Geography Impact factor: 1.358, year: 2014

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

    OpenAIRE

    Aronov, Dmitriy; Tank, David W.

    2014-01-01

    Virtual reality (VR) enables precise control of an animal’s environment and otherwise impossible experimental manipulations. Neural activity in navigating rodents has been studied on virtual linear tracks. However, the spatial navigation system’s engagement in complete two-dimensional environments has not been shown. We describe a VR setup for rats, including control software and a large-scale electrophysiology system, which supports 2D navigation by allowing animals to rotate and walk in any...

  10. Neural Systems Underlying Perceptual Adjustment to Non-Standard Speech Tokens.

    Science.gov (United States)

    Myers, Emily B; Mesite, Laura M

    2014-10-01

    It has long been noted that listeners use top-down information from context to guide perception of speech sounds. A recent line of work employing a phenomenon termed 'perceptual learning for speech' shows that listeners use top-down information to not only resolve the identity of perceptually ambiguous speech sounds, but also to adjust perceptual boundaries in subsequent processing of speech from the same talker. Even so, the neural mechanisms that underlie this process are not well understood. Of particular interest is whether this type of adjustment comes about because of a retuning of sensitivities to phonetic category structure early in the neural processing stream or whether the boundary shift results from decision-related or attentional mechanisms further downstream. In the current study, neural activation was measured using fMRI as participants categorized speech sounds that were perceptually shifted as a result of exposure to these sounds in lexically-unambiguous contexts. Sensitivity to lexically-mediated shifts in phonetic categorization emerged in right hemisphere frontal and middle temporal regions, suggesting that the perceptual learning for speech phenomenon relies on the adjustment of perceptual criteria downstream from primary auditory cortex. By the end of the session, this same sensitivity was seen in left superior temporal areas, which suggests that a rapidly-adapting system may be accompanied by more slowly evolving shifts in regions of the brain related to phonetic processing.

  11. 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.

  12. Fractionating the neural correlates of individual working memory components underlying arithmetic problem solving skills in children.

    Science.gov (United States)

    Metcalfe, Arron W S; Ashkenazi, Sarit; Rosenberg-Lee, Miriam; Menon, Vinod

    2013-10-01

    Baddeley and Hitch's multi-component working memory (WM) model has played an enduring and influential role in our understanding of cognitive abilities. Very little is known, however, about the neural basis of this multi-component WM model and the differential role each component plays in mediating arithmetic problem solving abilities in children. Here, we investigate the neural basis of the central executive (CE), phonological (PL) and visuo-spatial (VS) components of WM during a demanding mental arithmetic task in 7-9 year old children (N=74). The VS component was the strongest predictor of math ability in children and was associated with increased arithmetic complexity-related responses in left dorsolateral and right ventrolateral prefrontal cortices as well as bilateral intra-parietal sulcus and supramarginal gyrus in posterior parietal cortex. Critically, VS, CE and PL abilities were associated with largely distinct patterns of brain response. Overlap between VS and CE components was observed in left supramarginal gyrus and no overlap was observed between VS and PL components. Our findings point to a central role of visuo-spatial WM during arithmetic problem-solving in young grade-school children and highlight the usefulness of the multi-component Baddeley and Hitch WM model in fractionating the neural correlates of arithmetic problem solving during development. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. The potential of computer vision, optical backscattering parameters and artificial neural network modelling in monitoring the shrinkage of sweet potato (Ipomoea batatas L.) during drying.

    Science.gov (United States)

    Onwude, Daniel I; Hashim, Norhashila; Abdan, Khalina; Janius, Rimfiel; Chen, Guangnan

    2018-03-01

    Drying is a method used to preserve agricultural crops. During the drying of products with high moisture content, structural changes in shape, volume, area, density and porosity occur. These changes could affect the final quality of dried product and also the effective design of drying equipment. Therefore, this study investigated a novel approach in monitoring and predicting the shrinkage of sweet potato during drying. Drying experiments were conducted at temperatures of 50-70 °C and samples thicknesses of 2-6 mm. The volume and surface area obtained from camera vision, and the perimeter and illuminated area from backscattered optical images were analysed and used to evaluate the shrinkage of sweet potato during drying. The relationship between dimensionless moisture content and shrinkage of sweet potato in terms of volume, surface area, perimeter and illuminated area was found to be linearly correlated. The results also demonstrated that the shrinkage of sweet potato based on computer vision and backscattered optical parameters is affected by the product thickness, drying temperature and drying time. A multilayer perceptron (MLP) artificial neural network with input layer containing three cells, two hidden layers (18 neurons), and five cells for output layer, was used to develop a model that can monitor, control and predict the shrinkage parameters and moisture content of sweet potato slices under different drying conditions. The developed ANN model satisfactorily predicted the shrinkage and dimensionless moisture content of sweet potato with correlation coefficient greater than 0.95. Combined computer vision, laser light backscattering imaging and artificial neural network can be used as a non-destructive, rapid and easily adaptable technique for in-line monitoring, predicting and controlling the shrinkage and moisture changes of food and agricultural crops during drying. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  14. 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.

  15. Searching for memories, Sudoku, implicit check bits, and the iterative use of not-always-correct rapid neural computation.

    Science.gov (United States)

    Hopfield, J J

    2008-05-01

    The algorithms that simple feedback neural circuits representing a brain area can rapidly carry out are often adequate to solve easy problems but for more difficult problems can return incorrect answers. A new excitatory-inhibitory circuit model of associative memory displays the common human problem of failing to rapidly find a memory when only a small clue is present. The memory model and a related computational network for solving Sudoku puzzles produce answers that contain implicit check bits in the representation of information across neurons, allowing a rapid evaluation of whether the putative answer is correct or incorrect through a computation related to visual pop-out. This fact may account for our strong psychological feeling of right or wrong when we retrieve a nominal memory from a minimal clue. This information allows more difficult computations or memory retrievals to be done in a serial fashion by using the fast but limited capabilities of a computational module multiple times. The mathematics of the excitatory-inhibitory circuits for associative memory and for Sudoku, both of which are understood in terms of energy or Lyapunov functions, is described in detail.

  16. 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

  17. 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. .

  18. Neural Correlates of Choking Under Pressure: Athletes High in Sports Anxiety Monitor Errors More When Performance Is Being Evaluated.

    Science.gov (United States)

    Masaki, Hiroaki; Maruo, Yuya; Meyer, Alexandria; Hajcak, Greg

    2017-01-01

    We investigated the relationship between performance-related anxiety and the neural response to errors. Using the sport anxiety scale, we selected university athletes high in sports anxiety and low in sports anxiety. The two groups performed a spatial Stroop task while their performance was being evaluated by an experimenter and also during a control (i.e., no evaluation) condition. The error-related negativity was significantly larger during the evaluation than control condition among athletes who reported high performance-related anxiety. These results suggest that performance evaluation may make errors particularly aversive or salient for individuals who fail to perform well under pressure.

  19. Exponential synchronization of delayed neutral-type neural networks with Lévy noise under non-Lipschitz condition

    Science.gov (United States)

    Ma, Shuo; Kang, Yanmei

    2018-04-01

    In this paper, the exponential synchronization of stochastic neutral-type neural networks with time-varying delay and Lévy noise under non-Lipschitz condition is investigated for the first time. Using the general Itô's formula and the nonnegative semi-martingale convergence theorem, we derive general sufficient conditions of two kinds of exponential synchronization for the drive system and the response system with adaptive control. Numerical examples are presented to verify the effectiveness of the proposed criteria.

  20. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  1. 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.

  2. Computational neural networks: enhancing supervised learning algorithms via self-organization.

    Science.gov (United States)

    Holdaway, R M; White, M W

    1990-04-01

    A neural network processing scheme is proposed which utilizes a self-organizing Kohonen feature map as the front end to a feedforward classifier network. The results of a series of benchmarking studies based upon artificial statistical pattern recognition tasks indicate that the proposed architecture performs significantly better than conventional feedforward classifier networks when the decision regions are disjoint. This is attributed to the fact that the self-organization process allows internal units in the succeeding classifier network to be sensitive to a specific set of features in the input space at the outset of training.

  3. Comparisons of a Quantum Annealing and Classical Computer Neural Net Approach for Inferring Global Annual CO2 Fluxes over Land

    Science.gov (United States)

    Halem, M.; Radov, A.; Singh, D.

    2017-12-01

    Investigations of mid to high latitude atmospheric CO2 show growing amplitudes in seasonal variations over the past several decades. Recent high-resolution satellite measurements of CO2 concentration are now available for three years from the Orbiting Carbon Observatory-2. The Atmospheric Radiation Measurement (ARM) program of DOE has been making long-term CO2-flux measurements (in addition to CO2 concentration and an array of other meteorological quantities) at several towers and mobile sites located around the globe at half-hour frequencies. Recent papers have shown CO2 fluxes inferred by assimilating CO2 observations into ecosystem models are largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. Thus, new approaches for calculating CO2-flux for assimilation into land surface models are necessary for improving the prediction of annual carbon uptake. In this study, we calculate and compare the predicted CO2 fluxes results employing a Feed Forward Backward Propagation Neural Network model on two architectures, (i) an IBM Minsky Computer node and (ii) a hybrid version of the ARC D-Wave quantum annealing computer. We compare the neural net results of predictions of CO2 flux from ARM station data for three different DOE ecosystem sites; an arid plains near Oklahoma City, a northern arctic site at Barrows AL, and a tropical rainforest site in the Amazon. Training times and predictive results for the calculating annual CO2 flux for the two architectures for each of the three sites are presented. Comparative results of predictions as measured by RMSE and MAE are discussed. Plots and correlations of observed vs predicted CO2 flux are also presented for all three sites. We show the estimated training times for quantum and classical calculations when extended to calculating global annual Carbon Uptake over land. We also

  4. The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers

    Science.gov (United States)

    Botvinick, Matthew M.; Cohen, Jonathan D.

    2014-01-01

    Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on…

  5. Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs.

    Science.gov (United States)

    Cicero, Mark; Bilbily, Alexander; Colak, Errol; Dowdell, Tim; Gray, Bruce; Perampaladas, Kuhan; Barfett, Joseph

    2017-05-01

    Convolutional neural networks (CNNs) are a subtype of artificial neural network that have shown strong performance in computer vision tasks including image classification. To date, there has been limited application of CNNs to chest radiographs, the most frequently performed medical imaging study. We hypothesize CNNs can learn to classify frontal chest radiographs according to common findings from a sufficiently large data set. Our institution's research ethics board approved a single-center retrospective review of 35,038 adult posterior-anterior chest radiographs and final reports performed between 2005 and 2015 (56% men, average age of 56, patient type: 24% inpatient, 39% outpatient, 37% emergency department) with a waiver for informed consent. The GoogLeNet CNN was trained using 3 graphics processing units to automatically classify radiographs as normal (n = 11,702) or into 1 or more of cardiomegaly (n = 9240), consolidation (n = 6788), pleural effusion (n = 7786), pulmonary edema (n = 1286), or pneumothorax (n = 1299). The network's performance was evaluated using receiver operating curve analysis on a test set of 2443 radiographs with the criterion standard being board-certified radiologist interpretation. Using 256 × 256-pixel images as input, the network achieved an overall sensitivity and specificity of 91% with an area under the curve of 0.964 for classifying a study as normal (n = 1203). For the abnormal categories, the sensitivity, specificity, and area under the curve, respectively, were 91%, 91%, and 0.962 for pleural effusion (n = 782), 82%, 82%, and 0.868 for pulmonary edema (n = 356), 74%, 75%, and 0.850 for consolidation (n = 214), 81%, 80%, and 0.875 for cardiomegaly (n = 482), and 78%, 78%, and 0.861 for pneumothorax (n = 167). Current deep CNN architectures can be trained with modest-sized medical data sets to achieve clinically useful performance at detecting and excluding common pathology on chest radiographs.

  6. Temporal neural mechanisms underlying conscious access to different levels of facial stimulus contents.

    Science.gov (United States)

    Hsu, Shen-Mou; Yang, Yu-Fang

    2018-04-01

    An important issue facing the empirical study of consciousness concerns how the contents of incoming stimuli gain access to conscious processing. According to classic theories, facial stimuli are processed in a hierarchical manner. However, it remains unclear how the brain determines which level of stimulus content is consciously accessible when facing an incoming facial stimulus. Accordingly, with a magnetoencephalography technique, this study aims to investigate the temporal dynamics of the neural mechanism mediating which level of stimulus content is consciously accessible. Participants were instructed to view masked target faces at threshold so that, according to behavioral responses, their perceptual awareness alternated from consciously accessing facial identity in some trials to being able to consciously access facial configuration features but not facial identity in other trials. Conscious access at these two levels of facial contents were associated with a series of differential neural events. Before target presentation, different patterns of phase angle adjustment were observed between the two types of conscious access. This effect was followed by stronger phase clustering for awareness of facial identity immediately during stimulus presentation. After target onset, conscious access to facial identity, as opposed to facial configural features, was able to elicit more robust late positivity. In conclusion, we suggest that the stages of neural events, ranging from prestimulus to stimulus-related activities, may operate in combination to determine which level of stimulus contents is consciously accessed. Conscious access may thus be better construed as comprising various forms that depend on the level of stimulus contents accessed. NEW & NOTEWORTHY The present study investigates how the brain determines which level of stimulus contents is consciously accessible when facing an incoming facial stimulus. Using magnetoencephalography, we show that prestimulus

  7. Modulation of neural circuits underlying temporal production by facial expressions of pain.

    Directory of Open Access Journals (Sweden)

    Daniela Ballotta

    Full Text Available According to the Scalar Expectancy Theory, humans are equipped with a biological internal clock, possibly modulated by attention and arousal. Both emotions and pain are arousing and can absorb attentional resources, thus causing distortions of temporal perception. The aims of the present single-event fMRI study were to investigate: a whether observation of facial expressions of pain interferes with time production; and b the neural network subserving this kind of temporal distortions. Thirty healthy volunteers took part in the study. Subjects were asked to perform a temporal production task and a concurrent gender discrimination task, while viewing faces of unknown people with either pain-related or neutral expressions. Behavioural data showed temporal underestimation (i.e., longer produced intervals during implicit pain expression processing; this was accompanied by increased activity of right middle temporal gyrus, a region known to be active during the perception of emotional and painful faces. Psycho-Physiological Interaction analyses showed that: 1 the activity of middle temporal gyrus was positively related to that of areas previously reported to play a role in timing: left primary motor cortex, middle cingulate cortex, supplementary motor area, right anterior insula, inferior frontal gyrus, bilateral cerebellum and basal ganglia; 2 the functional connectivity of supplementary motor area with several frontal regions, anterior cingulate cortex and right angular gyrus was correlated to the produced interval during painful expression processing. Our data support the hypothesis that observing emotional expressions distorts subjective time perception through the interaction of the neural network subserving processing of facial expressions with the brain network involved in timing. Within this frame, middle temporal gyrus appears to be the key region of the interplay between the two neural systems.

  8. 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

  9. 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 4 th and 6 th 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 6 th grade. From the 4 th to the 6 th 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.

