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Sample records for neural complexity hypothesis

  1. Cortical Neural Computation by Discrete Results Hypothesis.

    Science.gov (United States)

    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

  2. The Criticality Hypothesis in Neural Systems

    Science.gov (United States)

    Karimipanah, Yahya

    There is mounting evidence that neural networks of the cerebral cortex exhibit scale invariant dynamics. At the larger scale, fMRI recordings have shown evidence for spatiotemporal long range correlations. On the other hand, at the smaller scales this scale invariance is marked by the power law distribution of the size and duration of spontaneous bursts of activity, which are referred as neuronal avalanches. The existence of such avalanches has been confirmed by several studies in vitro and in vivo, among different species and across multiple scales, from spatial scale of MEG and EEG down to single cell resolution. This prevalent scale free nature of cortical activity suggests the hypothesis that the cortex resides at a critical state between two phases of order (short-lasting activity) and disorder (long-lasting activity). In addition, it has been shown, both theoretically and experimentally, that being at criticality brings about certain functional advantages for information processing. However, despite the plenty of evidence and plausibility of the neural criticality hypothesis, still very little is known on how the brain may leverage such criticality to facilitate neural coding. Moreover, the emergent functions that may arise from critical dynamics is poorly understood. In the first part of this thesis, we review several pieces of evidence for the neural criticality hypothesis at different scales, as well as some of the most popular theories of self-organized criticality (SOC). Thereafter, we will focus on the most prominent evidence from small scales, namely neuronal avalanches. We will explore the effect of adaptation and how it can maintain scale free dynamics even at the presence of external stimuli. Using calcium imaging we also experimentally demonstrate the existence of scale free activity at the cellular resolution in vivo. Moreover, by exploring the subsampling issue in neural data, we will find some fundamental constraints of the conventional methods

  3. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  4. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  5. Neural networks supporting switching, hypothesis testing, and rule application.

    Science.gov (United States)

    Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S; Seger, Carol A

    2015-10-01

    We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example "choose the blue letter". Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest

  6. Spatio-temporal conditional inference and hypothesis tests for neural ensemble spiking precision

    Science.gov (United States)

    Harrison, Matthew T.; Amarasingham, Asohan; Truccolo, Wilson

    2014-01-01

    The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatio-temporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference, not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatio-temporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis testing adjustments and to design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peri-stimulus time histogram (PSTH) or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable in other areas of neurostatistical analysis. PMID:25380339

  7. Complex-valued Neural Networks

    Science.gov (United States)

    Hirose, Akira

    This paper reviews the features and applications of complex-valued neural networks (CVNNs). First we list the present application fields, and describe the advantages of the CVNNs in two application examples, namely, an adaptive plastic-landmine visualization system and an optical frequency-domain-multiplexed learning logic circuit. Then we briefly discuss the features of complex number itself to find that the phase rotation is the most significant concept, which is very useful in processing the information related to wave phenomena such as lightwave and electromagnetic wave. The CVNNs will also be an indispensable framework of the future microelectronic information-processing hardware where the quantum electron wave plays the principal role.

  8. Neural basis of major depressive disorder: Beyond monoamine hypothesis.

    Science.gov (United States)

    Boku, Shuken; Nakagawa, Shin; Toda, Hiroyuki; Hishimoto, Akitoyo

    2018-01-01

    The monoamine hypothesis has been accepted as the most common hypothesis of major depressive disorder (MDD) for a long period because of its simplicity and understandability. Actually, most currently used antidepressants have been considered to act based on the monoamine hypothesis. However, an important problem of the monoamine hypothesis has been pointed out as follows: it fails to explain the latency of response to antidepressants. In addition, many patients with MDD have remained refractory to currently used antidepressants. Therefore, monoamine-alternate hypotheses are required to explain the latency of response to antidepressants. Such hypotheses have been expected to contribute to identifying hopeful new therapeutic targets for MDD. Past studies have revealed that the volume of the hippocampus is decreased in patients with MDD, which is likely caused by the failure of the hypothalamic-pituitary-adrenal axis and following elevation of glucocorticoids. Two hypotheses have been proposed to explain the volume of the hippocampus: (i) the neuroplasticity hypothesis; and (ii) the neurogenesis hypothesis. The neuroplasticity hypothesis explains how the hippocampal volume is decreased by the morphological changes of hippocampal neurons, such as the shortening length of dendrites and the decreased number and density of spines. The neurogenesis hypothesis explains how the hippocampal volume is decreased by the decrease of neurogenesis in the hippocampal dentate gyrus. These hypotheses are able to explain the latency of response to antidepressants. In this review, we first overview how the neuroplasticity and neurogenesis hypotheses have been developed. We then describe the details of these hypotheses. © 2017 The Authors. Psychiatry and Clinical Neurosciences © 2017 Japanese Society of Psychiatry and Neurology.

  9. Minimalist social-affective value for use in joint action: A neural-computational hypothesis

    DEFF Research Database (Denmark)

    Lowe, Robert; Almér, Alexander; Lindblad, Gustaf

    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 expl...

  10. A cross-order integration hypothesis for the neural correlate of consciousness.

    Science.gov (United States)

    Kriegel, Uriah

    2007-12-01

    One major problem many hypotheses regarding the neural correlate of consciousness (NCC), face is what we might call "the why question": why would this particular neural feature, rather than another, correlate with consciousness? The purpose of the present paper is to develop an NCC hypothesis that answers this question. The proposed hypothesis is inspired by the cross-order integration (COI) theory of consciousness, according to which consciousness arises from the functional integration of a first-order representation of an external stimulus and a second-order representation of that first-order representation. The proposal comes in two steps. The first step concerns the "general shape" of the NCC and can be directly derived from COI theory. The second step is a concrete hypothesis that can be arrived at by combining the general shape with empirical considerations.

  11. Unlocking neural complexity with a robotic key.

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    Stratton, Peter; Hasselmo, Michael; Milford, Michael

    2016-11-15

    Complex brains evolved in order to comprehend and interact with complex environments in the real world. Despite significant progress in our understanding of perceptual representations in the brain, our understanding of how the brain carries out higher level processing remains largely superficial. This disconnect is understandable, since the direct mapping of sensory inputs to perceptual states is readily observed, while mappings between (unknown) stages of processing and intermediate neural states is not. We argue that testing theories of higher level neural processing on robots in the real world offers a clear path forward, since (1) the complexity of the neural robotic controllers can be staged as necessary, avoiding the almost intractable complexity apparent in even the simplest current living nervous systems; (2) robotic controller states are fully observable, avoiding the enormous technical challenge of recording from complete intact brains; and (3) unlike computational modelling, the real world can stand for itself when using robots, avoiding the computational intractability of simulating the world at an arbitrary level of detail. We suggest that embracing the complex and often unpredictable closed-loop interactions between robotic neuro-controllers and the physical world will bring about deeper understanding of the role of complex brain function in the high-level processing of information and the control of behaviour. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

  12. Why would musical training benefit the neural encoding of speech? The OPERA hypothesis.

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    Aniruddh D. Patel

    2011-06-01

    Full Text Available Mounting evidence suggests that musical training benefits the neural encoding of speech. This paper offers a hypothesis specifying why such benefits occur. The OPERA hypothesis proposes that such benefits are driven by adaptive plasticity in speech-processing networks, and that this plasticity occurs when five conditions are met. These are: 1 Overlap: there is anatomical overlap in the brain networks that process an acoustic feature used in both music and speech (e.g., waveform periodicity, amplitude envelope, 2 Precision: music places higher demands on these shared networks than does speech, in terms of the precision of processing, 3 Emotion: the musical activities that engage this network elicit strong positive emotion, 4 Repetition: the musical activities that engage this network are frequently repeated, and 5 Attention: the musical activities that engage this network are associated with focused attention. According to the OPERA hypothesis, when these conditions are met neural plasticity drives the networks in question to function with higher precision than needed for ordinary speech communication. Yet since speech shares these networks with music, speech processing benefits. The OPERA hypothesis is used to account for the observed superior subcortical encoding of speech in musically trained individuals, and to suggest mechanisms by which musical training might improve linguistic reading abilities.

  13. A Complex-Valued Projection Neural Network for Constrained Optimization of Real Functions in Complex Variables.

    Science.gov (United States)

    Zhang, Songchuan; Xia, Youshen; Wang, Jun

    2015-12-01

    In this paper, we present a complex-valued projection neural network for solving constrained convex optimization problems of real functions with complex variables, as an extension of real-valued projection neural networks. Theoretically, by developing results on complex-valued optimization techniques, we prove that the complex-valued projection neural network is globally stable and convergent to the optimal solution. Obtained results are completely established in the complex domain and thus significantly generalize existing results of the real-valued projection neural networks. Numerical simulations are presented to confirm the obtained results and effectiveness of the proposed complex-valued projection neural network.

  14. Rapid-onset antidepressant efficacy of glutamatergic system modulators: the neural plasticity hypothesis of depression.

    Science.gov (United States)

    Wang, Jing; Jing, Liang; Toledo-Salas, Juan-Carlos; Xu, Lin

    2015-02-01

    Depression is a devastating psychiatric disorder widely attributed to deficient monoaminergic signaling in the central nervous system. However, most clinical antidepressants enhance monoaminergic neurotransmission with little delay but require 4-8 weeks to reach therapeutic efficacy, a paradox suggesting that the monoaminergic hypothesis of depression is an oversimplification. In contrast to the antidepressants targeting the monoaminergic system, a single dose of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine produces rapid (within 2 h) and sustained (over 7 days) antidepressant efficacy in treatment-resistant patients. Glutamatergic transmission mediated by NMDARs is critical for experience-dependent synaptic plasticity and learning, processes that can be modified indirectly by the monoaminergic system. To better understand the mechanisms of action of the new antidepressants like ketamine, we review and compare the monoaminergic and glutamatergic antidepressants, with emphasis on neural plasticity. The pathogenesis of depression may involve maladaptive neural plasticity in glutamatergic circuits that may serve as a new class of targets to produce rapid antidepressant effects.

  15. Classes of feedforward neural networks and their circuit complexity

    NARCIS (Netherlands)

    Shawe-Taylor, John S.; Anthony, Martin H.G.; Kern, Walter

    1992-01-01

    This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a

  16. Cardio-cephalic neural crest syndrome: A novel hypothesis of vascular neurocristopathy.

    Science.gov (United States)

    Komiyama, M

    2017-12-01

    A novel hypothesis proposes that "cardio-cephalic neural crest (NC) syndrome," i.e. cephalic NC including cardiac NC, contributes to the concurrent occurrence of vascular diseases in the cardio- and cerebrovascular regions. NC is a transient structure present in early embryogenesis. Cephalic NC provides mesenchymal cells to the vascular media in these regions. Concurrent cardio- and cerebrovascular lesions have been reported in PHACE syndrome, ACTA2 mutation syndrome, and less frequently in the spontaneous occlusion of the circle of Willis (so-called moyamoya disease). Cardiovascular lesions in these syndromes include coarctation of the aorta, persistent truncus arteriosus, patent ductus arteriosus, and coronary artery disease, and cerebrovascular lesions include agenesis and stenosis/occlusion of the internal carotid arteries, and moyamoya phenomenon. These concurrent vascular lesions both in the cardio- and cerebrovascular regions might be related to cephalic NC. This hypothesis, although not proven, may facilitate a better understanding of the above-mentioned NC-related vascular pathologies and lead to appropriate diagnostic and therapeutic approaches for clinicians and chart future direction for researchers.

  17. Complex-valued neural networks advances and applications

    CERN Document Server

    Hirose, Akira

    2013-01-01

    Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

  18. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  19. 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

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

    Directory of Open Access Journals (Sweden)

    Robert J Lowe

    2016-08-01

    Full Text Available 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 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 Associative Two-Process (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.

  1. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  2. Localizing complex neural circuits with MEG data.

    Science.gov (United States)

    Belardinelli, P; Ciancetta, L; Pizzella, V; Del Gratta, C; Romani, G L

    2006-03-01

    During cognitive processing, the various cortical areas, with specialized functions, supply for different tasks. In most cases then, the information flows are processed in a parallel way by brain networks which work together integrating the single performances for a common goal. Such a step is generally performed at higher processing levels in the associative areas. The frequency range at which neuronal pools oscillate is generally wider than the one which is detectable by bold changes in fMRI studies. A high time resolution technique like magnetoencephalography or electroencephalography is therefore required as well as new data processing algorithms for detecting different coherent brain areas cooperating for one cognitive task. Our experiments show that no algorithm for the inverse problem solution is immune from bias. We propose therefore, as a possible solution, our software LOCANTO (LOcalization and Coherence ANalysis TOol). This new package features a set of tools for the detection of coherent areas. For such a task, as a default, it employs the algorithm with best performances for the neural landscape to be detected. If the neural landscape under attention involves more than two interacting areas the SLoreta algorithm is used. Our study shows in fact that SLoreta performance is not biased when the correlation among multiple sources is high. On the other hand, the Beamforming algorithm is more precise than SLoreta at localizing single or double sources but it gets a relevant localization bias when the sources are more than three and are highly correlated.

  3. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  4. The Complexity of Dynamics in Small Neural Circuits.

    Directory of Open Access Journals (Sweden)

    Diego Fasoli

    2016-08-01

    Full Text Available Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing.

  5. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...

  6. Complexity and competition in appetitive and aversive neural circuits

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    Crista L. Barberini

    2012-11-01

    Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

  7. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...

  8. Water deprivation and the double- depletion hypothesis: common neural mechanisms underlie thirst and salt appetite

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    L.A. Jr De Luca

    2007-05-01

    Full Text Available Water deprivation-induced thirst is explained by the double-depletion hypothesis, which predicts that dehydration of the two major body fluid compartments, the extracellular and intracellular compartments, activates signals that combine centrally to induce water intake. However, sodium appetite is also elicited by water deprivation. In this brief review, we stress the importance of the water-depletion and partial extracellular fluid-repletion protocol which permits the distinction between sodium appetite and thirst. Consistent enhancement or a de novo production of sodium intake induced by deactivation of inhibitory nuclei (e.g., lateral parabrachial nucleus or hormones (oxytocin, atrial natriuretic peptide, in water-deprived, extracellular-dehydrated or, contrary to tradition, intracellular-dehydrated rats, suggests that sodium appetite and thirst share more mechanisms than previously thought. Water deprivation has physiological and health effects in humans that might be related to the salt craving shown by our species.

  9. Sex differences in the neural mechanisms mediating addiction: a new synthesis and hypothesis

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    Becker Jill B

    2012-06-01

    Full Text Available Abstract In this review we propose that there are sex differences in how men and women enter onto the path that can lead to addiction. Males are more likely than females to engage in risky behaviors that include experimenting with drugs of abuse, and in susceptible individuals, they are drawn into the spiral that can eventually lead to addiction. Women and girls are more likely to begin taking drugs as self-medication to reduce stress or alleviate depression. For this reason women enter into the downward spiral further along the path to addiction, and so transition to addiction more rapidly. We propose that this sex difference is due, at least in part, to sex differences in the organization of the neural systems responsible for motivation and addiction. Additionally, we suggest that sex differences in these systems and their functioning are accentuated with addiction. In the current review we discuss historical, cultural, social and biological bases for sex differences in addiction with an emphasis on sex differences in the neurotransmitter systems that are implicated.

  10. GIN'n'CIN hypothesis of brain aging: deciphering the role of somatic genetic instabilities and neural aneuploidy during ontogeny

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    Iourov Ivan Y

    2009-11-01

    Full Text Available Abstract Genomic instability (GIN and chromosome instability (CIN are two closely related ways to produce a variety of pathogenic conditions, i.e. cancer, neurodegeneration, chromosomal and genomic diseases. The GIN and CIN manifestation that possesses the most appreciable impact on cell physiology and viability is aneuploidy. The latter has been consistently shown to be associated with aging. Classically, it has been considered that a failure of mitotic machinery leads to aneuploidy acquiring throughout aging in dividing cells. Paradoxically, this model is inapplicable for the human brain, which is composed of post-mitotic cells persisting throughout the lifetime. To solve this paradox, we have focused on mosaic neural aneuploidy, a remarkable biomarker of GIN and CIN in the normal and diseased brain (i.e. Alzheimer's disease and ataxia-telangiectasia. Looking through the available data on genomic variations in the developing and adult human central nervous system, we were able to propose a hypothesis suggesting that neural aneuploidy produced during early brain development plays a crucial role of genetic determinant of aging in the healthy and diseased brain.

  11. Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation.

    Science.gov (United States)

    Di Domenico, Stefano I; Rodrigo, Achala H; Ayaz, Hasan; Fournier, Marc A; Ruocco, Anthony C

    2015-04-01

    Research on the neural efficiency hypothesis of intelligence (NEH) has revealed that the brains of more intelligent individuals consume less energy when performing easy cognitive tasks but more energy when engaged in difficult mental operations. However, previous studies testing the NEH have relied on cognitive tasks that closely resemble psychometric tests of intelligence, potentially confounding efficiency during intelligence-test performance with neural efficiency per se. The present study sought to provide a novel test of the NEH by examining patterns of prefrontal activity while participants completed an experimental paradigm that is qualitatively distinct from the contents of psychometric tests of intelligence. Specifically, participants completed a personal decision-making task (e.g., which occupation would you prefer, dancer or chemist?) in which they made a series of forced choices according to their subjective preferences. The degree of decisional conflict (i.e., choice difficulty) between the available response options was manipulated on the basis of participants' unique preference ratings for the target stimuli, which were obtained prior to scanning. Evoked oxygenation of the prefrontal cortex was measured using 16-channel continuous-wave functional near-infrared spectroscopy. Consistent with the NEH, intelligence predicted decreased activation of the right inferior frontal gyrus (IFG) during low-conflict situations and increased activation of the right-IFG during high-conflict situations. This pattern of right-IFG activity among more intelligent individuals was complemented by faster reaction times in high-conflict situations. These results provide new support for the NEH and suggest that the neural efficiency of more intelligent individuals generalizes to the performance of cognitive tasks that are distinct from intelligence tests. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

    Science.gov (United States)

    Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

    2016-12-22

    In this paper, based on CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

  13. Emotional complexity and the neural representation of emotion in motion.

    Science.gov (United States)

    Tavares, Paula; Barnard, Philip J; Lawrence, Andrew D

    2011-01-01

    According to theories of emotional complexity, individuals low in emotional complexity encode and represent emotions in visceral or action-oriented terms, whereas individuals high in emotional complexity encode and represent emotions in a differentiated way, using multiple emotion concepts. During functional magnetic resonance imaging, participants viewed valenced animated scenarios of simple ball-like figures attending either to social or spatial aspects of the interactions. Participant's emotional complexity was assessed using the Levels of Emotional Awareness Scale. We found a distributed set of brain regions previously implicated in processing emotion from facial, vocal and bodily cues, in processing social intentions, and in emotional response, were sensitive to emotion conveyed by motion alone. Attention to social meaning amplified the influence of emotion in a subset of these regions. Critically, increased emotional complexity correlated with enhanced processing in a left temporal polar region implicated in detailed semantic knowledge; with a diminished effect of social attention; and with increased differentiation of brain activity between films of differing valence. Decreased emotional complexity was associated with increased activity in regions of pre-motor cortex. Thus, neural coding of emotion in semantic vs action systems varies as a function of emotional complexity, helping reconcile puzzling inconsistencies in neuropsychological investigations of emotion recognition.

  14. A complex-valued neural dynamical optimization approach and its stability analysis.

    Science.gov (United States)

    Zhang, Songchuan; Xia, Youshen; Zheng, Weixing

    2015-01-01

    In this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach.

  15. Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks

    Directory of Open Access Journals (Sweden)

    Huisheng Zhang

    2009-01-01

    Full Text Available The batch split-complex backpropagation (BSCBP algorithm for training complex-valued neural networks is considered. For constant learning rate, it is proved that the error function of BSCBP algorithm is monotone during the training iteration process, and the gradient of the error function tends to zero. By adding a moderate condition, the weights sequence itself is also proved to be convergent. A numerical example is given to support the theoretical analysis.

  16. A long distance dispersal hypothesis for the Pandanaceae and the origins of the Pandanus tectorius complex.

    Science.gov (United States)

    Gallaher, Timothy; Callmander, Martin W; Buerki, Sven; Keeley, Sterling C

    2015-02-01

    Pandanaceae (screwpines) is a monocot family composed of c. 750 species widely distributed in the Paleotropics. It has been proposed that the family may have a Gondwanan origin with an extant Paleotropical distribution resulting from the breakup of that supercontinent. However, fossils supporting that hypothesis have been recently reassigned to other families while new fossil discoveries suggest an alternate hypothesis. In the present study, nuclear and chloroplast sequences were used to resolve relationships among Pandanaceae genera. Two well-supported fossils were used to produce a chronogram to infer whether the age of major intra-familial lineages corresponds with the breakup of Gondwana. The Pandanaceae has a Late Cretaceous origin, and genera on former Gondwanan landmasses began to diverge in the Late Eocene, well after many of the southern hemisphere continents became isolated. The results suggest an extant distribution influenced by long-distance-dispersal. The most widespread group within the family, the Pandanus tectorius species complex, originated in Eastern Queensland within the past six million years and has spread to encompass nearly the entire geographic extent of the family from Africa through Polynesia. The spread of that group is likely due to dispersal via hydrochory as well as a combination of traits such as agamospermy, anemophily, and multi-seeded propagules which can facilitate the establishment of new populations in remote locations. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Unaccusative verb production in agrammatic aphasia: the argument structure complexity hypothesis.

    Science.gov (United States)

    Thompson, Cynthia K

    2003-03-01

    This study examined patterns of verb production in narrative samples of eight individuals with agrammatic aphasia and seven education- and age-matched normal subjects. Comprehension and constrained production of two types of intransitive verbs-unaccusatives whose argument structure triggers a complex syntactic derivation and unergatives that are considered syntactically simple- was also tested. Results showed that in narrative tasks a hierarchy of verb production difficulty as seen in previous studies [Aphasiology 11 (1997) 473; Brain and Language 74 (2000) 1] emerged for the aphasic participants, with a preference noted for production of verbs with a fewer number of arguments. Both normal and agrammatic subjects also showed fewer productions of unaccusative intransitive verbs in their narrative samples as compared to other verb types (supporting findings reported by Kegl [Brain and Language 50 (1995) 151]. In contrast to relatively spared comprehension of both unaccusative and unergative intransitives, the aphasic participants showed significantly greater difficulty producing unaccusatives as compared to unergatives in the constrained task. These findings suggest that deficits in accessing verbs for production are influenced by the verb's argument structure entry and led to what is referred to as the 'argument structure complexity hypothesis'. When verbs become more complex in terms of the number of associated arguments or when the argument structure entry of the verb does not directly map to its s-structure representation, production difficulty increases.

  18. Matched, mismatched, and robust scatter matrix estimation and hypothesis testing in complex t-distributed data

    Science.gov (United States)

    Fortunati, Stefano; Gini, Fulvio; Greco, Maria S.

    2016-12-01

    Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications. In this paper, we investigate and compare the matched, mismatched, and robust approaches to solve these problems in the context of the complex elliptically symmetric (CES) distributions. The matched approach is when the estimation and detection algorithms are tailored on the correct data distribution, whereas the mismatched approach refers to the case when the scatter matrix estimator and the decision rule are derived under a model assumption that is not correct. The robust approach aims at providing good estimation and detection performance, even if suboptimal, over a large set of possible data models, irrespective of the actual data distribution. Specifically, due to its central importance in both the statistical and engineering applications, we assume for the input data a complex t-distribution. We analyze scatter matrix estimators derived under the three different approaches and compare their mean square error (MSE) with the constrained Cramér-Rao bound (CCRB) and the constrained misspecified Cramér-Rao bound (CMCRB). In addition, the detection performance and false alarm rate (FAR) of the various detection algorithms are compared with that of the clairvoyant optimum detector.

  19. Complexity of VTA DA neural activities in response to PFC transection in nicotine treated rats

    Directory of Open Access Journals (Sweden)

    Akay Yasemin M

    2011-02-01

    Full Text Available Abstract Background The dopaminergic (DA neurons in the ventral tegmental area (VTA are widely implicated in the addiction and natural reward circuitry of the brain. These neurons project to several areas of the brain, including prefrontal cortex (PFC, nucleus accubens (NAc and amygdala. The functional coupling between PFC and VTA has been demonstrated, but little is known about how PFC mediates nicotinic modulation in VTA DA neurons. The objectives of this study were to investigate the effect of acute nicotine exposure on the VTA DA neuronal firing and to understand how the disruption of communication from PFC affects the firing patterns of VTA DA neurons. Methods Extracellular single-unit recordings were performed on Sprague-Dawley rats and nicotine was administered after stable recording was established as baseline. In order to test how input from PFC affects the VTA DA neuronal firing, bilateral transections were made immediate caudal to PFC to mechanically delete the interaction between VTA and PFC. Results The complexity of the recorded neural firing was subsequently assessed using a method based on the Lempel-Ziv estimator. The results were compared with those obtained when computing the entropy of neural firing. Exposure to nicotine triggered a significant increase in VTA DA neurons firing complexity when communication between PFC and VTA was present, while transection obliterated the effect of nicotine. Similar results were obtained when entropy values were estimated. Conclusions Our findings suggest that PFC plays a vital role in mediating VTA activity. We speculate that increased firing complexity with acute nicotine administration in PFC intact subjects is due to the close functional coupling between PFC and VTA. This hypothesis is supported by the fact that deletion of PFC results in minor alterations of VTA DA neural firing when nicotine is acutely administered.

  20. Natural lecithin promotes neural network complexity and activity.

    Science.gov (United States)

    Latifi, Shahrzad; Tamayol, Ali; Habibey, Rouhollah; Sabzevari, Reza; Kahn, Cyril; Geny, David; Eftekharpour, Eftekhar; Annabi, Nasim; Blau, Axel; Linder, Michel; Arab-Tehrany, Elmira

    2016-05-27

    Phospholipids in the brain cell membranes contain different polyunsaturated fatty acids (PUFAs), which are critical to nervous system function and structure. In particular, brain function critically depends on the uptake of the so-called "essential" fatty acids such as omega-3 (n-3) and omega-6 (n-6) PUFAs that cannot be readily synthesized by the human body. We extracted natural lecithin rich in various PUFAs from a marine source and transformed it into nanoliposomes. These nanoliposomes increased neurite outgrowth, network complexity and neural activity of cortical rat neurons in vitro. We also observed an upregulation of synapsin I (SYN1), which supports the positive role of lecithin in synaptogenesis, synaptic development and maturation. These findings suggest that lecithin nanoliposomes enhance neuronal development, which may have an impact on devising new lecithin delivery strategies for therapeutic applications.

  1. Complex Environmental Data Modelling Using Adaptive General Regression Neural Networks

    Science.gov (United States)

    Kanevski, Mikhail

    2015-04-01

    The research deals with an adaptation and application of Adaptive General Regression Neural Networks (GRNN) to high dimensional environmental data. GRNN [1,2,3] are efficient modelling tools both for spatial and temporal data and are based on nonparametric kernel methods closely related to classical Nadaraya-Watson estimator. Adaptive GRNN, using anisotropic kernels, can be also applied for features selection tasks when working with high dimensional data [1,3]. In the present research Adaptive GRNN are used to study geospatial data predictability and relevant feature selection using both simulated and real data case studies. The original raw data were either three dimensional monthly precipitation data or monthly wind speeds embedded into 13 dimensional space constructed by geographical coordinates and geo-features calculated from digital elevation model. GRNN were applied in two different ways: 1) adaptive GRNN with the resulting list of features ordered according to their relevancy; and 2) adaptive GRNN applied to evaluate all possible models N [in case of wind fields N=(2^13 -1)=8191] and rank them according to the cross-validation error. In both cases training were carried out applying leave-one-out procedure. An important result of the study is that the set of the most relevant features depends on the month (strong seasonal effect) and year. The predictabilities of precipitation and wind field patterns, estimated using the cross-validation and testing errors of raw and shuffled data, were studied in detail. The results of both approaches were qualitatively and quantitatively compared. In conclusion, Adaptive GRNN with their ability to select features and efficient modelling of complex high dimensional data can be widely used in automatic/on-line mapping and as an integrated part of environmental decision support systems. 1. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning for Spatial Environmental Data. Theory, applications and software. EPFL Press

  2. Neural complexity as a potential translational biomarker for psychosis.

    Science.gov (United States)

    Hager, Brandon; Yang, Albert C; Brady, Roscoe; Meda, Shashwath; Clementz, Brett; Pearlson, Godfrey D; Sweeney, John A; Tamminga, Carol; Keshavan, Matcheri

    2017-07-01

    probands shared increased brain signal randomness in ventral lateral PFC, whereas SAD and SZ probands shared increased brain signal randomness in dorsal medial PFC. Only SZ showed increased brain signal randomness in dorsal lateral PFC. The increased brain signal randomness in dorsal or ventral PFC was weakly associated with reduced cognitive performance in psychotic probands. These observations support the loss of brain complexity hypothesis in psychotic probands. Furthermore, we found significant differences as well as overlaps of pathologic brain signal complexity between psychotic probands by DSM diagnoses, thus suggesting a biological approach to categorizing psychosis based on functional neuroimaging data. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Complexity, Compassion and Self-Organisation: Human Evolution and the Vulnerable Ape Hypothesis

    Directory of Open Access Journals (Sweden)

    Nick P. Winder

    2015-06-01

    Full Text Available Humans are agents capable of helping others, learning new behaviours and forgetting old ones. The evolutionary approach to archaeological systems has therefore been hampered by the 'modern synthesis' - a gene-centred model of evolution as a process that eliminates those that cannot handle stress. The result has been a form of environmental determinism that explains human evolution in terms of heroic struggles and selective winnowing. Biologists committed to the modern synthesis have either dismissed agency as a delusion wrought in our bodies by natural selection, or imposed a sharp, Cartesian split between 'natural' and 'artificial' ecologies. We revisit the seminal literature of evolutionary biology and show that the paradigmatic fault lines of 21st century anthropology can be traced back to the 19th century and beyond. Lamarck had developed a two-factor evolutionary theory - one factor an endogenous tendency to become more advanced and complex, the other an exogenous constraint that drove organisms into conformity with environment. Darwin tried to eliminate the progressive tendency and imposed linearity constraints on evolution that Thomas Henry Huxley rejected. When experimental evidence falsified Darwin's linear hypothesis, the race began to develop a new, gene-centred model of evolution. This became the modern synthesis. The modern synthesis is now under pressure from the evidence of anthropology, sociology, palaeontology, ecology and genetics. An 'extended synthesis' is emerging. If evolution is adequately summarised by the aphorism survival of the fittest, then 'fitness' cannot always be defined in the heroic sense of 'better able to compete and reproduce'. The fittest organisms are often those that evade selective winnowing, even when their ability to compete and reproduce has been compromised by their genes. Characteristically human traits like language, abstraction, compassion and altruism may have arisen as coping strategies that

  4. Biphasic influence of Miz1 on neural crest development by regulating cell survival and apical adhesion complex formation in the developing neural tube

    Science.gov (United States)

    Kerosuo, Laura; Bronner, Marianne E.