  10. Modulation of neural circuits underlying temporal production by facial expressions of pain

    Science.gov (United States)

    Lui, Fausta; Porro, Carlo Adolfo; Nichelli, Paolo Frigio; Benuzzi, Francesca

    2018-01-01

    According to the Scalar Expectancy Theory, humans are equipped with a biological internal clock, possibly modulated by attention and arousal. Both emotions and pain are arousing and can absorb attentional resources, thus causing distortions of temporal perception. The aims of the present single-event fMRI study were to investigate: a) whether observation of facial expressions of pain interferes with time production; and b) the neural network subserving this kind of temporal distortions. Thirty healthy volunteers took part in the study. Subjects were asked to perform a temporal production task and a concurrent gender discrimination task, while viewing faces of unknown people with either pain-related or neutral expressions. Behavioural data showed temporal underestimation (i.e., longer produced intervals) during implicit pain expression processing; this was accompanied by increased activity of right middle temporal gyrus, a region known to be active during the perception of emotional and painful faces. Psycho-Physiological Interaction analyses showed that: 1) the activity of middle temporal gyrus was positively related to that of areas previously reported to play a role in timing: left primary motor cortex, middle cingulate cortex, supplementary motor area, right anterior insula, inferior frontal gyrus, bilateral cerebellum and basal ganglia; 2) the functional connectivity of supplementary motor area with several frontal regions, anterior cingulate cortex and right angular gyrus was correlated to the produced interval during painful expression processing. Our data support the hypothesis that observing emotional expressions distorts subjective time perception through the interaction of the neural network subserving processing of facial expressions with the brain network involved in timing. Within this frame, middle temporal gyrus appears to be the key region of the interplay between the two neural systems. PMID:29447256

  11. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  12. COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE

    Science.gov (United States)

    Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

    2015-01-01

    Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies. PMID:27027010

  13. Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters

    Directory of Open Access Journals (Sweden)

    Yuta Katsumi

    2018-01-01

    Full Text Available Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants’ ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds.

  14. COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE.

    Science.gov (United States)

    Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; Dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

    2011-01-01

    To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies.

  15. From image processing to computational neuroscience: a neural model based on histogram equalization.

    Science.gov (United States)

    Bertalmío, Marcelo

    2014-01-01

    There are many ways in which the human visual system works to reduce the inherent redundancy of the visual information in natural scenes, coding it in an efficient way. The non-linear response curves of photoreceptors and the spatial organization of the receptive fields of visual neurons both work toward this goal of efficient coding. A related, very important aspect is that of the existence of post-retinal mechanisms for contrast enhancement that compensate for the blurring produced in early stages of the visual process. And alongside mechanisms for coding and wiring efficiency, there is neural activity in the human visual cortex that correlates with the perceptual phenomenon of lightness induction. In this paper we propose a neural model that is derived from an image processing technique for histogram equalization, and that is able to deal with all the aspects just mentioned: this new model is able to predict lightness induction phenomena, and improves the efficiency of the representation by flattening both the histogram and the power spectrum of the image signal.

  16. From Image Processing to Computational Neuroscience: A Neural Model Based on Histogram Equalization

    Directory of Open Access Journals (Sweden)

    Marcelo eBertalmío

    2014-07-01

    Full Text Available There are many ways in which the human visual system works to reduce the inherent redundancy of the visual information in natural scenes, coding it in an efficient way.The non-linear response curves of photoreceptors and the spatial organization of the receptive fields of visual neurons both work towards this goal of efficient coding. A related, very important aspect is that of the existence of post-retinal mechanisms for contrast enhancement that compensate for the blurring produced in early stages of the visual process. And alongside mechanisms for coding and wiring efficiency, there is neural activity in the human visual cortex that correlates with the perceptual phenomenon of lightness induction.In this paper we propose a neural model that is derived from an image processing technique for histogram equalization, and that is able to deal with all the aspects just mentioned: this new model is able to predict lightness induction phenomena, and improves the efficiency of the representation by flattening both the histogram and the power spectrum of the image signal.

  17. Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters.

    Science.gov (United States)

    Katsumi, Yuta; Dolcos, Sanda

    2017-01-01

    Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants' ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds.

  18. Neural and fuzzy computation techniques for playout delay adaptation in VoIP networks.

    Science.gov (United States)

    Ranganathan, Mohan Krishna; Kilmartin, Liam

    2005-09-01

    Playout delay adaptation algorithms are often used in real time voice communication over packet-switched networks to counteract the effects of network jitter at the receiver. Whilst the conventional algorithms developed for silence-suppressed speech transmission focused on preserving the relative temporal structure of speech frames/packets within a talkspurt (intertalkspurt adaptation), more recently developed algorithms strive to achieve better quality by allowing for playout delay adaptation within a talkspurt (intratalkspurt adaptation). The adaptation algorithms, both intertalkspurt and intratalkspurt based, rely on short term estimations of the characteristics of network delay that would be experienced by up-coming voice packets. The use of novel neural networks and fuzzy systems as estimators of network delay characteristics are presented in this paper. Their performance is analyzed in comparison with a number of traditional techniques for both inter and intratalkspurt adaptation paradigms. The design of a novel fuzzy trend analyzer system (FTAS) for network delay trend analysis and its usage in intratalkspurt playout delay adaptation are presented in greater detail. The performance of the proposed mechanism is analyzed based on measured Internet delays. Index Terms-Fuzzy delay trend analysis, intertalkspurt, intratalkspurt, multilayer perceptrons (MLPs), network delay estimation, playout buffering, playout delay adaptation, time delay neural networks (TDNNs), voice over Internet protocol (VoIP).

  19. The Prediction of Bandwidth On Need Computer Network Through Artificial Neural Network Method of Backpropagation

    Directory of Open Access Journals (Sweden)

    Ikhthison Mekongga

    2014-02-01

    Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network.  ANN  is  chosen  to  predict  the  consumption  of  the  bandwidth  because  ANN  has  good  approachability  to  non-linearity.  The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks  with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation

  20. Computed tomography of the thymus of children under 10 years

    International Nuclear Information System (INIS)

    Salonen, O.L.M.; Kivisaari, M.L.; Somer, J.K.

    1984-01-01

    The CT appearance of the mediastinum of 27 patients under 10 years of age was analysed retrospectively, paying special attention to the anterior mediastinum and thymus. None of the patients had thymic disease. The purpose of the study was to record features which might be regarded as characteristic of the normal thymus. The lateral contour of the thymus was clearly seen. There were no infiltrations, but in one case there were displacements due to a prominent thymus. All the thymus glands appeared to be homogeneous. The use of contrast medium helped in distinguishing the thymus from other mediastinal structures; its use is well justified in studying the anterior mediastinum. (orig.)

  1. Violence exposure and neural systems underlying working memory for emotional stimuli in youth.

    Science.gov (United States)

    Jenness, Jessica L; Rosen, Maya L; Sambrook, Kelly A; Dennison, Meg J; Lambert, Hilary K; Sheridan, Margaret A; McLaughlin, Katie A

    2017-11-16

    Violence exposure during childhood is common and associated with poor cognitive and academic functioning. However, little is known about how violence exposure influences cognitive processes that might contribute to these disparities, such as working memory, or their neural underpinnings, particularly for cognitive processes that occur in emotionally salient contexts. We address this gap in a sample of 54 participants aged 8 to 19 years (50% female), half with exposure to interpersonal violence. Participants completed a delayed match to sample task for emotional faces while undergoing functional magnetic resonance imaging scanning. Violence-exposed youth performed worse than controls on happy and neutral, but not angry, trials. In whole-brain analysis, violence-exposed youth had reduced activation in the left middle frontal gyrus and right intraparietal sulcus during encoding and the left superior temporal sulcus and temporal-parietal junction during retrieval compared to control youth. Reduced activation in the left middle frontal gyrus during encoding and the left superior temporal sulcus during retrieval mediated the association between violence exposure and task performance. Violence exposure influences the frontoparietal network that supports working memory as well as regions involved in facial processing during working memory for emotional stimuli. Reduced neural recruitment in these regions may explain atypical patterns of cognitive processing seen among violence-exposed youth, particularly within emotional contexts.

  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 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.

  4. Tuning to the significant: neural and genetic processes underlying affective enhancement of visual perception and memory.

    Science.gov (United States)

    Markovic, Jelena; Anderson, Adam K; Todd, Rebecca M

    2014-02-01

    Emotionally arousing events reach awareness more easily and evoke greater visual cortex activation than more mundane events. Recent studies have shown that they are also perceived more vividly and that emotionally enhanced perceptual vividness predicts memory vividness. We propose that affect-biased attention (ABA) - selective attention to emotionally salient events - is an endogenous attentional system tuned by an individual's history of reward and punishment. We present the Biased Attention via Norepinephrine (BANE) model, which unifies genetic, neuromodulatory, neural and behavioural evidence to account for ABA. We review evidence supporting BANE's proposal that a key mechanism of ABA is locus coeruleus-norepinephrine (LC-NE) activity, which interacts with activity in hubs of affective salience networks to modulate visual cortex activation and heighten the subjective vividness of emotionally salient stimuli. We further review literature on biased competition and look at initial evidence for its potential as a neural mechanism behind ABA. We also review evidence supporting the role of the LC-NE system as a driving force of ABA. Finally, we review individual differences in ABA and memory including differences in sensitivity to stimulus category and valence. We focus on differences arising from a variant of the ADRA2b gene, which codes for the alpha2b adrenoreceptor as a way of investigating influences of NE availability on ABA in humans. Copyright © 2013 Elsevier B.V. All rights reserved.

  5. Anything goes? Regulation of the neural processes underlying response inhibition in TBI patients.

    Science.gov (United States)

    Moreno-López, Laura; Manktelow, Anne E; Sahakian, Barbara J; Menon, David K; Stamatakis, Emmanuel A

    2017-02-01

    Despite evidence for beneficial use of methylphenidate in response inhibition, no studies so far have investigated the effects of this drug in the neurobiology of inhibitory control in traumatic brain injury (TBI), even though impulsive behaviours are frequently reported in this patient group. We investigated the neural basis of response inhibition in a group of TBI patients using functional magnetic resonance imaging and a stop-signal paradigm. In a randomised double-blinded crossover study, the patients received either a single 30mg dose of methylphenidate or placebo and performed the stop-signal task. Activation in the right inferior frontal gyrus (RIFG), an area associated with response inhibition, was significantly lower in patients compared to healthy controls. Poor response inhibition in this group was associated with greater connectivity between the RIFG and a set of regions considered to be part of the default mode network (DMN), a finding that suggests the interplay between DMN and frontal executive networks maybe compromised. A single dose of methylphenidate rendered activity and connectivity profiles of the patients RIFG near normal. The results of this study indicate that the neural circuitry involved in response inhibition in TBI patients may be partially restored with methylphenidate. Given the known mechanisms of action of methylphenidate, the effect we observed may be due to increased dopamine and noradrenaline levels. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

  6. Bad and worse: neural systems underlying reappraisal of high- and low-intensity negative emotions.

    Science.gov (United States)

    Silvers, Jennifer A; Weber, Jochen; Wager, Tor D; Ochsner, Kevin N

    2015-02-01

    One of the most effective strategies for regulating emotional responses is cognitive reappraisal. While prior work has made great strides in characterizing reappraisal's neural mechanisms and behavioral outcomes, the key issue of how regulation varies as a function of emotional intensity remains unaddressed. We compared the behavioral and neural correlates of reappraisal of high- and low-intensity emotional responses using functional magnetic resonance imaging (fMRI). We found that successful reappraisal of both high- and low-intensity emotions depends upon recruitment of dorsomedial (dmPFC) as well as left dorsolateral (dlPFC) and ventrolateral (vlPFC) prefrontal cortex. However, reappraisal of high-intensity emotions more strongly activated left dlPFC, and in addition, activated right lateral and dorsomedial PFC regions not recruited by low-intensity reappraisal. No brain regions were more strongly recruited during reappraisal of low when compared with high-intensity emotions. Taken together, these results suggest that reappraisal of high-intensity emotion requires greater cognitive resources as evidenced by quantitative and qualitative differences in prefrontal recruitment. These data have implications for understanding how and when specific PFC systems are needed to regulate different types of emotional responses. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  7. Gender Differences in Behavioral and Neural Responses to Unfairness Under Social Pressure.

    Science.gov (United States)

    Zheng, Li; Ning, Reipeng; Li, Lin; Wei, Chunli; Cheng, Xuemei; Zhou, Chu; Guo, Xiuyan

    2017-10-18

    Numerous studies have revealed the key role of social pressure on individuals' decision-making processes. However, the impact of social pressure on unfairness-related decision-making processes remains unclear. In the present study, we investigated how social pressure modulated men's and women's responses in an ultimatum game. Twenty women and eighteen men played the ultimatum game as responders in the scanner, where fair and unfair offers were tendered by proposers acting alone (low pressure) or by proposers endorsed by three supporters (high pressure). Results showed that men rejected more, whereas women accepted more unfair offers in the high versus low pressure context. Neurally, pregenual anterior cingulate cortex activation in women positively predicted their acceptance rate difference between contexts. In men, stronger right anterior insula activation and increased connectivity between right anterior insula and dorsal anterior cingulate cortex were observed when they receiving unfair offers in the high than low pressure context. Furthermore, more bilateral anterior insula and left dorsolateral prefrontal cortex activations were found when men rejected (relative to accepted) unfair offers in the high than low pressure context. These findings highlighted gender differences in the modulation of behavioral and neural responses to unfairness by social pressure.

  8. Neural dynamics underlying attentional orienting to auditory representations in short-term memory.

    Science.gov (United States)

    Backer, Kristina C; Binns, Malcolm A; Alain, Claude

    2015-01-21

    Sounds are ephemeral. Thus, coherent auditory perception depends on "hearing" back in time: retrospectively attending that which was lost externally but preserved in short-term memory (STM). Current theories of auditory attention assume that sound features are integrated into a perceptual object, that multiple objects can coexist in STM, and that attention can be deployed to an object in STM. Recording electroencephalography from humans, we tested these assumptions, elucidating feature-general and feature-specific neural correlates of auditory attention to STM. Alpha/beta oscillations and frontal and posterior event-related potentials indexed feature-general top-down attentional control to one of several coexisting auditory representations in STM. Particularly, task performance during attentional orienting was correlated with alpha/low-beta desynchronization (i.e., power suppression). However, attention to one feature could occur without simultaneous processing of the second feature of the representation. Therefore, auditory attention to memory relies on both feature-specific and feature-general neural dynamics. Copyright © 2015 the authors 0270-6474/15/351307-12$15.00/0.

  9. Pattern recognition, neural networks, genetic algorithms and high performance computing in nuclear reactor diagnostics. Results and perspectives

    International Nuclear Information System (INIS)

    Dzwinel, W.; Pepyolyshev, N.

    1996-01-01

    The main goal of this paper is the presentation of our experience in development of the diagnostic system for the IBR-2 (Russia - Dubna) nuclear reactor. The authors show the principal results of the system modifications to make it work more reliable and much faster. The former needs the adaptation of new techniques of data processing, the latter, implementation of the newest computational facilities. The results of application of the clustering techniques and a method of visualization of the multi-dimensional information directly on the operator display are presented. The experiences with neural nets, used for prediction of the reactor operation, are discussed. The genetic algorithms were also tested, to reduce the quantity of data nd extracting the most informative components of the analyzed spectra. (authors)

  10. A neural network based computational model to predict the output power of different types of photovoltaic cells.

    Directory of Open Access Journals (Sweden)

    WenBo Xiao

    Full Text Available In this article, we introduced an artificial neural network (ANN based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-, multi-crystalline (multi-, and amorphous (amor- crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

  11. 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.