    2014-01-01

    Myc interacting zinc finger protein-1 (Miz1) is a transcription factor known to regulate cell cycle– and cell adhesion–related genes in cancer. Here we show that Miz1 also plays a critical role in neural crest development. In the chick, Miz1 is expressed throughout the neural plate and closing neural tube. Its morpholino-mediated knockdown affects neural crest precursor survival, leading to reduction of neural plate border and neural crest specifier genes Msx-1, Pax7, FoxD3, and Sox10. Of interest, Miz1 loss also causes marked reduction of adhesion molecules (N-cadherin, cadherin6B, and α1-catenin) with a concomitant increase of E-cadherin in the neural folds, likely leading to delayed and decreased neural crest emigration. Conversely, Miz1 overexpression results in up-regulation of cadherin6B and FoxD3 expression in the neural folds/neural tube, leading to premature neural crest emigration and increased number of migratory crest cells. Although Miz1 loss effects cell survival and proliferation throughout the neural plate, the neural progenitor marker Sox2 was unaffected, suggesting a neural crest–selective effect. The results suggest that Miz1 is important not only for survival of neural crest precursors, but also for maintenance of integrity of the neural folds and tube, via correct formation of the apical adhesion complex therein. PMID:24307680

  5. An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E. P.

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

  6. An attractor-based complexity measurement for Boolean recurrent neural networks.

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E P

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

  7. Fast Recall for Complex-Valued Hopfield Neural Networks with Projection Rules

    Directory of Open Access Journals (Sweden)

    Masaki Kobayashi

    2017-01-01

    Full Text Available Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local minima. In the present work, we propose a new recall algorithm that eliminates the local minima. We show that our proposed recall algorithm not only accelerated the recall but also improved the noise tolerance through computer simulations.

  8. The Oedipal Complex and Child Sexual Abuse Research: A Re-examination of Freud's Hypothesis.

    Science.gov (United States)

    Kendall-Tackett, Kathleen A.

    In 1896, Sigmund Freud stated that early childhood seduction caused hysteria in his female patients. He later recanted his original finding and claimed that the reports of abuse he heard from his patients were not descriptions of real events, but his patients' expressions of unconscious childhood wishes. The theory of the Oedipal complex gave…

  9. A note on the complexity of reliability in neural networks.

    Science.gov (United States)

    Berman, P; Parberry, I; Schnitger, G

    1992-01-01

    It is shown that in a standard discrete neural network model with small fan-in, tolerance to random malicious faults can be achieved with a log-linear increase in the number of neurons and a constant factor increase in parallel time, provided fan-in can increase arbitrarily. A similar result is obtained for a nonstandard but closely related model with no restriction on fan-in.

  10. Unaccusative verb production in agrammatic aphasia: the argument structure complexity hypothesis

    Science.gov (United States)

    Thompson, Cynthia K.

    2011-01-01

    This study examined patterns of verb production in narrative samples of eight individuals with agrammatic aphasia and seven education- and age-matched normal subjects. Comprehension and constrained production of two types of intransitive verbs—unaccusatives whose argument structure triggers a complex syntactic derivation and unergatives that are considered syntactically simple— was also tested. Results showed that in narrative tasks a hierarchy of verb production difficulty as seen in previous studies [Aphasiology 11 (1997) 473; Brain and Language 74 (2000) 1] emerged for the aphasic participants, with a preference noted for production of verbs with a fewer number of arguments. Both normal and agrammatic subjects also showed fewer productions of unaccusative intransitive verbs in their narrative samples as compared to other verb types (supporting findings reported by Kegl [Brain and Language 50 (1995) 151]. In contrast to relatively spared comprehension of both unaccusative and unergative intransitives, the aphasic participants showed significantly greater difficulty producing unaccusatives as compared to unergatives in the constrained task. These findings suggest that deficits in accessing verbs for production are influenced by the verb’s argument structure entry and led to what is referred to as the ‘argument structure complexity hypothesis’. When verbs become more complex in terms of the number of associated arguments or when the argument structure entry of the verb does not directly map to its s-structure representation, production difficulty increases. PMID:21274410

  11. Vocal production complexity correlates with neural instructions in the oyster toadfish (Opsanus tau)

    DEFF Research Database (Denmark)

    Elemans, C. P. H.; Mensinger, A. F.; Rome, L. C.

    2014-01-01

    Sound communication is fundamental to many social interactions and essential to courtship and agonistic behaviours in many vertebrates. The swimbladder and associated muscles in batrachoidid fishes (midshipman and toadfish) is a unique vertebrate sound production system, wherein fundamental frequ...... across vocal tetrapods have selected for muscles and motorneurons adapted for speed, which can execute complex neural instructions into equivalently complex vocalisations....

  12. Emotion processing in words: a test of the neural re-use hypothesis using surface and intracranial EEG.

    Science.gov (United States)

    Ponz, Aurélie; Montant, Marie; Liegeois-Chauvel, Catherine; Silva, Catarina; Braun, Mario; Jacobs, Arthur M; Ziegler, Johannes C

    2014-05-01

    This study investigates the spatiotemporal brain dynamics of emotional information processing during reading using a combination of surface and intracranial electroencephalography (EEG). Two different theoretical views were opposed. According to the standard psycholinguistic perspective, emotional responses to words are generated within the reading network itself subsequent to semantic activation. According to the neural re-use perspective, brain regions that are involved in processing emotional information contained in other stimuli (faces, pictures, smells) might be in charge of the processing of emotional information in words as well. We focused on a specific emotion-disgust-which has a clear locus in the brain, the anterior insula. Surface EEG showed differences between disgust and neutral words as early as 200 ms. Source localization suggested a cortical generator of the emotion effect in the left anterior insula. These findings were corroborated through the intracranial recordings of two epileptic patients with depth electrodes in insular and orbitofrontal areas. Both electrodes showed effects of disgust in reading as early as 200 ms. The early emotion effect in a brain region (insula) that responds to specific emotions in a variety of situations and stimuli clearly challenges classic sequential theories of reading in favor of the neural re-use perspective.

  13. A Hypothesis for Using Pathway Genetic Load Analysis for Understanding Complex Outcomes in Bilirubin Encephalopathy

    Science.gov (United States)

    Riordan, Sean M.; Bittel, Douglas C.; Le Pichon, Jean-Baptiste; Gazzin, Silvia; Tiribelli, Claudio; Watchko, Jon F.; Wennberg, Richard P.; Shapiro, Steven M.

    2016-01-01

    Genetic-based susceptibility to bilirubin neurotoxicity and chronic bilirubin encephalopathy (kernicterus) is still poorly understood. Neonatal jaundice affects 60–80% of newborns, and considerable effort goes into preventing this relatively benign condition from escalating into the development of kernicterus making the incidence of this potentially devastating condition very rare in more developed countries. The current understanding of the genetic background of kernicterus is largely comprised of mutations related to alterations of bilirubin production, elimination, or both. Less is known about mutations that may predispose or protect against CNS bilirubin neurotoxicity. The lack of a monogenetic source for this risk of bilirubin neurotoxicity suggests that disease progression is dependent upon an overall decrease in the functionality of one or more essential genetically controlled metabolic pathways. In other words, a “load” is placed on key pathways in the form of multiple genetic variants that combine to create a vulnerable phenotype. The idea of epistatic interactions creating a pathway genetic load (PGL) that affects the response to a specific insult has been previously reported as a PGL score. We hypothesize that the PGL score can be used to investigate whether increased susceptibility to bilirubin-induced CNS damage in neonates is due to a mutational load being placed on key genetic pathways important to the central nervous system's response to bilirubin neurotoxicity. We propose a modification of the PGL score method that replaces the use of a canonical pathway with custom gene lists organized into three tiers with descending levels of evidence combined with the utilization of single nucleotide polymorphism (SNP) causality prediction methods. The PGL score has the potential to explain the genetic background of complex bilirubin induced neurological disorders (BIND) such as kernicterus and could be the key to understanding ranges of outcome severity

  14. A hypothesis for using pathway genetic load analysis for understanding complex outcomes in bilirubin encephalopathy

    Directory of Open Access Journals (Sweden)

    Sean M. Riordan

    2016-08-01

    Full Text Available Genetic-based susceptibility to bilirubin neurotoxicity and chronic bilirubin encephalopathy (kernicterus is still poorly understood. Neonatal jaundice affects 60-80% of newborns, and considerable effort goes into preventing this relatively benign condition from escalating into the development of kernicterus making the incidence of this potentially devastating condition very rare in more developed countries. The current understanding of the genetic background of kernicterus is largely comprised of mutations related to alterations of bilirubin production, elimination, or both. Less is known about mutations that may predispose or protect against CNS bilirubin neurotoxicity. The lack of a monogenetic source for this risk of bilirubin neurotoxicity suggests that disease progression is dependent upon an overall decrease in the functionality of one or more essential genetically controlled metabolic pathways. In other words, a load is placed on key pathways in the form of multiple genetic variants that combine to create a vulnerable phenotype. The idea of epistatic interactions creating a pathway genetic load that affects the response to a specific insult has been previously reported as a pathway genetic load (PGL score. We hypothesize that the PGL score can be used to investigate whether increased susceptibility to bilirubin-induced CNS damage in neonates is due to a mutational load being placed on key genetic pathways important to the central nervous system’s response to bilirubin neurotoxicity. We propose a modification of the PGL score method that replaces the use of a canonical pathway with custom gene lists organized into three tiers with descending levels of evidence combined with the utilization of single nucleotide polymorphism (SNP causality prediction methods. The PGL score has the potential to explain the genetic background of complex bilirubin-induced neurological disorders (BIND such as kernicterus and could be the key to understanding

  15. Gastrin release: Antrum microdialysis reveals a complex neural control

    DEFF Research Database (Denmark)

    Ericsson, P; Håkanson, R; Rehfeld, Jens F.

    2010-01-01

    in serum regardless of the prandial state. The rats were conscious during microdialysis except when subjected to electrical vagal stimulation. Acid blockade (omeprazole treatment of freely fed rats for 4 days), or bilateral sectioning of the abdominal vagal trunks (fasted, 3 days post-op.), raised...... the gastrin concentration in blood as well as microdialysate. The high gastrin concentration following omeprazole treatment was not affected by vagotomy. Vagal excitation stimulated the G cells: electrical vagal stimulation and pylorus ligation (fasted rats) raised the gastrin concentration transiently...... microdialysate gastrin concentration in omeprazole-treated rats by 65%. We conclude that activated gastrin release, unlike basal gastrin release, is highly dependent on a neural input: 1) Vagal excitation has a transient stimulating effect on the G cells. The transient nature of the response suggests...

  16. Cooperation of deterministic dynamics and random noise in production of complex syntactical avian song sequences: a neural network model

    Directory of Open Access Journals (Sweden)

    Yuichi eYamashita

    2011-04-01

    Full Text Available How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC, a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf-HVC interaction.

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

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

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

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  19. Do mothers really know best? Complexities in testing the preference-performance hypothesis in polyphagous frugivorous fruit flies.

    Science.gov (United States)

    Birke, A; Aluja, M

    2017-12-04

    The preference-performance hypothesis (PPH) has widely been used to explain host exploitation patterns by phytophagous insects. However, this hypothesis often fails in the case of polyphagous species when compared with specialists. One explanation, validated by the information-processing hypothesis (IPH), considers that polyphagous insects are unable to process a large array of cues, which hinders females from distinguishing between high- and low- quality hosts. Here we analyzed Anastrepha ludens female host preference and offspring performance, and tested if neuronal limitations could possibly play a role in the incapacity of the polyphagous A. ludens to make 'accurate decisions' and therefore partially explain mismatches related to PPH. Results testing the PPH by correlating female preference to six naturally occurring hosts and its offspring outcomes show that A. ludens females oviposited greater proportions of eggs on fruit according to hierarchical preferences. Infestation level was low in white sapote, the preferential and seemingly putative ancestral host, likely due to sapote defence mechanisms. Pupal weight and adult size were lower when A. ludens larvae developed in guava (conditional host that was artificially infested) and peach, a lower ranked host compared with 'Marsh' grapefruit, white sapote, and 'Manila' mango (three preferred hosts). Larvae reared in 'Manzano' pepper, a low-ranked host, performed better than in peach and guava. Results testing the IPH, show that polyphagous A. ludens females were less accurate when discerning between a non natural host (guava) when compared with a preferred, natural host (grapefruit): error rate was significantly higher, number of oviposited fruit in a 6-h period was extremely low, time searching and ovipositing took longer, and pupae recovery was extremely low. Our findings indicate that both hypotheses tested are complementary and help better understand host use by A. ludens. However, we also discuss the

  20. Neural and response correlations to natural complex sounds in the auditory midbrain

    Directory of Open Access Journals (Sweden)

    Dominika Lyzwa

    2016-11-01

    Full Text Available How natural communication sounds are spatially represented across the inferior colliculus, the main center of convergence for auditory information in the midbrain, is not known. The neural representation of the acoustic stimuli results from the interplay of locally differing input and the organization of spectral and temporal neural preferences that change gradually across the nucleus. This raises the question how similar the neural representation of the communication sounds is across these gradients of neural preferences, and whether it also changes gradually. Analyzed neural recordings were multi-unit cluster spike trains from guinea pigs presented with a spectrotemporally rich set of eleven species-specific communication sounds. Using cross-correlation, we analyzed the response similarity of spiking activity across a broad frequency range for neurons of similar and different frequency tuning. Furthermore, we separated the contribution of the stimulus to the correlations to investigate whether similarity is only attributable to the stimulus, or, whether interactions exist between the multi-unit clusters that lead to neural correlations and whether these follow the same representation as the response correlations. We found that similarity of responses is dependent on the neurons' spatial distance for similarly and differently frequency-tuned neurons, and that similarity decreases gradually with spatial distance. Significant neural correlations exist, and contribute to the total response similarity. Our findings suggest that for multi-unit clusters in the mammalian inferior colliculus, the gradual response similarity with spatial distance to natural complex sounds is shaped by neural interactions and the gradual organization of neural preferences.

  1. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream

    NARCIS (Netherlands)

    Güçlü, U.; Gerven, M.A.J. van

    2015-01-01

    Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping

  2. Environmental layout complexity affects neural activity during navigation in humans.

    Science.gov (United States)

    Slone, Edward; Burles, Ford; Iaria, Giuseppe

    2016-05-01

    Navigating large-scale surroundings is a fundamental ability. In humans, it is commonly assumed that navigational performance is affected by individual differences, such as age, sex, and cognitive strategies adopted for orientation. We recently showed that the layout of the environment itself also influences how well people are able to find their way within it, yet it remains unclear whether differences in environmental complexity are associated with changes in brain activity during navigation. We used functional magnetic resonance imaging to investigate how the brain responds to a change in environmental complexity by asking participants to perform a navigation task in two large-scale virtual environments that differed solely in interconnection density, a measure of complexity defined as the average number of directional choices at decision points. The results showed that navigation in the simpler, less interconnected environment was faster and more accurate relative to the complex environment, and such performance was associated with increased activity in a number of brain areas (i.e. precuneus, retrosplenial cortex, and hippocampus) known to be involved in mental imagery, navigation, and memory. These findings provide novel evidence that environmental complexity not only affects navigational behaviour, but also modulates activity in brain regions that are important for successful orientation and navigation. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  3. Neural responses to complex auditory rhythms: the role of attending

    Directory of Open Access Journals (Sweden)

    Heather L Chapin

    2010-12-01

    Full Text Available The aim of this study was to explore the role of attention in pulse and meter perception using complex rhythms. We used a selective attention paradigm in which participants attended to either a complex auditory rhythm or a visually presented word list. Performance on a reproduction task was used to gauge whether participants were attending to the appropriate stimulus. We hypothesized that attention to complex rhythms – which contain no energy at the pulse frequency – would lead to activations in motor areas involved in pulse perception. Moreover, because multiple repetitions of a complex rhythm are needed to perceive a pulse, activations in pulse related areas would be seen only after sufficient time had elapsed for pulse perception to develop. Selective attention was also expected to modulate activity in sensory areas specific to the modality. We found that selective attention to rhythms led to increased BOLD responses in basal ganglia, and basal ganglia activity was observed only after the rhythms had cycled enough times for a stable pulse percept to develop. These observations suggest that attention is needed to recruit motor activations associated with the perception of pulse in complex rhythms. Moreover, attention to the auditory stimulus enhanced activity in an attentional sensory network including primary auditory, insula, anterior cingulate, and prefrontal cortex, and suppressed activity in sensory areas associated with attending to the visual stimulus.

  4. Artificial neural networks using complex numbers and phase encoded weights.

    Science.gov (United States)

    Michel, Howard E; Awwal, Abdul Ahad S

    2010-04-01

    The model of a simple perceptron using phase-encoded inputs and complex-valued weights is proposed. The aggregation function, activation function, and learning rule for the proposed neuron are derived and applied to Boolean logic functions and simple computer vision tasks. The complex-valued neuron (CVN) is shown to be superior to traditional perceptrons. An improvement of 135% over the theoretical maximum of 104 linearly separable problems (of three variables) solvable by conventional perceptrons is achieved without additional logic, neuron stages, or higher order terms such as those required in polynomial logic gates. The application of CVN in distortion invariant character recognition and image segmentation is demonstrated. Implementation details are discussed, and the CVN is shown to be very attractive for optical implementation since optical computations are naturally complex. The cost of the CVN is less in all cases than the traditional neuron when implemented optically. Therefore, all the benefits of the CVN can be obtained without additional cost. However, on those implementations dependent on standard serial computers, CVN will be more cost effective only in those applications where its increased power can offset the requirement for additional neurons.

  5. Prototyping of chemical composition of complex crystals using method of neural networks

    Science.gov (United States)

    Blagin, A. V.; Nefedov, V. V.; Nefedova, N. A.

    2017-10-01

    The traditional methods of study of multi-component crystal heterojunction structures by means of X-ray diffractometry and electroscopy are expensive and complex. The present paper discusses the information technology based on the use of artificial neural networks for identification of the chemical composition of complex semiconductor structures. The obtained results allow supposing the successful use of this method for many multi-component systems.

  6. Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

    Science.gov (United States)

    Cui, Yiqian; Shi, Junyou; Wang, Zili

    2015-11-01

    Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Study of the neural dynamics for understanding communication in terms of complex hetero systems.

    Science.gov (United States)

    Tsuda, Ichiro; Yamaguchi, Yoko; Hashimoto, Takashi; Okuda, Jiro; Kawasaki, Masahiro; Nagasaka, Yasuo

    2015-01-01

    The purpose of the research project was to establish a new research area named "neural information science for communication" by elucidating its neural mechanism. The research was performed in collaboration with applied mathematicians in complex-systems science and experimental researchers in neuroscience. The project included measurements of brain activity during communication with or without languages and analyses performed with the help of extended theories for dynamical systems and stochastic systems. The communication paradigm was extended to the interactions between human and human, human and animal, human and robot, human and materials, and even animal and animal. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  8. Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach

    Directory of Open Access Journals (Sweden)

    Ajit Kumar Rout

    2017-10-01

    Full Text Available The paper presents a low complexity recurrent Functional Link Artificial Neural Network for predicting the financial time series data like the stock market indices over a time frame varying from 1 day ahead to 1 month ahead. Although different types of basis functions have been used for low complexity neural networks earlier for stock market prediction, a comparative study is needed to choose the optimal combinations of these for a reasonably accurate forecast. Further several evolutionary learning methods like the Particle Swarm Optimization (PSO and modified version of its new variant (HMRPSO, and the Differential Evolution (DE are adopted here to find the optimal weights for the recurrent computationally efficient functional link neural network (RCEFLANN using a combination of linear and hyperbolic tangent basis functions. The performance of the recurrent computationally efficient FLANN model is compared with that of low complexity neural networks using the Trigonometric, Chebyshev, Laguerre, Legendre, and tangent hyperbolic basis functions in predicting stock prices of Bombay Stock Exchange data and Standard & Poor’s 500 data sets using different evolutionary methods and has been presented in this paper and the results clearly reveal that the recurrent FLANN model trained with the DE outperforms all other FLANN models similarly trained.

  9. Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay.

    Science.gov (United States)

    Velmurugan, G; Rakkiyappan, R; Vembarasan, V; Cao, Jinde; Alsaedi, Ahmed

    2017-02-01

    As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay. Moreover, some sufficient conditions are derived in order to ensure the global asymptotic stability of the addressed FCVNNs with time delay. Finally, two numerical simulations are posed to ensure that the attention of our main results are valuable. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Brain morphology of the threespine stickleback (Gasterosteus aculeatus) varies inconsistently with respect to habitat complexity: A test of the Clever Foraging Hypothesis.

    Science.gov (United States)

    Ahmed, Newaz I; Thompson, Cole; Bolnick, Daniel I; Stuart, Yoel E

    2017-05-01

    The Clever Foraging Hypothesis asserts that organisms living in a more spatially complex environment will have a greater neurological capacity for cognitive processes related to spatial memory, navigation, and foraging. Because the telencephalon is often associated with spatial memory and navigation tasks, this hypothesis predicts a positive association between telencephalon size and environmental complexity. The association between habitat complexity and brain size has been supported by comparative studies across multiple species but has not been widely studied at the within-species level. We tested for covariation between environmental complexity and neuroanatomy of threespine stickleback (Gasterosteus aculeatus) collected from 15 pairs of lakes and their parapatric streams on Vancouver Island. In most pairs, neuroanatomy differed between the adjoining lake and stream populations. However, the magnitude and direction of this difference were inconsistent between watersheds and did not covary strongly with measures of within-site environmental heterogeneity. Overall, we find weak support for the Clever Foraging Hypothesis in our study.

  11. Neuron's eye view: Inferring features of complex stimuli from neural responses.

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2017-08-01

    Full Text Available Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.

  12. Neural correlates of simple and complex mental calculation.

    Science.gov (United States)

    Zago, L; Pesenti, M; Mellet, E; Crivello, F; Mazoyer, B; Tzourio-Mazoyer, N

    2001-02-01

    Some authors proposed that exact mental calculation is based on linguistic representations and relies on the perisylvian language cortices, while the understanding of proximity relations between numerical quantities implicates the parietal cortex. However, other authors opposed developmental arguments to suggest that number sense emerges from nonspecific visuospatial processing areas in the parietal cortex. Within this debate, the present study aimed at revealing the functional anatomy of the two basic resolution strategies involved in mental calculation, namely arithmetical fact retrieval and actual computation, questioning in particular the respective role of language and/or visuospatial cerebral areas. Regional cerebral blood flow was measured with positron emission tomography while subjects were at rest (Rest), read digits (Read), retrieved simple arithmetic facts from memory (i.e., 2 x 4, Retrieve), and performed mental complex calculation (i.e., 32 x 24, Compute). Compared to Read, Retrieve engaged a left parieto-premotor circuit representing a developmental trace of a finger-counting representation that mediates, by extension, the numerical knowledge in adult. Beside this basic network, Retrieve involved a naming network, including the left anterior insula and the right cerebellar cortex, while it did not engage the perisylvian language areas, which were deactivated as compared to Rest. In addition to this retrieval network, Compute specifically involved two functional networks: a left parieto-frontal network in charge of the holding of the multidigit numbers in visuospatial working memory and a bilateral inferior temporal gyri related to the visual mental imagery resolution strategy. Overall, these results provide strong evidence of the involvement of visuospatial representations in different levels of mental calculation. Copyright 2001 Academic Press.

  13. Neural associations of the early retinotopic cortex with the lateral occipital complex during visual perception.

    Directory of Open Access Journals (Sweden)

    Delong Zhang

    Full Text Available Previous studies have demonstrated that the early retinotopic cortex (ERC, i.e., V1/V2/V3 is highly associated with the lateral occipital complex (LOC during visual perception. However, it remains largely unclear how to evaluate their associations in quantitative way. The present study tried to apply a multivariate pattern analysis (MVPA to quantify the neural activity in ERC and its association with that of the LOC when participants saw visual images. To this end, we assessed whether low-level visual features (Gabor features could predict the neural activity in the ERC and LOC according to a voxel-based encoding model (VBEM, and then quantified the association of the neural activity between these regions by using an analogical VBEM. We found that the Gabor features remarkably predicted the activity of the ERC (e.g., the predicted accuracy was 52.5% for a participant instead of that of the LOC (4.2%. Moreover, the MVPA approach can also be used to establish corresponding relationships between the activity patterns in the LOC and those in the ERC (64.2%. In particular, we found that the integration of the Gabor features and LOC visual information could dramatically improve the 'prediction' of ERC activity (88.3%. Overall, the present study provides new evidences for the possibility of quantifying the association of the neural activity between the regions of ERC and LOC. This approach will help to provide further insights into the neural substrates of the visual processing.

  14. Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

    Science.gov (United States)

    Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

    2016-01-01

    Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

  15. The exon junction complex component Magoh controls brain size by regulating neural stem cell division

    Science.gov (United States)

    Silver, Debra L.; Watkins-Chow, Dawn E.; Schreck, Karisa C.; Pierfelice, Tarran J.; Larson, Denise M.; Burnetti, Anthony J.; Liaw, Hung-Jiun; Myung, Kyungjae; Walsh, Christopher A.; Gaiano, Nicholas; Pavan, William J.

    2010-01-01

    Summary Brain structure and size requires precise division of neural stem cells (NSCs), which self-renew and generate intermediate neural progenitors (INPs) and neurons. The factors that regulate NSCs remain poorly understood, as do mechanistic explanations of how aberrant NSC division causes reduced brain size as seen in microcephaly. Here we demonstrate that Magoh, a component of the exon junction complex (EJC) that binds RNA, controls mouse cerebral cortical size by regulating NSC division. Magoh haploinsufficiency causes microcephaly due to INP depletion and neuronal apoptosis. Defective mitosis underlies these phenotypes as depletion of EJC components disrupts mitotic spindle orientation and integrity, chromosome number, and genomic stability. In utero rescue experiments revealed that a key function of Magoh is to control levels of the microcephaly-associated protein, LIS1, during neurogenesis. This study uncovers new requirements for the EJC in brain development, NSC maintenance, and mitosis, thus implicating this complex in the pathogenesis of microcephaly. PMID:20364144

  16. Complex Dynamical Network Control for Trajectory Tracking Using Delayed Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Jose P. Perez

    2014-01-01

    Full Text Available In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results, we present a tracking simulation of a dynamical network with each node being just one Lorenz’s dynamical system and three identical Chen’s dynamical systems.

  17. Determination of Complex-Valued Parametric Model Coefficients Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    A. M. Aibinu

    2010-01-01

    Full Text Available A new approach for determining the coefficients of a complex-valued autoregressive (CAR and complex-valued autoregressive moving average (CARMA model coefficients using complex-valued neural network (CVNN technique is discussed in this paper. The CAR and complex-valued moving average (CMA coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. The performance of the proposed technique has been evaluated using simulated complex-valued data (CVD with three different types of activation functions. The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. Furthermore, application of the developed CVNN-based technique for MRI K-space reconstruction results in images with improve resolution.

  18. A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

    Directory of Open Access Journals (Sweden)

    Stefan Fürtinger

    2014-11-01

    Full Text Available Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number

  19. 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

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

  20. Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule.

    Science.gov (United States)

    Lee, D L

    2001-01-01

    Jankowski et al. proposed (1996) a complex-valued neural network (CVNN) which is capable of storing and recalling gray-scale images. The convergence property of the CVNN has also been proven by means of the energy function approach. However, the memory capacity of the CVNN is very low because they use a generalized Hebb rule to construct the connection matrix. In this letter, a modified gradient descent learning rule (MGDR) is proposed to enhance the capacity of the CVNN. The proposed technique is derived by applying gradient search over a complex error surface. Simulation shows that the capacity of CVNN with MGDR is greatly improved.

  1. Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

    Science.gov (United States)

    Yang, Bin

    2017-07-01

    Accurate models play an important role in capturing the significant characteristics of the network traffic, analyzing the network dynamic, and improving the forecasting accuracy for system dynamics. In this study, complex-valued neural network (CVNN) model is proposed to further improve the accuracy of small-time scale network traffic forecasting. Artificial bee colony (ABC) algorithm is proposed to optimize the complex-valued and real-valued parameters of CVNN model. Small-scale traffic measurements data namely the TCP traffic data is used to test the performance of CVNN model. Experimental results reveal that CVNN model forecasts the small-time scale network traffic measurement data very accurately

  2. Convergence analysis of an augmented algorithm for fully complex-valued neural networks.

    Science.gov (United States)

    Xu, Dongpo; Zhang, Huisheng; Mandic, Danilo P

    2015-09-01

    This paper presents an augmented algorithm for fully complex-valued neural network based on Wirtinger calculus, which simplifies the derivation of the algorithm and eliminates the Schwarz symmetry restriction on the activation functions. A unified mean value theorem is first established for general functions of complex variables, covering the analytic functions, non-analytic functions and real-valued functions. Based on so introduced theorem, convergence results of the augmented algorithm are obtained under mild conditions. Simulations are provided to support the analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. On the Keyhole Hypothesis

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare B.; Kidmose, Preben; Hansen, Lars Kai

    2017-01-01

    We propose and test the keyhole hypothesis that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10...... simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG "keyhole," furthermore we...