  12. 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

  13. Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

    Science.gov (United States)

    Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

    2018-04-01

    Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

  14. Realization for Chinese vehicle license plate recognition based on computer vision and fuzzy neural network

    Science.gov (United States)

    Yang, Yun; Zhang, Weigang; Guo, Pan

    2010-07-01

    The proposed approach in this paper is divided into three steps namely the location of plate, the segmentation of the characters and the recognition of the characters. The location algorithm is firstly consisted of two video captures to get high quality images, and estimates the size of vehicle plate in these images via parallel binocular stereo vision algorithm. Then the segmentation method extracts the edge of vehicle plate based on second generation non-orthogonal Haar wavelet transformation, and locates the vehicle plate according to the estimated result in the first step. Finally, the recognition algorithm is realized based on the Radial Basis Function Fuzzy Neural Network. Experiments have been conducted for real images. The results show this method can decrease the error recognition rate of Chinese license plate recognition.

  15. 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.

  16. 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.

  17. 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.

  18. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  19. Fast computation of a string duplication history under no-breakpoint-reuse

    Science.gov (United States)

    Brejová, Broňa; Kravec, Martin; Landau, Gad M.; Vinař, Tomáš

    2014-01-01

    In this paper, we provide an O(n log2 n log log n log* n) algorithm to compute a duplication history of a string under no-breakpoint-reuse condition. The motivation of this problem stems from computational biology, in particular, from analysis of complex gene clusters. The problem is also related to computing edit distance with block operations, but, in our scenario, the start of the history is not fixed, but chosen to minimize the distance measure. PMID:24751867

  20. Blunt oesophageal perforation: treatment with surgical exclusion and percutaneous drainage under computed tomographic guidance

    International Nuclear Information System (INIS)

    Vauthey, J.N.; Lerut, J.; Laube, M.; Gertsch, P.

    1992-01-01

    We report a patient with a left thoracic contusion and rupture of the distal oesophagus. Persistent sepsis developed after oesophageal exclusion without closure. Two collection were drained percutaneously under computed tomographic guidance and the sepsis resolved. (11 refs., 1 fig.)

  1. Creating and Using a Computer Networking and Systems Administration Laboratory Built under Relaxed Financial Constraints

    Science.gov (United States)

    Conlon, Michael P.; Mullins, Paul

    2011-01-01

    The Computer Science Department at Slippery Rock University created a laboratory for its Computer Networks and System Administration and Security courses under relaxed financial constraints. This paper describes the department's experience designing and using this laboratory, including lessons learned and descriptions of some student projects…

  2. An autonomous implantable computer for neural recording and stimulation in unrestrained primates.

    Science.gov (United States)

    Mavoori, Jaideep; Jackson, Andrew; Diorio, Chris; Fetz, Eberhard

    2005-10-15

    To perform neurobiological experiments on freely behaving primates, we have developed a miniature battery-powered implantable computer capable of recording and stimulating through chronic electrodes in the cortex. The device has: (1) an analog front end with a four-pole bandpass filter (500 Hz-5 kHz), programmable gain and offset nulling; (2) an analog-to-digital converter to sample the data at 11.7 ksps; (3) a programmable microcontroller to discriminate spikes in real time and perform computations; (4) a stimulator to deliver biphasic current pulses of up to 100 muA with variable pulse width and frequency; (5) a 4 Mbit non-volatile memory to store biological data; (6) a 57.6 kbps infrared data link for wireless communications with a hand-held or desktop computer. The device is enclosed in a 5.5 cm x 5 cm x 3 cm titanium casing on the monkey's head along with a 3.3 V lithium battery and an array of cortical electrodes. In in vivo tests, the device was able to record stable cell discharge continuously for time periods of a week or more. After downloading the parameters for recording, stimulation, discrimination, and other computations, the device is capable of operating autonomously, delivering stimuli to one electrode triggered by spikes recorded at a separate site.

  3. Dynamic indoor thermal comfort model identification based on neural computing PMV index

    Science.gov (United States)

    Sahari, K. S. Mohamed; Jalal, M. F. Abdul; Homod, R. Z.; Eng, Y. K.

    2013-06-01

    This paper focuses on modelling and simulation of building dynamic thermal comfort control for non-linear HVAC system. Thermal comfort in general refers to temperature and also humidity. However in reality, temperature or humidity is just one of the factors affecting the thermal comfort but not the main measures. Besides, as HVAC control system has the characteristic of time delay, large inertia, and highly nonlinear behaviour, it is difficult to determine the thermal comfort sensation accurately if we use traditional Fanger's PMV index. Hence, Artificial Neural Network (ANN) has been introduced due to its ability to approximate any nonlinear mapping. Using ANN to train, we can get the input-output mapping of HVAC control system or in other word; we can propose a practical approach to identify thermal comfort of a building. Simulations were carried out to validate and verify the proposed method. Results show that the proposed ANN method can track down the desired thermal sensation for a specified condition space.

  4. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Muammar Sadrawi

    2015-01-01

    Full Text Available This study evaluated the depth of anesthesia (DoA index using artificial neural networks (ANN which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD is utilized to purify between the electroencephalography (EEG signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG, heart rate (HR, pulse, systolic blood pressure (SBP, diastolic blood pressure (DBP, and signal quality index (SQI to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS. The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

  5. [Calculation of soil water erosion modulus based on RUSLE and its assessment under support of artificial neural network].

    Science.gov (United States)

    Li, Yuhuan; Wang, Jing; Zhang, Jixian

    2006-06-01

    With Hengshan County of Shanxi Province in the North Loess Plateau as an example, and by using ETM + and remote sensing data and RUSLE module, this paper quantitatively derived the soil and water loss in loess hilly region based on "3S" technology, and assessed the derivation results under the support of artificial neural network. The results showed that the annual average erosion modulus of Hengshan County was 103.23 t x hm(-2), and the gross erosion loss per year was 4. 38 x 10(7) t. The erosion was increased from northwest to southeast, and varied significantly with topographic position. A slight erosion or no erosion happened in walled basin, flat-headed mountain ridges and sandy area, which always suffered from dropping erosion, while strip erosion often happened on the upslope of mountain ridge and mountaintop flat. Moderate rill erosion always occurred on the middle and down slope of mountain ridge and mountaintop flat, and weighty rushing erosion occurred on the steep ravine and brink. The RUSLE model and artificial neural network technique were feasible and could be propagandized for drainage areas control and preserved practice.

  6. 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.

  7. Neural correlates and network connectivity underlying narrative production and comprehension: a combined fMRI and PET study.

    Science.gov (United States)

    AbdulSabur, Nuria Y; Xu, Yisheng; Liu, Siyuan; Chow, Ho Ming; Baxter, Miranda; Carson, Jessica; Braun, Allen R

    2014-08-01

    The neural correlates of narrative production and comprehension remain poorly understood. Here, using positron emission tomography (PET), functional magnetic resonance imaging (fMRI), contrast and functional network connectivity analyses we comprehensively characterize the neural mechanisms underlying these complex behaviors. Eighteen healthy subjects told and listened to fictional stories during scanning. In addition to traditional language areas (e.g., left inferior frontal and posterior middle temporal gyri), both narrative production and comprehension engaged regions associated with mentalizing and situation model construction (e.g., dorsomedial prefrontal cortex, precuneus and inferior parietal lobules) as well as neocortical premotor areas, such as the pre-supplementary motor area and left dorsal premotor cortex. Narrative comprehension alone showed marked bilaterality, activating right hemisphere homologs of perisylvian language areas. Narrative production remained predominantly left lateralized, uniquely activating executive and motor-related regions essential to language formulation and articulation. Connectivity analyses revealed strong associations between language areas and the superior and middle temporal gyri during both tasks. However, only during storytelling were these same language-related regions connected to cortical and subcortical motor regions. In contrast, during story comprehension alone, they were strongly linked to regions supporting mentalizing. Thus, when employed in a more complex, ecologically-valid context, language production and comprehension show both overlapping and idiosyncratic patterns of activation and functional connectivity. Importantly, in each case the language system is integrated with regions that support other cognitive and sensorimotor domains. Copyright © 2014. Published by Elsevier Ltd.

  8. Conservative treatment of neural arch fractures of the axis: computed tomography scan and X-ray study on consolidation time.

    Science.gov (United States)

    Ramieri, Alessandro; Domenicucci, Maurizio; Landi, Alessandro; Rastelli, Emanuela; Raco, Antonino

    2011-02-01

    Computed tomography (CT) scan and X-ray study on consolidation time of C2 neural arch fractures treated conservatively were examined. A prospective study was undertaken: 28 conservatively treated fractures of the neural arch of the axis (11 hanged-man type I, 5 type II, and 12 miscellaneous fractures) were monitored during the period of orthosis by means of CT scan and plain X-rays performed on admission, and then at intervals between 2 and 120 days afterward. In patients treated with a halo vest (20 cases), a CT scan of the skull was performed to make sure that the pins were correctly positioned and to evaluate the osteolytic processes at the pin-bone interface. Two patients died as a result of other severe brain or thoracoabdominal injuries, and the remaining 26 fractures healed in an average time of 109 days (range 90-120). The process of bone consolidation was documented in detail by CT, which showed how the newly formed osteofibrous tissue (iso-hypodense) progressively filled the interfragmentary space. In 2 cases of cranial pin loosening, CT demonstrated an osteolytic rim at the interface, which prompted early removal of the halo system. At clinical follow-up (mean 32 months; range 24-84), functional status was evaluated: all of the patients were neurologically intact with the exception of one, who presented with persistent paresthesias. The most frequent disturbance was cervical pain (12 cases, 46%). CT with two- and three-dimensional reconstructed images has been shown to be the most reliable method for clarifying the evolution of bone consolidation and to show any osteolytic processes at the pin-bone interface during halo vest immobilization. Follow-up results of our series suggest that surgical treatment would not have improved the quality of life in these patients. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. 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.

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

    Science.gov (United States)

    Aronov, Dmitriy; Tank, David W.

    2015-01-01

    SUMMARY Virtual reality (VR) enables precise control of an animal’s environment and otherwise impossible experimental manipulations. Neural activity in navigating rodents has been studied on virtual linear tracks. However, the spatial navigation system’s engagement in complete two-dimensional environments has not been shown. We describe a VR setup for rats, including control software and a large-scale electrophysiology system, which supports 2D navigation by allowing animals to rotate and walk in any direction. The entorhinal-hippocampal circuit, including place cells, grid cells, head direction cells and border cells, showed 2D activity patterns in VR similar to those in the real world. Hippocampal neurons exhibited various remapping responses to changes in the appearance or the shape of the virtual environment, including a novel form in which a VR-induced cue conflict caused remapping to lock to geometry rather than salient cues. These results suggest a general-purpose tool for novel types of experimental manipulations in navigating rats. PMID:25374363

  11. 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.

  12. Neural mechanisms underlying valence inferences to sound: The role of the right angular gyrus.

    Science.gov (United States)

    Bravo, Fernando; Cross, Ian; Hawkins, Sarah; Gonzalez, Nadia; Docampo, Jorge; Bruno, Claudio; Stamatakis, Emmanuel Andreas

    2017-07-28

    We frequently infer others' intentions based on non-verbal auditory cues. Although the brain underpinnings of social cognition have been extensively studied, no empirical work has yet examined the impact of musical structure manipulation on the neural processing of emotional valence during mental state inferences. We used a novel sound-based theory-of-mind paradigm in which participants categorized stimuli of different sensory dissonance level in terms of positive/negative valence. Whilst consistent with previous studies which propose facilitated encoding of consonances, our results demonstrated that distinct levels of consonance/dissonance elicited differential influences on the right angular gyrus, an area implicated in mental state attribution and attention reorienting processes. Functional and effective connectivity analyses further showed that consonances modulated a specific inhibitory interaction from associative memory to mental state attribution substrates. Following evidence suggesting that individuals with autism may process social affective cues differently, we assessed the relationship between participants' task performance and self-reported autistic traits in clinically typical adults. Higher scores on the social cognition scales of the AQ were associated with deficits in recognising positive valence in consonant sound cues. These findings are discussed with respect to Bayesian perspectives on autistic perception, which highlight a functional failure to optimize precision in relation to prior beliefs. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. 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.

  14. 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.

  15. 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.

  16. Switching from neural networks (PAPNET) to the Imager (Hologic) for computer-assisted screening.

    Science.gov (United States)

    Boon, Mathilde E; Ouwerkerk-Noordam, Elisabeth; Meijer-Marres, Elisabeth M; Bontekoe, T Romke

    2011-01-01

    The large set of ThinPrep slides prepared in the Leiden Cytology and Pathology Laboratory is exploited for calculating the impact of the transition from PAPNET neural network scanning to the Imager technology. All cervical samples were suspended and fixed in the coagulant fixative BoonFix. We compared 57,541 ThinPrep slides which were scanned by PAPNET and 64,273 ThinPrep slides processed with the Imager: 99,157 cases originated from the Dutch population screening program of asymptomatic women (screenees) and the remaining 22,657 samples were of symptomatic women. In the PAPNET series, 23% were diagnosed by additional light microscopy; in the Imager method, all slides were studied light microscopically. The cytoscores (positive cytology per 1,000 samples) were calculated for normal, atypical squamous cells of undetermined significance (ASC-US), cervical intraepithelial neoplasia (CIN) grades I-II, and for CIN III+. The odds ratios (ORs) for the positive cytoscores were assessed for both the screenees and the symptomatic women. The cytoscores, per 1,000 cases, for ASC-US varied from 17.77 to 40.59, for CIN I-II from 7.17 to 33.35, and for CIN III+ from 2.81 to 8.8. These 6 cytoscores were higher for symptomatic women than for screenees. We observe significantly elevated ORs for the Imager for ASC-US (1.26 and 1.23), CIN I-II (1.45) and for CIN III+ (1.58 and 1.45). These 3 ORs are higher for screenees than for symptomatic women. The Imager technology is more efficacious, particularly for handling screenee slides. Copyright © 2011 S. Karger AG, Basel.

  17. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  18. 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, Ali Reza; 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 eight input variables: soil layer intervals (D), percentages of sand (Sa), silt (Si), and clay (Cl), bulk density of soil layers (Bd), weighted soil moisture deficit during the irrigation strategies period (SMD), geometric mean particle size diameter (dg), and geometric standard deviation (σg...

  19. 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.

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

    Science.gov (United States)

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

    2017-01-01

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

  1. 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

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

    Science.gov (United States)

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

    2017-01-01

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

  3. A machine for neural computation of acoustical patterns with application to real time speech recognition

    Science.gov (United States)

    Mueller, P.; Lazzaro, J.

    1986-08-01

    400 analog electronic neurons have been assembled and connected for the analysis and recognition of acoustical patterns, including speech. Input to the net comes from a set of 18 band pass filters (Qmax 300 dB/octave; 180 to 6000 Hz, log scale). The net is organized into two parts, the first performs in real time the decomposition of the input patterns into their primitives of energy, space (frequency) and time relations. The other part decodes the set of primitives. 216 neurons are dedicated to pattern decomposition. The output of the individual filters is rectified and fed to two sets of 18 neurons in an opponent center-surround organization of synaptic connections (``on center'' and (``off center''). These units compute maxima and minima of energy at different frequencies. The next two sets of neutrons compute the temporal boundaries (``on'') and ``off'') and the following two the movement of the energy maxima (formants) up or down the frequency axis. There are in addition ``hyperacuity'' units which expand the frequency resolution to 36, other units tuned to a particular range of duration of the ``on center'' units and others tuned exclusively to very low energy sounds. In order to recognize speech sounds at the phoneme or diphone level, the set of primitives belonging to the phoneme is decoded such that only one neuron or a non-overlapping group of neurons fire when the sound pattern is present at the input. For display and translation into phonetic symbols the output from these neurons is fed into an EPROM decoder and computer which displays in real time a phonetic representation of the speech input.

  4. Multilayered perceptron neural networks to compute energy losses in magnetic cores

    International Nuclear Information System (INIS)

    Kucuk, Ilker

    2006-01-01

    This paper presents a new approach based on multilayered perceptrons (MLPs) to compute the specific energy losses of toroidal wound cores built from 3% SiFe 0.27 mm thick M4, 0.1 and 0.08 mm thin gauge electrical steel strips. The MLP has been trained by a back-propagation and extended delta-bar-delta learning algorithm. The results obtained by using the MLP model were compared with a commonly used conventional method. The comparison has shown that the proposed model improved loss estimation with respect to the conventional method

  5. A neural computational model for animal's time-to-collision estimation.

    Science.gov (United States)

    Wang, Ling; Yao, Dezhong

    2013-04-17

    The time-to-collision (TTC) is the time elapsed before a looming object hits the subject. An accurate estimation of TTC plays a critical role in the survival of animals in nature and acts as an important factor in artificial intelligence systems that depend on judging and avoiding potential dangers. The theoretic formula for TTC is 1/τ≈θ'/sin θ, where θ and θ' are the visual angle and its variation, respectively, and the widely used approximation computational model is θ'/θ. However, both of these measures are too complex to be implemented by a biological neuronal model. We propose a new simple computational model: 1/τ≈Mθ-P/(θ+Q)+N, where M, P, Q, and N are constants that depend on a predefined visual angle. This model, weighted summation of visual angle model (WSVAM), can achieve perfect implementation through a widely accepted biological neuronal model. WSVAM has additional merits, including a natural minimum consumption and simplicity. Thus, it yields a precise and neuronal-implemented estimation for TTC, which provides a simple and convenient implementation for artificial vision, and represents a potential visual brain mechanism.

  6. The integration of social influence and reward: Computational approaches and neural evidence.

    Science.gov (United States)

    Tomlin, Damon; Nedic, Andrea; Prentice, Deborah A; Holmes, Philip; Cohen, Jonathan D

    2017-08-01

    Decades of research have established that decision-making is dramatically impacted by both the rewards an individual receives and the behavior of others. How do these distinct influences exert their influence on an individual's actions, and can the resulting behavior be effectively captured in a computational model? To address this question, we employed a novel spatial foraging game in which groups of three participants sought to find the most rewarding location in an unfamiliar two-dimensional space. As the game transitioned from one block to the next, the availability of information regarding other group members was varied systematically, revealing the relative impacts of feedback from the environment and information from other group members on individual decision-making. Both reward-based and socially-based sources of information exerted a significant influence on behavior, and a computational model incorporating these effects was able to recapitulate several key trends in the behavioral data. In addition, our findings suggest how these sources were processed and combined during decision-making. Analysis of reaction time, location of gaze, and functional magnetic resonance imaging (fMRI) data indicated that these distinct sources of information were integrated simultaneously for each decision, rather than exerting their influence in a separate, all-or-none fashion across separate subsets of trials. These findings add to our understanding of how the separate influences of reward from the environment and information derived from other social agents are combined to produce decisions.

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

    Science.gov (United States)

    2017-03-01

    the digital representation of xin are updated in each clock cycle for F(xin) = u in Fig. 6(b). Thereby, with a random uniform distribution of u...network, and enables power-performance trade-off. Circuit in Fig. 8(a) is triggered by a spike, and produces a pulse controlled by a digital weight-bits...sign(dx/dt) for input variable x. Circuit schematic in Fig. 9(b) computes sign of derivative of its input. Based on clock signals CLK/CLKB

  8. Basic mechanisms of DBS for Parkinson’s disease: computational and experimental studies on neural dynamics

    NARCIS (Netherlands)

    Çağnan, H.

    2010-01-01

    Deep Brain Stimulation (DBS) has become an accepted therapy of last resort for Parkinson’s disease (PD). The acceptance of DBS for the management of PD motor symptoms is based on its success rate and contrasts sharply with ones understanding of the pathophysiology underlying the disease state and

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

    Directory of Open Access Journals (Sweden)

    Viet Tra

    2017-12-01

    Full Text Available 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.

  10. What's the gist? The influence of schemas on the neural correlates underlying true and false memories.

    Science.gov (United States)

    Webb, Christina E; Turney, Indira C; Dennis, Nancy A

    2016-12-01

    The current study used a novel scene paradigm to investigate the role of encoding schemas on memory. Specifically, the study examined the influence of a strong encoding schema on retrieval of both schematic and non-schematic information, as well as false memories for information associated with the schema. Additionally, the separate roles of recollection and familiarity in both veridical and false memory retrieval were examined. The study identified several novel results. First, while many common neural regions mediated both schematic and non-schematic retrieval success, schematic recollection exhibited greater activation in visual cortex and hippocampus, regions commonly shown to mediate detailed retrieval. More effortful cognitive control regions in the prefrontal and parietal cortices, on the other hand, supported non-schematic recollection, while lateral temporal cortices supported familiarity-based retrieval of non-schematic items. Second, both true and false recollection, as well as familiarity, were mediated by activity in left middle temporal gyrus, a region associated with semantic processing and retrieval of schematic gist. Moreover, activity in this region was greater for both false recollection and false familiarity, suggesting a greater reliance on lateral temporal cortices for retrieval of illusory memories, irrespective of memory strength. Consistent with previous false memory studies, visual cortex showed increased activity for true compared to false recollection, suggesting that visual cortices are critical for distinguishing between previously viewed targets and related lures at retrieval. Additionally, the absence of common visual activity between true and false retrieval suggests that, unlike previous studies utilizing visual stimuli, when false memories are predicated on schematic gist and not perceptual overlap, there is little reliance on visual processes during false memory retrieval. Finally, the medial temporal lobe exhibited an

  11. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    is by active truncated models. In these models only the very top part of the system is represented by a physical model whereas the behavior of the part below the truncation is calculated by numerical models and accounted for in the physical model by active actuators applying relevant forces to the physical...... orders of magnitude faster than conventional numerical methods. The AAN ability to learn and predict the nonlinear relation between a given input and the corresponding output makes the hybrid method tailor made for the active actuators used in the truncated experiments. All the ANN training can be done...... prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time-without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system...

  12. Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface

    Science.gov (United States)

    Blakely, Tim M.; Miller, Kai J.; Rao, Rajesh P. N.; Ojemann, Jeffrey G.

    2014-01-01

    Human subjects can learn to control a one-dimensional electrocorticographic (ECoG) brain-computer interface (BCI) using modulation of primary motor (M1) high-gamma activity (signal power in the 75–200 Hz range). However, the stability and dynamics of the signals over the course of new BCI skill acquisition have not been investigated. In this study, we report 3 characteristic periods in evolution of the high-gamma control signal during BCI training: initial, low task accuracy with corresponding low power modulation in the gamma spectrum, followed by a second period of improved task accuracy with increasing average power separation between activity and rest, and a final period of high task accuracy with stable (or decreasing) power separation and decreasing trial-to-trial variance. These findings may have implications in the design and implementation of BCI control algorithms. PMID:25599079

  13. Neural Correlates of User-initiated Motor Success and Failure - A Brain-Computer Interface Perspective.

    Science.gov (United States)

    Yazmir, Boris; Reiner, Miriam

    2016-11-02

    Any motor action is, by nature, potentially accompanied by human errors. In order to facilitate development of error-tailored Brain-Computer Interface (BCI) correction systems, we focused on internal, human-initiated errors, and investigated EEG correlates of user outcome successes and errors during a continuous 3D virtual tennis game against a computer player. We used a multisensory, 3D, highly immersive environment. Missing and repelling the tennis ball were considered, as 'error' (miss) and 'success' (repel). Unlike most previous studies, where the environment "encouraged" the participant to perform a mistake, here errors happened naturally, resulting from motor-perceptual-cognitive processes of incorrect estimation of the ball kinematics, and can be regarded as user internal, self-initiated errors. Results show distinct and well-defined Event-Related Potentials (ERPs), embedded in the ongoing EEG, that differ across conditions by waveforms, scalp signal distribution maps, source estimation results (sLORETA) and time-frequency patterns, establishing a series of typical features that allow valid discrimination between user internal outcome success and error. The significant delay in latency between positive peaks of error- and success-related ERPs, suggests a cross-talk between top-down and bottom-up processing, represented by an outcome recognition process, in the context of the game world. Success-related ERPs had a central scalp distribution, while error-related ERPs were centro-parietal. The unique characteristics and sharp differences between EEG correlates of error/success provide the crucial components for an improved BCI system. The features of the EEG waveform can be used to detect user action outcome, to be fed into the BCI correction system. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  14. 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

  15. Cognitive and neural plasticity in older adults’ prospective memory following training with the Virtual Week computer game

    Science.gov (United States)

    Rose, Nathan S.; Rendell, Peter G.; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M.; Craik, Fergus I. M.

    2015-01-01

    Prospective memory (PM) – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood. PMID:26578936

  16. Cognitive and Neural Plasticity in Older Adults’ Prospective Memory Following Training with the Virtual Week Computer Game

    Directory of Open Access Journals (Sweden)

    Nathan S Rose

    2015-10-01

    Full Text Available Prospective memory (PM – the ability to remember and successfully execute our intentions and planned activities – is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in twelve, 1-hour sessions over one month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood.

  17. Cognitive and neural plasticity in older adults' prospective memory following training with the Virtual Week computer game.

    Science.gov (United States)

    Rose, Nathan S; Rendell, Peter G; Hering, Alexandra; Kliegel, Matthias; Bidelman, Gavin M; Craik, Fergus I M

    2015-01-01

    Prospective memory (PM) - the ability to remember and successfully execute our intentions and planned activities - is critical for functional independence and declines with age, yet few studies have attempted to train PM in older adults. We developed a PM training program using the Virtual Week computer game. Trained participants played the game in 12, 1-h sessions over 1 month. Measures of neuropsychological functions, lab-based PM, event-related potentials (ERPs) during performance on a lab-based PM task, instrumental activities of daily living, and real-world PM were assessed before and after training. Performance was compared to both no-contact and active (music training) control groups. PM on the Virtual Week game dramatically improved following training relative to controls, suggesting PM plasticity is preserved in older adults. Relative to control participants, training did not produce reliable transfer to laboratory-based tasks, but was associated with a reduction of an ERP component (sustained negativity over occipito-parietal cortex) associated with processing PM cues, indicative of more automatic PM retrieval. Most importantly, training produced far transfer to real-world outcomes including improvements in performance on real-world PM and activities of daily living. Real-world gains were not observed in either control group. Our findings demonstrate that short-term training with the Virtual Week game produces cognitive and neural plasticity that may result in real-world benefits to supporting functional independence in older adulthood.

  18. Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks.

    Science.gov (United States)

    Iqbal, Abdullah; Valous, Nektarios A; Sun, Da-Wen; Allen, Paul

    2011-02-01

    Lacunarity is about quantifying the degree of spatial heterogeneity in the visual texture of imagery through the identification of the relationships between patterns and their spatial configurations in a two-dimensional setting. The computed lacunarity data can designate a mathematical index of spatial heterogeneity, therefore the corresponding feature vectors should possess the necessary inter-class statistical properties that would enable them to be used for pattern recognition purposes. The objectives of this study is to construct a supervised parsimonious classification model of binary lacunarity data-computed by Valous et al. (2009)-from pork ham slice surface images, with the aid of kernel principal component analysis (KPCA) and artificial neural networks (ANNs), using a portion of informative salient features. At first, the dimension of the initial space (510 features) was reduced by 90% in order to avoid any noise effects in the subsequent classification. Then, using KPCA, the first nineteen kernel principal components (99.04% of total variance) were extracted from the reduced feature space, and were used as input in the ANN. An adaptive feedforward multilayer perceptron (MLP) classifier was employed to obtain a suitable mapping from the input dataset. The correct classification percentages for the training, test and validation sets were 86.7%, 86.7%, and 85.0%, respectively. The results confirm that the classification performance was satisfactory. The binary lacunarity spatial metric captured relevant information that provided a good level of differentiation among pork ham slice images. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd. All rights reserved.

  19. Modulating Conscious Movement Intention by Noninvasive Brain Stimulation and the Underlying Neural Mechanisms

    OpenAIRE

    Douglas, Zachary H.; Maniscalco, Brian; Hallett, Mark; Wassermann, Eric M.; He, Biyu J.

    2015-01-01

    Conscious intention is a fundamental aspect of the human experience. Despite long-standing interest in the basis and implications of intention, its underlying neurobiological mechanisms remain poorly understood. Using high-definition transcranial DC stimulation (tDCS), we observed that enhancing spontaneous neuronal excitability in both the angular gyrus and the primary motor cortex caused the reported time of conscious movement intention to be ∼60–70 ms earlier. Slow brain waves recorded ∼2–...

  20. Research on elastic resource management for multi-queue under cloud computing environment

    Science.gov (United States)

    CHENG, Zhenjing; LI, Haibo; HUANG, Qiulan; Cheng, Yaodong; CHEN, Gang

    2017-10-01

    As a new approach to manage computing resource, virtualization technology is more and more widely applied in the high-energy physics field. A virtual computing cluster based on Openstack was built at IHEP, using HTCondor as the job queue management system. In a traditional static cluster, a fixed number of virtual machines are pre-allocated to the job queue of different experiments. However this method cannot be well adapted to the volatility of computing resource requirements. To solve this problem, an elastic computing resource management system under cloud computing environment has been designed. This system performs unified management of virtual computing nodes on the basis of job queue in HTCondor based on dual resource thresholds as well as the quota service. A two-stage pool is designed to improve the efficiency of resource pool expansion. This paper will present several use cases of the elastic resource management system in IHEPCloud. The practical run shows virtual computing resource dynamically expanded or shrunk while computing requirements change. Additionally, the CPU utilization ratio of computing resource was significantly increased when compared with traditional resource management. The system also has good performance when there are multiple condor schedulers and multiple job queues.

  1. Invariant visual object and face recognition: neural and computational bases, and a model, VisNet

    Directory of Open Access Journals (Sweden)

    Edmund T eRolls

    2012-06-01

    Full Text Available Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy modelin which invariant representations can be built by self-organizing learning based on the temporal and spatialstatistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associativesynaptic learning rule with a short term memory trace, and/or it can use spatialcontinuity in Continuous Spatial Transformation learning which does not require a temporal trace. The model of visual processing in theventral cortical stream can build representations of objects that are invariant withrespect to translation, view, size, and also lighting. The modelhas been extended to provide an account of invariant representations in the dorsal visualsystem of the global motion produced by objects such as looming, rotation, and objectbased movement. The model has been extended to incorporate top-down feedback connectionsto model the control of attention by biased competition in for example spatial and objectsearch tasks. The model has also been extended to account for how the visual system canselect single objects in complex visual scenes, and how multiple objects can berepresented in a scene. The model has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

  2. Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform

    Directory of Open Access Journals (Sweden)

    Massimiliano eGiulioni

    2016-02-01

    Full Text Available We demonstrate robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission and follows the basic theoretical principles presented in (Benosman et al. 2014: the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. The same basic principle is embedded in the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina. Mimicking those cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. We built a 3x3 test grid of independent detectors, each observing a different portion of the scene, so that our final output is a spike train encoding a 3x3 optical flow vector field. In this work we focus on the architectural aspects, and we demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene.