  4. Determining the Effects of Cognitive Style, Problem Complexity, and Hypothesis Generation on the Problem Solving Ability of School-Based Agricultural Education Students

    Science.gov (United States)

    Blackburn, J. Joey; Robinson, J. Shane

    2016-01-01

    The purpose of this experimental study was to assess the effects of cognitive style, problem complexity, and hypothesis generation on the problem solving ability of school-based agricultural education students. Problem solving ability was defined as time to solution. Kirton's Adaption-Innovation Inventory was employed to assess students' cognitive…

  5. Revisiting the Scrambling Complexity Hypothesis in Sentence Processing: A Self-Paced Reading Study on Anomaly Detection and Scrambling in Hindi

    Science.gov (United States)

    Mishra, Ramesh K.; Pandey, Aparna; Srinivasan, Narayanan

    2011-01-01

    The scrambling complexity hypothesis based on working memory or locality accounts as well as syntactic accounts have proposed that processing a scrambled structure is difficult. However, the locus of this difficulty in sentence processing remains debatable. Several studies on multiple languages have explored the effect of scrambling on sentence…

  6. The neural correlates of emotional prosody comprehension: disentangling simple from complex emotion.

    Directory of Open Access Journals (Sweden)

    Lucy Alba-Ferrara

    Full Text Available BACKGROUND: Emotional prosody comprehension (EPC, the ability to interpret another person's feelings by listening to their tone of voice, is crucial for effective social communication. Previous studies assessing the neural correlates of EPC have found inconsistent results, particularly regarding the involvement of the medial prefrontal cortex (mPFC. It remained unclear whether the involvement of the mPFC is linked to an increased demand in socio-cognitive components of EPC such as mental state attribution and if basic perceptual processing of EPC can be performed without the contribution of this region. METHODS: fMRI was used to delineate neural activity during the perception of prosodic stimuli conveying simple and complex emotion. Emotional trials in general, as compared to neutral ones, activated a network comprising temporal and lateral frontal brain regions, while complex emotion trials specifically showed an additional involvement of the mPFC, premotor cortex, frontal operculum and left insula. CONCLUSION: These results indicate that the mPFC and premotor areas might be associated, but are not crucial to EPC. However, the mPFC supports socio-cognitive skills necessary to interpret complex emotion such as inferring mental states. Additionally, the premotor cortex involvement may reflect the participation of the mirror neuron system for prosody processing particularly of complex emotion.

  7. THE FRACTAL MARKET HYPOTHESIS

    Directory of Open Access Journals (Sweden)

    FELICIA RAMONA BIRAU

    2012-05-01

    Full Text Available In this article, the concept of capital market is analysed using Fractal Market Hypothesis which is a modern, complex and unconventional alternative to classical finance methods. Fractal Market Hypothesis is in sharp opposition to Efficient Market Hypothesis and it explores the application of chaos theory and fractal geometry to finance. Fractal Market Hypothesis is based on certain assumption. Thus, it is emphasized that investors did not react immediately to the information they receive and of course, the manner in which they interpret that information may be different. Also, Fractal Market Hypothesis refers to the way that liquidity and investment horizons influence the behaviour of financial investors.

  8. Reservoir-based Online Adaptive Forward Models with Neural Control for Complex Locomotion in a Hexapod Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Dasgupta, Sakyasingha; Goldschmidt, Dennis

    2014-01-01

    Walking animals show fascinating locomotor abilities and complex behaviors. Biological study has revealed that such complex behaviors is a result of a combination of biomechanics and neural mechanisms. While biomechanics allows for flexibility and a variety of movements, neural mechanisms generate...... locomotion, make predictions, and provide adaptation. Inspired by this finding, we present here an artificial bio-inspired walking system which combines biomechanics (in terms of its body and leg structures) and neural mechanisms. The neural mechanisms consist of 1) central pattern generator-based control...... conditions. Simulation results show that this bio-inspired approach allows the walking robot to perform complex locomotor abilities including walking on undulated terrains, crossing a large gap, as well as climbing over a high obstacle and a fleet of stairs....

  9. Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

    Science.gov (United States)

    Hu, Jin; Zeng, Chunna

    2017-02-01

    The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat

    Directory of Open Access Journals (Sweden)

    Okut Hayrettin

    2011-10-01

    Full Text Available Abstract Background In the study of associations between genomic data and complex phenotypes there may be relationships that are not amenable to parametric statistical modeling. Such associations have been investigated mainly using single-marker and Bayesian linear regression models that differ in their distributions, but that assume additive inheritance while ignoring interactions and non-linearity. When interactions have been included in the model, their effects have entered linearly. There is a growing interest in non-parametric methods for predicting quantitative traits based on reproducing kernel Hilbert spaces regressions on markers and radial basis functions. Artificial neural networks (ANN provide an alternative, because these act as universal approximators of complex functions and can capture non-linear relationships between predictors and responses, with the interplay among variables learned adaptively. ANNs are interesting candidates for analysis of traits affected by cryptic forms of gene action. Results We investigated various Bayesian ANN architectures using for predicting phenotypes in two data sets consisting of milk production in Jersey cows and yield of inbred lines of wheat. For the Jerseys, predictor variables were derived from pedigree and molecular marker (35,798 single nucleotide polymorphisms, SNPS information on 297 individually cows. The wheat data represented 599 lines, each genotyped with 1,279 markers. The ability of predicting fat, milk and protein yield was low when using pedigrees, but it was better when SNPs were employed, irrespective of the ANN trained. Predictive ability was even better in wheat because the trait was a mean, as opposed to an individual phenotype in cows. Non-linear neural networks outperformed a linear model in predictive ability in both data sets, but more clearly in wheat. Conclusion Results suggest that neural networks may be useful for predicting complex traits using high

  11. Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat.

    Science.gov (United States)

    Gianola, Daniel; Okut, Hayrettin; Weigel, Kent A; Rosa, Guilherme Jm

    2011-10-07

    In the study of associations between genomic data and complex phenotypes there may be relationships that are not amenable to parametric statistical modeling. Such associations have been investigated mainly using single-marker and Bayesian linear regression models that differ in their distributions, but that assume additive inheritance while ignoring interactions and non-linearity. When interactions have been included in the model, their effects have entered linearly. There is a growing interest in non-parametric methods for predicting quantitative traits based on reproducing kernel Hilbert spaces regressions on markers and radial basis functions. Artificial neural networks (ANN) provide an alternative, because these act as universal approximators of complex functions and can capture non-linear relationships between predictors and responses, with the interplay among variables learned adaptively. ANNs are interesting candidates for analysis of traits affected by cryptic forms of gene action. We investigated various Bayesian ANN architectures using for predicting phenotypes in two data sets consisting of milk production in Jersey cows and yield of inbred lines of wheat. For the Jerseys, predictor variables were derived from pedigree and molecular marker (35,798 single nucleotide polymorphisms, SNPS) information on 297 individually cows. The wheat data represented 599 lines, each genotyped with 1,279 markers. The ability of predicting fat, milk and protein yield was low when using pedigrees, but it was better when SNPs were employed, irrespective of the ANN trained. Predictive ability was even better in wheat because the trait was a mean, as opposed to an individual phenotype in cows. Non-linear neural networks outperformed a linear model in predictive ability in both data sets, but more clearly in wheat. Results suggest that neural networks may be useful for predicting complex traits using high-dimensional genomic information, a situation where the number of unknowns

  12. Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks.

    Science.gov (United States)

    Carpentier, Sarah M; Moreno, Sylvain; McIntosh, Anthony R

    2016-10-01

    Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

  13. Radiovaccination Hypothesis

    National Research Council Canada - National Science Library

    Eapen Libni

    2017-01-01

    .... We review the relevant immune physiology and radiotherapy particulars and propose the hypothesis that radiovaccination with high fractional dose to skin metastases can stimulate the development...

  14. Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control.

    Science.gov (United States)

    Li, Xiaofan; Fang, Jian-An; Li, Huiyuan

    2017-09-01

    This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-07-12

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

  16. 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…

  17. Neuroautonomic evaluation of patients with unexplained syncope: incidence of complex neurally mediated diagnoses in the elderly

    Directory of Open Access Journals (Sweden)

    Rafanelli M

    2014-02-01

    Full Text Available Martina Rafanelli, Alessandro Morrione, Annalisa Landi, Emilia Ruffolo, Valentina M Chisciotti, Maria A Brunetti, Niccolò Marchionni, Andrea Ungar Syncope Unit, Cardiology and Geriatric Medicine, University of Florence and Azienda Ospedaliero-Universitaria Careggi, Florence, Italy Background: The incidence of syncope increases in individuals over the age of 70 years, but data about this condition in the elderly are limited. Little is known about tilt testing (TT, carotid sinus massage (CSM, or supine and upright blood pressure measurement related to age or about patients with complex diagnoses, for example, those with a double diagnosis, ie, positivity in two of these three tests. Methods: A total of 873 consecutive patients of mean age 66.5±18 years underwent TT, CSM, and blood pressure measurement in the supine and upright positions according to the European Society of Cardiology guidelines on syncope.1 Neuroautonomic evaluation was performed if the first-line evaluation (clinical history, physical examination, electrocardiogram was suggestive of neurally mediated syncope, or if the first-line evaluation was suggestive of cardiac syncope but this diagnosis was excluded after specific diagnostic tests according to European Society of Cardiology guidelines on syncope, or if certain or suspected diagnostic criteria were not present after the first-line evaluation. Results: A diagnosis was reached in 64.3% of cases. TT was diagnostic in 50.4% of cases, CSM was diagnostic in 11.8% of cases, and orthostatic hypotension was present in 19.9% of cases. Predictors of a positive tilt test were prodromal symptoms and typical situational syncope. Increased age and a pathologic electrocardiogram were predictors of carotid sinus syndrome. Varicose veins and alpha-receptor blockers, nitrates, and benzodiazepines were associated with orthostatic hypotension. Twenty-three percent of the patients had a complex diagnosis. The most frequent association was

  18. Multipotent neural stem cells generate glial cells of the central complex through transit amplifying intermediate progenitors in Drosophila brain development.

    Science.gov (United States)

    Viktorin, Gudrun; Riebli, Nadia; Popkova, Anna; Giangrande, Angela; Reichert, Heinrich

    2011-08-15

    The neural stem cells that give rise to the neural lineages of the brain can generate their progeny directly or through transit amplifying intermediate neural progenitor cells (INPs). The INP-producing neural stem cells in Drosophila are called type II neuroblasts, and their neural progeny innervate the central complex, a prominent integrative brain center. Here we use genetic lineage tracing and clonal analysis to show that the INPs of these type II neuroblast lineages give rise to glial cells as well as neurons during postembryonic brain development. Our data indicate that two main types of INP lineages are generated, namely mixed neuronal/glial lineages and neuronal lineages. Genetic loss-of-function and gain-of-function experiments show that the gcm gene is necessary and sufficient for gliogenesis in these lineages. The INP-derived glial cells, like the INP-derived neuronal cells, make major contributions to the central complex. In postembryonic development, these INP-derived glial cells surround the entire developing central complex neuropile, and once the major compartments of the central complex are formed, they also delimit each of these compartments. During this process, the number of these glial cells in the central complex is increased markedly through local proliferation based on glial cell mitosis. Taken together, these findings uncover a novel and complex form of neurogliogenesis in Drosophila involving transit amplifying intermediate progenitors. Moreover, they indicate that type II neuroblasts are remarkably multipotent neural stem cells that can generate both the neuronal and the glial progeny that make major contributions to one and the same complex brain structure. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes.

    Science.gov (United States)

    Costa, Tommaso; Cauda, Franco; Crini, Manuella; Tatu, Mona-Karina; Celeghin, Alessia; de Gelder, Beatrice; Tamietto, Marco

    2014-11-01

    The different temporal dynamics of emotions are critical to understand their evolutionary role in the regulation of interactions with the surrounding environment. Here, we investigated the temporal dynamics underlying the perception of four basic emotions from complex scenes varying in valence and arousal (fear, disgust, happiness and sadness) with the millisecond time resolution of Electroencephalography (EEG). Event-related potentials were computed and each emotion showed a specific temporal profile, as revealed by distinct time segments of significant differences from the neutral scenes. Fear perception elicited significant activity at the earliest time segments, followed by disgust, happiness and sadness. Moreover, fear, disgust and happiness were characterized by two time segments of significant activity, whereas sadness showed only one long-latency time segment of activity. Multidimensional scaling was used to assess the correspondence between neural temporal dynamics and the subjective experience elicited by the four emotions in a subsequent behavioral task. We found a high coherence between these two classes of data, indicating that psychological categories defining emotions have a close correspondence at the brain level in terms of neural temporal dynamics. Finally, we localized the brain regions of time-dependent activity for each emotion and time segment with the low-resolution brain electromagnetic tomography. Fear and disgust showed widely distributed activations, predominantly in the right hemisphere. Happiness activated a number of areas mostly in the left hemisphere, whereas sadness showed a limited number of active areas at late latency. The present findings indicate that the neural signature of basic emotions can emerge as the byproduct of dynamic spatiotemporal brain networks as investigated with millisecond-range resolution, rather than in time-independent areas involved uniquely in the processing one specific emotion. © The Author (2013

  20. Neural mechanisms of brain plasticity with complex cognitive training in healthy seniors.

    Science.gov (United States)

    Chapman, Sandra B; Aslan, Sina; Spence, Jeffrey S; Hart, John J; Bartz, Elizabeth K; Didehbani, Nyaz; Keebler, Molly W; Gardner, Claire M; Strain, Jeremy F; DeFina, Laura F; Lu, Hanzhang

    2015-02-01

    Complex mental activity induces improvements in cognition, brain function, and structure in animals and young adults. It is not clear to what extent the aging brain is capable of such plasticity. This study expands previous evidence of generalized cognitive gains after mental training in healthy seniors. Using 3 MRI-based measurements, that is, arterial spin labeling MRI, functional connectivity, and diffusion tensor imaging, we examined brain changes across 3 time points pre, mid, and post training (12 weeks) in a randomized sample (n = 37) who received cognitive training versus a control group. We found significant training-related brain state changes at rest; specifically, 1) increases in global and regional cerebral blood flow (CBF), particularly in the default mode network and the central executive network, 2) greater connectivity in these same networks, and 3) increased white matter integrity in the left uncinate demonstrated by an increase in fractional anisotropy. Improvements in cognition were identified along with significant CBF correlates of the cognitive gains. We propose that cognitive training enhances resting-state neural activity and connectivity, increasing the blood supply to these regions via neurovascular coupling. These convergent results provide preliminary evidence that neural plasticity can be harnessed to mitigate brain losses with cognitive training in seniors. © The Author 2013. Published by Oxford University Press.

  1. Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions.

    Science.gov (United States)

    Ding, Xiaoshuai; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E; Hayat, Tasawar

    2017-06-01

    This paper is concerned with the fixed-time synchronization for a class of complex-valued neural networks in the presence of discontinuous activation functions and parameter uncertainties. Fixed-time synchronization not only claims that the considered master-slave system realizes synchronization within a finite time segment, but also requires a uniform upper bound for such time intervals for all initial synchronization errors. To accomplish the target of fixed-time synchronization, a novel feedback control procedure is designed for the slave neural networks. By means of the Filippov discontinuity theories and Lyapunov stability theories, some sufficient conditions are established for the selection of control parameters to guarantee synchronization within a fixed time, while an upper bound of the settling time is acquired as well, which allows to be modulated to predefined values independently on initial conditions. Additionally, criteria of modified controller for assurance of fixed-time anti-synchronization are also derived for the same system. An example is included to illustrate the proposed methodologies. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Optimal system size for complex dynamics in random neural networks near criticality

    Energy Technology Data Exchange (ETDEWEB)

    Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)

    2013-12-15

    In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

  3. On the Keyhole Hypothesis

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare B.; Kidmose, Preben; Hansen, Lars Kai

    2017-01-01

    We propose and test the keyhole hypothesis that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10...... subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from...... simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG "keyhole," furthermore we...

  4. Extracting, Recognizing, and Counting White Blood Cells from Microscopic Images by Using Complex-valued Neural Networks.

    Science.gov (United States)

    Akramifard, Hamid; Firouzmand, Mohammad; Moghadam, Reza Askari

    2012-07-01

    In this paper a method related to extracting white blood cells (WBCs) from blood microscopic images and recognizing them and counting each kind of WBCs is presented. In medical science diagnosis by check the number of WBCs and compared with normal number of them is a new challenge and in this context has been discussed it. After reviewing the methods of extracting WBCs from hematology images, because of high applicability of artificial neural networks (ANNs) in classification we decided to use this effective method to classify WBCs, and because of high speed and stable convergence of complex-valued neural networks (CVNNs) compare to the real one, we used them to classification purpose. In the method that will be introduced, first the white blood cells are extracted by RGB color system's help. In continuance, by using the features of each kind of globules and their color scheme, a normalized feature vector is extracted, and for classifying, it is sent to a complex-valued back-propagation neural network. And at last, the results are sent to the output in the shape of the quantity of each of white blood cells. Despite the low quality of the used images, our method has high accuracy in extracting and recognizing WBCs by CVNNs, and because of this, certainly its result on high quality images will be acceptable. Learning time of complex-valued neural networks, that are used here, was significantly less than real-valued neural networks.

  5. The development of neural synchrony and large-scale cortical networks during adolescence: relevance for the pathophysiology of schizophrenia and neurodevelopmental hypothesis.

    Science.gov (United States)

    Uhlhaas, Peter J; Singer, Wolf

    2011-05-01

    Recent data from developmental cognitive neuroscience highlight the profound changes in the organization and function of cortical networks during the transition from adolescence to adulthood. While previous studies have focused on the development of gray and white matter, recent evidence suggests that brain maturation during adolescence extends to fundamental changes in the properties of cortical circuits that in turn promote the precise temporal coding of neural activity. In the current article, we will highlight modifications in the amplitude and synchrony of neural oscillations during adolescence that may be crucial for the emergence of cognitive deficits and psychotic symptoms in schizophrenia. Specifically, we will suggest that schizophrenia is associated with impaired parameters of synchronous oscillations that undergo changes during late brain maturation, suggesting an important role of adolescent brain development for the understanding, treatment, and prevention of the disorder. © The Author 2011. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved.

  6. Neural networks for modelling of chemical reaction systems with complex kinetics: oxidation of 2-octanol with nitric acid

    NARCIS (Netherlands)

    Molga, E.J.; van Woezik, B.A.A.; Westerterp, K.R.

    2000-01-01

    Application of neural networks to model the conversion rates of a heterogeneous oxidation reaction has been investigated — oxidation of 2-octanol with nitric acid has been considered as a case study. Due to a more complex and unknown kinetics of the investigated reaction the proposed approach based

  7. Low-complexity object detection with deep convolutional neural network for embedded systems

    Science.gov (United States)

    Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

    2017-09-01

    We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

  8. Examining neural correlates of skill acquisition in a complex videogame training program

    Directory of Open Access Journals (Sweden)

    Ruchika S Prakash

    2012-05-01

    Full Text Available Acquisition of complex skills is a universal feature of human behavior that has been conceptualized as a process that starts with intense resource dependency, requires effortful cognitive control, and ends in relative automaticity on the multi-faceted task. The present study examined the effects of different theoretically-based training strategies on cortical recruitment during acquisition of complex videogame skills. Seventy-five participants were recruited and assigned to one of three training groups: Fixed Emphasis Training (FET, in which participants practiced the game, Hybrid Variable Priority Training (HVT, in which participants practiced using a combination of part-task training and variable priority training, or a Control group that received limited game play. After 30 hours of training, game data indicated a significant advantage for the two training groups relative to the control group. The HVT group demonstrated enhanced benefits of training, as indexed by an improvement in overall game score and a reduction in cortical recruitment post-training. Specifically, while both groups demonstrated a significant reduction of activation in attentional control areas, namely the right middle frontal gyrus, right superior frontal gyrus, and the ventral medial prefrontal cortex, participants in the control group continued to engage these areas post-training, suggesting a sustained reliance on attentional regions during challenging task demands. The HVT group showed a further reduction in neural resources post-training compared to the FET group in these cognitive control regions, along with reduced activation in the motor and sensory cortices and the posteromedial cortex. Findings suggest that training, specifically one that emphasizes cognitive flexibility can reduce the attentional demands of a complex cognitive task, along with reduced reliance on the motor network.

  9. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies.

    Science.gov (United States)

    Lancashire, Lee J; Lemetre, Christophe; Ball, Graham R

    2009-05-01

    Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

  10. Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target.

    Science.gov (United States)

    Dror, I E; Florer, F L; Rios, D; Zagaeski, M

    1996-04-01

    Two sets of studies examined the viability of using bat-like sonar input for artificial neural networks in complex pattern recognition tasks. In the first set of studies, a sonar neural network was required to perform two face recognition tasks. In the first task, the network was trained to recognize different faces regardless of facial expressions. Following training, the network was tested on its ability to generalize and correctly recognize faces using echoes of novel facial expressions that were not included in the training set. The neural network was able to recognize novel echoes of faces almost perfectly (above 96% accuracy) when it was required to recognize up to five faces. In the second face recognition task, a sonar neural network was trained to recognize the sex of 16 faces (eight males and eight females). After training, the network was able to correctly recognize novel echoes of those faces as 'male' or as 'female' faces with accuracy levels of 88%. However, the network was not able to recognize novel faces as 'male' or 'female' faces. In the second set of studies, a sonar neural network was required to learn to recognize the speed of a target that was moving towards the viewer. During training, the target was presented in a variety of orientations, and the network's performance was evaluated when the target was presented in novel orientations that were not included in the training set. The different orientations dramatically affected the amplitude and the frequency composition of the echoes. The neural network was able to learn and recognize the speed of a moving target, and to generalize to new orientations of the target. However, the network was not able to generalize to new speeds that were not included in the training set. The potential and limitations of using bat-like sonar as input for artifical neural networks are discussed.

  11. Evolutionary programming technique for reducing complexity of artifical neural networks for breast cancer diagnosis

    Science.gov (United States)

    Lo, Joseph Y.; Land, Walker H., Jr.; Morrison, Clayton T.

    2000-06-01

    An evolutionary programming (EP) technique was investigated to reduce the complexity of artificial neural network (ANN) models that predict the outcome of mammography-induced breast biopsy. By combining input variables consisting of mammography lesion descriptors and patient history data, the ANN predicted whether the lesion was benign or malignant, which may aide in reducing the number of unnecessary benign biopsies and thus the cost of mammography screening of breast cancer. The EP has the ability to optimize the ANN both structurally and parametrically. An EP was partially optimized using a data set of 882 biopsy-proven cases from Duke University Medical Center. Although many different architectures were evolved, the best were often perceptrons with no hidden nodes. A rank ordering of the inputs was performed using twenty independent EP runs. This confirmed the predictive value of the mass margin and patient age variables, and revealed the unexpected usefulness of the history of previous breast cancer. Further work is required to improve the performance of the EP over all cases in general and calcification cases in particular.

  12. Characterization of K-complexes and slow wave activity in a neural mass model.

    Directory of Open Access Journals (Sweden)

    Arne Weigenand

    2014-11-01

    Full Text Available NREM sleep is characterized by two hallmarks, namely K-complexes (KCs during sleep stage N2 and cortical slow oscillations (SOs during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep.

  13. Enhancement of Cognitive and Neural Functions through Complex Reasoning Training: Evidence from Normal and Clinical Populations

    Directory of Open Access Journals (Sweden)

    Sandra Bond Chapman

    2014-04-01

    Full Text Available Public awareness of cognitive health is fairly recent compared to physical health. Growing evidence suggests that cognitive training offers promise in augmenting cognitive brain performance in normal and clinical populations. Targeting higher-order cognitive functions, such as reasoning in particular, may promote generalized cognitive changes necessary for supporting the complexities of daily life. This data-driven perspective highlights cognitive and brain changes measured in randomized clinical trials that trained gist reasoning strategies in populations ranging from teenagers to healthy older adults, individuals with brain injury to those at-risk for Alzheimer’s disease. The evidence presented across studies support the potential for Gist reasoning training to strengthen cognitive performance in trained and untrained domains and to engage more efficient communication across widespread neural networks that support higher-order cognition. The meaningful benefits of Gist training provide compelling motivation to examine optimal dose for sustained benefits as well as to explore additive benefits of meditation, physical exercise, and/or improved sleep in future studies.

  14. Disrupting neural activity related to awake-state sharp wave-ripple complexes prevents hippocampal learning

    Directory of Open Access Journals (Sweden)

    Miriam Shirin Nokia

    2012-12-01

    Full Text Available Oscillations in hippocampal local-field potentials reflect the crucial involvement of the hippocampus in memory trace formation: theta (4-8 Hz oscillations and ripples (~200 Hz occurring during sharp waves are thought to mediate encoding and consolidation, respectively. During sharp wave-ripple complexes (SPW-Rs, hippocampal cell firing closely follows the pattern that took place during the initial experience, most likely reflecting replay of that event. Disrupting hippocampal ripples using electrical stimulation either during training in awake animals or during sleep after training retards spatial learning. Here, adult rabbits were trained in trace eyeblink conditioning, a hippocampus-dependent associative learning task. A bright light was presented to the animals during the inter-trial interval, when awake, either during SPW-Rs or irrespective of their neural state. Learning was particularly poor when the light was presented following SPW-Rs. While the light did not disrupt the ripple itself, it elicited a theta-band oscillation, a state that does not usually coincide with SPW-Rs. Thus, it seems that consolidation depends on neuronal activity within and beyond the hippocampus taking place immediately after, but by no means limited to, hippocampal SPW-Rs.

  15. Influence of the complex-shape light signal on the neural network

    Science.gov (United States)

    Melnikov, Leonid A.; Novosselova, Anna V.; Blinova, Nadejda V.

    1999-03-01

    The effect of external signals of different shapes (constant, serrated and others) on the ring neural network modeling the visual perception is investigated numerically. New specific features in the dynamics of the neural network, such as the excitation, the swapping and the depression, were observed. The cooperative amplication of the external signal and the memory effect have been observed.

  16. Distributed Recurrent Neural Forward Models with Synaptic Adaptation and CPG-based control for Complex Behaviors of Walking Robots

    Directory of Open Access Journals (Sweden)

    Sakyasingha eDasgupta

    2015-09-01

    Full Text Available Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures with the underlying neural mechanisms. The neural mechanisms consist of 1 central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2 distributed (at each leg recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3 searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron

  17. Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots.

    Science.gov (United States)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models

  18. "Hypothesis for the Modern RNA World": A pervasive Non-coding RNA-Based Genetic Regulation is a Prerequisite for the Emergence of Multicellular Complexity

    Science.gov (United States)

    Lozada-Chávez, Irma; Stadler, Peter F.; Prohaska, Sonja J.

    2011-12-01

    The transitions to multicellularity mark the most pivotal and distinctive events in life's history on Earth. Although several transitions to "simple" multicellularity (SM) have been recorded in both bacterial and eukaryotic clades, transitions to complex multicellularity (CM) have only happened a few times in eukaryotes. A large number of cell types (associated with large body size), increased energy consumption per gene expressed, and an increment of non-protein-coding DNA positively correlate with CM. These three factors can indeed be understood as the causes and consequences of the regulation of gene expression. Here, we discuss how a vast expansion of non-protein-coding RNA (ncRNAs) regulators rather than large numbers of novel protein regulators can easily contribute to the emergence of CM. We also propose that the evolutionary advantage of RNA-based gene regulation derives from the robustness of the RNA structure that makes it easy to combine genetic drift with functional exploration. We describe a model which aims to explain how the evolutionary dynamic of ncRNAs becomes dominated by the accessibility of advantageous mutations to innovate regulation in complex multicellular organisms. The information and models discussed here outline the hypothesis that pervasive ncRNA-based regulatory systems, only capable of being expanded and explored in higher eukaryotes, are prerequisite to complex multicellularity. Thereby, regulatory RNA molecules in Eukarya have allowed intensification of morphological complexity by stabilizing critical phenotypes and controlling developmental precision. Although the origin of RNA on early Earth is still controversial, it is becoming clear that once RNA emerged into a protocellular system, its relevance within the evolution of biological systems has been greater than we previously thought.

  19. A unique missense allele of BAF155, a core BAF chromatin remodeling complex protein, causes neural tube closure defects in mice.

    Science.gov (United States)

    Harmacek, Laura; Watkins-Chow, Dawn E; Chen, Jianfu; Jones, Kenneth L; Pavan, William J; Salbaum, J Michael; Niswander, Lee

    2014-05-01

    Failure of embryonic neural tube closure results in the second most common class of birth defects known as neural tube defects (NTDs). While NTDs are likely the result of complex multigenic dysfunction, it is not known whether polymorphisms in epigenetic regulators may be risk factors for NTDs. Here we characterized Baf155(msp3) , a unique ENU-induced allele in mice. Homozygous Baf155(mps3) embryos exhibit highly penetrant exencephaly, allowing us to investigate the roles of an assembled, but malfunctional BAF chromatin remodeling complex in vivo at the time of neural tube closure. Evidence of defects in proliferation and apoptosis were found within the neural tube. RNA-Seq analysis revealed that surprisingly few genes showed altered expression in Baf155 mutant neural tissue, given the broad epigenetic role of the BAF complex, but included genes involved in neural development and cell survival. Moreover, gene expression changes between individual mutants were variable even though the NTD was consistently observed. This suggests that inconsistent gene regulation contributes to failed neural tube closure. These results shed light on the role of the BAF complex in the process of neural tube closure and highlight the importance of studying missense alleles to understand epigenetic regulation during critical phases of development. Copyright © 2013 Wiley Periodicals, Inc.