  3. Polynomial neural network for robot forward and inverse kinematics learning computations

    Science.gov (United States)

    Chen, C. L. P.; McAulay, Alastair D.

    1991-03-01

    Knowing the end-effector location (position and orientation) and the joint angles of the robot manipulator in real-time will assist the manipulator in negotiating around the obstacles when the manipulator is moving in a crowded environment. Thus, Forward and Inverse Kinematics Computations (FKC and IKC) play very important roles in robotic manipulators. The main objective of this paper is to demonstrate the capability of learning different trajectories of the robot reachable space by using the proposed PNN model. A software package has been developed for solving both FKC and IKC. The software can discover both the structure and the coefficients of a model to describe the dependent output variables in terms of the independent input variables identified by the users. The simulation is performed in a two degree-of-freedom manipulator. The solutions of the built FKC and IKC networks are compared with the analytic equations. The PNN learns successfully the indicated path. The simulation result shows that the PNN can interpolate the indicated path better than 99.87% of accuracy by only training the built PNN network 361 data pairs (out of 2D space point). The approach presented here can be expanded to six degree-of-freedom type of manipulators. Detailed algorithms of the GMDH to construct the PNN kinematics models will be discussed.

  4. A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

    Science.gov (United States)

    Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

    2013-01-01

    The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

  5. 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.

  6. Shafting Alignment Computing Method of New Multibearing Rotor System under Specific Installation Requirement

    Directory of Open Access Journals (Sweden)

    Qian Chen

    2016-01-01

    Full Text Available The shafting of large steam turbine generator set is composed of several rotors which are connected by couplings. The computing method of shafting with different structure under specific installation requirement is studied in this paper. Based on three-moment equation, shafting alignment mathematical model is established. The computing method of bearing elevations and loads under corresponding installation requirements, where bending moment of each coupling is zero and there exist preset sag and gap in some couplings, is proposed, respectively. Bearing elevations and loads of shafting with different structure under specific installation requirement are calculated; calculation results are compared with installation data measured on site which verifies the validity and accuracy of the proposed shafting alignment computing method. The above work provides a reliable approach to analyze shafting alignment and could guide installation on site.

  7. The neural correlates of subjective utility of monetary outcome and probability weight in economic and in motor decision under risk

    Science.gov (United States)

    Wu, Shih-Wei; Delgado, Mauricio R.; Maloney, Laurence T.

    2011-01-01

    In decision under risk, people choose between lotteries that contain a list of potential outcomes paired with their probabilities of occurrence. We previously developed a method for translating such lotteries to mathematically equivalent motor lotteries. The probability of each outcome in a motor lottery is determined by the subject’s noise in executing a movement. In this study, we used functional magnetic resonance imaging in humans to compare the neural correlates of monetary outcome and probability in classical lottery tasks where information about probability was explicitly communicated to the subjects and in mathematically equivalent motor lottery tasks where probability was implicit in the subjects’ own motor noise. We found that activity in the medial prefrontal cortex (mPFC) and the posterior cingulate cortex (PCC) quantitatively represent the subjective utility of monetary outcome in both tasks. For probability, we found that the mPFC significantly tracked the distortion of such information in both tasks. Specifically, activity in mPFC represents probability information but not the physical properties of the stimuli correlated with this information. Together, the results demonstrate that mPFC represents probability from two distinct forms of decision under risk. PMID:21677166

  8. Modeling the state dependent impulse control for computer virus propagation under media coverage

    Science.gov (United States)

    Liang, Xiyin; Pei, Yongzhen; Lv, Yunfei

    2018-02-01

    A state dependent impulsive control model is proposed to model the spread of computer virus incorporating media coverage. By the successor function, the sufficient conditions for the existence and uniqueness of order-1 periodic solution are presented first. Secondly, for two classes of periodic solutions, the geometric property of successor function and the analogue of the Poincaré criterion are employed to obtain the stability results. These results show that the number of the infective computers is under the threshold all the time. Finally, the theoretic and numerical analysis show that media coverage can delay the spread of computer virus.

  9. 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.

  10. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  11. 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…

  12. 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

  13. 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.

  14. Revealing the Neural Mechanisms Underlying the Beneficial Effects of Tai Chi: A Neuroimaging Perspective.

    Science.gov (United States)

    Yu, Angus P; Tam, Bjorn T; Lai, Christopher W; Yu, Doris S; Woo, Jean; Chung, Ka-Fai; Hui, Stanley S; Liu, Justina Y; Wei, Gao X; Siu, Parco M

    2018-01-01

    Tai Chi Chuan (TCC), a traditional Chinese martial art, is well-documented to result in beneficial consequences in physical and mental health. TCC is regarded as a mind-body exercise that is comprised of physical exercise and meditation. Favorable effects of TCC on body balance, gait, bone mineral density, metabolic parameters, anxiety, depression, cognitive function, and sleep have been previously reported. However, the underlying mechanisms explaining the effects of TCC remain largely unclear. Recently, advances in neuroimaging technology have offered new investigative opportunities to reveal the effects of TCC on anatomical morphologies and neurological activities in different regions of the brain. These neuroimaging findings have provided new clues for revealing the mechanisms behind the observed effects of TCC. In this review paper, we discussed the possible effects of TCC-induced modulation of brain morphology, functional homogeneity and connectivity, regional activity and macro-scale network activity on health. Moreover, we identified possible links between the alterations in brain and beneficial effects of TCC, such as improved motor functions, pain perception, metabolic profile, cognitive functions, mental health and sleep quality. This paper aimed to stimulate further mechanistic neuroimaging studies in TCC and its effects on brain morphology, functional homogeneity and connectivity, regional activity and macro-scale network activity, which ultimately lead to a better understanding of the mechanisms responsible for the beneficial effects of TCC on human health.

  15. 45 CFR 233.35 - Computing the assistance payment under retrospective budgeting after the initial one or two...

    Science.gov (United States)

    2010-10-01

    ... 45 Public Welfare 2 2010-10-01 2010-10-01 false Computing the assistance payment under... FINANCIAL ASSISTANCE PROGRAMS § 233.35 Computing the assistance payment under retrospective budgeting after... shall be computed retrospectively, i.e., shall be based on income and other relevant circumstances in...

  16. 41 CFR 102-36.475 - What is the authority for transfers underComputers for Learning”?

    Science.gov (United States)

    2010-07-01

    ... for transfers under âComputers for Learningâ? 102-36.475 Section 102-36.475 Public Contracts and....475 What is the authority for transfers underComputers for Learning”? (a) The Stevenson-Wydler... permitted by law and where appropriate, the transfer of computer equipment for use by schools or nonprofit...

  17. Automated segmentation of synchrotron radiation micro-computed tomography biomedical images using Graph Cuts and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alvarenga de Moura Meneses, Anderson, E-mail: ameneses@ieee.org [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil); Giusti, Alessandro [IDSIA (Dalle Molle Institute for Artificial Intelligence), University of Lugano (Switzerland); Pereira de Almeida, Andre; Parreira Nogueira, Liebert; Braz, Delson [Nuclear Engineering Program, Federal University of Rio de Janeiro, RJ (Brazil); Cely Barroso, Regina [Laboratory of Applied Physics on Biomedical Sciences, Physics Department, Rio de Janeiro State University, RJ (Brazil); Almeida, Carlos Eduardo de [Radiological Sciences Laboratory, Rio de Janeiro State University, Rua Sao Francisco Xavier 524, CEP 20550-900, RJ (Brazil)

    2011-12-21

    Synchrotron Radiation (SR) X-ray micro-Computed Tomography ({mu}CT) enables magnified images to be used as a non-invasive and non-destructive technique with a high space resolution for the qualitative and quantitative analyses of biomedical samples. The research on applications of segmentation algorithms to SR-{mu}CT is an open problem, due to the interesting and well-known characteristics of SR images for visualization, such as the high resolution and the phase contrast effect. In this article, we describe and assess the application of the Energy Minimization via Graph Cuts (EMvGC) algorithm for the segmentation of SR-{mu}CT biomedical images acquired at the Synchrotron Radiation for MEdical Physics (SYRMEP) beam line at the Elettra Laboratory (Trieste, Italy). We also propose a method using EMvGC with Artificial Neural Networks (EMANNs) for correcting misclassifications due to intensity variation of phase contrast, which are important effects and sometimes indispensable in certain biomedical applications, although they impair the segmentation provided by conventional techniques. Results demonstrate considerable success in the segmentation of SR-{mu}CT biomedical images, with average Dice Similarity Coefficient 99.88% for bony tissue in Wistar Rats rib samples (EMvGC), as well as 98.95% and 98.02% for scans of Rhodnius prolixus insect samples (Chagas's disease vector) with EMANNs, in relation to manual segmentation. The techniques EMvGC and EMANNs cope with the task of performing segmentation in images with the intensity variation due to phase contrast effects, presenting a superior performance in comparison to conventional segmentation techniques based on thresholding and linear/nonlinear image filtering, which is also discussed in the present article.

  18. Quality assessment of microwave-vacuum dried material with the use of computer image analysis and neural model

    Science.gov (United States)

    Koszela, K.; OtrzÄ sek, J.; Zaborowicz, M.; Boniecki, P.; Mueller, W.; Raba, B.; Lewicki, A.; Przybył, K.

    2014-04-01

    The farming area for vegetables in Poland is constantly changed and modified. Each year the cultivation structure of particular vegetables is different. However, it is the cultivation of carrots that plays a significant role among vegetables. According to the Main Statistical Office (GUS), in 2012 carrot held second position among the cultivated root vegetables, and it was estimated at 835 thousand tons. In the world we are perceived as the leading producer of carrot, due to the fourth place in the ranking of global producers. Poland is the largest producer of this vegetable in the EU [1]. It is also noteworthy, that the demand for dried vegetables is still increasing. This tendency affects the development of drying industry in our country, contributing to utilization of the product surplus. Dried vegetables are used increasingly often in various sectors of food products industry, due to high nutrition value, as well as to changing alimentary preferences of consumers [2-3]. Dried carrot plays a crucial role among dried vegetables, because of its wide scope of use and high nutrition value. It contains a lot of carotene and sugar present in the form of crystals. Carrot also undergoes many different drying processes, which makes it difficult to perform a reliable quality assessment and classification of this dried material. One of many qualitative properties of dried carrot, having important influence on a positive or negative result of the quality assessment, is color and shape. The aim of the research project was to develop a method for the analysis of microwave-vacuum dried carrot images, and its application for the classification of individual fractions in the sample studied for quality assessment. During the research digital photographs of dried carrot were taken, which constituted the basis for assessment performed by a dedicated computer programme developed as a part of the research. Consequently, using a neural model, the dried material was classified [4-6].

  19. Fish and chips: implementation of a neural network model into computer chips to maximize swimming efficiency in autonomous underwater vehicles.

    Science.gov (United States)

    Blake, R W; Ng, H; Chan, K H S; Li, J

    2008-09-01

    Recent developments in the design and propulsion of biomimetic autonomous underwater vehicles (AUVs) have focused on boxfish as models (e.g. Deng and Avadhanula 2005 Biomimetic micro underwater vehicle with oscillating fin propulsion: system design and force measurement Proc. 2005 IEEE Int. Conf. Robot. Auto. (Barcelona, Spain) pp 3312-7). Whilst such vehicles have many potential advantages in operating in complex environments (e.g. high manoeuvrability and stability), limited battery life and payload capacity are likely functional disadvantages. Boxfish employ undulatory median and paired fins during routine swimming which are characterized by high hydromechanical Froude efficiencies (approximately 0.9) at low forward speeds. Current boxfish-inspired vehicles are propelled by a low aspect ratio, 'plate-like' caudal fin (ostraciiform tail) which can be shown to operate at a relatively low maximum Froude efficiency (approximately 0.5) and is mainly employed as a rudder for steering and in rapid swimming bouts (e.g. escape responses). Given this and the fact that bioinspired engineering designs are not obligated to wholly duplicate a biological model, computer chips were developed using a multilayer perception neural network model of undulatory fin propulsion in the knifefish Xenomystus nigri that would potentially allow an AUV to achieve high optimum values of propulsive efficiency at any given forward velocity, giving a minimum energy drain on the battery. We envisage that externally monitored information on flow velocity (sensory system) would be conveyed to the chips residing in the vehicle's control unit, which in turn would signal the locomotor unit to adopt kinematics (e.g. fin frequency, amplitude) associated with optimal propulsion efficiency. Power savings could protract vehicle operational life and/or provide more power to other functions (e.g. communications).

  20. Neuroscience-inspired computational systems for speech recognition under noisy conditions

    Science.gov (United States)

    Schafer, Phillip B.

    Humans routinely recognize speech in challenging acoustic environments with background music, engine sounds, competing talkers, and other acoustic noise. However, today's automatic speech recognition (ASR) systems perform poorly in such environments. In this dissertation, I present novel methods for ASR designed to approach human-level performance by emulating the brain's processing of sounds. I exploit recent advances in auditory neuroscience to compute neuron-based representations of speech, and design novel methods for decoding these representations to produce word transcriptions. I begin by considering speech representations modeled on the spectrotemporal receptive fields of auditory neurons. These representations can be tuned to optimize a variety of objective functions, which characterize the response properties of a neural population. I propose an objective function that explicitly optimizes the noise invariance of the neural responses, and find that it gives improved performance on an ASR task in noise compared to other objectives. The method as a whole, however, fails to significantly close the performance gap with humans. I next consider speech representations that make use of spiking model neurons. The neurons in this method are feature detectors that selectively respond to spectrotemporal patterns within short time windows in speech. I consider a number of methods for training the response properties of the neurons. In particular, I present a method using linear support vector machines (SVMs) and show that this method produces spikes that are robust to additive noise. I compute the spectrotemporal receptive fields of the neurons for comparison with previous physiological results. To decode the spike-based speech representations, I propose two methods designed to work on isolated word recordings. The first method uses a classical ASR technique based on the hidden Markov model. The second method is a novel template-based recognition scheme that takes

  1. Suspected atlantoaxial rotatory fixation-subluxation: the value of multidetector computed tomography scanning under general anesthesia

    NARCIS (Netherlands)

    Been, Henk D.; Kerkhoffs, Gino M. M. J.; Maas, Mario

    2007-01-01

    STUDY DESIGN: Clinical case reports and radiologic study. OBJECTIVES: To emphasize the value of computed tomography (CT) scan under general anesthesia in order to prevent misdiagnosing atlantoaxial rotatory fixation-subluxation in children with acute torticollis. SUMMARY OF BACKGROUND DATA: A

  2. An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

    Science.gov (United States)

    Vrettaros, John; Vouros, George; Drigas, Athanasios S.

    This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

  3. 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.

  4. 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. PMID:22837740

  5. Modelling the release of volatile fission product cesium from CANDU fuel under severe accident conditions using artificial neural networks

    International Nuclear Information System (INIS)

    Andrews, W.S.; Lewis, B.J.; Cox, D.S.