  20. Fractal Hypothesis of the Pelagic Microbial Ecosystem—Can Simple Ecological Principles Lead to Self-Similar Complexity in the Pelagic Microbial Food Web?

    Science.gov (United States)

    Våge, Selina; Thingstad, T. Frede

    2015-01-01

    Trophic interactions are highly complex and modern sequencing techniques reveal enormous biodiversity across multiple scales in marine microbial communities. Within the chemically and physically relatively homogeneous pelagic environment, this calls for an explanation beyond spatial and temporal heterogeneity. Based on observations of simple parasite-host and predator-prey interactions occurring at different trophic levels and levels of phylogenetic resolution, we present a theoretical perspective on this enormous biodiversity, discussing in particular self-similar aspects of pelagic microbial food web organization. Fractal methods have been used to describe a variety of natural phenomena, with studies of habitat structures being an application in ecology. In contrast to mathematical fractals where pattern generating rules are readily known, however, identifying mechanisms that lead to natural fractals is not straight-forward. Here we put forward the hypothesis that trophic interactions between pelagic microbes may be organized in a fractal-like manner, with the emergent network resembling the structure of the Sierpinski triangle. We discuss a mechanism that could be underlying the formation of repeated patterns at different trophic levels and discuss how this may help understand characteristic biomass size-spectra that hint at scale-invariant properties of the pelagic environment. If the idea of simple underlying principles leading to a fractal-like organization of the pelagic food web could be formalized, this would extend an ecologists mindset on how biological complexity could be accounted for. It may furthermore benefit ecosystem modeling by facilitating adequate model resolution across multiple scales. PMID:26648929

  1. Fractal hypothesis of the pelagic microbial ecosystem - Can simple ecological principles lead to self-similar complexity in the pelagic microbial food web?

    Directory of Open Access Journals (Sweden)

    Selina eVåge

    2015-12-01

    Full Text Available Trophic interactions are highly complex and modern sequencing techniques reveal enormous biodiversity across multiple scales in marine microbial communities . Within the chemically and physically relatively homogeneous pelagic environment, this calls for an explanation beyond spatial and temporal heterogeneity. Based on observations of simple parasite-host and predator-prey interactions occurring at different trophic levels and levels of phylogenetic resolution, we present a theoretical perspective on this enormous biodiversity, discussing in particular self-similar aspects of pelagic microbial food web organization. Fractal methods have been used to describe a variety of natural phenomena, with studies of habitat structures being an application in ecology. In contrast to mathematical fractals where pattern generating rules are readily known, however, identifying mechanisms that lead to natural fractals is not straight-forward. Here we put forward the hypothesis that trophic interactions between pelagic microbes may be organized in a fractal-like manner, with the emergent network resembling the structure of the Sierpinski triangle. We discuss a mechanism that could be underlying the formation of repeated patterns at different trophic levels and discuss how this may help understand characteristic biomass size-spectra that hint at scale-invariant properties of the pelagic environment. If the idea of simple underlying principles leading to a fractal-like organization of the pelagic food web could be formalized, this would extend an ecologists mindset on how biological complexity could be accounted for. It may furthermore benefit ecosystem modeling by facilitating adequate model resolution across multiple scales.

  2. Where and when does a ring start and end? Testing the ring-species hypothesis in a species complex of Australian parrots

    Science.gov (United States)

    Joseph, Leo; Dolman, Gaynor; Donnellan, Stephen; Saint, Kathleen M; Berg, Mathew L; Bennett, Andrew T.D

    2008-01-01

    Speciation, despite ongoing gene flow can be studied directly in nature in ring species that comprise two reproductively isolated populations connected by a chain or ring of intergrading populations. We applied three tiers of spatio-temporal analysis (phylogeny/historical biogeography, phylogeography and landscape/population genetics) to the data from mitochondrial and nuclear genomes of eastern Australian parrots of the Crimson Rosella Platycercus elegans complex to understand the history and present genetic structure of the ring they have long been considered to form. A ring speciation hypothesis does not explain the patterns we have observed in our data (e.g. multiple genetic discontinuities, discordance in genotypic and phenotypic assignments where terminal differentiates meet). However, we cannot reject that a continuous circular distribution has been involved in the group's history or indeed that one was formed through secondary contact at the ‘ring's’ east and west; however, we reject a simple ring-species hypothesis as traditionally applied, with secondary contact only at its east. We discuss alternative models involving historical allopatry of populations. We suggest that population expansion shown by population genetics parameters in one of these isolates was accompanied by geographical range expansion, secondary contact and hybridization on the eastern and western sides of the ring. Pleistocene landscape and sea-level and habitat changes then established the birds' current distributions and range disjunctions. Populations now show idiosyncratic patterns of selection and drift. We suggest that selection and drift now drive evolution in different populations within what has been considered the ring. PMID:18664434

  3. Neural Responses to Peer Rejection in Anxious Adolescents: Contributions from the Amygdala-Hippocampal Complex

    Science.gov (United States)

    Lau, Jennifer Y. F.; Guyer, Amanda E.; Tone, Erin B.; Jenness, Jessica; Parrish, Jessica M.; Pine, Daniel S.; Nelson, Eric E.

    2012-01-01

    Peer rejection powerfully predicts adolescent anxiety. While cognitive differences influence anxious responses to social feedback, little is known about neural contributions. Twelve anxious and twelve age-, gender- and IQ-matched, psychiatrically healthy adolescents received "not interested" and "interested" feedback from unknown peers during a…

  4. Neural detection of complex sound sequences in the absence of consciousness

    OpenAIRE

    Tzovara, Athina; Simonin, Alexandre; Oddo, Mauro; Rossetti, Andrea O; De Lucia, Marzia

    2017-01-01

    Neural responses to violations of global regularities are thought to require consciousness. However, Tzovara et al. show that some comatose patients can also detect deviations in sequences composed of repeated groups of sounds, suggesting that the unconscious brain has a greater capacity to track sensory inputs than previously believed

  5. High Precision Neural Decoding of Complex Movement Trajectories using Recursive Bayesian Estimation with Dynamic Movement Primitives.

    Science.gov (United States)

    Hotson, Guy; Smith, Ryan J; Rouse, Adam G; Schieber, Marc H; Thakor, Nitish V; Wester, Brock A

    2016-07-01

    Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance. Here we present a novel sensor fusion paradigm for BMIs that capitalizes on information able to be extracted from the environment to greatly improve the performance of control. This was accomplished by using dynamic movement primitives to model the 3D endpoint trajectories of manipulating various objects. We then used a switching unscented Kalman filter to continuously arbitrate between the 3D endpoint kinematics predicted by the dynamic movement primitives and control derived from neural signals. We experimentally validated our system by decoding 3D endpoint trajectories executed by a non-human primate manipulating four different objects at various locations. Performance using our system showed a dramatic improvement over using neural signals alone, with median distance between actual and decoded trajectories decreasing from 31.1 cm to 9.9 cm, and mean correlation increasing from 0.80 to 0.98. Our results indicate that our sensor fusion framework can dramatically increase the fidelity of neural prosthetic trajectory decoding.

  6. Research of Recurrent Dynamic Neural Networks for Adaptive Control of Complex Dynamic Systems

    Science.gov (United States)

    2010-07-08

    of human brain . Neural Dynamic Associative Memory can be considered as an analogue of mechanisms of brain memory that explains processes of forming...4402.85 UAH. Total, without VAT 13164.30 UAH. Pure VAT 2632.86 UAH. Total with VAT

  7. Sequence conservation and combinatorial complexity of Drosophila neural precursor cell enhancers

    Directory of Open Access Journals (Sweden)

    Kuzin Alexander

    2008-08-01

    Full Text Available Abstract Background The presence of highly conserved sequences within cis-regulatory regions can serve as a valuable starting point for elucidating the basis of enhancer function. This study focuses on regulation of gene expression during the early events of Drosophila neural development. We describe the use of EvoPrinter and cis-Decoder, a suite of interrelated phylogenetic footprinting and alignment programs, to characterize highly conserved sequences that are shared among co-regulating enhancers. Results Analysis of in vivo characterized enhancers that drive neural precursor gene expression has revealed that they contain clusters of highly conserved sequence blocks (CSBs made up of shorter shared sequence elements which are present in different combinations and orientations within the different co-regulating enhancers; these elements contain either known consensus transcription factor binding sites or consist of novel sequences that have not been functionally characterized. The CSBs of co-regulated enhancers share a large number of sequence elements, suggesting that a diverse repertoire of transcription factors may interact in a highly combinatorial fashion to coordinately regulate gene expression. We have used information gained from our comparative analysis to discover an enhancer that directs expression of the nervy gene in neural precursor cells of the CNS and PNS. Conclusion The combined use EvoPrinter and cis-Decoder has yielded important insights into the combinatorial appearance of fundamental sequence elements required for neural enhancer function. Each of the 30 enhancers examined conformed to a pattern of highly conserved blocks of sequences containing shared constituent elements. These data establish a basis for further analysis and understanding of neural enhancer function.

  8. Threshold Hypothesis: Fact or Artifact?

    Science.gov (United States)

    Karwowski, Maciej; Gralewski, Jacek

    2013-01-01

    The threshold hypothesis (TH) assumes the existence of complex relations between creative abilities and intelligence: linear associations below 120 points of IQ and weaker or lack of associations above the threshold. However, diverse results have been obtained over the last six decades--some confirmed the hypothesis and some rejected it. In this…

  9. Haptic fMRI: accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks.

    Science.gov (United States)

    Menon, Samir; Yu, Michelle; Kay, Kendrick; Khatib, Oussama

    2014-01-01

    Haptics combined with functional magnetic resonance imaging (Haptic fMRI) can non-invasively study how the human brain coordinates movement during complex manipulation tasks, yet avoiding associated fMRI artifacts remains a challenge. Here, we demonstrate confound-free neural activation measurements using Haptic fMRI for an unconstrained three degree-of-freedom motor task that involves planning, reaching, and visually guided trajectory tracking. Our haptic interface tracked subjects' hand motions, velocities, and accelerations (sample-rate, 350Hz), and provided continuous realtime visual feedback. During fMRI acquisition, we achieved uniform response latencies (reaching, 0.7-1.1s; tracking, 0.4-0.65s); minimized hand jitter (neural activation across cortex; unreliable motions and response latencies, which reduce statistical power; and task-correlated head motion, which causes spurious fMRI activation. Haptic fMRI can thus reliably elicit and localize heterogeneous neural activation for different tasks in motor (movement), pre-motor (planning), and somatosensory (limb displacement) cortex, demonstrating that it is feasible to use the technique to study how the brain achieves three dimensional motor control.

  10. Spintronic characteristics of self-assembled neurotransmitter acetylcholine molecular complexes enable quantum information processing in neural networks and brain

    Science.gov (United States)

    Tamulis, Arvydas; Majauskaite, Kristina; Kairys, Visvaldas; Zborowski, Krzysztof; Adhikari, Kapil; Krisciukaitis, Sarunas

    2016-09-01

    Implementation of liquid state quantum information processing based on spatially localized electronic spin in the neurotransmitter stable acetylcholine (ACh) neutral molecular radical is discussed. Using DFT quantum calculations we proved that this molecule possesses stable localized electron spin, which may represent a qubit in quantum information processing. The necessary operating conditions for ACh molecule are formulated in self-assembled dimer and more complex systems. The main quantum mechanical research result of this paper is that the neurotransmitter ACh systems, which were proposed, include the use of quantum molecular spintronics arrays to control the neurotransmission in neural networks.

  11. Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

    Directory of Open Access Journals (Sweden)

    Achmad Arwan

    2016-07-01

    Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results

  12. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method

    OpenAIRE

    Zhang, Li; Gan, John Q.; Wang, Haixian

    2015-01-01

    Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection meth...

  13. Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks

    OpenAIRE

    Carpentier, Sarah M.; Moreno, Sylvain; McIntosh, Anthony R.

    2016-01-01

    Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudi...

  14. A Hypothesis for the Cause of Complex Regional Pain Syndrome - Type I (Reflex Sympathetic Dystrophy): Pain Due to Deep-Tissue Microvascular Pathology

    Science.gov (United States)

    Coderre, Terence J.; Bennett, Gary J.

    2015-01-01

    Complex regional pain syndrome - type I (CRPS-I; Reflex Sympathetic Dystrophy) is a chronic pain condition that usually follows a deep-tissue injury such as fracture or sprain. The cause of the pain is unknown. We have developed an animal model (chronic post-ischemia pain; CPIP) that creates CRPS-I –like symptomology. The model is produced by occluding the blood flow to one hind paw for 3 hr under general anesthesia. Following reperfusion, the treated hind paw exhibits an initial phase of hyperemia and edema. This is followed by mechano-hyperalgesia, mechano-allodynia, and cold-allodynia that last for at least one month. Light- and electron microscopic analyses of the nerves at the site of the tourniquet show that the majority of these animals have no sign of injury to myelinated or unmyelinated axons. However, electron microscopy shows that the ischemia-reperfusion (I-R) injury produces a microvascular injury, slow-flow/no-reflow, in the capillaries of the hind paw muscle and digital nerves. We propose that the slow-flow/no-reflow phenomenon initiates and maintains deep tissue ischemia and inflammation, leading to the activation of muscle nociceptors, and the ectopic activation of sensory afferent axons due to endoneurial ischemia and inflammation. These data, and a large body of clinical evidence, suggest that in at least a subset of CRPS-I patients, the fundamental cause of the abnormal pain sensations is ischemia and inflammation due to microvascular pathology in deep tissues, leading to a combination of inflammatory and neuropathic pain processes. Moreover, we suggest a unifying idea that relates the pathogenesis of CRPS-I to that of CRPS-II. Lastly, our hypothesis suggests that the role of the sympathetic nervous system in CRPS-I is a factor that is not fundamentally causative, but may have an important contributory role in early stage disease. PMID:20704671

  15. Age-related shift in neural complexity related to task performance and physical activity.

    Science.gov (United States)

    Heisz, Jennifer J; Gould, Michelle; McIntosh, Anthony R

    2015-03-01

    The human brain undergoes marked structural changes with age including cortical thinning and reduced connectivity because of the degradation of myelin. Although these changes can compromise cognitive function, the brain is able to functionally reorganize to compensate for some of this structural loss. However, there are interesting individual differences in outcome: When comparing individuals of similar age, those who engage in regular physical activity are less affected by the typical age-related decline in cognitive function. This study used multiscale entropy to reveal a shift in the way the brain processes information in older adults that is related to physical activity. Specifically, older adults who were more physically active engaged in more local neural information processing. Interestingly, this shift toward local information processing was also associated with improved executive function performance in older adults, suggesting that physical activity may help to improve aspects of cognitive function in older adults by biasing the neural system toward local information processing. In the face of age-related structural decline, the neural plasticity that is enhanced through physical activity may help older adults maintain cognitive health longer into their lifespan.

  16. Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

    Science.gov (United States)

    Zhang, Junming; Wu, Yan

    2017-02-21

    Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

  17. Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm.

    Science.gov (United States)

    Peker, Musa; Sen, Baha; Delen, Dursun

    2015-01-01

    Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.

  18. A potential neural substrate for processing functional classes of complex acoustic signals.

    Directory of Open Access Journals (Sweden)

    Isabelle George

    Full Text Available Categorization is essential to all cognitive processes, but identifying the neural substrates underlying categorization processes is a real challenge. Among animals that have been shown to be able of categorization, songbirds are particularly interesting because they provide researchers with clear examples of categories of acoustic signals allowing different levels of recognition, and they possess a system of specialized brain structures found only in birds that learn to sing: the song system. Moreover, an avian brain nucleus that is analogous to the mammalian secondary auditory cortex (the caudo-medial nidopallium, or NCM has recently emerged as a plausible site for sensory representation of birdsong, and appears as a well positioned brain region for categorization of songs. Hence, we tested responses in this non-primary, associative area to clear and distinct classes of songs with different functions and social values, and for a possible correspondence between these responses and the functional aspects of songs, in a highly social songbird species: the European starling. Our results clearly show differential neuronal responses to the ethologically defined classes of songs, both in the number of neurons responding, and in the response magnitude of these neurons. Most importantly, these differential responses corresponded to the functional classes of songs, with increasing activation from non-specific to species-specific and from species-specific to individual-specific sounds. These data therefore suggest a potential neural substrate for sorting natural communication signals into categories, and for individual vocal recognition of same-species members. Given the many parallels that exist between birdsong and speech, these results may contribute to a better understanding of the neural bases of speech.

  19. 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.

  20. Fetal Alcohol Spectrum Disorder (FASD Associated Neural Defects: Complex Mechanisms and Potential Therapeutic Targets

    Directory of Open Access Journals (Sweden)

    James A. Marrs

    2013-06-01

    Full Text Available Fetal alcohol spectrum disorder (FASD, caused by prenatal alcohol exposure, can result in craniofacial dysmorphism, cognitive impairment, sensory and motor disabilities among other defects. FASD incidences are as high as 2% to 5 % children born in the US, and prevalence is higher in low socioeconomic populations. Despite various mechanisms being proposed to explain the etiology of FASD, the molecular targets of ethanol toxicity during development are unknown. Proposed mechanisms include cell death, cell signaling defects and gene expression changes. More recently, the involvement of several other molecular pathways was explored, including non-coding RNA, epigenetic changes and specific vitamin deficiencies. These various pathways may interact, producing a wide spectrum of consequences. Detailed understanding of these various pathways and their interactions will facilitate the therapeutic target identification, leading to new clinical intervention, which may reduce the incidence and severity of these highly prevalent preventable birth defects. This review discusses manifestations of alcohol exposure on the developing central nervous system, including the neural crest cells and sensory neural placodes, focusing on molecular neurodevelopmental pathways as possible therapeutic targets for prevention or protection.

  1. Opaque for the Reader but Transparent for the Brain: Neural Signatures of Morphological Complexity

    Science.gov (United States)

    Meinzer, Marcus; Lahiri, Aditi; Flaisch, Tobias; Hannemann, Ronny; Eulitz, Carsten

    2009-01-01

    Within linguistics, words with a complex internal structure are commonly assumed to be decomposed into their constituent morphemes (e.g., un-help-ful). Nevertheless, an ongoing debate concerns the brain structures that subserve this process. Using functional magnetic resonance imaging, the present study varied the internal complexity of derived…

  2. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence

    Directory of Open Access Journals (Sweden)

    Esperanza Fernández

    2017-10-01

    Full Text Available Arc is an activity-regulated neuronal protein, but little is known about its interactions, assembly into multiprotein complexes, and role in human disease and cognition. We applied an integrated proteomic and genetic strategy by targeting a tandem affinity purification (TAP tag and Venus fluorescent protein into the endogenous Arc gene in mice. This allowed biochemical and proteomic characterization of native complexes in wild-type and knockout mice. We identified many Arc-interacting proteins, of which PSD95 was the most abundant. PSD95 was essential for Arc assembly into 1.5-MDa complexes and activity-dependent recruitment to excitatory synapses. Integrating human genetic data with proteomic data showed that Arc-PSD95 complexes are enriched in schizophrenia, intellectual disability, autism, and epilepsy mutations and normal variants in intelligence. We propose that Arc-PSD95 postsynaptic complexes potentially affect human cognitive function.

  3. Component neural systems for the creation of emotional memories during free viewing of a complex, real-world event

    Directory of Open Access Journals (Sweden)

    Anne Botzung

    2010-05-01

    Full Text Available To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI. During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets or from non-viewed portions of the same game (foils. After an old-new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan’s perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences.

  4. Neural correlates of conflict between gestures and words: A domain-specific role for a temporal-parietal complex

    National Research Council Canada - National Science Library

    J Adam Noah; Swethasri Dravida; Xian Zhang; Shaul Yahil; Joy Hirsch

    2017-01-01

    .... To gain insight into these underlying neural processes, we compared neural responses in a traditional color/word conflict task and to a gesture/word conflict task to test hypotheses of domain-general...

  5. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence.

    Science.gov (United States)

    Fernández, Esperanza; Collins, Mark O; Frank, René A W; Zhu, Fei; Kopanitsa, Maksym V; Nithianantharajah, Jess; Lemprière, Sarah A; Fricker, David; Elsegood, Kathryn A; McLaughlin, Catherine L; Croning, Mike D R; Mclean, Colin; Armstrong, J Douglas; Hill, W David; Deary, Ian J; Cencelli, Giulia; Bagni, Claudia; Fromer, Menachem; Purcell, Shaun M; Pocklington, Andrew J; Choudhary, Jyoti S; Komiyama, Noboru H; Grant, Seth G N

    2017-10-17

    Arc is an activity-regulated neuronal protein, but little is known about its interactions, assembly into multiprotein complexes, and role in human disease and cognition. We applied an integrated proteomic and genetic strategy by targeting a tandem affinity purification (TAP) tag and Venus fluorescent protein into the endogenous Arc gene in mice. This allowed biochemical and proteomic characterization of native complexes in wild-type and knockout mice. We identified many Arc-interacting proteins, of which PSD95 was the most abundant. PSD95 was essential for Arc assembly into 1.5-MDa complexes and activity-dependent recruitment to excitatory synapses. Integrating human genetic data with proteomic data showed that Arc-PSD95 complexes are enriched in schizophrenia, intellectual disability, autism, and epilepsy mutations and normal variants in intelligence. We propose that Arc-PSD95 postsynaptic complexes potentially affect human cognitive function. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  6. The neural substrates of complex argument structure representations: Processing 'alternating transitivity' verbs.

    Science.gov (United States)

    Meltzer-Asscher, Aya; Schuchard, Julia; den Ouden, Dirk-Bart; Thompson, Cynthia K

    This study examines the neural correlates of processing verbal entries with multiple argument structures using fMRI. We compared brain activation in response to 'alternating transitivity' verbs, corresponding to two different verbal alternates - one transitive and one intransitive - and simple verbs, with only one, intransitive, thematic grid. Fourteen young healthy participants performed a lexical decision task with the two verb types. Results showed significantly greater activation in the angular and supramarginal gyri (BAs 39 and 40) extending to the posterior superior and middle temporal gyri bilaterally, for alternating compared to simple verbs. Additional activation was detected in bilateral middle and superior frontal gyri (BAs 8 and 9). The opposite contrast, simple compared to alternating verbs, showed no significant differential activation in any regions of the brain. These findings are consistent with previous studies implicating a posterior network including the superior temporal, supramarginal and angular gyri for processing verbs with multiple thematic roles, as well as with those suggesting involvement of the middle and superior frontal gyri in lexical ambiguity processing. However, because 'alternating transitivity' verbs differ from simple intransitives with regard to both the number of thematic grids (two vs. one) and the number of thematic roles (two vs. one), our findings do not distinguish between activations associated with these two differences.

  7. The neural substrates of complex argument structure representations: Processing ‘alternating transitivity’ verbs

    Science.gov (United States)

    Meltzer-Asscher, Aya; Schuchard, Julia; den Ouden, Dirk-Bart; Thompson, Cynthia K.

    2015-01-01

    This study examines the neural correlates of processing verbal entries with multiple argument structures using fMRI. We compared brain activation in response to ‘alternating transitivity’ verbs, corresponding to two different verbal alternates – one transitive and one intransitive - and simple verbs, with only one, intransitive, thematic grid. Fourteen young healthy participants performed a lexical decision task with the two verb types. Results showed significantly greater activation in the angular and supramarginal gyri (BAs 39 and 40) extending to the posterior superior and middle temporal gyri bilaterally, for alternating compared to simple verbs. Additional activation was detected in bilateral middle and superior frontal gyri (BAs 8 and 9). The opposite contrast, simple compared to alternating verbs, showed no significant differential activation in any regions of the brain. These findings are consistent with previous studies implicating a posterior network including the superior temporal, supramarginal and angular gyri for processing verbs with multiple thematic roles, as well as with those suggesting involvement of the middle and superior frontal gyri in lexical ambiguity processing. However, because ‘alternating transitivity’ verbs differ from simple intransitives with regard to both the number of thematic grids (two vs. one) and the number of thematic roles (two vs. one), our findings do not distinguish between activations associated with these two differences. PMID:26139954

  8. Nutritional and other types of oedema, albumin, complex carbohydrates and the interstitium - a response to Malcolm Coulthard's hypothesis: Oedema in kwashiorkor is caused by hypo-albuminaemia.

    Science.gov (United States)

    Golden, Michael Henry

    2015-05-01

    The various types of oedema in man are considered in relation to Starling's hypothesis of fluid movement from capillaries, with the main emphasis on nutritional oedema and the nephrotic syndrome in children. It is concluded that each condition has sufficient anomalous findings to render Starling's hypothesis untenable. The finding that the endothelial glycocalyx is key to control of fluid movement from and into the capillaries calls for complete revision of our understanding of oedema formation. The factors so far known to affect the function of the glycocalyx are reviewed. As these depend upon sulphated proteoglycans and other glycosaminoglycans, the argument is advanced that the same abnormalities will extend to the interstitial space and that kwashiorkor is fundamentally related to a defect in sulphur metabolism which can explain all the clinical features of the condition, including the formation of oedema.

  9. Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes

    NARCIS (Netherlands)

    Costa, T.; Cauda, F.; Crini, M.; Tatu, M.K.; Celeghin, A.; de Gelder, B.; Tamietto, M.

    2014-01-01

    The different temporal dynamics of emotions are critical to understand their evolutionary role in the regulation of interactions with the surrounding environment. Here, we investigated the temporal dynamics underlying the perception of four basic emotions from complex scenes varying in valence and

  10. Use of artificial neural networks for analysis of complex physical systems

    Energy Technology Data Exchange (ETDEWEB)

    Benjamin, A.; Altman, B.; O`Gorman, C.; Rodeman, R.; Paez, T.L.

    1996-12-31

    Mathematical models of physical systems are used, among other purposes, to improve our understanding of the behavior of physical systems, predict physical system response, and control the responses of systems. Phenomenological models are frequently used to simulate system behavior, but an alternative is available - the artificial neural network (ANN). The ANN is an inductive, or data-based model for the simulation of input/output mappings. The ANN can be used in numerous frameworks to simulate physical system behavior. ANNs require training data to learn patterns of input/output behavior, and once trained, they can be used to simulate system behavior within the space where they were trained.They do this by interpolating specified inputs among the training inputs to yield outputs that are interpolations of =Ming outputs. The reason for using ANNs for the simulation of system response is that they provide accurate approximations of system behavior and are typically much more efficient than phenomenological models. This efficiency is very important in situations where multiple response computations are required, as in, for example, Monte Carlo analysis of probabilistic system response. This paper describes two frameworks in which we have used ANNs to good advantage in the approximate simulation of the behavior of physical system response. These frameworks are the non-recurrent and recurrent frameworks. It is assumed in these applications that physical experiments have been performed to obtain data characterizing the behavior of a system, or that an accurate finite element model has been run to establish system response. The paper provides brief discussions on the operation of ANNs, the operation of two different types of mechanical systems, and approaches to the solution of some special problems that occur in connection with ANN simulation of physical system response. Numerical examples are presented to demonstrate system simulation with ANNs.

  11. Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems.

    Science.gov (United States)

    Oparaji, Uchenna; Sheu, Rong-Jiun; Bankhead, Mark; Austin, Jonathan; Patelli, Edoardo

    2017-12-01

    Artificial Neural Networks (ANNs) are commonly used in place of expensive models to reduce the computational burden required for uncertainty quantification, reliability and sensitivity analyses. ANN with selected architecture is trained with the back-propagation algorithm from few data representatives of the input/output relationship of the underlying model of interest. However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN. On the other hand, using cross-validation to select the best performing ANN based on the ANN with the highest R2 value can lead to biassing in the prediction. This is as a result of the fact that the use of R2 cannot determine if the prediction made by ANN is biased. Additionally, R2 does not indicate if a model is adequate, as it is possible to have a low R2 for a good model and a high R2 for a bad model. Hence, in this paper, we propose an approach to improve the robustness of a prediction made by ANN. The approach is based on a systematic combination of identical trained ANNs, by coupling the Bayesian framework and model averaging. Additionally, the uncertainties of the robust prediction derived from the approach are quantified in terms of confidence intervals. To demonstrate the applicability of the proposed approach, two synthetic numerical examples are presented. Finally, the proposed approach is used to perform a reliability and sensitivity analyses on a process simulation model of a UK nuclear effluent treatment plant developed by National Nuclear Laboratory (NNL) and treated in this study as a black-box employing a set of training data as a test case. This model has been extensively validated against plant and experimental data and used to support the UK effluent discharge strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. The neural stem cell fate determinant TRIM32 regulates complex behavioral traits

    Directory of Open Access Journals (Sweden)

    Anna-Lena eHillje

    2015-03-01

    Full Text Available In mammals, new neurons are generated throughout the entire lifespan in two restricted areas of the brain, the dentate gyrus (DG of the hippocampus and the subventricular zone (SVZ – olfactory bulb (OB system. In both regions newborn neurons display unique properties that clearly distinguish them from mature neurons. Enhanced excitability and increased synaptic plasticity enables them to add specific properties to information processing by modulating the existing local circuitry of already established mature neurons. Hippocampal neurogenesis has been suggested to play a role in spatial-navigation learning, spatial memory and spatial pattern separation. Cumulative evidences implicate that adult-born OB neurons contribute to learning processes and odor memory. We recently demonstrated that the cell fate determinant TRIM32 is upregulated in differentiating neuroblasts of the SVZ-OB system in the adult mouse brain. The absence of TRIM32 leads to increased progenitor cell proliferation and less cell death. Both effects accumulate in an overproduction of adult-generated OB neurons. Here, we present novel data from behavioral studies showing that such an enhancement of OB neurogenesis not necessarily leads to increased olfactory performance but in contrast even results in impaired olfactory capabilities. In addition, we show at the cellular level that TRIM32 protein levels increase during differentiation of neural stem cells. At the molecular level, several metabolic intermediates that are connected to glycolysis, glycine or cysteine metabolism are deregulated in TRIM32 knockout mice brain tissue. These metabolomics pathways are directly or indirectly linked to anxiety or depression like behavior. In summary, our study provides comprehensive data on how the impairment of neurogenesis caused by the loss of the cell fate determinant TRIM32 causes a decrease of olfactory performance as well as a deregulation of metabolomic pathways that are linked to

  13. Practical aspects of complex permittivity reconstruction with neural-network-controlled FDTD modeling of a two-port fixture.