    1997-01-01

    An artificial neural network (ANN) model has been developed to predict the release of volatile fission products from CANDU fuel under severe accident conditions. The model was based on data for the release Of 134 Cs measured during three annealing experiments (Hot Cell Experiments 1 and 2, or HCE- 1, HCE-2 and Metallurgical Cell Experiment 1, or MCE- 1) at Chalk River Laboratories. These experiments were comprised of a total of 30 separate tests. The ANN established a correlation among 14 separate input variables and predicted the cumulative fractional release for a set of 386 data points drawn from 29 tests to a normalized error, E n , of 0.104 and an average absolute error, E abs , of 0.064. Predictions for a blind validation set (test HCE2-CM6) had an E n of 0.064 and an E abs of 0.054. A methodology is presented for deploying the ANN model by providing the connection weights. Finally, the performance of an ANN model was compared to a fuel oxidation model developed by Lewis et al. and to the U.S. Nuclear Regulatory Commission's CORSOR-M. (author)

  6. 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.

  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.

    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.

  9. Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

    Science.gov (United States)

    Hsu, Ken-Yuh (Editor); Liu, Hua-Kuang (Editor)

    1992-01-01

    The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

  10. 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

    2018-04-01

    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

  11. Mechanisms underlying metabolic and neural defects in zebrafish and human multiple acyl-CoA dehydrogenase deficiency (MADD.

    Directory of Open Access Journals (Sweden)

    Yuanquan Song

    2009-12-01

    Full Text Available In humans, mutations in electron transfer flavoprotein (ETF or electron transfer flavoprotein dehydrogenase (ETFDH lead to MADD/glutaric aciduria type II, an autosomal recessively inherited disorder characterized by a broad spectrum of devastating neurological, systemic and metabolic symptoms. We show that a zebrafish mutant in ETFDH, xavier, and fibroblast cells from MADD patients demonstrate similar mitochondrial and metabolic abnormalities, including reduced oxidative phosphorylation, increased aerobic glycolysis, and upregulation of the PPARG-ERK pathway. This metabolic dysfunction is associated with aberrant neural proliferation in xav, in addition to other neural phenotypes and paralysis. Strikingly, a PPARG antagonist attenuates aberrant neural proliferation and alleviates paralysis in xav, while PPARG agonists increase neural proliferation in wild type embryos. These results show that mitochondrial dysfunction, leading to an increase in aerobic glycolysis, affects neurogenesis through the PPARG-ERK pathway, a potential target for therapeutic intervention.

  12. The position of a standard optical computer mouse affects cardiorespiratory responses during the operation of a computer under time constraints

    Directory of Open Access Journals (Sweden)

    Shunji Sako

    2014-08-01

    Full Text Available Objectives: This study investigated the association between task-induced stress and fatigue by examining the cardiovascular responses of subjects using different mouse positions while operating a computer under time constraints. Material and Methods: The study was participated by 16 young, healthy men and examined the use of optical mouse devices affixed to laptop computers. Two mouse positions were investigated: (1 the distal position (DP, in which the subjects place their forearms on the desk accompanied by the abduction and flexion of their shoulder joints, and (2 the proximal position (PP, in which the subjects place only their wrists on the desk without using an armrest. The subjects continued each task for 16 min. We assessed differences in several characteristics according to mouse position, including expired gas values, autonomic nerve activities (based on cardiorespiratory responses, operating efficiencies (based on word counts, and fatigue levels (based on the visual analog scale – VAS. Results: Oxygen consumption (VO2, the ratio of inspiration time to respiration time (Ti/Ttotal, respiratory rate (RR, minute ventilation (VE, and the ratio of expiration to inspiration (Te/Ti were significantly lower when the participants were performing the task in the DP than those obtained in the PP. Tidal volume (VT, carbon dioxide output rates (VCO2/VE, and oxygen extraction fractions (VO2/VE were significantly higher for the DP than they were for the PP. No significant difference in VAS was observed between the positions; however, as the task progressed, autonomic nerve activities were lower and operating efficiencies were significantly higher for the DP than they were for the PP. Conclusions: Our results suggest that the DP has fewer effects on cardiorespiratory functions, causes lower levels of sympathetic nerve activity and mental stress, and produces a higher total workload than the PP. This suggests that the DP is preferable to the PP when

  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. 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

  15. The neural processing of voluntary completed, real and virtual violent and nonviolent computer game scenarios displaying predefined actions in gamers and nongamers.

    Science.gov (United States)

    Regenbogen, Christina; Herrmann, Manfred; Fehr, Thorsten

    2010-01-01

    Studies investigating the effects of violent computer and video game playing have resulted in heterogeneous outcomes. It has been assumed that there is a decreased ability to differentiate between virtuality and reality in people that play these games intensively. FMRI data of a group of young males with (gamers) and without (controls) a history of long-term violent computer game playing experience were obtained during the presentation of computer game and realistic video sequences. In gamers the processing of real violence in contrast to nonviolence produced activation clusters in right inferior frontal, left lingual and superior temporal brain regions. Virtual violence activated a network comprising bilateral inferior frontal, occipital, postcentral, right middle temporal, and left fusiform regions. Control participants showed extended left frontal, insula and superior frontal activations during the processing of real, and posterior activations during the processing of virtual violent scenarios. The data suggest that the ability to differentiate automatically between real and virtual violence has not been diminished by a long-term history of violent video game play, nor have gamers' neural responses to real violence in particular been subject to desensitization processes. However, analyses of individual data indicated that group-related analyses reflect only a small part of actual individual different neural network involvement, suggesting that the consideration of individual learning history is sufficient for the present discussion.

  16. EAST-AIA deployment under vacuum: Calibration of laser diagnostic system using computer vision

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Yang, E-mail: yangyang@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, 350 Shushanhu Rd, Hefei, Anhui (China); Song, Yuntao; Cheng, Yong; Feng, Hansheng; Wu, Zhenwei; Li, Yingying; Sun, Yongjun; Zheng, Lei [Institute of Plasma Physics, Chinese Academy of Sciences, 350 Shushanhu Rd, Hefei, Anhui (China); Bruno, Vincent; Eric, Villedieu [CEA-IRFM, F-13108 Saint-Paul-Lez-Durance (France)

    2016-11-15

    Highlights: • The first deployment of the EAST articulated inspection arm robot under vacuum is presented. • A computer vision based approach to measure the laser spot displacement is proposed. • An experiment on the real EAST tokamak is performed to validate the proposed measure approach, and the results shows that the measurement accuracy satisfies the requirement. - Abstract: For the operation of EAST tokamak, it is crucial to ensure that all the diagnostic systems are in the good condition in order to reflect the plasma status properly. However, most of the diagnostic systems are mounted inside the tokamak vacuum vessel, which makes them extremely difficult to maintain under high vacuum condition during the tokamak operation. Thanks to a system called EAST articulated inspection arm robot (EAST-AIA), the examination of these in-vessel diagnostic systems can be performed by an embedded camera carried by the robot. In this paper, a computer vision algorithm has been developed to calibrate a laser diagnostic system with the help of a monocular camera at the robot end. In order to estimate the displacement of the laser diagnostic system with respect to the vacuum vessel, several visual markers were attached to the inner wall. This experiment was conducted both on the EAST vacuum vessel mock-up and the real EAST tokamak under vacuum condition. As a result, the accuracy of the displacement measurement was within 3 mm under the current camera resolution, which satisfied the laser diagnostic system calibration.

  17. A collaborative computer auditing system under SOA-based conceptual model

    Science.gov (United States)

    Cong, Qiushi; Huang, Zuoming; Hu, Jibing

    2013-03-01

    Some of the current challenges of computer auditing are the obstacles to retrieving, converting and translating data from different database schema. During the last few years, there are many data exchange standards under continuous development such as Extensible Business Reporting Language (XBRL). These XML document standards can be used for data exchange among companies, financial institutions, and audit firms. However, for many companies, it is still expensive and time-consuming to translate and provide XML messages with commercial application packages, because it is complicated and laborious to search and transform data from thousands of tables in the ERP databases. How to transfer transaction documents for supporting continuous auditing or real time auditing between audit firms and their client companies is a important topic. In this paper, a collaborative computer auditing system under SOA-based conceptual model is proposed. By utilizing the widely used XML document standards and existing data transformation applications developed by different companies and software venders, we can wrap these application as commercial web services that will be easy implemented under the forthcoming application environments: service-oriented architecture (SOA). Under the SOA environments, the multiagency mechanism will help the maturity and popularity of data assurance service over the Internet. By the wrapping of data transformation components with heterogeneous databases or platforms, it will create new component markets composed by many software vendors and assurance service companies to provide data assurance services for audit firms, regulators or third parties.

  18. EAST-AIA deployment under vacuum: Calibration of laser diagnostic system using computer vision

    International Nuclear Information System (INIS)

    Yang, Yang; Song, Yuntao; Cheng, Yong; Feng, Hansheng; Wu, Zhenwei; Li, Yingying; Sun, Yongjun; Zheng, Lei; Bruno, Vincent; Eric, Villedieu

    2016-01-01

    Highlights: • The first deployment of the EAST articulated inspection arm robot under vacuum is presented. • A computer vision based approach to measure the laser spot displacement is proposed. • An experiment on the real EAST tokamak is performed to validate the proposed measure approach, and the results shows that the measurement accuracy satisfies the requirement. - Abstract: For the operation of EAST tokamak, it is crucial to ensure that all the diagnostic systems are in the good condition in order to reflect the plasma status properly. However, most of the diagnostic systems are mounted inside the tokamak vacuum vessel, which makes them extremely difficult to maintain under high vacuum condition during the tokamak operation. Thanks to a system called EAST articulated inspection arm robot (EAST-AIA), the examination of these in-vessel diagnostic systems can be performed by an embedded camera carried by the robot. In this paper, a computer vision algorithm has been developed to calibrate a laser diagnostic system with the help of a monocular camera at the robot end. In order to estimate the displacement of the laser diagnostic system with respect to the vacuum vessel, several visual markers were attached to the inner wall. This experiment was conducted both on the EAST vacuum vessel mock-up and the real EAST tokamak under vacuum condition. As a result, the accuracy of the displacement measurement was within 3 mm under the current camera resolution, which satisfied the laser diagnostic system calibration.

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

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

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

  20. Neural Machine Translation

    OpenAIRE

    Koehn, Philipp

    2017-01-01

    Draft of textbook chapter on neural machine translation. a comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges. Written as chapter for the textbook Statistical Machine Translation. Used in the JHU Fall 2017 class on machine translation.

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

    Science.gov (United States)

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

    2017-01-01

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

  2. Development of a signal-analysis algorithm for the ZEUS transition-radiation detector under application of a neural network

    International Nuclear Information System (INIS)

    Wollschlaeger, U.

    1992-07-01

    The aim of this thesis consisted in the development of a procedure for the analysis of the data of the transition-radiation detector at ZEUS. For this a neural network was applied and first studied, which results concerning the separation power between electron an pions can be reached by this procedure. It was shown that neural nets yield within the error limits as well results as standard algorithms (total charge, cluster analysis). At an electron efficiency of 90% pion contaminations in the range 1%-2% were reached. Furthermore it could be confirmed that neural networks can be considered for the here present application field as robust in relatively insensitive against external perturbations. For the application in the experiment beside the separation power also the time-behaviour is of importance. The requirement to keep dead-times small didn't allow the application of standard method. By a simulation the time availabel for the signal analysis was estimated. For the testing of the processing time in a neural network subsequently the corresponding algorithm was implemented into an assembler code for the digital signal processor DSP56001. (orig./HSI) [de

  3. 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

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

    Science.gov (United States)

    Lindstrom, Kara M; 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

    2009-10-06

    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 correlations in attention-orienting biases to positive and negative face emotions in a healthy sample (N=37; 9-40 years old) using functional magnetic resonance imaging and a dot-probe task. The dot-probe task in an fMRI setting yields both behavioral and neural indices of attention biases towards or away from an emotional cue (happy or angry face). In the full sample, angry-face attention bias scores did not correlate with age, and age did not correlate with brain activation to angry faces. However, age did positively correlate with attention bias towards happy faces; age also negatively correlated with left cuneus and left caudate activation to a happy bias fMRI contrast. Secondary analyses suggested age-related changes in attention bias to happy faces. The tendency in younger children to direct attention away from happy faces (relative to neutral faces) was diminished in the older age groups, in tandem with increasing neural deactivation. Implications for future work on developmental changes in attention-emotion processing are discussed.

  5. Punchless Drawing of Magnesium Alloy Sheet under Cold Condition and its Computation

    International Nuclear Information System (INIS)

    Yamashita, Minoru; Hattori, Toshio; Sato, Joji

    2011-01-01

    The punchless drawing with Maslennikov's technique was applied to the circular cup drawing of magnesium alloy AZ31B sheet under cold condition. The elastic rubber ring was used instead of the 'hard' punch, where the compressed ring dragged the sheet inward the die cavity. Attainable circumferential strain of the blank was increased by this technique with repetitive drawing operation. Thickness of the rubber pad affected little the attainable strain. The shape appearance became better when a harder rubber was used. The cup forming by single drawing operation was also tested using a small die shoulder radius. The LDR of 1.250 was obtained with the straight cup wall. Further, the computation of the punchless drawing was also conducted for the single drawing operation. The computed deformation pattern was well consistent with the corresponding experimental result.

  6. Modeling nanoscale gas sensors under realistic conditions: Computational screening of metal-doped carbon nanotubes

    DEFF Research Database (Denmark)

    García Lastra, Juan Maria; Mowbray, Duncan; Thygesen, Kristian Sommer

    2010-01-01

    We use computational screening to systematically investigate the use of transition-metal-doped carbon nanotubes for chemical-gas sensing. For a set of relevant target molecules (CO, NH3, and H2S) and the main components of air (N2, O2, and H2O), we calculate the binding energy and change in condu......We use computational screening to systematically investigate the use of transition-metal-doped carbon nanotubes for chemical-gas sensing. For a set of relevant target molecules (CO, NH3, and H2S) and the main components of air (N2, O2, and H2O), we calculate the binding energy and change...... the change in the nanotube resistance per doping site as a function of the target molecule concentration assuming charge transport in the diffusive regime. Our analysis points to Ni-doped nanotubes as candidates for CO sensors working under typical atmospheric conditions....

  7. Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy.

    Science.gov (United States)

    Hoffmann, K; Gambichler, T; Rick, A; Kreutz, M; Anschuetz, M; Grünendick, T; Orlikov, A; Gehlen, S; Perotti, R; Andreassi, L; Newton Bishop, J; Césarini, J-P; Fischer, T; Frosch, P J; Lindskov, R; Mackie, R; Nashan, D; Sommer, A; Neumann, M; Ortonne, J P; Bahadoran, P; Penas, P F; Zoras, U; Altmeyer, P

    2003-10-01

    Early detection of melanomas by means of diverse screening campaigns is an important step towards a reduction in mortality. Computer-aided analysis of digital images obtained by dermoscopy has been reported to be an accurate, practical and time-saving tool for the evaluation of pigmented skin lesions (PSLs). A prototype for the computer-aided diagnosis of PSLs using artificial neural networks (NNs) has recently been developed: diagnostic and neural analysis of skin cancer (DANAOS). To demonstrate the accuracy of PSL diagnosis by the DANAOS expert system, a multicentre study on a diverse multinational population was conducted. A calibrated camera system was developed and used to collect images of PSLs in a multicentre study in 13 dermatology centres in nine European countries. The dataset was used to train an NN expert system for the computer-aided diagnosis of melanoma. We analysed different aspects of the data collection and its influence on the performance of the expert system. The NN expert system was trained with a dataset of 2218 dermoscopic images of PSLs. The resulting expert system showed a performance similar to that of dermatologists as published in the literature. The performance depended on the size and quality of the database and its selection. The need for a large database, the usefulness of multicentre data collection, as well as the benefit of a representative collection of cases from clinical practice, were demonstrated in this trial. Images that were difficult to classify using the NN expert system were not identical to those found difficult to classify by clinicians. We suggest therefore that the combination of clinician and computer may potentially increase the accuracy of PSL diagnosis. This may result in improved detection of melanoma and a reduction in unnecessary excisions.