    Science.gov (United States)

    Eves, E Eugene; Murphy, Ethan K; Yakovlev, Vadim V

    2007-01-01

    The paper discusses characteristics of a new modeling-based technique for determining dielectric properties of materials. Complex permittivity is found with an optimization algorithm designed to match complex S-parameters obtained from measurements and from 3D FDTD simulation. The method is developed on a two-port (waveguide-type) fixture and deals with complex reflection and transmission characteristics at the frequency of interest. A computational part is constructed as an inverse-RBF-network-based procedure that reconstructs dielectric constant and the loss factor of the sample from the FDTD modeling data sets and the measured reflection and transmission coefficients. As such, it is applicable to samples and cavities of arbitrary configurations provided that the geometry of the experimental setup is adequately represented by the FDTD model. The practical implementation of the method considered in this paper is a section of a WR975 waveguide containing a sample of a liquid in a cylindrical cutout of a rectangular Teflon cup. The method is run in two stages and employs two databases--first, built for a sparse grid on the complex permittivity plane, in order to locate a domain with an anticipated solution and, second, made as a denser grid covering the determined domain, for finding an exact location of the complex permittivity point. Numerical tests demonstrate that the computational part of the method is highly accurate even when the modeling data is represented by relatively small data sets. When working with reflection and transmission coefficients measured in an actual experimental fixture and reconstructing a low dielectric constant and the loss factor the technique may be less accurate. It is shown that the employed neural network is capable of finding complex permittivity of the sample when experimental data on the reflection and transmission coefficients are numerically dispersive (noise-contaminated). A special modeling test is proposed for validating the

  14. 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.

  15. Wind speed and wind power short and medium range predictions for complex terrain using artificial neural networks and ensemble calibration

    Science.gov (United States)

    Schicker, Irene; Papazek, Petrina; Kann, Alexander; Wang, Yong

    2017-04-01

    Reliable predictions of wind speed and wind power are vital for balancing the electricity network. Within the last two decades the amount of energy stemming from renewable sources increased substantially relying heavily on the prevailing synoptic conditions. Especially for regions with complex terrain and forested surfaces providing reliable predictions is a challenging task. Forecasts in the nowcasting as well as in the (two) day-ahead range are thus essential for the network balancing. Predictions of wind speed and wind power from the nowcasting to the +72-hour forecast range using NWP models in regions with complex terrain need a suitable horizontal, vertical and temporal resolution (e.g. 10 - 15 minute forecasts for the Nowcasting range) requiring high performance computing. To be able to provide sub-hourly to hourly forecasts different approaches such as model output statistics (MOS) or artificial neural networks (ANN) - including feed forward recurrent neural networks, fuzzy logic, particle swarm optimizations - are needed as computational costs are too high. To represent the forecast uncertainties additional probabilistic ensemble predictions are required increasing the computational needs. Ensemble prediction systems account for errors and uncertainties in the initial and boundary conditions, parameterizations, numeric, etc. Due to the underestimation of model and sampling errors ensemble predictions tend to be underdispersive and biased. They lack, too, sharpness and reliability. These shortcomings can be accounted for using statistical post-processing methods such as the non-homogeneous Gaussian regression (NGR) to calibrate an ensemble. These calibrated ensembles provide forecasts in the medium range for any arbitrary location where observations are available. In this study an ANN is used to provide forecasts for the nowcasting and medium-range with sub-hourly to hourly predictions for different Austrian sites, including high alpine sites as well as low

  16. Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

    Science.gov (United States)

    Laloy, Eric; Hérault, Romain; Lee, John; Jacques, Diederik; Linde, Niklas

    2017-12-01

    Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200-500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

  17. Neural bases of event knowledge and syntax integration in comprehension of complex sentences.

    Science.gov (United States)

    Malaia, Evie; Newman, Sharlene

    2015-01-01

    Comprehension of complex sentences is necessarily supported by both syntactic and semantic knowledge, but what linguistic factors trigger a readers' reliance on a specific system? This functional neuroimaging study orthogonally manipulated argument plausibility and verb event type to investigate cortical bases of the semantic effect on argument comprehension during reading. The data suggest that telic verbs facilitate online processing by means of consolidating the event schemas in episodic memory and by easing the computation of syntactico-thematic hierarchies in the left inferior frontal gyrus. The results demonstrate that syntax-semantics integration relies on trade-offs among a distributed network of regions for maximum comprehension efficiency.

  18. Neural substrates of decision-making.

    Science.gov (United States)

    Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

    2016-06-01

    Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

  19. Complex Membrane Channel Blockade: A Unifying Hypothesis for the Prodromal and Acute Neuropsychiatric Sequelae Resulting from Exposure to the Antimalarial Drug Mefloquine

    Directory of Open Access Journals (Sweden)

    Jane C. Quinn

    2015-01-01

    Full Text Available The alkaloid toxin quinine and its derivative compounds have been used for many centuries as effective medications for the prevention and treatment of malaria. More recently, synthetic derivatives, such as the quinoline derivative mefloquine (bis(trifluoromethyl-(2-piperidyl-4-quinolinemethanol, have been widely used to combat disease caused by chloroquine-resistant strains of the malaria parasite, Plasmodium falciparum. However, the parent compound quinine, as well as its more recent counterparts, suffers from an incidence of adverse neuropsychiatric side effects ranging from mild mood disturbances and anxiety to hallucinations, seizures, and psychosis. This review considers how the pharmacology, cellular neurobiology, and membrane channel kinetics of mefloquine could lead to the significant and sometimes life-threatening neurotoxicity associated with mefloquine exposure. A key role for mefloquine blockade of ATP-sensitive potassium channels and connexins in the substantia nigra is considered as a unifying hypothesis for the pathogenesis of severe neuropsychiatric events after mefloquine exposure in humans.

  20. An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting.

    Science.gov (United States)

    Hippert, Henrique S; Taylor, James W

    2010-04-01

    Artificial neural networks have frequently been proposed for electricity load forecasting because of their capabilities for the nonlinear modelling of large multivariate data sets. Modelling with neural networks is not an easy task though; two of the main challenges are defining the appropriate level of model complexity, and choosing the input variables. This paper evaluates techniques for automatic neural network modelling within a Bayesian framework, as applied to six samples containing daily load and weather data for four different countries. We analyse input selection as carried out by the Bayesian 'automatic relevance determination', and the usefulness of the Bayesian 'evidence' for the selection of the best structure (in terms of number of neurones), as compared to methods based on cross-validation. Copyright 2009 Elsevier Ltd. All rights reserved.

  1. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

  2. Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

    Science.gov (United States)

    Valdés, Julio J; Bonham-Carter, Graeme

    2006-03-01

    A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

  3. The Vascular Depression Hypothesis: Mechanisms Linking Vascular Disease with Depression

    Science.gov (United States)

    Taylor, Warren D.; Aizenstein, Howard J.; Alexopoulos, George S.

    2013-01-01

    The ‘Vascular Depression’ hypothesis posits that cerebrovascular disease may predispose, precipitate, or perpetuate some geriatric depressive syndromes. This hypothesis stimulated much research that has improved our understanding of the complex relationships between late-life depression (LLD), vascular risk factors, and cognition. Succinctly, there are well-established relationships between late-life depression, vascular risk factors, and cerebral hyperintensities, the radiological hallmark of vascular depression. Cognitive dysfunction is common in late-life depression, particularly executive dysfunction, a finding predictive of poor antidepressant response. Over time, progression of hyperintensities and cognitive deficits predicts a poor course of depression and may reflect underlying worsening of vascular disease. This work laid the foundation for examining the mechanisms by which vascular disease influences brain circuits and influences the development and course of depression. We review data testing the vascular depression hypothesis with a focus on identifying potential underlying vascular mechanisms. We propose a disconnection hypothesis, wherein focal vascular damage and white matter lesion location is a crucial factor influencing neural connectivity that contributes to clinical symptomatology. We also propose inflammatory and hypoperfusion hypotheses, concepts that link underlying vascular processes with adverse effects on brain function that influence the development of depression. Testing such hypotheses will not only inform the relationship between vascular disease and depression but also provide guidance on the potential repurposing of pharmacological agents that may improve late-life depression outcomes. PMID:23439482

  4. Neural Representation of Harmonic Complex Tones in Primary Auditory Cortex of the Awake Monkey

    Science.gov (United States)

    Micheyl, Christophe; Steinschneider, Mitchell

    2013-01-01

    Many natural sounds are periodic and consist of frequencies (harmonics) that are integer multiples of a common fundamental frequency (F0). Such harmonic complex tones (HCTs) evoke a pitch corresponding to their F0, which plays a key role in the perception of speech and music. “Pitch-selective” neurons have been identified in non-primary auditory cortex of marmoset monkeys. Noninvasive studies point to a putative “pitch center” located in a homologous cortical region in humans. It remains unclear whether there is sufficient spectral and temporal information available at the level of primary auditory cortex (A1) to enable reliable pitch extraction in non-primary auditory cortex. Here we evaluated multiunit responses to HCTs in A1 of awake macaques using a stimulus design employed in auditory nerve studies of pitch encoding. The F0 of the HCTs was varied in small increments, such that harmonics of the HCTs fell either on the peak or on the sides of the neuronal pure tone tuning functions. Resultant response-amplitude-versus-harmonic-number functions (“rate-place profiles”) displayed a periodic pattern reflecting the neuronal representation of individual HCT harmonics. Consistent with psychoacoustic findings in humans, lower harmonics were better resolved in rate-place profiles than higher harmonics. Lower F0s were also temporally represented by neuronal phase-locking to the periodic waveform of the HCTs. Findings indicate that population responses in A1 contain sufficient spectral and temporal information for extracting the pitch of HCTs by neurons in downstream cortical areas that receive their input from A1. PMID:23785145

  5. Physiopathological Hypothesis of Cellulite

    OpenAIRE

    de Godoy, Jos? Maria Pereira; de Godoy, Maria de F?tima Guerreiro

    2009-01-01

    A series of questions are asked concerning this condition including as regards to its name, the consensus about the histopathological findings, physiological hypothesis and treatment of the disease. We established a hypothesis for cellulite and confirmed that the clinical response is compatible with this hypothesis. Hence this novel approach brings a modern physiological concept with physiopathologic basis and clinical proof of the hypothesis. We emphasize that the choice of patient, correct ...

  6. Niche Overlap of Congeneric Invaders Supports a Single-Species Hypothesis and Provides Insight into Future Invasion Risk: Implications for Global Management of the Bactrocera dorsalis Complex

    Science.gov (United States)

    Hill, Matthew P.; Terblanche, John S.

    2014-01-01

    Background The invasive fruit fly, Bactrocera invadens, has expanded its range rapidly over the past 10 years. Here we aimed to determine if the recent range expansion of Bactrocera invadens into southern Africa can be better understood through niche exploration tools, ecological niche models (ENMs), and through incorporating information about Bactrocera dorsalis s.s., a putative conspecific species from Asia. We test for niche overlap of environmental variables between Bactrocera invadens and Bactrocera dorsalis s.s. as well as two other putative conspecific species, Bactrocera philippinensis and B. papayae. We examine overlap and similarity in the geographical expression of each species’ realised niche through reciprocal distribution models between Africa and Asia. We explore different geographical backgrounds, environmental variables and model complexity with multiple and single Bactrocera species hypotheses in an attempt to predict the recent range expansion of B. invadens into northern parts of South Africa. Principal Findings Bactrocera invadens has a high degree of niche overlap with B. dorsalis s.s. (and B. philippinensis and B. papayae). Ecological niche models built for Bactrocera dorsalis s.s. have high transferability to describe the range of B. invadens, and B. invadens is able to project to the core range of B. dorsalis s.s. The ENMs of both Bactrocera dorsalis and B. dorsalis combined with B. philipenesis and B. papayae have significantly higher predictive ability to capture the distribution points in South Africa than for B. invadens alone. Conclusions/Significance Consistent with other studies proposing these Bactrocera species as conspecific, niche similarity and overlap between these species is high. Considering these other Bactrocera dorsalis complex species simultaneously better describes the range expansion and invasion potential of B. invadens in South Africa. We suggest that these species should be considered the same–at least

  7. Niche overlap of congeneric invaders supports a single-species hypothesis and provides insight into future invasion risk: implications for global management of the Bactrocera dorsalis complex.

    Directory of Open Access Journals (Sweden)

    Matthew P Hill

    Full Text Available BACKGROUND: The invasive fruit fly, Bactrocera invadens, has expanded its range rapidly over the past 10 years. Here we aimed to determine if the recent range expansion of Bactrocera invadens into southern Africa can be better understood through niche exploration tools, ecological niche models (ENMs, and through incorporating information about Bactrocera dorsalis s.s., a putative conspecific species from Asia. We test for niche overlap of environmental variables between Bactrocera invadens and Bactrocera dorsalis s.s. as well as two other putative conspecific species, Bactrocera philippinensis and B. papayae. We examine overlap and similarity in the geographical expression of each species' realised niche through reciprocal distribution models between Africa and Asia. We explore different geographical backgrounds, environmental variables and model complexity with multiple and single Bactrocera species hypotheses in an attempt to predict the recent range expansion of B. invadens into northern parts of South Africa. PRINCIPAL FINDINGS: Bactrocera invadens has a high degree of niche overlap with B. dorsalis s.s. (and B. philippinensis and B. papayae. Ecological niche models built for Bactrocera dorsalis s.s. have high transferability to describe the range of B. invadens, and B. invadens is able to project to the core range of B. dorsalis s.s. The ENMs of both Bactrocera dorsalis and B. dorsalis combined with B. philipenesis and B. papayae have significantly higher predictive ability to capture the distribution points in South Africa than for B. invadens alone. CONCLUSIONS/SIGNIFICANCE: Consistent with other studies proposing these Bactrocera species as conspecific, niche similarity and overlap between these species is high. Considering these other Bactrocera dorsalis complex species simultaneously better describes the range expansion and invasion potential of B. invadens in South Africa. We suggest that these species should be considered the same

  8. Neurodevelopmental hypothesis of schizophrenia

    National Research Council Canada - National Science Library

    Owen, Michael J; O'Donovan, Michael C; Thapar, Anita; Craddock, Nicholas

    2011-01-01

    The neurodevelopmental hypothesis of schizophrenia provided a valuable framework that allowed a condition that usually presents with frank disorder in adolescence or early adulthood to be understood...

  9. Physiopathological Hypothesis of Cellulite

    Science.gov (United States)

    de Godoy, José Maria Pereira; de Godoy, Maria de Fátima Guerreiro

    2009-01-01

    A series of questions are asked concerning this condition including as regards to its name, the consensus about the histopathological findings, physiological hypothesis and treatment of the disease. We established a hypothesis for cellulite and confirmed that the clinical response is compatible with this hypothesis. Hence this novel approach brings a modern physiological concept with physiopathologic basis and clinical proof of the hypothesis. We emphasize that the choice of patient, correct diagnosis of cellulite and the technique employed are fundamental to success. PMID:19756187

  10. Life Origination Hydrate Hypothesis (LOH-Hypothesis

    Directory of Open Access Journals (Sweden)

    Victor Ostrovskii

    2012-01-01

    Full Text Available The paper develops the Life Origination Hydrate Hypothesis (LOH-hypothesis, according to which living-matter simplest elements (LMSEs, which are N-bases, riboses, nucleosides, nucleotides, DNA- and RNA-like molecules, amino-acids, and proto-cells repeatedly originated on the basis of thermodynamically controlled, natural, and inevitable processes governed by universal physical and chemical laws from CH4, niters, and phosphates under the Earth's surface or seabed within the crystal cavities of the honeycomb methane-hydrate structure at low temperatures; the chemical processes passed slowly through all successive chemical steps in the direction that is determined by a gradual decrease in the Gibbs free energy of reacting systems. The hypothesis formulation method is based on the thermodynamic directedness of natural movement and consists ofan attempt to mentally backtrack on the progression of nature and thus reveal principal milestones alongits route. The changes in Gibbs free energy are estimated for different steps of the living-matter origination process; special attention is paid to the processes of proto-cell formation. Just the occurrence of the gas-hydrate periodic honeycomb matrix filled with LMSEs almost completely in its final state accounts for size limitation in the DNA functional groups and the nonrandom location of N-bases in the DNA chains. The slowness of the low-temperature chemical transformations and their “thermodynamic front” guide the gross process of living matter origination and its successive steps. It is shown that the hypothesis is thermodynamically justified and testable and that many observed natural phenomena count in its favor.

  11. Life Origination Hydrate Hypothesis (LOH-Hypothesis)

    Science.gov (United States)

    Ostrovskii, Victor; Kadyshevich, Elena

    2012-01-01

    The paper develops the Life Origination Hydrate Hypothesis (LOH-hypothesis), according to which living-matter simplest elements (LMSEs, which are N-bases, riboses, nucleosides, nucleotides), DNA- and RNA-like molecules, amino-acids, and proto-cells repeatedly originated on the basis of thermodynamically controlled, natural, and inevitable processes governed by universal physical and chemical laws from CH4, niters, and phosphates under the Earth's surface or seabed within the crystal cavities of the honeycomb methane-hydrate structure at low temperatures; the chemical processes passed slowly through all successive chemical steps in the direction that is determined by a gradual decrease in the Gibbs free energy of reacting systems. The hypothesis formulation method is based on the thermodynamic directedness of natural movement and consists ofan attempt to mentally backtrack on the progression of nature and thus reveal principal milestones alongits route. The changes in Gibbs free energy are estimated for different steps of the living-matter origination process; special attention is paid to the processes of proto-cell formation. Just the occurrence of the gas-hydrate periodic honeycomb matrix filled with LMSEs almost completely in its final state accounts for size limitation in the DNA functional groups and the nonrandom location of N-bases in the DNA chains. The slowness of the low-temperature chemical transformations and their “thermodynamic front” guide the gross process of living matter origination and its successive steps. It is shown that the hypothesis is thermodynamically justified and testable and that many observed natural phenomena count in its favor. PMID:25382120

  12. Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio P [Richland, WA; Cowell, Andrew J [Kennewick, WA; Gregory, Michelle L [Richland, WA; Baddeley, Robert L [Richland, WA; Paulson, Patrick R [Pasco, WA; Tratz, Stephen C [Richland, WA; Hohimer, Ryan E [West Richland, WA

    2012-03-20

    Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the hypothesis.

  13. A specific hygiene hypothesis.

    Science.gov (United States)

    Shunsheng Han, Cliff

    2016-08-01

    Allergic diseases have reached epidemic proportions in Western populations in the last several decades. The hygiene hypothesis proposed more than twenty years ago has helped us to understand the epidemic and has been verified with numerous studies. However, translational measures deduced from these studies to prevent allergic diseases have not proven effective. Recent studies on immigrants' allergies and any potential association between oral infection and allergic diseases prompt me to propose a specific hygiene hypothesis to explain how oral hygiene practices might have contributed to the uprising of hay fever, the most common allergic disease. The historic oral hygiene level in US is closely associated with the emerging allergic epidemic. Future studies to test the hypothesis are needed and verification of the hypothesis can potentially yield highly effective measures to prevent allergic diseases. Published by Elsevier Ltd.

  14. Complexity and evolution of dissipative systems an analytical approach

    CERN Document Server

    Vakulenko, Sergey

    2013-01-01

    This book focusses ondynamic complexity of neural and genetic networks, reaction diffusion systems and equations of fluid dynamics.It considersviability problems for such systems and discusses an interesting hypothesis of M. Gromov andA. Carbone on biological evolution.Several applications are considered.

  15. The Qualitative Expectations Hypothesis

    DEFF Research Database (Denmark)

    Frydman, Roman; Johansen, Søren; Rahbek, Anders

    2017-01-01

    We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the predictions...... of an economistís model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining specific values that outcomes take...

  16. The Qualitative Expectations Hypothesis

    DEFF Research Database (Denmark)

    Frydman, Roman; Johansen, Søren; Rahbek, Anders

    We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the predictions...... of an economist's model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining specific values that outcomes take...

  17. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  18. Understanding the Functional Plasticity in Neural Networks of the Basal Ganglia in Cocaine Use Disorder: A Role for Allosteric Receptor-Receptor Interactions in A2A-D2 Heteroreceptor Complexes

    Directory of Open Access Journals (Sweden)

    Dasiel O. Borroto-Escuela

    2016-01-01

    Full Text Available Our hypothesis is that allosteric receptor-receptor interactions in homo- and heteroreceptor complexes may form the molecular basis of learning and memory. This principle is illustrated by showing how cocaine abuse can alter the adenosine A2AR-dopamine D2R heterocomplexes and their receptor-receptor interactions and hereby induce neural plasticity in the basal ganglia. Studies with A2AR ligands using cocaine self-administration procedures indicate that antagonistic allosteric A2AR-D2R heterocomplexes of the ventral striatopallidal GABA antireward pathway play a significant role in reducing cocaine induced reward, motivation, and cocaine seeking. Anticocaine actions of A2AR agonists can also be produced at A2AR homocomplexes in these antireward neurons, actions in which are independent of D2R signaling. At the A2AR-D2R heterocomplex, they are dependent on the strength of the antagonistic allosteric A2AR-D2R interaction and the number of A2AR-D2R and A2AR-D2R-sigma1R heterocomplexes present in the ventral striatopallidal GABA neurons. It involves a differential cocaine-induced increase in sigma1Rs in the ventral versus the dorsal striatum. In contrast, the allosteric brake on the D2R protomer signaling in the A2AR-D2R heterocomplex of the dorsal striatopallidal GABA neurons is lost upon cocaine self-administration. This is potentially due to differences in composition and allosteric plasticity of these complexes versus those in the ventral striatopallidal neurons.

  19. The Lehman Sisters Hypothesis

    NARCIS (Netherlands)

    I.P. van Staveren (Irene)

    2014-01-01

    markdownabstract__Abstract__ This article explores the Lehman Sisters Hypothesis. It reviews empirical literature about gender differences in behavioral, experimental, and neuro-economics as well as in other fields of behavioral research. It discusses gender differences along three dimensions of

  20. The Riemann Hypothesis

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 11; Issue 11. The Riemann Hypothesis. Renuka Ravindran. General Article Volume 11 Issue 11 November 2006 pp 40-47. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/011/11/0040-0047 ...

  1. Dirac's Large Numbers Hypothesis

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 8; Issue 8. Dirac's Large Numbers Hypothesis. Biman Nath. Article-in-a-Box Volume 8 Issue 8 August 2003 pp 7-7. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/008/08/0007-0007. Author Affiliations.

  2. An exploration of the social brain hypothesis in insects

    Directory of Open Access Journals (Sweden)

    Mathieu eLihoreau

    2012-11-01

    Full Text Available The ‘social brain hypothesis’ posits that the cognitive demands of sociality have driven the evolution of substantially enlarged brains in primates and some other mammals. Whether such reasoning can apply to all social animals is an open question. Here we examine the evolutionary relationships between sociality, cognition and brain size in insects, a taxonomic group characterized by an extreme sophistication of social behaviors and relatively simple nervous systems. We discuss the application of the social brain hypothesis in this group based on comparative studies of brain volumes across species exhibiting various levels of social complexity. We illustrate how some of the major behavioral innovations of social insects may in fact require little information processing and minor adjustments of neural circuitry, thus potentially selecting for more specialized rather than bigger brains. We argue that future work aiming to understand how animal behavior, cognition and brains are shaped by the environment (including social interactions should focus on brain functions and identify neural correlates of social tasks, not only brain sizes.

  3. Invariant recognition drives neural representations of action sequences.

    Directory of Open Access Journals (Sweden)

    Andrea Tacchetti

    2017-12-01

    Full Text Available Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs, that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.

  4. Bayesian Hypothesis Testing

    Energy Technology Data Exchange (ETDEWEB)

    Andrews, Stephen A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sigeti, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-11-15

    These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ2 which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H0.

  5. Novel high-viscosity polyacrylamidated chitosan for neural tissue engineering: fabrication of anisotropic neurodurable scaffold via molecular disposition of persulfate-mediated polymer slicing and complexation.

    Science.gov (United States)

    Kumar, Pradeep; Choonara, Yahya E; du Toit, Lisa C; Modi, Girish; Naidoo, Dinesh; Pillay, Viness

    2012-10-29

    Macroporous polyacrylamide-grafted-chitosan scaffolds for neural tissue engineering were fabricated with varied synthetic and viscosity profiles. A novel approach and mechanism was utilized for polyacrylamide grafting onto chitosan using potassium persulfate (KPS) mediated degradation of both polymers under a thermally controlled environment. Commercially available high molecular mass polyacrylamide was used instead of the acrylamide monomer for graft copolymerization. This grafting strategy yielded an enhanced grafting efficiency (GE = 92%), grafting ratio (GR = 263%), intrinsic viscosity (IV = 5.231 dL/g) and viscometric average molecular mass (MW = 1.63 × 106 Da) compared with known acrylamide that has a GE = 83%, GR = 178%, IV = 3.901 dL/g and MW = 1.22 × 106 Da. Image processing analysis of SEM images of the newly grafted neurodurable scaffold was undertaken based on the polymer-pore threshold. Attenuated Total Reflectance-FTIR spectral analyses in conjugation with DSC were used for the characterization and comparison of the newly grafted copolymers. Static Lattice Atomistic Simulations were employed to investigate and elucidate the copolymeric assembly and reaction mechanism by exploring the spatial disposition of chitosan and polyacrylamide with respect to the reactional profile of potassium persulfate. Interestingly, potassium persulfate, a peroxide, was found to play a dual role initially degrading the polymers-"polymer slicing"-thereby initiating the formation of free radicals and subsequently leading to synthesis of the high molecular mass polyacrylamide-grafted-chitosan (PAAm-g-CHT)-"polymer complexation". Furthermore, the applicability of the uniquely grafted scaffold for neural tissue engineering was evaluated via PC12 neuronal cell seeding. The novel PAAm-g-CHT exhibited superior neurocompatibility in terms of cell infiltration owing to the anisotropic porous architecture, high molecular mass mediated robustness, superior hydrophilicity as well as

  6. Mesoamerican cosmovision: an hypothesis.

    Science.gov (United States)

    Franch, J. A.

    In the present conference the author explains a new hypothesis to interpret the cosmogonic vision of the people and the cultures from the Mesoamerican area during the precolumbian period. The hypothesis at issue consists in irregular octahedrical form, or as two pyramids jointed by the base in such a manner that the celestial pyramid has thirteen heavens in the form of platforms in such a way that the zenith is the seventh platform; on the contrary, the infraworld pyramid has nine platforms. The sequence of the heavens comes to an end in the number 13 heaven, or the West side of the world, that is to say the Omeyocan or the Tamoanchan, whereas the ninth infraworld is the Apochcalocan. This is the point of the intercommunication between the celestial world and the infraworld, the place of Death and Birth. In order to develop that hypothesis the author has a great number of ethnographic testimonies taken from Totonacs, Tzotziles, Mayas and, along with this, from Southamerican areas, as it is the case of the Kogi, of Colombia. The author has also considered the evidence that proceeds from the ancient codices as well as numerous samples of sculptures and reliefs, especially from the Aztec culture.

  7. Neural correlates of conflict between gestures and words: A domain-specific role for a temporal-parietal complex.

    Directory of Open Access Journals (Sweden)

    J Adam Noah

    Full Text Available The interpretation of social cues is a fundamental function of human social behavior, and resolution of inconsistencies between spoken and gestural cues plays an important role in successful interactions. To gain insight into these underlying neural processes, we compared neural responses in a traditional color/word conflict task and to a gesture/word conflict task to test hypotheses of domain-general and domain-specific conflict resolution. In the gesture task, recorded spoken words ("yes" and "no" were presented simultaneously with video recordings of actors performing one of the following affirmative or negative gestures: thumbs up, thumbs down, head nodding (up and down, or head shaking (side-to-side, thereby generating congruent and incongruent communication stimuli between gesture and words. Participants identified the communicative intent of the gestures as either positive or negative. In the color task, participants were presented the words "red" and "green" in either red or green font and were asked to identify the color of the letters. We observed a classic "Stroop" behavioral interference effect, with participants showing increased response time for incongruent trials relative to congruent ones for both the gesture and color tasks. Hemodynamic signals acquired using functional near-infrared spectroscopy (fNIRS were increased in the right dorsolateral prefrontal cortex (DLPFC for incongruent trials relative to congruent trials for both tasks consistent with a common, domain-general mechanism for detecting conflict. However, activity in the left DLPFC and frontal eye fields and the right temporal-parietal junction (TPJ, superior temporal gyrus (STG, supramarginal gyrus (SMG, and primary and auditory association cortices was greater for the gesture task than the color task. Thus, in addition to domain-general conflict processing mechanisms, as suggested by common engagement of right DLPFC, socially specialized neural modules localized to

  8. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering

    Science.gov (United States)

    Elangasinghe, M. A.; Singhal, N.; Dirks, K. N.; Salmond, J. A.; Samarasinghe, S.