  8. COMPUTING

    CERN Multimedia

    I. Fisk

    2010-01-01

    Introduction The first data taking period of November produced a first scientific paper, and this is a very satisfactory step for Computing. It also gave the invaluable opportunity to learn and debrief from this first, intense period, and make the necessary adaptations. The alarm procedures between different groups (DAQ, Physics, T0 processing, Alignment/calibration, T1 and T2 communications) have been reinforced. A major effort has also been invested into remodeling and optimizing operator tasks in all activities in Computing, in parallel with the recruitment of new Cat A operators. The teams are being completed and by mid year the new tasks will have been assigned. CRB (Computing Resource Board) The Board met twice since last CMS week. In December it reviewed the experience of the November data-taking period and could measure the positive improvements made for the site readiness. It also reviewed the policy under which Tier-2 are associated with Physics Groups. Such associations are decided twice per ye...

  9. COMPUTING

    CERN Multimedia

    M. Kasemann

    Introduction A large fraction of the effort was focused during the last period into the preparation and monitoring of the February tests of Common VO Computing Readiness Challenge 08. CCRC08 is being run by the WLCG collaboration in two phases, between the centres and all experiments. The February test is dedicated to functionality tests, while the May challenge will consist of running at all centres and with full workflows. For this first period, a number of functionality checks of the computing power, data repositories and archives as well as network links are planned. This will help assess the reliability of the systems under a variety of loads, and identifying possible bottlenecks. Many tests are scheduled together with other VOs, allowing the full scale stress test. The data rates (writing, accessing and transfer¬ring) are being checked under a variety of loads and operating conditions, as well as the reliability and transfer rates of the links between Tier-0 and Tier-1s. In addition, the capa...

  10. Computational modeling of dynamic mechanical properties of pure polycrystalline magnesium under high loading strain rates

    Directory of Open Access Journals (Sweden)

    Li Qizhen

    2015-01-01

    Full Text Available Computational simulations were performed to investigate the dynamic mechanical behavior of pure polycrystalline magnesium under different high loading strain rates with the values of 800, 1000, 2000, and 3600 s−1. The Johnson-Cook model was utilized in the simulations based on finite element modeling. The results showed that the simulations provided well-matched predictions of the material behavior such as the strain rate-time history, the stress-strain curve, and the temperature increase. Under high loading strain rates, the tested material experienced linear strain hardening at the early stage of plastic deformation, increased strain hardening at the intermediate plastic deformation region, and decreased strain hardening at the region before fracture. The strain hardening rates for the studied high loading strain rate cases do not vary much with the change of strain rates.

  11. Elastic Scheduling of Scientific Workflows under Deadline Constraints in Cloud Computing Environments

    Directory of Open Access Journals (Sweden)

    Nazia Anwar

    2018-01-01

    Full Text Available Scientific workflow applications are collections of several structured activities and fine-grained computational tasks. Scientific workflow scheduling in cloud computing is a challenging research topic due to its distinctive features. In cloud environments, it has become critical to perform efficient task scheduling resulting in reduced scheduling overhead, minimized cost and maximized resource utilization while still meeting the user-specified overall deadline. This paper proposes a strategy, Dynamic Scheduling of Bag of Tasks based workflows (DSB, for scheduling scientific workflows with the aim to minimize financial cost of leasing Virtual Machines (VMs under a user-defined deadline constraint. The proposed model groups the workflow into Bag of Tasks (BoTs based on data dependency and priority constraints and thereafter optimizes the allocation and scheduling of BoTs on elastic, heterogeneous and dynamically provisioned cloud resources called VMs in order to attain the proposed method’s objectives. The proposed approach considers pay-as-you-go Infrastructure as a Service (IaaS clouds having inherent features such as elasticity, abundance, heterogeneity and VM provisioning delays. A trace-based simulation using benchmark scientific workflows representing real world applications, demonstrates a significant reduction in workflow computation cost while the workflow deadline is met. The results validate that the proposed model produces better success rates to meet deadlines and cost efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

  12. 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.

  13. 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.

  14. Computer Vision Utilization for Detection of Green House Tomato under Natural Illumination

    Directory of Open Access Journals (Sweden)

    H Mohamadi Monavar

    2013-02-01

    Full Text Available Agricultural sector experiences the application of automated systems since two decades ago. These systems are applied to harvest fruits in agriculture. Computer vision is one of the technologies that are most widely used in food industries and agriculture. In this paper, an automated system based on computer vision for harvesting greenhouse tomatoes is presented. A CCD camera takes images from workspace and tomatoes with over 50 percent ripeness are detected through an image processing algorithm. In this research three color spaces including RGB, HSI and YCbCr and three algorithms including threshold recognition, curvature of the image and red/green ratio were used in order to identify the ripe tomatoes from background under natural illumination. The average error of threshold recognition, red/green ratio and curvature of the image algorithms were 11.82%, 10.03% and 7.95% in HSI, RGB and YCbCr color spaces, respectively. Therefore, the YCbCr color space and curvature of the image algorithm were identified as the most suitable for recognizing fruits under natural illumination condition.

  15. Computational modeling and validation of human nasal airflow under various breathing conditions.

    Science.gov (United States)

    Li, Chengyu; Jiang, Jianbo; Dong, Haibo; Zhao, Kai

    2017-11-07

    The human nose serves vital physiological functions, including warming, filtration, humidification, and olfaction. These functions are based on transport phenomena that depend on nasal airflow patterns and turbulence. Accurate prediction of these airflow properties requires careful selection of computational fluid dynamics models and rigorous validation. The validation studies in the past have been limited by poor representations of the complex nasal geometry, lack of detailed airflow comparisons, and restricted ranges of flow rate. The objective of this study is to validate various numerical methods based on an anatomically accurate nasal model against published experimentally measured data under breathing flow rates from 180 to 1100ml/s. The numerical results of velocity profiles and turbulence intensities were obtained using the laminar model, four widely used Reynolds-averaged Navier-Stokes (RANS) turbulence models (i.e., k-ε, standard k-ω, Shear Stress Transport k-ω, and Reynolds Stress Model), large eddy simulation (LES) model, and direct numerical simulation (DNS). It was found that, despite certain irregularity in the flow field, the laminar model achieved good agreement with experimental results under restful breathing condition (180ml/s) and performed better than the RANS models. As the breathing flow rate increased, the RANS models achieved more accurate predictions but still performed worse than LES and DNS. As expected, LES and DNS can provide accurate predictions of the nasal airflow under all flow conditions but have an approximately 100-fold higher computational cost. Among all the RANS models tested, the standard k-ω model agrees most closely with the experimental values in terms of velocity profile and turbulence intensity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Comparing between predicted output temperature of flat-plate solar collector and experimental results: computational fluid dynamics and artificial neural network

    Directory of Open Access Journals (Sweden)

    F Nadi

    2017-05-01

    Full Text Available Introduction The significant of solar energy as a renewable energy source, clean and without damage to the environment, for the production of electricity and heat is of great importance. Furthermore, due to the oil crisis as well as reducing the cost of home heating by 70%, solar energy in the past two decades has been a favorite of many researchers. Solar collectors are devices for collecting solar radiant energy through which this energy is converted into heat and then heat is transferred to a fluid (usually air or water. Therefore, a key component in performance improvement of solar heating system is a solar collector optimization under different testing conditions. However, estimation of output parameters under different testing conditions is costly, time consuming and mostly impossible. As a result, smart use of neural networks as well as CFD (computational fluid dynamics to predict the properties with which desired output would have been acquired is valuable. To the best of our knowledge, there are no any studies that compare experimental results with CFD and ANN. Materials and Methods A corrugated galvanized iron sheet of 2 m length, 1 m wide and 0.5 mm in thickness was used as an absorber plate for absorbing the incident solar radiation (Fig. 1 and 2. Corrugations in absorber were caused turbulent air and improved heat transfer coefficient. Computational fluid dynamics K-ε turbulence model was used for simulation. The following assumptions are made in the analysis. (1 Air is a continuous medium and incompressible. (2 The flow is steady and possesses have turbulent flow characteristics, due to the high velocity of flow. (3 The thermal-physical properties of the absorber sheet and the absorber tube are constant with respect to the operating temperature. (4 The bottom side of the absorber tube and the absorber plate are assumed to be adiabatic. Artificial neural network In this research a one-hidden-layer feed-forward network based on the

  17. 29 CFR 778.313 - Computing overtime pay under the Act for employees compensated on task basis.

    Science.gov (United States)

    2010-07-01

    ... 29 Labor 3 2010-07-01 2010-07-01 false Computing overtime pay under the Act for employees compensated on task basis. 778.313 Section 778.313 Labor Regulations Relating to Labor (Continued) WAGE AND... TO REGULATIONS OVERTIME COMPENSATION Special Problems âtaskâ Basis of Payment § 778.313 Computing...

  18. Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography.

    Science.gov (United States)

    Suzuki, Kenji; Zhang, Jun; Xu, Jianwu

    2010-11-01

    A major challenge in the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is to reduce the number of false-positive (FP) detections while maintaining a high sensitivity level. A pattern-recognition technique based on the use of an artificial neural network (ANN) as a filter, which is called a massive-training ANN (MTANN), has been developed recently for this purpose. The MTANN is trained with a massive number of subvolumes extracted from input volumes together with the teaching volumes containing the distribution for the "likelihood of being a polyp;" hence the term "massive training." Because of the large number of subvolumes and the high dimensionality of voxels in each input subvolume, the training of an MTANN is time-consuming. In order to solve this time issue and make an MTANN work more efficiently, we propose here a dimension reduction method for an MTANN by using Laplacian eigenfunctions (LAPs), denoted as LAP-MTANN. Instead of input voxels, the LAP-MTANN uses the dependence structures of input voxels to compute the selected LAPs of the input voxels from each input subvolume and thus reduces the dimensions of the input vector to the MTANN. Our database consisted of 246 CTC datasets obtained from 123 patients, each of whom was scanned in both supine and prone positions. Seventeen patients had 29 polyps, 15 of which were 5-9 mm and 14 were 10-25 mm in size. We divided our database into a training set and a test set. The training set included 10 polyps in 10 patients and 20 negative patients. The test set had 93 patients including 19 polyps in seven patients and 86 negative patients. To investigate the basic properties of a LAP-MTANN, we trained the LAP-MTANN with actual polyps and a single source of FPs, which were rectal tubes. We applied the trained LAP-MTANN to simulated polyps and rectal tubes. The results showed that the performance of LAP-MTANNs with 20 LAPs was advantageous over that of the original MTANN with 171 inputs. To

  19. Massive-Training Artificial Neural Network Coupled With Laplacian-Eigenfunction-Based Dimensionality Reduction for Computer-Aided Detection of Polyps in CT Colonography

    Science.gov (United States)

    Suzuki, Kenji; Zhang, Jun; Xu, Jianwu

    2014-01-01

    A major challenge in the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is to reduce the number of false-positive (FP) detections while maintaining a high sensitivity level. A pattern-recognition technique based on the use of an artificial neural network (ANN) as a filter, which is called a massive-training ANN (MTANN), has been developed recently for this purpose. The MTANN is trained with a massive number of subvolumes extracted from input volumes together with the teaching volumes containing the distribution for the “likelihood of being a polyp;” hence the term “massive training.” Because of the large number of subvolumes and the high dimensionality of voxels in each input subvolume, the training of an MTANN is time-consuming. In order to solve this time issue and make an MTANN work more efficiently, we propose here a dimension reduction method for an MTANN by using Laplacian eigenfunctions (LAPs), denoted as LAP-MTANN. Instead of input voxels, the LAP-MTANN uses the dependence structures of input voxels to compute the selected LAPs of the input voxels from each input subvolume and thus reduces the dimensions of the input vector to the MTANN. Our database consisted of 246 CTC datasets obtained from 123 patients, each of whom was scanned in both supine and prone positions. Seventeen patients had 29 polyps, 15 of which were 5–9 mm and 14 were 10–25 mm in size. We divided our database into a training set and a test set. The training set included 10 polyps in 10 patients and 20 negative patients. The test set had 93 patients including 19 polyps in seven patients and 86 negative patients. To investigate the basic properties of a LAP-MTANN, we trained the LAP-MTANN with actual polyps and a single source of FPs, which were rectal tubes. We applied the trained LAP-MTANN to simulated polyps and rectal tubes. The results showed that the performance of LAP-MTANNs with 20 LAPs was advantageous over that of the original MTANN with 171

  20. 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 ...... 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....

  1. COMPUTING

    CERN Document Server

    2010-01-01

    Introduction Just two months after the “LHC First Physics” event of 30th March, the analysis of the O(200) million 7 TeV collision events in CMS accumulated during the first 60 days is well under way. The consistency of the CMS computing model has been confirmed during these first weeks of data taking. This model is based on a hierarchy of use-cases deployed between the different tiers and, in particular, the distribution of RECO data to T1s, who then serve data on request to T2s, along a topology known as “fat tree”. Indeed, during this period this model was further extended by almost full “mesh” commissioning, meaning that RECO data were shipped to T2s whenever possible, enabling additional physics analyses compared with the “fat tree” model. Computing activities at the CMS Analysis Facility (CAF) have been marked by a good time response for a load almost evenly shared between ALCA (Alignment and Calibration tasks - highest p...

  2. Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

    Science.gov (United States)

    Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

    2016-08-31

    Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

  3. Three dimensional computational fluid dynamic analysis of debris transport under emergency cooling water recirculation

    International Nuclear Information System (INIS)

    Park, Jong Woon

    2010-01-01

    This paper provides a computational fluid dynamic (CFD) analysis method on the evaluation of debris transport under emergency recirculation mode after loss of coolant accident of a nuclear power plant. Three dimensional reactor building floor geometrical model is constructed including flow obstacles larger than 6 inches such as mechanical components and equipments and considering various inlet flow paths from the upper reactor building such as break and spray flow. In the modeling of the inlet flows from the upper floors, effect of gravitational force was also reflected. For the precision of the analysis, 3 millions of tetrahedral-shaped meshes were generated. Reference calculation showed physically reasonable results. Sensitivity studies for mesh type and turbulence model showed very similar results to the reference case. This study provides useful information on the application of CFD to the evaluation of debris transport fraction for the design of new emergency sump filters. (orig.)

  4. Computer Simulations of Resonant Coherent Excitation of Heavy Hydrogen-Like Ions Under Planar Channeling

    Science.gov (United States)

    Babaev, A. A.; Pivovarov, Yu L.