    2014-09-01

    This paper uses artificial neural networks (ANN), combined with k-means clustering, to understand the complex time series of PM10 and PM2.5 concentrations at a coastal location of New Zealand based on data from a single site. Out of available meteorological parameters from the network (wind speed, wind direction, solar radiation, temperature, relative humidity), key factors governing the pattern of the time series concentrations were identified through input sensitivity analysis performed on the trained neural network model. The transport pathways of particulate matter under these key meteorological parameters were further analysed through bivariate concentration polar plots and k-means clustering techniques. The analysis shows that the external sources such as marine aerosols and local sources such as traffic and biomass burning contribute equally to the particulate matter concentrations at the study site. These results are in agreement with the results of receptor modelling by the Auckland Council based on Positive Matrix Factorization (PMF). Our findings also show that contrasting concentration-wind speed relationships exist between marine aerosols and local traffic sources resulting in very noisy and seemingly large random PM10 concentrations. The inclusion of cluster rankings as an input parameter to the ANN model showed a statistically significant (p transport characteristics prior to the implementation of costly chemical analysis techniques or advanced air dispersion models.

  9. The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis.

    Science.gov (United States)

    Patel, Aniruddh D; Iversen, John R

    2014-01-01

    a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement). More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This "action simulation for auditory prediction" (ASAP) hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in non-human primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi.

  10. Serotonergic hypothesis of sleepwalking.

    Science.gov (United States)

    Juszczak, Grzegorz R; Swiergiel, Artur H

    2005-01-01

    Despite widespread prevalence of sleepwalking, its etiology and pathophysiology are not well understood. However, there is some evidence that sleepwalking can be precipitated by sleep-disordered breathing. A hypothesis is proposed that serotonergic system may be a link between sleep-disordered breathing and sleepwalking. Serotonergic neurons meet basic requirements for such a role because they are activated by hypercapnia, provide a tonic excitatory drive that gates afferent inputs to motoneurons, and the activity of serotonergic neurons can be dissociated from the level of arousal. This paper discusses also drug-induced somnambulism and co-occurrence of sleepwalking and other disorders such as migraine and febrile illness.

  11. Neural responses to a modified Stroop paradigm in patients with complex chronic musculoskeletal pain compared to matched controls: an experimental functional magnetic resonance imaging study.

    Science.gov (United States)

    Taylor, Ann M; Harris, Ashley D; Varnava, Alice; Phillips, Rhiannon; Hughes, Owen; Wilkes, Antony R; Hall, Judith E; Wise, Richard G

    2016-02-01

    Chronic musculoskeletal pain (CMSKP) is attentionally demanding, complex and multi-factorial; neuroimaging research in the population seen in pain clinics is sparse. A better understanding of the neural activity underlying attentional processes to pain related information compared to healthy controls may help inform diagnosis and management in the future. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD fMRI) compared brain responses in patients with CMSKP (n = 15) and healthy controls (n = 14) while completing a modified Stroop task using pain-related, positive-emotional, and neutral control words. Response times in the Stroop task were no different for CMSKP patients compared with controls, but patients were less accurate in their responses to all word types. BOLD fMRI responses during presentation of pain-related words suggested increases in neural activation in patients compared to controls in regions previously reported as being involved in pain perception and emotion: the anterior cingulate cortex, insula and primary and secondary somatosensory cortex. No fMRI differences were seen between groups in response to positive or control words. Using this modified Stroop tasks, specific differences were identified in brain activity between CMSKP patients and controls in response to pain-related information using fMRI. This provided evidence of differences in the way that pain-related information is processed in those with chronic complex musculoskeletal pain that were not detectable using the behavioural measures of speed and accuracy. The study may be helpful in gaining new insights into the impact of attention in those living with chronic pain.

  12. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  13. Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

    Science.gov (United States)

    Oustimov, Andrew; Gastounioti, Aimilia; Hsieh, Meng-Kang; Pantalone, Lauren; Conant, Emily F.; Kontos, Despina

    2017-03-01

    We assess the feasibility of a parenchymal texture feature fusion approach, utilizing a convolutional neural network (ConvNet) architecture, to benefit breast cancer risk assessment. Hypothesizing that by capturing sparse, subtle interactions between localized motifs present in two-dimensional texture feature maps derived from mammographic images, a multitude of texture feature descriptors can be optimally reduced to five meta-features capable of serving as a basis on which a linear classifier, such as logistic regression, can efficiently assess breast cancer risk. We combine this methodology with our previously validated lattice-based strategy for parenchymal texture analysis and we evaluate the feasibility of this approach in a case-control study with 424 digital mammograms. In a randomized split-sample setting, we optimize our framework in training/validation sets (N=300) and evaluate its descriminatory performance in an independent test set (N=124). The discriminatory capacity is assessed in terms of the the area under the curve (AUC) of the receiver operator characteristic (ROC). The resulting meta-features exhibited strong classification capability in the test dataset (AUC = 0.90), outperforming conventional, non-fused, texture analysis which previously resulted in an AUC=0.85 on the same case-control dataset. Our results suggest that informative interactions between localized motifs exist and can be extracted and summarized via a fairly simple ConvNet architecture.

  14. Cortical gene expression in spinal cord injury and repair: insight into the functional complexity of the neural regeneration program

    Directory of Open Access Journals (Sweden)

    Fabian eKruse

    2011-09-01

    Full Text Available Traumatic spinal cord injury (SCI results in the formation of a fibrous scar acting as a growth barrier for regenerating axons at the lesion site. We have previously shown (Klapka et al., 2005 that transient suppression of the inhibitory lesion scar in rat spinal cord leads to long distance axon regeneration, retrograde rescue of axotomized cortical motoneurons and improvement of locomotor function. Here we applied a systemic approach to investigate for the first time specific and dynamic alterations in the cortical gene expression profile following both thoracic SCI and regeneration-promoting anti-scarring treatment (AST. In order to monitor cortical gene expression we carried out microarray analyses using total RNA isolated from layer V/VI of rat sensorimotor cortex at 1-60 days post-operation (dpo. We demonstrate that cortical neurons respond to injury by massive changes in gene expression, starting as early as 1 dpo. AST, in turn, results in profound modifications of the lesion-induced expression profile. The treatment attenuates SCI-triggered transcriptional changes of genes related to inhibition of axon growth and impairment of cell survival, while upregulating the expression of genes associated with axon outgrowth, cell protection and neural development. Thus, AST not only modifies the local environment impeding spinal cord regeneration by reduction of fibrous scarring in the injured spinal cord, but, in addition, strikingly changes the intrinsic capacity of cortical pyramidal neurons towards enhanced cell maintenance and axonal regeneration.

  15. The locust standard brain: a 3D standard of the central complex as a platform for neural network analysis

    Directory of Open Access Journals (Sweden)

    Basil El Jundi

    2010-02-01

    Full Text Available Many insects use the pattern of polarized light in the sky for spatial orientation and navigation. We have investigated the polarization vision system in the desert locust. To create a common platform for anatomical studies on polarization vision pathways, Kurylas et al. (2008 have generated a three-dimensional (3D standard brain from confocal microscopy image stacks of 10 male brains, using two different standardization methods, the Iterative Shape Averaging (ISA procedure and the Virtual Insect Brain (VIB protocol. Comparison of both standardization methods showed that the VIB standard is ideal for comparative volume analysis of neuropils, whereas the ISA standard is the method of choice to analyze the morphology and connectivity of neurons. The central complex is a key processing stage for polarization information in the locust brain. To investigate neuronal connections between diverse central-complex neurons, we generated a higher-resolution standard atlas of the central complex and surrounding areas, using the ISA method based on brain sections from 20 individual central complexes. To explore the usefulness of this atlas, two central-complex neurons, a polarization-sensitive columnar neuron (type CPU1a and a tangential neuron that is activated during flight, the giant-fan shaped (GFS neuron, were reconstructed three-dimensionally from brain sections. To examine whether the GFS neuron is a candidate to contribute to synaptic input to the CPU1a neuron, we registered both neurons into the standardized central complex. Visualization of both neurons revealed a potential connection of the CPU1a and GFS neurons in layer II of the upper division of the central body.

  16. Understanding the Implications of Neural Population Activity on Behavior

    Science.gov (United States)

    Briguglio, John

    Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests

  17. The Drift Burst Hypothesis

    DEFF Research Database (Denmark)

    Christensen, Kim; Oomen, Roel; Renò, Roberto

    The Drift Burst Hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent US equity and Treasury flash crashes can be viewed as two high profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude....... We then develop a non-parametric test statistic that allows for the identification of drift bursts from noisy high-frequency data. We apply this methodology to a comprehensive set of tick data and show that drift bursts form an integral part of the price dynamics across equities, fixed income......, currencies and commodities. We find that the majority of identified drift bursts are accompanied by strong price reversals and these can therefore be regarded as “flash crashes” that span brief periods of severe market disruption without any material longer term price impacts....

  18. Subsystem eigenstate thermalization hypothesis

    Science.gov (United States)

    Dymarsky, Anatoly; Lashkari, Nima; Liu, Hong

    2018-01-01

    Motivated by the qualitative picture of canonical typicality, we propose a refined formulation of the eigenstate thermalization hypothesis (ETH) for chaotic quantum systems. This formulation, which we refer to as subsystem ETH, is in terms of the reduced density matrix of subsystems. This strong form of ETH outlines the set of observables defined within the subsystem for which it guarantees eigenstate thermalization. We discuss the limits when the size of the subsystem is small or comparable to its complement. In the latter case we outline the way to calculate the leading volume-proportional contribution to the von Neumann and Renyi entanglment entropies. Finally, we provide numerical evidence for the proposal in the case of a one-dimensional Ising spin chain.

  19. Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

    Science.gov (United States)

    MaBouDi, HaDi; Shimazaki, Hideaki; Giurfa, Martin; Chittka, Lars

    2017-06-01

    The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons' outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several-but not all-types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.

  20. Complexity

    Indian Academy of Sciences (India)

    Rahul Pandit

    2008-10-31

    Oct 31, 2008 ... ”The more complex a thing is, the more you can talk about it.” - attributed to Giorgio Parisi. ▻ ”C'est magnifique, mais ce n'est pas de la science.” (It is magnificent, but not all of it is science.) - attributed ... Earliest examples: theoretical computer science, algorithmic complexity, etc. ▻ Rapid progress after the ...

  1. The venom optimization hypothesis revisited.

    Science.gov (United States)

    Morgenstern, David; King, Glenn F

    2013-03-01

    Animal venoms are complex chemical mixtures that typically contain hundreds of proteins and non-proteinaceous compounds, resulting in a potent weapon for prey immobilization and predator deterrence. However, because venoms are protein-rich, they come with a high metabolic price tag. The metabolic cost of venom is sufficiently high to result in secondary loss of venom whenever its use becomes non-essential to survival of the animal. The high metabolic cost of venom leads to the prediction that venomous animals may have evolved strategies for minimizing venom expenditure. Indeed, various behaviors have been identified that appear consistent with frugality of venom use. This has led to formulation of the "venom optimization hypothesis" (Wigger et al. (2002) Toxicon 40, 749-752), also known as "venom metering", which postulates that venom is metabolically expensive and therefore used frugally through behavioral control. Here, we review the available data concerning economy of venom use by animals with either ancient or more recently evolved venom systems. We conclude that the convergent nature of the evidence in multiple taxa strongly suggests the existence of evolutionary pressures favoring frugal use of venom. However, there remains an unresolved dichotomy between this economy of venom use and the lavish biochemical complexity of venom, which includes a high degree of functional redundancy. We discuss the evidence for biochemical optimization of venom as a means of resolving this conundrum. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Non-SMC condensin I complex proteins control chromosome segregation and survival of proliferating cells in the zebrafish neural retina

    Directory of Open Access Journals (Sweden)

    Harris William A

    2009-07-01

    Full Text Available Abstract Background The condensation of chromosomes and correct sister chromatid segregation during cell division is an essential feature of all proliferative cells. Structural maintenance of chromosomes (SMC and non-SMC proteins form the condensin I complex and regulate chromosome condensation and segregation during mitosis. However, due to the lack of appropriate mutants, the function of the condensin I complex during vertebrate development has not been described. Results Here, we report the positional cloning and detailed characterization of retinal phenotypes of a zebrafish mutation at the cap-g locus. High resolution live imaging reveals that the progression of mitosis between prometa- to telophase is delayed and that sister chromatid segregation is impaired upon loss of CAP-G. CAP-G associates with chromosomes between prometa- and telophase of the cell cycle. Loss of the interaction partners CAP-H and CAP-D2 causes cytoplasmic mislocalization of CAP-G throughout mitosis. DNA content analysis reveals increased genomic imbalances upon loss of non-SMC condensin I subunits. Within the retina, loss of condensin I function causes increased rates of apoptosis among cells within the proliferative ciliary marginal zone (CMZ whereas postmitotic retinal cells are viable. Inhibition of p53-mediated apoptosis partially rescues cell numbers in cap-g mutant retinae and allows normal layering of retinal cell types without alleviating their aberrant nuclear sizes. Conclusion Our findings indicate that the condensin I complex is particularly important within rapidly amplifying progenitor cell populations to ensure faithful chromosome segregation. In contrast, differentiation of postmitotic retinal cells is not impaired upon polyploidization.

  3. Visual motion imagery neurofeedback based on the hMT+/V5 complex: evidence for a feedback-specific neural circuit involving neocortical and cerebellar regions

    Science.gov (United States)

    Banca, Paula; Sousa, Teresa; Catarina Duarte, Isabel; Castelo-Branco, Miguel

    2015-12-01

    Objective. Current approaches in neurofeedback/brain-computer interface research often focus on identifying, on a subject-by-subject basis, the neural regions that are best suited for self-driven modulation. It is known that the hMT+/V5 complex, an early visual cortical region, is recruited during explicit and implicit motion imagery, in addition to real motion perception. This study tests the feasibility of training healthy volunteers to regulate the level of activation in their hMT+/V5 complex using real-time fMRI neurofeedback and visual motion imagery strategies. Approach. We functionally localized the hMT+/V5 complex to further use as a target region for neurofeedback. An uniform strategy based on motion imagery was used to guide subjects to neuromodulate hMT+/V5. Main results. We found that 15/20 participants achieved successful neurofeedback. This modulation led to the recruitment of a specific network as further assessed by psychophysiological interaction analysis. This specific circuit, including hMT+/V5, putative V6 and medial cerebellum was activated for successful neurofeedback runs. The putamen and anterior insula were recruited for both successful and non-successful runs. Significance. Our findings indicate that hMT+/V5 is a region that can be modulated by focused imagery and that a specific cortico-cerebellar circuit is recruited during visual motion imagery leading to successful neurofeedback. These findings contribute to the debate on the relative potential of extrinsic (sensory) versus intrinsic (default-mode) brain regions in the clinical application of neurofeedback paradigms. This novel circuit might be a good target for future neurofeedback approaches that aim, for example, the training of focused attention in disorders such as ADHD.

  4. Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

    Directory of Open Access Journals (Sweden)

    Andrea Maesani

    2015-11-01

    Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

  5. Neural Integration in Learning and Memory: A Hypothesis

    Science.gov (United States)

    1975-04-01

    in synthetic chains through repression of enzyme synthesis and end-product inhibition of enzyme activity (Datta 1969) The dynamics of this type of...RNA or protein synthesis (Atkinson, 1966). However Mitchison (1969) reviewing patterns of enzyme synthesis in synchronous throuZ tCtH i/1Ŕ...1969. Enzyme synthesis in S^nchronous cultures. Science 165: 657-663. Monod. J.. J. Wymanand J.D. Changeux. 1965. On the nature of

  6. The migratory fascia hypothesis.

    Science.gov (United States)

    Lelean, Peter

    2009-10-01

    its possible implications for lumbo-pelvic function. Although a review of anatomy atlases has failed to reveal mention of migratory fascia, the author respectfully suggests that dissection, specifically aimed at this task, may demonstrate its presence. It is also suggested that a retrospective review of lumbo-pelvic MRI records be initiated to identify the presence of this proposed fascial feature in the general population. Finally, magnetic resonance elastography may be useful in defining areas of increased muscular tension, in order to test the migratory fascia hypothesis.

  7. A computational analysis of the neural bases of Bayesian inference.

    Science.gov (United States)

    Kolossa, Antonio; Kopp, Bruno; Fingscheidt, Tim

    2015-02-01

    Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. This hypothesis posits simply that neural activities code and compute Bayesian probabilities. Here, we introduce an urn-ball paradigm to relate event-related potentials (ERPs) such as the P300 wave to Bayesian inference. Bayesian model comparison is conducted to compare various models in terms of their ability to explain trial-by-trial variation in ERP responses at different points in time and over different regions of the scalp. Specifically, we are interested in dissociating specific ERP responses in terms of Bayesian updating and predictive surprise. Bayesian updating refers to changes in probability distributions given new observations, while predictive surprise equals the surprise about observations under current probability distributions. Components of the late positive complex (P3a, P3b, Slow Wave) provide dissociable measures of Bayesian updating and predictive surprise. Specifically, the updating of beliefs about hidden states yields the best fit for the anteriorly distributed P3a, whereas the updating of predictions of observations accounts best for the posteriorly distributed Slow Wave. In addition, parietally distributed P3b responses are best fit by predictive surprise. These results indicate that the three components of the late positive complex reflect distinct neural computations. As such they are consistent with the Bayesian brain hypothesis, but these neural computations seem to be subject to nonlinear probability weighting. We integrate these findings with the free-energy principle that instantiates the Bayesian brain hypothesis. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Remote sensing of harmful algal events in optically complex waters using regionally specific neural network-based algorithms for MERIS data

    Science.gov (United States)

    Gonzalez Vilas, L.; Castro Fernandez, M.; Spyrakos, E.; Torres Palenzuela, J.

    2013-08-01

    In typical case 2 waters an accurate remote sensing retrieval of chlorophyll a (chla) is still challenging. There is a widespread understanding that universally applicable water constituent retrieval algorithms are currently not feasible, shifting the research focus to regionally specific implementations of powerful inversion methods. This study takes advantage of regionally specific chlorophyll a (chla) algorithms, which were developed by the authors of this abstract in previous works, and the characteristics of Medium Resolution Imaging Spectrometer (MERIS) in order to study harmful algal events in the optically complex waters of the Galician Rias (NW). Harmful algal events are a frequent phenomenon in this area with direct and indirect impacts to the mussel production that constitute a very important economic activity for the local community. More than 240 106 kg of mussel per year are produced in these highly primary productive upwelling systems. A MERIS archive from nine years (2003-2012) was analysed using regionally specific chla algorithms. The latter were developed based on Multilayer perceptron (MLP) artificial neural networks and fuzzy c-mean clustering techniques (FCM). FCM specifies zones (based on water leaving reflectances) where the retrieval algorithms normally provide more reliable results. Monthly chla anomalies and other statistics were calculated for the nine years MERIS archive. These results were then related to upwelling indices and other associated measurements to determine the driver forces for specific phytoplankton blooms. The distribution and changes of chla are also discussed.

  9. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  10. Identifying Regulators of Morphogenesis Common to Vertebrate Neural Tube Closure and Caenorhabditis elegans Gastrulation.

    Science.gov (United States)

    Sullivan-Brown, Jessica L; Tandon, Panna; Bird, Kim E; Dickinson, Daniel J; Tintori, Sophia C; Heppert, Jennifer K; Meserve, Joy H; Trogden, Kathryn P; Orlowski, Sara K; Conlon, Frank L; Goldstein, Bob

    2016-01-01

    Neural tube defects including spina bifida are common and severe congenital disorders. In mice, mutations in more than 200 genes can result in neural tube defects. We hypothesized that this large gene set might include genes whose homologs contribute to morphogenesis in diverse animals. To test this hypothesis, we screened a set of Caenorhabditis elegans homologs for roles in gastrulation, a topologically similar process to vertebrate neural tube closure. Both C. elegans gastrulation and vertebrate neural tube closure involve the internalization of surface cells, requiring tissue-specific gene regulation, actomyosin-driven apical constriction, and establishment and maintenance of adhesions between specific cells. Our screen identified several neural tube defect gene homologs that are required for gastrulation in C. elegans, including the transcription factor sptf-3. Disruption of sptf-3 in C. elegans reduced the expression of early endodermally expressed genes as well as genes expressed in other early cell lineages, establishing sptf-3 as a key contributor to multiple well-studied C. elegans cell fate specification pathways. We also identified members of the actin regulatory WAVE complex (wve-1, gex-2, gex-3, abi-1, and nuo-3a). Disruption of WAVE complex members reduced the narrowing of endodermal cells' apical surfaces. Although WAVE complex members are expressed broadly in C. elegans, we found that expression of a vertebrate WAVE complex member, nckap1, is enriched in the developing neural tube of Xenopus. We show that nckap1 contributes to neural tube closure in Xenopus. This work identifies in vivo roles for homologs of mammalian neural tube defect genes in two manipulable genetic model systems. Copyright © 2016 by the Genetics Society of America.

  11. The oxidative hypothesis of senescence

    Directory of Open Access Journals (Sweden)

    Gilca M

    2007-01-01

    Full Text Available The oxidative hypothesis of senescence, since its origin in 1956, has garnered significant evidence and growing support among scientists for the notion that free radicals play an important role in ageing, either as "damaging" molecules or as signaling molecules. Age-increasing oxidative injuries induced by free radicals, higher susceptibility to oxidative stress in short-lived organisms, genetic manipulations that alter both oxidative resistance and longevity and the anti-ageing effect of caloric restriction and intermittent fasting are a few examples of accepted scientific facts that support the oxidative theory of senescence. Though not completely understood due to the complex "network" of redox regulatory systems, the implication of oxidative stress in the ageing process is now well documented. Moreover, it is compatible with other current ageing theories (e.g., those implicating the mitochondrial damage/mitochondrial-lysosomal axis, stress-induced premature senescence, biological "garbage" accumulation, etc. This review is intended to summarize and critically discuss the redox mechanisms involved during the ageing process: sources of oxidant agents in ageing (mitochondrial -electron transport chain, nitric oxide synthase reaction- and non-mitochondrial- Fenton reaction, microsomal cytochrome P450 enzymes, peroxisomal β -oxidation and respiratory burst of phagocytic cells, antioxidant changes in ageing (enzymatic- superoxide dismutase, glutathione-reductase, glutathion peroxidase, catalase- and non-enzymatic glutathione, ascorbate, urate, bilirubine, melatonin, tocopherols, carotenoids, ubiquinol, alteration of oxidative damage repairing mechanisms and the role of free radicals as signaling molecules in ageing.

  12. A cholinergic hypothesis of the unconscious in affective disorders.

    Directory of Open Access Journals (Sweden)

    Costa eVakalopoulos

    2013-11-01

    Full Text Available The interactions between distinct pharmacological systems are proposed as a key dynamic in the formation of unconscious memories underlying rumination and mood disorder, but also reflect the plastic capacity of neural networks that can aid recovery. An inverse and reciprocal relationship is postulated between cholinergic and monoaminergic receptor subtypes. M1-type muscarinic receptor transduction facilitates encoding of unconscious, prepotent behavioural repertoires at the core of affective disorders and ADHD. Behavioural adaptation to new contingencies is mediated by the classic prototype receptor: 5-HT1A (Gi/o and its modulation of m1-plasticity. Reversal of learning is dependent on increased phasic activation of midbrain monoaminergic nuclei and is a function of hippocampal theta. Acquired hippocampal dysfunction due to abnormal activation of the hypothalamic-pituitary-adrenal (HPA axis predicts deficits in hippocampal-dependent memory and executive function and further impairments to cognitive inhibition. Encoding of explicit memories is mediated by Gq/11 and Gs signalling of monoamines only. A role is proposed for the phasic activation of the basal forebrain cholinergic nucleus by cortical projections from the complex consisting of the insula and claustrum. Although controversial. recent studies suggest a common ontogenetic origin of the two structures and a functional coupling. Lesions of the region result in loss of motivational behaviour and familiarity based judgements. A major hypothesis of the paper is that these lost faculties result indirectly, from reduced cholinergic tone.

  13. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Hyperbolic Hopfield neural networks.

    Science.gov (United States)

    Kobayashi, M

    2013-02-01

    In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states.

  15. Is the Aluminum Hypothesis Dead?

    Science.gov (United States)

    2014-01-01

    The Aluminum Hypothesis, the idea that aluminum exposure is involved in the etiology of Alzheimer disease, dates back to a 1965 demonstration that aluminum causes neurofibrillary tangles in the brains of rabbits. Initially the focus of intensive research, the Aluminum Hypothesis has gradually been abandoned by most researchers. Yet, despite this current indifference, the Aluminum Hypothesis continues to attract the attention of a small group of scientists and aluminum continues to be viewed with concern by some of the public. This review article discusses reasons that mainstream science has largely abandoned the Aluminum Hypothesis and explores a possible reason for some in the general public continuing to view aluminum with mistrust. PMID:24806729

  16. Is the Aluminum Hypothesis dead?

    Science.gov (United States)

    Lidsky, Theodore I

    2014-05-01

    The Aluminum Hypothesis, the idea that aluminum exposure is involved in the etiology of Alzheimer disease, dates back to a 1965 demonstration that aluminum causes neurofibrillary tangles in the brains of rabbits. Initially the focus of intensive research, the Aluminum Hypothesis has gradually been abandoned by most researchers. Yet, despite this current indifference, the Aluminum Hypothesis continues to attract the attention of a small group of scientists and aluminum continues to be viewed with concern by some of the public. This review article discusses reasons that mainstream science has largely abandoned the Aluminum Hypothesis and explores a possible reason for some in the general public continuing to view aluminum with mistrust.

  17. Memory in astrocytes: a hypothesis

    Directory of Open Access Journals (Sweden)

    Caudle Robert M

    2006-01-01

    Full Text Available Abstract Background Recent work has indicated an increasingly complex role for astrocytes in the central nervous system. Astrocytes are now known to exchange information with neurons at synaptic junctions and to alter the information processing capabilities of the neurons. As an extension of this trend a hypothesis was proposed that astrocytes function to store information. To explore this idea the ion channels in biological membranes were compared to models known as cellular automata. These comparisons were made to test the hypothesis that ion channels in the membranes of astrocytes form a dynamic information storage device. Results Two dimensional cellular automata were found to behave similarly to ion channels in a membrane when they function at the boundary between order and chaos. The length of time information is stored in this class of cellular automata is exponentially related to the number of units. Therefore the length of time biological ion channels store information was plotted versus the estimated number of ion channels in the tissue. This analysis indicates that there is an exponential relationship between memory and the number of ion channels. Extrapolation of this relationship to the estimated number of ion channels in the astrocytes of a human brain indicates that memory can be stored in this system for an entire life span. Interestingly, this information is not affixed to any physical structure, but is stored as an organization of the activity of the ion channels. Further analysis of two dimensional cellular automata also demonstrates that these systems have both associative and temporal memory capabilities. Conclusion It is concluded that astrocytes may serve as a dynamic information sink for neurons. The memory in the astrocytes is stored by organizing the activity of ion channels and is not associated with a physical location such as a synapse. In order for this form of memory to be of significant duration it is necessary

  18. Proneness to social anxiety modulates neural complexity in the absence of exposure: A resting state fMRI study using Hurst exponent.

    Science.gov (United States)

    Gentili, Claudio; Vanello, Nicola; Cristea, Ioana; David, Daniel; Ricciardi, Emiliano; Pietrini, Pietro

    2015-05-30

    To test the hypothesis that brain activity is modulated by trait social anxiety, we measured the Hurst Exponent (HE), an index of complexity in time series, in healthy individuals at rest in the absence of any social trigger. Functional magnetic resonance imaging (fMRI) time series were recorded in 36 subjects at rest. All volunteers were healthy without any psychiatric, medical or neurological disorder. Subjects completed the Liebowitz Social Anxiety Scale (LSAS) and the Brief Fear of Negative Evaluation (BFNE) to assess social anxiety and thoughts in social contexts. We also obtained the fractional Amplitude of Low Frequency Fluctuations (fALFF) of the BOLD signal as an independent control measure for HE data. BFNE scores correlated positively with HE in the posterior cingulate/precuneus, while LSAS scores correlated positively with HE in the precuneus, in the inferior parietal sulci and in the parahippocamus. Results from fALFF were highly consistent with those obtained using LSAS and BFNE to predict HE. Overall our data indicate that spontaneous brain activity is influenced by the degree of social anxiety, on a continuum and in the absence of social stimuli. These findings suggest that social anxiety is a trait characteristic that shapes brain activity and predisposes to different reactions in social contexts. Copyright © 2015. Published by Elsevier Ireland Ltd.

  19. Riemann hypothesis is not correct

    OpenAIRE

    Fei, JinHua

    2014-01-01

    This paper use Nevanlinna's Second Main Theorem of the value distribution theory, we got an important conclusion by Riemann hypothesis. this conclusion contradicts the Theorem 8.12 in Titchmarsh's book "Theory of the Riemann Zeta-functions", therefore we prove that Riemann hypothesis is incorrect.

  20. The Comprehension Hypothesis: Recent Evidence.

    Science.gov (United States)

    Krashen, Stephen

    1997-01-01

    Research published in recent years that deals with the Comprehension (Input) Hypothesis is reviewed, and evidence supporting the hypothesis is underlined. The research is from the areas of literacy development, second-language learning, and foreign-language learning and confirms the claim that development of language and literacy operate in much…

  1. A Puzzle About Stalnaker's Hypothesis

    NARCIS (Netherlands)

    Douven, Igor; Dietz, Richard

    According to Stalnaker's Hypothesis, the probability of an indicative conditional, Pr(phi -> psi), equals the probability of the consequent conditional on its antecedent, Pr(phi -> psi). While the hypothesis is generally taken to have been conclusively refuted by Lewis' and others' triviality

  2. Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances.

    Science.gov (United States)

    Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Yao, Yang

    2013-07-01

    Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Hypothesis Designs for Three-Hypothesis Test Problems

    OpenAIRE

    Yan Li; Xiaolong Pu

    2010-01-01

    As a helpful guide for applications, the alternative hypotheses of the three-hypothesis test problems are designed under the required error probabilities and average sample number in this paper. The asymptotic formulas and the proposed numerical quadrature formulas are adopted, respectively, to obtain the hypothesis designs and the corresponding sequential test schemes under the Koopman-Darmois distributions. The example of the normal mean test shows that our methods are qu...

  4. The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis

    Science.gov (United States)

    Patel, Aniruddh D.; Iversen, John R.

    2013-01-01

    Every human culture has some form of music with a beat: a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement). More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This “action simulation for auditory prediction” (ASAP) hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in non-human primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi. PMID:24860439

  5. The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP hypothesis.

    Directory of Open Access Journals (Sweden)

    Aniruddh D. Patel

    2014-05-01

    Full Text Available Every human culture has some form of music with a beat: a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement. More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This action simulation for auditory prediction (ASAP hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in nonhuman primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi.

  6. Vehicle Detection Based on Probability Hypothesis Density Filter

    Directory of Open Access Journals (Sweden)

    Feihu Zhang

    2016-04-01

    Full Text Available In the past decade, the developments of vehicle detection have been significantly improved. By utilizing cameras, vehicles can be detected in the Regions of Interest (ROI in complex environments. However, vision techniques often suffer from false positives and limited field of view. In this paper, a LiDAR based vehicle detection approach is proposed by using the Probability Hypothesis Density (PHD filter. The proposed approach consists of two phases: the hypothesis generation phase to detect potential objects and the hypothesis verification phase to classify objects. The performance of the proposed approach is evaluated in complex scenarios, compared with the state-of-the-art.

  7. [The glutamate hypothesis of schizophrenia].

    Science.gov (United States)

    Hasan, A; Malchow, B; Falkai, P; Schmitt, A

    2014-08-01

    For many years, the dopamine hypothesis of schizophrenia has been the leading theory explaining the aetiology of schizophrenia. However, since the first observation showed that NMDA-receptor antagonists (e. g., PCP) can induce all kinds of schizophrenia symptoms in humans, the glutamate hypothesis of schizophrenia has been established as an additional explanation model. Apart from the PCP-induced psychoses, many other findings from all areas of modern neuroscience have confirmed and extended the glutamate hypothesis. This review discusses the available evidence for the glutamate hypothesis and puts the different findings into relation. Consecutively, the possibilities for a pharmacological modulation of the glutamate system and recent clinical trials are discussed. To sum up, one could note that the glutamate hypothesis of schizophrenia is now well-established. The development of glutamatergic antipsychotics is still in the early stages, but there is hope for a new generation of antipsychotics based on the glutamate hypothesis of schizophrenia. However, recent findings from registration trials could not provide positive findings for the recently developed glutamatergic drugs. © Georg Thieme Verlag KG Stuttgart · New York.

  8. DNA methylation analysis of Homeobox genes implicates HOXB7 hypomethylation as risk factor for neural tube defects

    OpenAIRE

    Rochtus, Anne; Izzi, Benedetta; Vangeel, Elise; Louwette, Sophie; Wittevrongel, Christine; Lambrechts, Diether; Moreau, Yves; Winand, Raf; Verpoorten, Carla; Jansen, Katrien; van Geet, Chris; Freson, Kathleen

    2015-01-01

    Abstract Neural tube defects (NTDs) are common birth defects of complex etiology. Though family- and population-based studies have confirmed a genetic component, the responsible genes for NTDs are still largely unknown. Based on the hypothesis that folic acid prevents NTDs by stimulating methylation reactions, epigenetic factors, such as DNA methylation, are predicted to be involved in NTDs. Homeobox (HOX) genes play a role in spinal cord development and are tightly regulated in a spatiotempo...

  9. Pathogenesis of bladder exstrophy: A new hypothesis.

    Science.gov (United States)

    K V, Satish Kumar; Mammen, Abraham; Varma, Karthikeya K

    2015-12-01

    Classical bladder exstrophy affects 1 in 30 000 live births. Results of surgical treatment from different institutions employing various surgical techniques are not uniform, thus there is a need for a consensus on the best technique for bladder exstrophy repair. Surgical correction in bladder exstrophy would be more effective if the exact pathogenetic mechanism was deduced and the procedure was directed to correct the cause, which is responsible for the defect. The anatomy of exstrophy shows that the infraumbilical abdominal wall, the anterior wall of the bladder, and the urethra are split, with splayed out genitalia and musculature along with pubic diastasis. There is no tissue loss and hence embryological defect is unlikely to be the cause of bladder exstrophy. Thus there is a need to examine pathogenesis of bladder exstrophy. A literature search was made of the various hypotheses for cause of bladder exstrophy, and attempts were made to propose a new hypothesis. The present hypothesis is also the basis for a technique of mobilization of pelvic musculature, done in two stages. The functional outcomes of 38 children with bladder exstrophy managed over a period of 10 years were reviewed. At a mean follow-up of 4.5 years (range 2.5-8 years), 82% of patients were functionally continent. The exact embryopathogenesis of bladder exstrophy is unknown. In this study a new hypothesis is proposed, with the aim of tailoring the surgical procedure to correct this defect. Bladder exstrophy epispadias complex (BEEC) is a deformative disruption occurring after embryogenic phase and pubic diastasis, and is central to exstrophy development. A working hypothesis can be formulated in line with our observation so that future experiments based this new hypothesis can aim to elucidate the exact pathogenesis. Copyright © 2015 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

  10. A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

    Science.gov (United States)

    Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

    2016-12-01

    The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management

  11. Neural crest does not contribute to the neck and shoulder in the axolotl (Ambystoma mexicanum.

    Directory of Open Access Journals (Sweden)

    Hans-Henning Epperlein

    Full Text Available BACKGROUND: A major step during the evolution of tetrapods was their transition from water to land. This process involved the reduction or complete loss of the dermal bones that made up connections to the skull and a concomitant enlargement of the endochondral shoulder girdle. In the mouse the latter is derived from three separate embryonic sources: lateral plate mesoderm, somites, and neural crest. The neural crest was suggested to sustain the muscle attachments. How this complex composition of the endochondral shoulder girdle arose during evolution and whether it is shared by all tetrapods is unknown. Salamanders that lack dermal bone within their shoulder girdle were of special interest for a possible contribution of the neural crest to the endochondral elements and muscle attachment sites, and we therefore studied them in this context. RESULTS: We grafted neural crest from GFP+ fluorescent transgenic axolotl (Ambystoma mexicanum donor embryos into white (d/d axolotl hosts and followed the presence of neural crest cells within the cartilage of the shoulder girdle and the connective tissue of muscle attachment sites of the neck-shoulder region. Strikingly, neural crest cells did not contribute to any part of the endochondral shoulder girdle or to the connective tissue at muscle attachment sites in axolotl. CONCLUSIONS: Our results in axolotl suggest that neural crest does not serve a general function in vertebrate shoulder muscle attachment sites as predicted by the "muscle scaffold theory," and that it is not necessary to maintain connectivity of the endochondral shoulder girdle to the skull. Our data support the possibility that the contribution of the neural crest to the endochondral shoulder girdle, which is observed in the mouse, arose de novo in mammals as a developmental basis for their skeletal synapomorphies. This further supports the hypothesis of an increased neural crest diversification during vertebrate evolution.

  12. Action perception as hypothesis testing.

    Science.gov (United States)

    Donnarumma, Francesco; Costantini, Marcello; Ambrosini, Ettore; Friston, Karl; Pezzulo, Giovanni

    2017-04-01

    We present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions - and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. The thrifty phenotype hypothesis revisited

    DEFF Research Database (Denmark)

    Vaag, A A; Grunnet, L G; Arora, G P

    2012-01-01

    Twenty years ago, Hales and Barker along with their co-workers published some of their pioneering papers proposing the 'thrifty phenotype hypothesis' in Diabetologia (4;35:595-601 and 3;36:62-67). Their postulate that fetal programming could represent an important player in the origin of type 2...... control is inadequate to reduce the excess CVD mortality in type 2 diabetic patients. Today, the thrifty phenotype hypothesis has been established as a promising conceptual framework for a more sustainable intergenerational prevention of type 2 diabetes....

  14. RANDOM WALK HYPOTHESIS IN FINANCIAL MARKETS

    Directory of Open Access Journals (Sweden)

    Nicolae-Marius JULA

    2017-05-01

    Full Text Available Random walk hypothesis states that the stock market prices do not follow a predictable trajectory, but are simply random. If you are trying to predict a random set of data, one should test for randomness, because, despite the power and complexity of the used models, the results cannot be trustworthy. There are several methods for testing these hypotheses and the use of computational power provided by the R environment makes the work of the researcher easier and with a cost-effective approach. The increasing power of computing and the continuous development of econometric tests should give the potential investors new tools in selecting commodities and investing in efficient markets.

  15. The Stress Acceleration Hypothesis of Nightmares

    Science.gov (United States)

    Nielsen, Tore

    2017-01-01

    Adverse childhood experiences can deleteriously affect future physical and mental health, increasing risk for many illnesses, including psychiatric problems, sleep disorders, and, according to the present hypothesis, idiopathic nightmares. Much like post-traumatic nightmares, which are triggered by trauma and lead to recurrent emotional dreaming about the trauma, idiopathic nightmares are hypothesized to originate in early adverse experiences that lead in later life to the expression of early memories and emotions in dream content. Accordingly, the objectives of this paper are to (1) review existing literature on sleep, dreaming and nightmares in relation to early adverse experiences, drawing upon both empirical studies of dreaming and nightmares and books and chapters by recognized nightmare experts and (2) propose a new approach to explaining nightmares that is based upon the Stress Acceleration Hypothesis of mental illness. The latter stipulates that susceptibility to mental illness is increased by adversity occurring during a developmentally sensitive window for emotional maturation—the infantile amnesia period—that ends around age 3½. Early adversity accelerates the neural and behavioral maturation of emotional systems governing the expression, learning, and extinction of fear memories and may afford short-term adaptive value. But it also engenders long-term dysfunctional consequences including an increased risk for nightmares. Two mechanisms are proposed: (1) disruption of infantile amnesia allows normally forgotten early childhood memories to influence later emotions, cognitions and behavior, including the common expression of threats in nightmares; (2) alterations of normal emotion regulation processes of both waking and sleep lead to increased fear sensitivity and less effective fear extinction. These changes influence an affect network previously hypothesized to regulate fear extinction during REM sleep, disruption of which leads to nightmares. This

  16. The Stress Acceleration Hypothesis of Nightmares

    Directory of Open Access Journals (Sweden)

    Tore Nielsen

    2017-06-01

    Full Text Available Adverse childhood experiences can deleteriously affect future physical and mental health, increasing risk for many illnesses, including psychiatric problems, sleep disorders, and, according to the present hypothesis, idiopathic nightmares. Much like post-traumatic nightmares, which are triggered by trauma and lead to recurrent emotional dreaming about the trauma, idiopathic nightmares are hypothesized to originate in early adverse experiences that lead in later life to the expression of early memories and emotions in dream content. Accordingly, the objectives of this paper are to (1 review existing literature on sleep, dreaming and nightmares in relation to early adverse experiences, drawing upon both empirical studies of dreaming and nightmares and books and chapters by recognized nightmare experts and (2 propose a new approach to explaining nightmares that is based upon the Stress Acceleration Hypothesis of mental illness. The latter stipulates that susceptibility to mental illness is increased by adversity occurring during a developmentally sensitive window for emotional maturation—the infantile amnesia period—that ends around age 3½. Early adversity accelerates the neural and behavioral maturation of emotional systems governing the expression, learning, and extinction of fear memories and may afford short-term adaptive value. But it also engenders long-term dysfunctional consequences including an increased risk for nightmares. Two mechanisms are proposed: (1 disruption of infantile amnesia allows normally forgotten early childhood memories to influence later emotions, cognitions and behavior, including the common expression of threats in nightmares; (2 alterations of normal emotion regulation processes of both waking and sleep lead to increased fear sensitivity and less effective fear extinction. These changes influence an affect network previously hypothesized to regulate fear extinction during REM sleep, disruption of which leads to

  17. On informal hypothesis testing in hydrology: the example of the "two water worlds" hypothesis

    Science.gov (United States)

    Geris, Josie; Soulsby, Chris; Tetzlaff, Doerthe

    2017-04-01

    Rigorous hypothesis tests provide useful tools for making statistical inferences about hydrological processes and have indeed led to major advances in the field of hydrology. However, the formulation of such (typically rather simple) tests with valid assumptions is not always realistic for complex hydrological problems with limited data. Moreover, ill-defined hypothesis tests can lead to meaningless results and increased risks of drawing ambiguous conclusions. In such cases, data plots can be more powerful than p-values. Nevertheless, the formulation and evaluation of (working) hypotheses can offer an important framework to structure data collection and analyses of a more exploratory nature. Here we demonstrate the power of such an approach using the example of the topical "two water worlds" hypothesis in (eco)hydrology. Several recent studies in this field have suggested that there may be "ecohydrological separation" of distinct soil water pools ("water worlds") comprising plant-available water on one hand and water that drains to streams on the other. However, contrary to findings in most other climates, preliminary investigations in humid northern environments did not find strong evidence to support the hypothesis, which has further highlighted the complex nature of subsurface soil water storage processes and vegetation water use. While unambiguously rejecting or verifying the "two water worlds" hypothesis might be an unrealistic aim, studies addressing it more informally have so far led to new insights into e.g. soil-vegetation water interactions, the potential drivers of such separation and advances in our commonly used data collection and analyses techniques.

  18. Capture of fixation by rotational flow; a deterministic hypothesis regarding scaling and stochasticity in fixational eye movements

    Science.gov (United States)

    Wilkinson, Nicholas M.; Metta, Giorgio

    2014-01-01

    Visual scan paths exhibit complex, stochastic dynamics. Even during visual fixation, the eye is in constant motion. Fixational drift and tremor are thought to reflect fluctuations in the persistent neural activity of neural integrators in the oculomotor brainstem, which integrate sequences of transient saccadic velocity signals into a short term memory of eye position. Despite intensive research and much progress, the precise mechanisms by which oculomotor posture is maintained remain elusive. Drift exhibits a stochastic statistical profile which has been modeled using random walk formalisms. Tremor is widely dismissed as noise. Here we focus on the dynamical profile of fixational tremor, and argue that tremor may be a signal which usefully reflects the workings of oculomotor postural control. We identify signatures reminiscent of a certain flavor of transient neurodynamics; toric traveling waves which rotate around a central phase singularity. Spiral waves play an organizational role in dynamical systems at many scales throughout nature, though their potential functional role in brain activity remains a matter of educated speculation. Spiral waves have a repertoire of functionally interesting dynamical properties, including persistence, which suggest that they could in theory contribute to persistent neural activity in the oculomotor postural control system. Whilst speculative, the singularity hypothesis of oculomotor postural control implies testable predictions, and could provide the beginnings of an integrated dynamical framework for eye movements across scales. PMID:24616670

  19. Capture of fixation by rotational flow; a deterministic hypothesis regarding scaling and stochasticity in fixational eye movements

    Directory of Open Access Journals (Sweden)

    Nicholas Mansel Wilkinson

    2014-02-01

    Full Text Available Visual scan paths exhibit complex, stochastic dynamics. Even during visual fixation, the eye is in constant motion. Fixational drift and tremor are thought to reflect fluctuations in the persistent neural activity of neural integrators in the oculomotor brainstem, which integrate sequences of transient saccadic velocity signals into a short term memory of eye position. Despite intensive research and much progress, the precise mechanisms by which oculomotor posture is maintained remain elusive. Drift exhibits a stochastic statistical profile which has been modelled using random walk formalisms. Tremor is widely dismissed as noise. Here we focus on the dynamical profile of fixational tremor, and argue that tremor may be a signal which usefully reflects the workings of the oculomotor postural control. We identify signatures reminiscent of a certain flavour of transient neurodynamics; toric travelling waves which rotate around a central phase singularity. Spiral waves play an organisational role in dynamical systems at many scales throughout nature, though their potential functional role in brain activity remains a matter of educated speculation. Spiral waves have a repertoire of functionally interesting dynamical properties, including persistence, which suggest that they could in theory contribute to persistent neural activity in the oculomotor postural control system. Whilst speculative, the singularity hypothesis of oculomotor postural control implies testable predictions, and could provide the beginnings of an integrated dynamical framework for eye movements across scales.

  20. Evolution of the C2RCC Neural Network for Sentinel 2 and 3 for the Retrieval of Ocean Colour Products in Normal and Extreme Optically Complex Waters

    Science.gov (United States)

    Brockmann, Carsten; Doerffer, Roland; Peters, Marco; Kerstin, Stelzer; Embacher, Sabine; Ruescas, Ana

    2016-08-01

    Retrieval of water constituents, or its optical properties, requires inversion of the water leaving reflectance spectrum, measured at top of atmosphere by ocean colour satellites. The Case 2 Regional processor, originally developed by Doerffer and Schiller [6], uses a large database of radiative transfer simulations inverted by neural networks as basic technology. Through the CoastColour project major improvements were introduced. It has been amended by a set of additional neural networks performing specific tasks and special neural networks have been trained to cover extreme ranges of scattering and absorption. The processor has been renamed into C2RCC (Case 2 Regional CoastColour) and is applicable to all past and current ocean colour sensors as well as Sentinel 2. It has been validated in various studies and is available through ESA's Sentinel toolbox SNAP. It is also used in the Sentinel 3 OLCI ground segment processor of ESA for the generation of the Case 2 water products, as well as in the processor for the upcoming MERIS 4th reprocessing.

  1. Investigating the complexity of respiratory patterns during the laryngeal chemoreflex

    Directory of Open Access Journals (Sweden)

    Curran Aidan K

    2008-06-01

    Full Text Available Abstract Background The laryngeal chemoreflex exists in infants as a primary sensory mechanism for defending the airway from the aspiration of liquids. Previous studies have hypothesized that prolonged apnea associated with this reflex may be life threatening and might be a cause of sudden infant death syndrome. Methods In this study we quantified the output of the respiratory neural network, the diaphragm EMG signal, during the laryngeal chemoreflex and eupnea in early postnatal (3–10 days piglets. We tested the hypothesis that diaphragm EMG activity corresponding to reflex-related events involved in clearance (restorative mechanisms such as cough and swallow exhibit lower complexity, suggesting that a synchronized homogeneous group of neurons in the central respiratory network are active during these events. Nonlinear dynamic analysis was performed using the approximate entropy to asses the complexity of respiratory patterns. Results Diaphragm EMG, genioglossal activity EMG, as well as other physiological signals (tracheal pressure, blood pressure and respiratory volume were recorded from 5 unanesthetized chronically instrumented intact piglets. Approximate entropy values of the EMG during cough and swallow were found significantly (p p Conclusion Reduced complexity values of the respiratory neural network output corresponding to coughs and swallows suggest synchronous neural activity of a homogeneous group of neurons. The higher complexity values exhibited by eupneic respiratory activity are the result of a more random behaviour, which is the outcome of the integrated action of several groups of neurons involved in the respiratory neural network.

  2. Metabolic hypothesis for human altriciality.

    Science.gov (United States)

    Dunsworth, Holly M; Warrener, Anna G; Deacon, Terrence; Ellison, Peter T; Pontzer, Herman

    2012-09-18

    The classic anthropological hypothesis known as the "obstetrical dilemma" is a well-known explanation for human altriciality, a condition that has significant implications for human social and behavioral evolution. The hypothesis holds that antagonistic selection for a large neonatal brain and a narrow, bipedal-adapted birth canal poses a problem for childbirth; the hominin "solution" is to truncate gestation, resulting in an altricial neonate. This explanation for human altriciality based on pelvic constraints persists despite data linking human life history to that of other species. Here, we present evidence that challenges the importance of pelvic morphology and mechanics in the evolution of human gestation and altriciality. Instead, our analyses suggest that limits to maternal metabolism are the primary constraints on human gestation length and fetal growth. Although pelvic remodeling and encephalization during hominin evolution contributed to the present parturitional difficulty, there is little evidence that pelvic constraints have altered the timing of birth.

  3. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

  4. The hypothesis of cardiac arrhythmias

    OpenAIRE

    Ermoshkin, Vladimir

    2013-01-01

    Background. Cardiovascular diseases(CVDs) are the main causes of death in all countries. Majority of these deaths occur due to arrhythmias. The aim of this review to attempt to propose new hypothesis regarding the pathogenesis of extrasystoles and pathological tachycardia. Methods. Internet search and discussion with experts: Frolov V.M., Shirokov E.A., Singh R.B. et al. Results. The extrasystoles and tachycardia occur in some people due to the pulse propagation through abnormal contour of ve...

  5. Hypothesis Formation, Paradigms, and Openness

    Directory of Open Access Journals (Sweden)

    Conrad P. Pritscher

    2008-01-01

    Full Text Available A part of hypothesis formation, while necessary for scientific investigation, is beyond direct observation. Powerful hypothesis formation is more than logical and is facilitated by mind­opening. As Percy Bridgeman, Nobel laureate, said, science is: “Nothing more than doing one's damnedest with one's mind, no holds barred.” This paper suggests more open schooling helps generate more open hypothesizing which helps one do one's damnedest with one's mind. It is hypothesized that a more open process of hypothesis formation may help schools and society forge new ways of living and learning so that more people more often can do their damnedest with their mind. This writing does not offer a new paradigm but rather attempts to elaborate on the notion that new paradigms are difficult to form without openness to what was previously quasi­unthinkable. More on these topics and issues is included in the author's Reopening Einstein's Thought: About What Can't Be Learned From Textbooks ­­to be published by Sense Publishers in June 2008.

  6. Language control in bilinguals: The adaptive control hypothesis.

    Science.gov (United States)

    Green, David W; Abutalebi, Jubin

    2013-08-01

    Speech comprehension and production are governed by control processes. We explore their nature and dynamics in bilingual speakers with a focus on speech production. Prior research indicates that individuals increase cognitive control in order to achieve a desired goal. In the adaptive control hypothesis we propose a stronger hypothesis: Language control processes themselves adapt to the recurrent demands placed on them by the interactional context. Adapting a control process means changing a parameter or parameters about the way it works (its neural capacity or efficiency) or the way it works in concert, or in cascade, with other control processes (e.g., its connectedness). We distinguish eight control processes (goal maintenance, conflict monitoring, interference suppression, salient cue detection, selective response inhibition, task disengagement, task engagement, opportunistic planning). We consider the demands on these processes imposed by three interactional contexts (single language, dual language, and dense code-switching). We predict adaptive changes in the neural regions and circuits associated with specific control processes. A dual-language context, for example, is predicted to lead to the adaptation of a circuit mediating a cascade of control processes that circumvents a control dilemma. Effective test of the adaptive control hypothesis requires behavioural and neuroimaging work that assesses language control in a range of tasks within the same individual.

  7. Case report 332: A complex anomaly of the craniovertebral junction representing a regressive malformation with agenesis of the neural arch of C-2, hypomorphogenesis at C5-C6 and instability of the upper cervical spine

    Energy Technology Data Exchange (ETDEWEB)

    Bernini, F.P.; Muras, I.

    1985-08-01

    A very complex anomaly, including agenesis of the neural arch of the axis, hypomorphogenesis of the cervical spine at the C5-C6 level with a partial ''blocked'' vertebra and disability of the upper cervical spine, is reported in a 31-year-old man. The anomalies associated with these changes are described in detail in the text and illustrated radiologically. The relationship of the embryological alterations in contrast with the normal is described and emphasized, particularly in the upper cervical area. It is stressed that narrowing of the space from the back of the odontoid (or the posterior lip of the foramen magnum) is a direct result of the complex anomalies described in this case, producing compression of the medulla and/or the upper cervical spinal cord. The literature on this subject is reviewed in depth.

  8. Biologically Inspired Modular Neural Networks

    OpenAIRE

    Azam, Farooq

    2000-01-01

    This dissertation explores the modular learning in artificial neural networks that mainly driven by the inspiration from the neurobiological basis of the human learning. The presented modularization approaches to the neural network design and learning are inspired by the engineering, complexity, psychological and neurobiological aspects. The main theme of this dissertation is to explore the organization and functioning of the brain to discover new structural and learning ...

  9. 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. .

  10. Novel High-Viscosity Polyacrylamidated Chitosan for Neural Tissue Engineering: Fabrication of Anisotropic Neurodurable Scaffold via Molecular Disposition of Persulfate-Mediated Polymer Slicing and Complexation

    Directory of Open Access Journals (Sweden)

    Viness Pillay

    2012-10-01

    Full Text Available Macroporous polyacrylamide-grafted-chitosan scaffolds for neural tissue engineering were fabricated with varied synthetic and viscosity profiles. A novel approach and mechanism was utilized for polyacrylamide grafting onto chitosan using potassium persulfate (KPS mediated degradation of both polymers under a thermally controlled environment. Commercially available high molecular mass polyacrylamide was used instead of the acrylamide monomer for graft copolymerization. This grafting strategy yielded an enhanced grafting efficiency (GE = 92%, grafting ratio (GR = 263%, intrinsic viscosity (IV = 5.231 dL/g and viscometric average molecular mass (MW = 1.63 × 106 Da compared with known acrylamide that has a GE = 83%, GR = 178%, IV = 3.901 dL/g and MW = 1.22 × 106 Da. Image processing analysis of SEM images of the newly grafted neurodurable scaffold was undertaken based on the polymer-pore threshold. Attenuated Total Reflectance-FTIR spectral analyses in conjugation with DSC were used for the characterization and comparison of the newly grafted copolymers. Static Lattice Atomistic Simulations were employed to investigate and elucidate the copolymeric assembly and reaction mechanism by exploring the spatial disposition of chitosan and polyacrylamide with respect to the reactional profile of potassium persulfate. Interestingly, potassium persulfate, a peroxide, was found to play a dual role initially degrading the polymers—“polymer slicing”—thereby initiating the formation of free radicals and subsequently leading to synthesis of the high molecular mass polyacrylamide-grafted-chitosan (PAAm-g-CHT—“polymer complexation”. Furthermore, the applicability of the uniquely grafted scaffold for neural tissue engineering was evaluated via PC12 neuronal cell seeding. The novel PAAm-g-CHT exhibited superior neurocompatibility in terms of cell infiltration owing to the anisotropic porous architecture, high molecular mass mediated robustness

  11. Antiaging therapy: a prospective hypothesis.

    Science.gov (United States)

    Shahidi Bonjar, Mohammad Rashid; Shahidi Bonjar, Leyla

    2015-01-01

    This hypothesis proposes a new prospective approach to slow the aging process in older humans. The hypothesis could lead to developing new treatments for age-related illnesses and help humans to live longer. This hypothesis has no previous documentation in scientific media and has no protocol. Scientists have presented evidence that systemic aging is influenced by peculiar molecules in the blood. Researchers at Albert Einstein College of Medicine, New York, and Harvard University in Cambridge discovered elevated titer of aging-related molecules (ARMs) in blood, which trigger cascade of aging process in mice; they also indicated that the process can be reduced or even reversed. By inhibiting the production of ARMs, they could reduce age-related cognitive and physical declines. The present hypothesis offers a new approach to translate these findings into medical treatment: extracorporeal adjustment of ARMs would lead to slower rates of aging. A prospective "antiaging blood filtration column" (AABFC) is a nanotechnological device that would fulfill the central role in this approach. An AABFC would set a near-youth homeostatic titer of ARMs in the blood. In this regard, the AABFC immobilizes ARMs from the blood while blood passes through the column. The AABFC harbors antibodies against ARMs. ARM antibodies would be conjugated irreversibly to ARMs on contact surfaces of the reaction platforms inside the AABFC till near-youth homeostasis is attained. The treatment is performed with the aid of a blood-circulating pump. Similar to a renal dialysis machine, blood would circulate from the body to the AABFC and from there back to the body in a closed circuit until ARMs were sufficiently depleted from the blood. The optimal application criteria, such as human age for implementation, frequency of treatments, dosage, ideal homeostasis, and similar concerns, should be revealed by appropriate investigations. If AABFC technology undergoes practical evaluations and gains approval

  12. Antiaging therapy: a prospective hypothesis

    Directory of Open Access Journals (Sweden)

    Shahidi Bonjar MR

    2015-01-01

    Full Text Available Mohammad Rashid Shahidi Bonjar,1 Leyla Shahidi Bonjar2 1School of Dentistry, Kerman University of Medical Sciences, Kerman Iran; 2Department of Pharmacology, College of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran Abstract: This hypothesis proposes a new prospective approach to slow the aging process in older humans. The hypothesis could lead to developing new treatments for age-related illnesses and help humans to live longer. This hypothesis has no previous documentation in scientific media and has no protocol. Scientists have presented evidence that systemic aging is influenced by peculiar molecules in the blood. Researchers at Albert Einstein College of Medicine, New York, and Harvard University in Cambridge discovered elevated titer of aging-related molecules (ARMs in blood, which trigger cascade of aging process in mice; they also indicated that the process can be reduced or even reversed. By inhibiting the production of ARMs, they could reduce age-related cognitive and physical declines. The present hypothesis offers a new approach to translate these findings into medical treatment: extracorporeal adjustment of ARMs would lead to slower rates of aging. A prospective “antiaging blood filtration column” (AABFC is a nanotechnological device that would fulfill the central role in this approach. An AABFC would set a near-youth homeostatic titer of ARMs in the blood. In this regard, the AABFC immobilizes ARMs from the blood while blood passes through the column. The AABFC harbors antibodies against ARMs. ARM antibodies would be conjugated irreversibly to ARMs on contact surfaces of the reaction platforms inside the AABFC till near-youth homeostasis is attained. The treatment is performed with the aid of a blood-circulating pump. Similar to a renal dialysis machine, blood would circulate from the body to the AABFC and from there back to the body in a closed circuit until ARMs were sufficiently depleted from the blood. The

  13. A Molecular–Structure Hypothesis

    Directory of Open Access Journals (Sweden)

    Jan C. A. Boeyens

    2010-11-01

    Full Text Available The self-similar symmetry that occurs between atomic nuclei, biological growth structures, the solar system, globular clusters and spiral galaxies suggests that a similar pattern should characterize atomic and molecular structures. This possibility is explored in terms of the current molecular structure-hypothesis and its extension into four-dimensional space-time. It is concluded that a quantum molecule only has structure in four dimensions and that classical (Newtonian structure, which occurs in three dimensions, cannot be simulated by quantum-chemical computation.