    2010-04-01

    Resonant coherent excitation (RCE) of relativistic hydrogen-like ions is investigated by computer simulations methods. The suggested theoretical model is applied to the simulations of recent experiments on RCE of 390 MeV/u Ar17+ ions under (220) planar channeling in a Si crystal performed by T.Azuma et al at HIMAC (Tokyo). Theoretical results are in a good agreement with these experimental data and clearly show the appearance of the doublet structure of RCE peaks. The simulations are also extended to greater ion energies in order to predict the new RCE features at the future accelerator facility FAIR OSI and as an example, RCE of II GeV/u U91+ ions is considered in detail.

  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. 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.

  7. Cognitive mechanisms underlying Armoni: a computer-assisted cognitive training programme for individuals with intellectual disabilities

    Directory of Open Access Journals (Sweden)

    Claudia Peñaloza

    2016-01-01

    Full Text Available Although a number of cognitive deficits have been described in individuals with intellectual disabilities (ID, few studies have examined the use of computer-assisted cognitive training programmes in this group of people. This study sought to determine the cognitive mechanisms underlying 16 activities included in Armoni, a computerized cognitive training programme for individuals with ID, in order to validate its use with this population. Fifty adults with ID from four residential care centres in Spain underwent neuropsychological testing tapping attention, verbal memory, visual memory, comprehension, visuoperception, visuoconstruction, naming ability, verbal fluency, verbal reasoning and motor function. In addition, they performed 16 activities included in the Armoni programme. The relationships between cognitive function and the computer-based activities were assessed using Spearman correlations. Stepwise multiple regression analyses were then used to explore how cognitive function predicted the performance of individuals with ID on the programme activities. Most programme activities correlated with visuoconstruction, comprehension and naming ability. Naming ability, visual memory, comprehension and visuoconstruction contributed the most to the predictive models regarding performance on the Armoni activities. Our findings support the validity of Armoni for cognitive training in individuals with ID.

  8. Differential effects of reward and punishment in decision making under uncertainty: a computational study.

    Directory of Open Access Journals (Sweden)

    Elaine eDuffin

    2014-02-01

    Full Text Available Computational models of learning have proved largely successful in characterising potentialmechanisms which allow humans to make decisions in uncertain and volatile contexts. We reporthere findings that extend existing knowledge and show that a modified reinforcement learningmodel which differentiates between prior reward and punishment can provide the best fit tohuman behaviour in decision making under uncertainty. More specifically, we examined thefit of our modified reinforcement learning model to human behavioural data in a probabilistictwo-alternative decision making task with rule reversals. Our results demonstrate that this modelpredicted human behaviour better than a series of other models based on reinforcement learningor Bayesian reasoning. Unlike the Bayesian models, our modified reinforcement learning modeldoes not include any representation of rule switches. When our task is considered purely as amachine learning task, to gain as many rewards as possible without trying to describe humanbehaviour, the performance of modified reinforcement learning and Bayesian methods is similar.Others have used various computational models to describe human behaviour in similar tasks,however, we are not aware of any who have compared Bayesian reasoning with reinforcementlearning modified to differentiate rewards and punishments.

  9. Successful combination of computationally inexpensive GIAO 13C NMR calculations and artificial neural network pattern recognition: a new strategy for simple and rapid detection of structural misassignments.

    Science.gov (United States)

    Sarotti, Ariel M

    2013-08-07

    GIAO NMR chemical shift calculations coupled with trained artificial neural networks (ANNs) have been shown to provide a powerful strategy for simple, rapid and reliable identification of structural misassignments of organic compounds using only one set of both computational and experimental data. The geometry optimization, usually the most time-consuming step in the overall procedure, was carried out using computationally inexpensive methods (MM+, AM1 or HF/3-21G) and the NMR shielding constants at the affordable mPW1PW91/6-31G(d) level of theory. As low quality NMR prediction is typically obtained with such protocols, the decision making was foreseen as a problem of pattern recognition. Thus, given a set of statistical parameters computed after correlation between experimental and calculated chemical shifts the classification was done using the knowledge derived from trained ANNs. The training process was carried out with a set of 200 molecules chosen to provide a wide array of chemical functionalities and molecular complexity, and the results were validated with a set of 26 natural products that had been incorrectly assigned along with their 26 revised structures. The high prediction effectiveness observed makes this method a suitable test for rapid identification of structural misassignments, preventing not only the publication of wrong structures but also avoiding the consequences of such a mistake.

  10. A method for computing association rate constants of atomistically represented proteins under macromolecular crowding

    Science.gov (United States)

    Qin, Sanbo; Cai, Lu; Zhou, Huan-Xiang

    2012-12-01

    In cellular environments, two protein molecules on their way to form a specific complex encounter many bystander macromolecules. The latter molecules, or crowders, affect both the energetics of the interaction between the test molecules and the dynamics of their relative motion. In earlier work (Zhou and Szabo 1991 J. Chem. Phys. 95 5948-52), it has been shown that, in modeling the association kinetics of the test molecules, the presence of crowders can be accounted for by their energetic and dynamic effects. The recent development of the transient-complex theory for protein association in dilute solutions makes it possible to easily incorporate the energetic and dynamic effects of crowders. The transient complex refers to a late on-pathway intermediate, in which the two protein molecules have near-native relative separation and orientation, but have yet to form the many short-range specific interactions of the native complex. The transient-complex theory predicts the association rate constant as ka = ka0exp( - ΔG*el/kBT), where ka0 is the ‘basal’ rate constant for reaching the transient complex by unbiased diffusion, and the Boltzmann factors captures the influence of long-range electrostatic interactions between the protein molecules. Crowders slow down the diffusion, therefore reducing the basal rate constant (to kac0), and induce an effective interaction energy ΔGc. We show that the latter interaction energy for atomistic proteins in the presence of spherical crowders is ‘long’-ranged, allowing the association rate constant under crowding to be computed as kac = kac0exp[ - (ΔG*el + ΔG*c)/kBT]. Applications demonstrate that this computational method allows for realistic modeling of protein association kinetics under crowding.

  11. Using computational neuroscience to investigate the neural correlates of cognitive-affective integration during covert decision making.

    Science.gov (United States)

    Wagar, Brandon M; Thagard, Paul

    2003-11-01

    We presented a proposed neural level mechanism for the integration of cognitive and affective information during covert decision making. The central idea is that the ventromedial prefrontal cortex establishes predicted outcomes of responses through its connections with the amygdala, and that this information is passed through the context-moderated gateway in the nucleus accumbens in order to promote behaviours that are most beneficial to the long term survival of the organism. We then implemented the proposed mechanism in a network of spiking neurons, and tested one of its central claims. Results showed that the model was capable of producing behaviour similar to that observed in normal humans, as well as that exhibited during VMPFC damage.

  12. Chest computed tomography in children under general anesthesia - cases of an atelectasis

    International Nuclear Information System (INIS)

    Laskowska, K.; Lasek, W.; Drewa, S.; Karolkiewicz, M.; Pogorzala, M.; Wysocki, M.

    2003-01-01

    Computed tomography is a routine examination in children with diagnosed or suspected cancer. Despite the procedure is painless, it requires stillness for some time. Thus, general anesthesia is provided in selected cases. An aim of this paper was an evaluation of an atelectasis incidence in children referred to CT examination under general anesthesia. Material consisted of 11 children aged 2-61 months with neoplasmatic disease diagnosed or suspected. All of them had a regular chest CT exam under general anesthesia with lungs parenchyma, mediastinum and chest wall analyzed. In 4 of 11 children (36%) atelectasis was seen, located in supradiaphragmatic and paravertebral segments of the lungs. None of the children had clinical symptoms of atelectasis. In two of them control chest radiograms did not show any changes. In some patients general anesthesia may reduce the lungs pneumatization which can hide metastases in lungs. It could be summarized that in infants and young children sedation instead of general anesthesia in chest CT should be considered, which could improve the quality of the imaging and the safety of the examination. (author)

  13. Preliminary Computational Hemodynamics Study of Double Aortic Aneurysms under Multistage Surgical Procedures: An Idealised Model Study

    Directory of Open Access Journals (Sweden)

    Yosuke Otsuki

    2013-01-01

    Full Text Available Double aortic aneurysm (DAA falls under the category of multiple aortic aneurysms. Repair is generally done through staged surgery due to low invasiveness. In this approach, one aneurysm is cured per operation. Therefore, two operations are required for DAA. However, post-first-surgery rupture cases have been reported. Although the problems involved with managing staged surgery have been discussed for more than 30 years, investigation from a hemodynamic perspective has not been attempted. Hence, this is the first computational fluid dynamics approach to the DAA problem. Three idealized geometries were prepared: presurgery, thoracic aortic aneurysm (TAA cured, and abdominal aortic aneurysm (AAA cured. By applying identical boundary conditions for flow rate and pressure, the Navier-Stokes equation and continuity equations were solved under the Newtonian fluid assumption. Average pressure in TAA was increased by AAA repair. On the other hand, average pressure in AAA was decreased after TAA repair. Average wall shear stress was decreased at the peak in post-first-surgery models. However, the wave profile of TAA average wall shear stress was changed in the late systole phase after AAA repair. Since the average wall shear stress in the post-first-surgery models decreased and pressure at TAA after AAA repair increased, the TAA might be treated first to prevent rupture.

  14. PANAMA. A computer code to predict TRISO particle failure under accident conditions

    International Nuclear Information System (INIS)

    Verfondern, K.; Nabielek, H.

    1985-02-01

    The computer code PANAMA and its underlying modeling assumptions are presented. The models are based on independent measurements of the properties of TRISO particles with a SiC interlayer. Essential features are the calculation of internal gas pressure, of coating strength and its decrease during irradiation and its weakening due to fission product interaction during accidents. At very high temperatures, particle life is determined by SiC thermal decomposition. Good comparison is obtained in the temperature range 1600 - 2500 0 C when applying PANAMA to a wide variation of existing accident simulation experiments with spherical fuel elements. At lower temperatures, PANAMA tends to be over-conservative. Predictions of particle failure during the depressurized accident sequence with the worst temperatures of the 200 MWsub(th) side-by-side Modular Reactor System remain below the level of normal operations. The same holds true for the HTR-500 MWsub(e) accident sequence with the system under pressure. In the depressurized case, however, failure of all particles has to be expected after approximately 100 hours in the least favourable core position. (orig.) [de

  15. Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

    Science.gov (United States)

    Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

    1991-01-01

    Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

  16. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  17. Forecasting the EMU inflation rate: Linear econometric vs. non-linear computational models using genetic neural fuzzy systems

    DEFF Research Database (Denmark)

    Kooths, Stefan; Mitze, Timo Friedel; Ringhut, Eric

    2004-01-01

    This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according...

  18. Neural computation to predict in-flight particle characteristic dependences from processing parameters in the APS process

    Science.gov (United States)

    Guessasma, Sofiane; Montavon, Ghislain; Coddet, Christian

    2004-12-01

    In-flight particle sensors for thermal spraying are used for real-time monitoring of coating manufacture. However, such tools do not offer facilities to tune the processing parameters when the monitoring reveals fluctuations or instabilities in the thermal jet. To complete the process control, any diagnostic sensors need to be coupled with a predictive system to separate the effect of each processing parameter on the in-flight particle characteristics. In this work, a nonlinear dynamic system based on an artificial neural network (ANN) model is proposed to play this role. It consists of a method that relates the processing parameters to the particle emitted signal characteristics recorded with a DPV2000 (TECNAR Automation, St-Bruno, QC, Canada) optical sensing device. In such a way, a database was built to train and optimize an ANN structure. The in-flight particle average velocity, temperature, and diameter of an alumina-13wt.%titania feedstock were correlated to the injection and power parameters. Correlations are discussed on the basis of these predictive results.

  19. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

    2017-12-01

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

  20. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  1. Computed tomographic angiography under sedation in the diagnosis of suspected canine pancreatitis: a pilot study.

    Science.gov (United States)

    Adrian, A M; Twedt, D C; Kraft, S L; Marolf, A J

    2015-01-01

    Computed tomography (CT) is highly accurate for diagnosing pancreatitis in humans. The diagnosis of pancreatitis in dogs is based on clinical signs, laboratory findings, and ultrasonographic (US) changes. There are, however, inherent limitations in relying on laboratory and ultrasound findings for the clinical diagnosis of pancreatitis in dogs. We hypothesized that CT angiography would be a rapid and reliable method to confirm pancreatitis in dogs compared to ultrasonography. The aim was to describe the CT characteristics and compare them to ultrasound findings and correlate the CT appearance to the severity of the patients' clinical course. A prospective pilot case series; 10 dogs with pancreatitis were enrolled if the history, clinical signs, laboratory, and ultrasonographic findings were indicative of pancreatitis. A 3-phase angiographic CT was performed under sedation. Afterward, each dog had US-guided aspirates of the pancreas collected and blood drawn for cPLi assay. Images were evaluated for portion of visible pancreas, pancreatic size and margin, pancreatic parenchyma, presence of peripancreatic changes and contrast enhancement pattern. The results were compared with outcome. An enlarged, homogeneously to heterogeneously attenuating and contrast-enhancing pancreas with ill-defined borders was identified in all dogs. CT identified more features characterizing pancreatic abnormalities compared to US. Thrombi were found in 3/10 dogs. Three dogs with heterogeneous contrast enhancement had an overall poorer outcome than those with homogenous enhancement. CT angiography under sedation was used in dogs to confirm clinically suspected pancreatitis and identified clinically relevant and potentially prognostic features of pancreatitis in dogs. Copyright © 2014 by the American College of Veterinary Internal Medicine.

  2. Anatomy of the soul as reflected in the cerebral hemispheres: neural circuits underlying voluntary control of basic motivated behaviors.

    Science.gov (United States)

    Swanson, Larry W

    2005-12-05

    Understanding the principles of cerebral hemisphere neural network organization is essential for understanding the biological foundations of cognition and affect-thinking and feeling. A tripartite model of cerebral structure-function organization is reviewed, with attention focused on a behavior control system differentiation that mediates voluntary influences on three fundamental classes of goal-oriented behavior common to all animals. The model postulates just three cerebral divisions, one cortical and two nuclear (lateral or striatal, and medial or pallidal), that together generate a triple descending projection to the brainstem/cord motor system. This minimal circuit element is topographically organized and regionally differentiated, with the map of cortical areas serving as a basic starting point. Virtually all of the cerebral hemisphere projects on the upper brainstem behavior control column, atop the motor system hierarchy. The latter's rostral segment helps control ingestive (eating and drinking), defensive (fight or flight), and reproductive (sexual and parental) motivated behaviors, whereas its caudal segment helps control foraging or exploratory behavior to obtain or avoid specific goal objects associated with all classes of motivated behavior. (c) 2005 Wiley-Liss, Inc.

  3. 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.

    2013-01-01

    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, 14–17 years-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 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. PMID:23707590

  4. Diagnostic and therapeutic manipulations under ultrasound and computed tomography guidance of pathologic pulmonary and pleural lesions

    International Nuclear Information System (INIS)

    Nedeva-Petkova, M.; Velkova, K.

    2006-01-01

    More than 50 000 patient with new indeterminate pulmonary nodules are identified by roentgenographic assessment each year. Forty percent to 50 % of patients will ultimately be found to have malignant lesions, of which 75% will be primary lung cancers. The most appropriate strategy for the diagnosis of such indeterminate pulmonary nodules is still debated. Conventional chest X-ray followed by computed tomography have been found to accurately predict the presence of malignancy in up to 60% of lesions on the basis of their morphologic and tissue density characteristics. However, a tissue diagnosis is required unless the lesion has remained unchanged during a 2-year period of observation or if benign calcification ca