  14. The ctenophore genome and the evolutionary origins of neural systems.

    Science.gov (United States)

    Moroz, Leonid L; Kocot, Kevin M; Citarella, Mathew R; Dosung, Sohn; Norekian, Tigran P; Povolotskaya, Inna S; Grigorenko, Anastasia P; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V; Jurka, Jerzy; Bobkov, Yuri V; Swore, Joshua J; Girardo, David O; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E; Rast, Jonathan P; Derelle, Romain; Solovyev, Victor V; Kondrashov, Fyodor A; Swalla, Billie J; Sweedler, Jonathan V; Rogaev, Evgeny I; Halanych, Kenneth M; Kohn, Andrea B

    2014-06-05

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes, and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well recognized in ctenophores, many bilaterian neuron-specific genes and genes of 'classical' neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

  15. Hypothesis tests for hydrologic alteration

    Science.gov (United States)

    Kroll, Charles N.; Croteau, Kelly E.; Vogel, Richard M.

    2015-11-01

    Hydrologic systems can be altered by anthropogenic and climatic influences. While there are a number of statistical frameworks for describing and evaluating the extent of hydrologic alteration, here we present a new framework for assessing whether statistically significant hydrologic alteration has occurred, or whether the shift in the hydrologic regime is consistent with the natural variability of the system. Four hypothesis tests based on shifts of flow duration curves (FDCs) are developed and tested using three different experimental designs based on different strategies for resampling of annual FDCs. The four hypothesis tests examined are the Kolmogorov-Smirnov (KS), Kuiper (K), confidence interval (CI), and ecosurplus and ecodeficit (Eco). Here 117 streamflow sites that have potentially undergone hydrologic alteration due to reservoir construction are examined. 20 years of pre-reservoir record is used to develop the critical value of the test statistic for type I errors of 5% and 10%, while 10 years of post-alteration record is used to examine the power of each test. The best experimental design, based on calculating the mean annual FDC from an exhaustive jackknife resampling regime, provided a larger number of unique values of each test statistic and properly reproduced type I errors. Of the four tests, the CI test consistently had the highest power, while the K test had the second highest power; KS and Eco always had the lowest power. The power of the CI test appeared related to the storage ratio of the reservoir, a rough measure of the hydrologic alteration of the system.

  16. Inoculation Stress Hypothesis of Environmental Enrichment

    Science.gov (United States)

    Crofton, Elizabeth J.; Zhang, Yafang; Green, Thomas A.

    2014-01-01

    One hallmark of psychiatric conditions is the vast continuum of individual differences in susceptibility vs. resilience resulting from the interaction of genetic and environmental factors. The environmental enrichment paradigm is an animal model that is useful for studying a range of psychiatric conditions, including protective phenotypes in addiction and depression models. The major question is how environmental enrichment, a non-drug and non-surgical manipulation, can produce such robust individual differences in such a wide range of behaviors. This paper draws from a variety of published sources to outline a coherent hypothesis of inoculation stress as a factor producing the protective enrichment phenotypes. The basic tenet suggests that chronic mild stress from living in a complex environment and interacting non-aggressively with conspecifics can inoculate enriched rats against subsequent stressors and/or drugs of abuse. This paper reviews the enrichment phenotypes, mulls the fundamental nature of environmental enrichment vs. isolation, discusses the most appropriate control for environmental enrichment, and challenges the idea that cortisol/corticosterone equals stress. The intent of the inoculation stress hypothesis of environmental enrichment is to provide a scaffold with which to build testable hypotheses for the elucidation of the molecular mechanisms underlying these protective phenotypes and thus provide new therapeutic targets to treat psychiatric/neurological conditions. PMID:25449533

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

    Science.gov (United States)

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

    2017-08-01

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

  18. Convergent evolution of neural systems in ctenophores.

    Science.gov (United States)

    Moroz, Leonid L

    2015-02-15

    Neurons are defined as polarized secretory cells specializing in directional propagation of electrical signals leading to the release of extracellular messengers - features that enable them to transmit information, primarily chemical in nature, beyond their immediate neighbors without affecting all intervening cells en route. Multiple origins of neurons and synapses from different classes of ancestral secretory cells might have occurred more than once during ~600 million years of animal evolution with independent events of nervous system centralization from a common bilaterian/cnidarian ancestor without the bona fide central nervous system. Ctenophores, or comb jellies, represent an example of extensive parallel evolution in neural systems. First, recent genome analyses place ctenophores as a sister group to other animals. Second, ctenophores have a smaller complement of pan-animal genes controlling canonical neurogenic, synaptic, muscle and immune systems, and developmental pathways than most other metazoans. However, comb jellies are carnivorous marine animals with a complex neuromuscular organization and sophisticated patterns of behavior. To sustain these functions, they have evolved a number of unique molecular innovations supporting the hypothesis of massive homoplasies in the organization of integrative and locomotory systems. Third, many bilaterian/cnidarian neuron-specific genes and 'classical' neurotransmitter pathways are either absent or, if present, not expressed in ctenophore neurons (e.g. the bilaterian/cnidarian neurotransmitter, γ-amino butyric acid or GABA, is localized in muscles and presumed bilaterian neuron-specific RNA-binding protein Elav is found in non-neuronal cells). Finally, metabolomic and pharmacological data failed to detect either the presence or any physiological action of serotonin, dopamine, noradrenaline, adrenaline, octopamine, acetylcholine or histamine - consistent with the hypothesis that ctenophore neural systems evolved

  19. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  20. Horizontal gene transfer among genomes: The complexity hypothesis

    OpenAIRE

    Jain, Ravi; Rivera, Maria C.; Lake, James A.

    1999-01-01

    Increasingly, studies of genes and genomes are indicating that considerable horizontal transfer has occurred between prokaryotes. Extensive horizontal transfer has occurred for operational genes (those involved in housekeeping), whereas informational genes (those involved in transcription, translation, and related processes) are seldomly horizontally transferred. Through phylogenetic analysis of six complete prokaryotic genomes and the identification of 312 sets of orthologous genes present i...

  1. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  2. Memristor-based neural networks

    Science.gov (United States)

    Thomas, Andy

    2013-03-01

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

  3. CHARGEd with neural crest defects.

    Science.gov (United States)

    Pauli, Silke; Bajpai, Ruchi; Borchers, Annette

    2017-10-30

    Neural crest cells are highly migratory pluripotent cells that give rise to diverse derivatives including cartilage, bone, smooth muscle, pigment, and endocrine cells as well as neurons and glia. Abnormalities in neural crest-derived tissues contribute to the etiology of CHARGE syndrome, a complex malformation disorder that encompasses clinical symptoms like coloboma, heart defects, atresia of the choanae, retarded growth and development, genital hypoplasia, ear anomalies, and deafness. Mutations in the chromodomain helicase DNA-binding protein 7 (CHD7) gene are causative of CHARGE syndrome and loss-of-function data in different model systems have firmly established a role of CHD7 in neural crest development. Here, we will summarize our current understanding of the function of CHD7 in neural crest development and discuss possible links of CHARGE syndrome to other developmental disorders. © 2017 Wiley Periodicals, Inc.

  4. Differences in Brain Information Transmission between Gifted and Normal Children during Scientific Hypothesis Generation

    Science.gov (United States)

    Jin, Seung-Hyun; Kwon, Yong-Ju; Jeong, Jin-Su; Kwon, Suk-Won; Shin, Dong-Hoon

    2006-01-01

    The purpose of the present study was to investigate differences in neural information transmission between gifted and normal children involved in scientific hypothesis generation. To investigate changes in the amount of information transmission, the children's averaged-cross mutual information (A-CMI) of EEGs was estimated during their generation…

  5. The Triple T Allergy Hypothesis

    Directory of Open Access Journals (Sweden)

    Matthias Wjst

    2004-01-01

    Full Text Available The early induction of allergy is a complex process involving protective and destructive gene variants, environmental and nutritional co-factors as well as allergen exposure. Although critical doses, interactions and susceptible time frames have not been identified so far, late gestation and early childhood seem to be important time periods for allergic sensitization.

  6. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

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

  7. Interrelationships between Hormones, Behavior, and Affect during Adolescence: Complex Relationships Exist between Reproductive Hormones, Stress‐Related Hormones, and the Activity of Neural Systems That Regulate Behavioral Affect. Comments on Part III

    National Research Council Canada - National Science Library

    CAMERON, JUDY L

    2004-01-01

    ..., and changes in behavioral affect regulation. The interactions between activity in the reproductive axis, the neural systems that regulate stress, hormones produced in response to stress, and neural systems governing behavioral affect regulation...

  8. Robust and distributed hypothesis testing

    CERN Document Server

    Gül, Gökhan

    2017-01-01

    This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the boo...

  9. Disrupting morphosyntactic and lexical semantic processing has opposite effects on the sample entropy of neural signals.

    Science.gov (United States)

    Fonseca, André; Boboeva, Vezha; Brederoo, Sanne; Baggio, Giosuè

    2015-04-16

    Converging evidence in neuroscience suggests that syntax and semantics are dissociable in brain space and time. However, it is possible that partly disjoint cortical networks, operating in successive time frames, still perform similar types of neural computations. To test the alternative hypothesis, we collected EEG data while participants read sentences containing lexical semantic or morphosyntactic anomalies, resulting in N400 and P600 effects, respectively. Next, we reconstructed phase space trajectories from EEG time series, and we measured the complexity of the resulting dynamical orbits using sample entropy - an index of the rate at which the system generates or loses information over time. Disrupting morphosyntactic or lexical semantic processing had opposite effects on sample entropy: it increased in the N400 window for semantic anomalies, and it decreased in the P600 window for morphosyntactic anomalies. These findings point to a fundamental divergence in the neural computations supporting meaning and grammar in language. Copyright © 2015 Elsevier B.V. All rights reserved.

  10. Borna disease virus infects human neural progenitor cells and impairs neurogenesis.

    Science.gov (United States)

    Brnic, Dragan; Stevanovic, Vladimir; Cochet, Marielle; Agier, Cécilia; Richardson, Jennifer; Montero-Menei, Claudia N; Milhavet, Ollivier; Eloit, Marc; Coulpier, Muriel

    2012-03-01

    Understanding the complex mechanisms by which infectious agents can disrupt behavior represents a major challenge. The Borna disease virus (BDV), a potential human pathogen, provides a unique model to study such mechanisms. Because BDV induces neurodegeneration in brain areas that are still undergoing maturation at the time of infection, we tested the hypothesis that BDV interferes with neurogenesis. We showed that human neural stem/progenitor cells are highly permissive to BDV, although infection does not alter their survival or undifferentiated phenotype. In contrast, upon the induction of differentiation, BDV is capable of severely impairing neurogenesis by interfering with the survival of newly generated neurons. Such impairment was specific to neurogenesis, since astrogliogenesis was unaltered. In conclusion, we demonstrate a new mechanism by which BDV might impair neural function and brain plasticity in infected individuals. These results may contribute to a better understanding of behavioral disorders associated with BDV infection.

  11. The Over-Pruning Hypothesis of Autism

    Science.gov (United States)

    Thomas, Michael S. C.; Davis, Rachael; Karmiloff-Smith, Annette; Knowland, Victoria C. P.; Charman, Tony

    2016-01-01

    This article outlines the "over-pruning hypothesis" of autism. The hypothesis originates in a neurocomputational model of the regressive sub-type (Thomas, Knowland & Karmiloff-Smith, 2011a, 2011b). Here we develop a more general version of the over-pruning hypothesis to address heterogeneity in the timing of manifestation of ASD,…

  12. Infantile amnesia: a neurogenic hypothesis.

    Science.gov (United States)

    Josselyn, Sheena A; Frankland, Paul W

    2012-08-16

    In the late 19th Century, Sigmund Freud described the phenomenon in which people are unable to recall events from early childhood as infantile amnesia. Although universally observed, infantile amnesia is a paradox; adults have surprisingly few memories of early childhood despite the seemingly exuberant learning capacity of young children. How can these findings be reconciled? The mechanisms underlying this form of amnesia are the subject of much debate. Psychological/cognitive theories assert that the ability to maintain detailed, declarative-like memories in the long term correlates with the development of language, theory of mind, and/or sense of "self." However, the finding that experimental animals also show infantile amnesia suggests that this phenomenon cannot be explained fully in purely human terms. Biological explanations of infantile amnesia suggest that protracted postnatal development of key brain regions important for memory interferes with stable long-term memory storage, yet they do not clearly specify which particular aspects of brain maturation are causally related to infantile amnesia. Here, we propose a hypothesis of infantile amnesia that focuses on one specific aspect of postnatal brain development--the continued addition of new neurons to the hippocampus. Infants (humans, nonhuman primates, and rodents) exhibit high levels of hippocampal neurogenesis and an inability to form lasting memories. Interestingly, the decline of postnatal neurogenesis levels corresponds to the emergence of the ability to form stable long-term memory. We propose that high neurogenesis levels negatively regulate the ability to form enduring memories, most likely by replacing synaptic connections in preexisting hippocampal memory circuits.

  13. Spike Neural Models Part II: Abstract Neural Models

    OpenAIRE

    Johnson, Melissa G.; Chartier, Sylvain

    2018-01-01

    Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN) though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF) model whic...

  14. Comparative approximations of criticality in a neural and quantum regime.

    Science.gov (United States)

    Bettinger, Jesse Sterling

    2017-12-01

    Under a variety of conditions, stochastic and non-linear systems with many degrees of freedom tend to evolve towards complexity and criticality. Over the last decades, a steady proliferation of models re: far-from-equilibrium thermodynamics of metastable, many-valued systems arose, serving as attributes of a 'critical' attractor landscape. Building off recent data citing trademark aspects of criticality in the brain-including: power-laws, scale-free (1/f) behavior (scale invariance, or scale independence), critical slowing, and avalanches-it has been conjectured that operating at criticality entails functional advantages such as: optimized neural computation and information processing; boosted memory; large dynamical ranges; long-range communication; and an increased ability to react to highly diverse stimuli. In short, critical dynamics provide a necessary condition for neurobiologically significant elements of brain dynamics. Theoretical predictions have been verified in specific models such as Boolean networks, liquid state machines, and neural networks. These findings inspired the neural criticality hypothesis, proposing that the brain operates in a critical state because the associated optimal computational capabilities provide an evolutionarily advantage. This paper develops in three parts: after developing the critical landscape, we will then shift gears to rediscover another inroad to criticality via stochastic quantum field theory and dissipative dynamics. The existence of these two approaches deserves some consideration, given both neural and quantum criticality hypotheses propose specific mechanisms that leverage the same phenomena. This suggests that understanding the quantum approach could help to shed light on brain-based modeling. In the third part, we will turn to Whitehead's actual entities and modes of perception in order to demonstrate a concomitant logic underwriting both models. In the discussion, I briefly motivate a reading of criticality and

  15. A computational hypothesis for allostasis: delineation of substance dependence, conventional therapies, and alternative treatments

    Directory of Open Access Journals (Sweden)

    Yariv Z. Levy

    2013-12-01

    Full Text Available The allostatic theory of drug abuse describes the brain's reward system alterations as substance misuse progresses. Neural adaptations arising from the reward system itself and from the antireward system provide the subject with functional stability, while affecting the person's mood. We propose a computational hypothesis describing how a virtual subject's drug consumption, cognitive substrate, and mood interface with reward and antireward systems. Reward system adaptations are assumed interrelated with the ongoing neural activity defining behavior towards drug intake, including activity in the nucleus accumbens, ventral tegmental area, and prefrontal cortex (PFC. Antireward system adaptations are assumed to mutually connect with higher-order cognitive processes occurring within PFC, orbitofrontal cortex, and anterior cingulate cortex. The subject's mood estimation is a provisional function of reward components.The presented knowledge repository model incorporates pharmacokinetic, pharmacodynamic, neuropsychological, cognitive, and behavioral components. Patterns of tobacco smoking exemplify the framework's predictive properties: escalation of cigarette consumption, conventional treatments similar to nicotine patches, and alternative medical practices comparable to meditation. The primary outcomes include an estimate of the virtual subject's mood and the daily account of drug intakes. The main limitation of this study resides in the 21 time-dependent processes which partially describe the complex phenomena of drug addiction and involve a large number of parameters which may underconstrain the framework.Our model predicts that reward system adaptations account for mood stabilization, whereas antireward system adaptations delineate mood improvement and reduction in drug consumption. This investigation provides formal arguments encouraging current rehabilitation therapies to include meditation-like practices along with pharmaceutical drugs and

  16. Neural Tube Defects

    Science.gov (United States)

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

  17. Optimizing neural network models: motivation and case studies

    OpenAIRE

    Harp, S A; T. Samad

    2012-01-01

    Practical successes have been achieved  with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally  rem...

  18. Serotonin, neural markers and memory

    Directory of Open Access Journals (Sweden)

    Alfredo eMeneses

    2015-07-01

    Full Text Available Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals’ species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6 and 5-HT7 receptors as well as SERT (serotonin transporter seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence

  19. Statistical inferences under the Null hypothesis: Common mistakes and pitfalls in neuroimaging studies.

    Directory of Open Access Journals (Sweden)

    Jean-Michel eHupé

    2015-02-01

    Full Text Available Published studies using functional and structural MRI include many errors in the way data are analyzed and conclusions reported. This was observed when working on a comprehensive review of the neural bases of synesthesia, but these errors are probably endemic to neuroimaging studies. All studies reviewed had based their conclusions using Null Hypothesis Significance Tests (NHST. NHST have yet been criticized since their inception because they are more appropriate for taking decisions related to a Null hypothesis (like in manufacturing than for making inferences about behavioral and neuronal processes. Here I focus on a few key problems of NHST related to brain imaging techniques, and explain why or when we should not rely on significance tests. I also observed that, often, the ill-posed logic of NHST was even not correctly applied, and describe what I identified as common mistakes or at least problematic practices in published papers, in light of what could be considered as the very basics of statistical inference. MRI statistics also involve much more complex issues than standard statistical inference. Analysis pipelines vary a lot between studies, even for those using the same software, and there is no consensus which pipeline is the best. I propose a synthetic view of the logic behind the possible methodological choices, and warn against the usage and interpretation of two statistical methods popular in brain imaging studies, the false discovery rate (FDR procedure and permutation tests. I suggest that current models for the analysis of brain imaging data suffer from serious limitations and call for a revision taking into account the new statistics (confidence intervals logic.

  20. 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.

  1. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.

    Science.gov (United States)

    Zhang, Li; Gan, John Q; Wang, Haixian

    2015-10-01

    Based on the neural efficiency hypothesis and task-induced EEG gamma-band response (GBR), this study investigated the brain regions where neural resource could be most efficiently recruited by the math-gifted adolescents in response to varying cognitive demands. In this experiment, various GBR-based mental states were generated with three factors (level of mathematical ability, task complexity, and short-term learning) modulating the level of neural activation. A feature subset selection method based on the sequential forward floating search algorithm was used to identify an "optimal" combination of EEG channel locations, where the corresponding GBR feature subset could obtain the highest accuracy in discriminating pairwise mental states influenced by each experiment factor. The integrative results from multi-factor selections suggest that the right-lateral fronto-parietal system is highly involved in neural efficiency of the math-gifted brain, primarily including the bilateral superior frontal, right inferior frontal, right-lateral central and right temporal regions. By means of the localization method based on single-trial classification of mental states, new GBR features and EEG channel-based brain regions related to mathematical giftedness were identified, which could be useful for the brain function improvement of children/adolescents in mathematical learning through brain-computer interface systems.

  2. [Hypothesis of "sinew-meridian system"].

    Science.gov (United States)

    Liu, Nongyu

    2017-01-12

    The author provides the hypothesis on the "sinew-meridian system" in terms of the physiology, pathology, diagnosis and treatment of meridians and sinew-meridians. Meridians are nourished with blood and sinew-meridians are softened with yang qi . Meridians are circulated in linear form and sinew-meridians are distributed in centripetal state. Meridians are communicated externally and internally and sinew-meridians are connected with tendons and bones. Meridians pertain to zangfu organs and sinew-meridians stabilize zangfu organs. Meridians nourish five sensory organs and sinew-meridians moisten nine orifices. Meridians are characterized as nourishment and sinew-meridians as solidity. Meridians emphasize the conditions of either deficiency or excess, and sinew-meridians as either cold or heat. The meridian disorder is located deeply and of complex and sinew-meridian's is located superficially and of simplicity. The meridian disorder is difficult to treat and with poor therapeutic effect and the sinew-meridian disorder is easy to treat and with rapid therapeutic effect. The "sinew-meridian system" composes of meridian-collateral system and tendon-skin system, in which the meridian-collateral system includes the twelve meridians, eight extra meridians and fifteen collaterals, being relevant with nutrition and blood, acting on transporting qi , blood and message; the tendon-skin system includes twelve sinew-meridians and twelve meridians of cutaneous regions, being relevant with defensive qi , acting on governing the motor function and protecting the body.

  3. The social brain hypothesis of schizophrenia

    Science.gov (United States)

    BURNS, JONATHAN

    2006-01-01

    The social brain hypothesis is a useful heuristic for understanding schizophrenia. It focuses attention on the core Bleulerian concept of autistic alienation and is consistent with well-replicated findings of social brain dysfunction in schizophrenia as well as contemporary theories of human cognitive and brain evolution. The contributions of Heidegger, Merleau-Ponty and Wittgenstein allow us to arrive at a new "philosophy of interpersonal relatedness", which better reflects the "embodied mind" and signifies the end of Cartesian dualistic thinking. In this paper I review the evolution, development and neurobiology of the social brain - the anatomical and functional substrate for adaptive social behaviour and cognition. Functional imaging identifies fronto-temporal and fronto-parietal cortical networks as comprising the social brain, while the discovery of "mirror neurons" provides an understanding of social cognition at a cellular level. Patients with schizophrenia display abnormalities in a wide range of social cognition tasks such as emotion recognition, theory of mind and affective responsiveness. Furthermore, recent research indicates that schizophrenia is a disorder of functional and structural connectivity of social brain networks. These findings lend support to the claim that schizophrenia represents a costly by-product of social brain evolution in Homo sapiens. Individuals with this disorder find themselves seriously disadvantaged in the social arena and vulnerable to the stresses of their complex social environments. This state of "disembodiment" and interpersonal alienation is the core phenomenon of schizophrenia and the root cause of intolerable suffering in the lives of those affected. PMID:16946939

  4. Spectral analysis and the Riemann hypothesis

    Science.gov (United States)

    Lachaud, Gilles

    2003-11-01

    The explicit formulas of Riemann and Guinand-Weil relate the set of prime numbers with the set of nontrivial zeros of the zeta function of Riemann. We recall Alain Connes' spectral interpretation of the critical zeros of the Riemann zeta function as eigenvalues of the absorption spectrum of an unbounded operator in a suitable Hilbert space. We then give a spectral interpretation of the zeros of the Dedekind zeta function of an algebraic number field K of degree n in an automorphic setting. If K is a complex quadratic field, the torical forms are the functions defined on the modular surface X, such that the sum of this function over the "Gauss set" of K is zero, and Eisenstein series provide such torical forms. In the case of a general number field, one can associate to K a maximal torus T of the general linear group G. The torical forms are the functions defined on the modular variety X associated to G, such that the integral over the subvariety induced by T is zero. Alternately, the torical forms are the functions which are orthogonal to orbital series on X. We show here that the Riemann hypothesis is equivalent to certain conditions bearing on spaces of torical forms, constructed from Eisenstein series, the torical wave packets. Furthermore, we define a Hilbert space and a self-adjoint operator on this space, whose spectrum equals the set of critical zeros of the Dedekind zeta function of K.

  5. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

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

    2000-01-01

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

  6. Hypothesis for the pathophysiology of delirium: role of baseline brain network connectivity and changes in inhibitory tone.

    Science.gov (United States)

    Sanders, Robert D

    2011-07-01

    Normal brain function is facilitated by a highly organized and interconnected structure allowing complex integration of sensory information and motor responses. The acute confusional state of delirium is characterized by a fluctuating disturbance in consciousness, arousal level and cognition-memory; as such, delirium represents a failure in the integration and appropriate processing of information. The pathogenesis of this cognitive disintegration is unclear; herein a hypothesis is proposed that delirium results from an acute breakdown in network connectivity within the brain. The hypothesis predicts that the extent to which the network connectivity breaks down is dependent on two factors: (i) the baseline connectivity within the brain and (ii) the level of inhibitory tone. Baseline connectivity is the connectivity of neural networks within the brain before the precipitating insult provoking delirium. Many non-modifiable risk factors for delirium influence baseline connectivity such as age, cognitive impairment, dementia and depression. Precipitant events that provoke delirium (modifiable risk factors) are hypothesized to further, and acutely, breakdown network connectivity by increasing inhibitory tone within the brain. Modifiable risk factors include inflammation, metabolic abnormalities, sleep deprivation and medication such as benzodiazepines. An important role for GABAergic neurotransmission is implicated in increasing the inhibitory tone to produce delirium. This theory accounts for the various forms of delirium, hypoactive, hyperactive and mixed. The form of delirium that ensues will depend upon how and which networks breakdown (dependent on both the individual's baseline network connectivity and the degree change in inhibitory tone produced). Copyright © 2011 Elsevier Ltd. All rights reserved.

  7. Reassessing the Trade-off Hypothesis

    DEFF Research Database (Denmark)

    Rosas, Guillermo; Manzetti, Luigi

    2015-01-01

    Do economic conditions drive voters to punish politicians that tolerate corruption? Previous scholarly work contends that citizens in young democracies support corrupt governments that are capable of promoting good economic outcomes, the so-called trade-off hypothesis. We test this hypothesis based...... by good economic performance. However, we find some evidence for a weaker form of the trade-off hypothesis: presidential disapproval among corruption victims might be more pronounced in contexts of high inflation and high unemployment....

  8. Hypothesis-Driven Treatment of Naming Deficits.

    Science.gov (United States)

    Robinson, K M; Grossman, M

    1997-01-01

    This article proposes to use information processing models of cognition to guide behaviorally based treatments of language deficits, specifically, single-word object naming. Our approach is illustrated with a clinical case of a transcortical sensory aphasic. Clinical neuropsychological and functional imaging data demonstrate that the components comprising the information processing network that underpins naming can be mapped onto a cerebral neural network in the neurologically intact and that reorganization of function seen in transcortical sensory aphasia can demonstrate plasticity in this neural network. The observed balance of impaired and preserved clinical and physiological components in reorganizing neural networks such as this can be used to design treatment strategies to alleviate naming deficits.

  9. [Neural repair].

    Science.gov (United States)

    Kitada, Masaaki; Dezawa, Mari

    2008-05-01

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

  10. The Neural Crest in Cardiac Congenital Anomalies

    Science.gov (United States)

    Keyte, Anna; Hutson, Mary Redmond

    2012-01-01

    This review discusses the function of neural crest as they relate to cardiovascular defects. The cardiac neural crest cells are a subpopulation of cranial neural crest discovered nearly 30 years ago by ablation of premigratory neural crest. The cardiac neural crest cells are necessary for normal cardiovascular development. We begin with a description of the crest cells in normal development, including their function in remodeling the pharyngeal arch arteries, outflow tract septation, valvulogenesis, and development of the cardiac conduction system. The cells are also responsible for modulating signaling in the caudal pharynx, including the second heart field. Many of the molecular pathways that are known to influence specification, migration, patterning and final targeting of the cardiac neural crest cells are reviewed. The cardiac neural crest cells play a critical role in the pathogenesis of various human cardiocraniofacial syndromes such as DiGeorge, Velocardiofacial, CHARGE, Fetal Alcohol, Alagille, LEOPARD, and Noonan syndromes, as well as Retinoic Acid Embryopathy. The loss of neural crest cells or their dysfunction may not always directly cause abnormal cardiovascular development, but are involved secondarily because crest cells represent a major component in the complex tissue interactions in the head, pharynx and outflow tract. Thus many of the human syndromes linking defects in the heart, face and brain can be better understood when considered within the context of a single cardiocraniofacial developmental module with the neural crest being a key cell type that interconnects the regions. PMID:22595346

  11. Predictions from high scale mixing unification hypothesis

    Indian Academy of Sciences (India)

    2016-01-09

    Jan 9, 2016 ... Starting with 'high scale mixing unification' hypothesis, we investigate the renormalization group evolution of mixing parameters and masses for both Dirac and Majorana-type neutrinos. Following this hypothesis, the PMNS mixing parameters are taken to be identical to the CKM ones at a unifying high ...

  12. Hypothesis Testing in the Real World

    Science.gov (United States)

    Miller, Jeff

    2017-01-01

    Critics of null hypothesis significance testing suggest that (a) its basic logic is invalid and (b) it addresses a question that is of no interest. In contrast to (a), I argue that the underlying logic of hypothesis testing is actually extremely straightforward and compelling. To substantiate that, I present examples showing that hypothesis…

  13. Mazur's hypothesis on technology controversy and media.

    NARCIS (Netherlands)

    Gutteling, Jan M.

    2005-01-01

    In the early 1980s, Allan Mazur published his hypothesis on the direct relation between media coverage and public reaction toward technological issues. This hypothesis stated, ‘the rise in reaction against a scientific technology appears to coincide with a rise in quantity of media coverage,

  14. A Test of the Urban Overload Hypothesis.

    Science.gov (United States)

    McCauley, Clark R.

    This paper briefly discusses three studies aimed at exploring the overload hypothesis posited by Stanley Milgram. That hypothesis suggests that impoverished social interaction in the city is an adaptation to overload of interpersonal contacts. The three studies examine various aspects of the phenomenon using different methodologies. Comparing city…

  15. 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

  16. Linking neural and symbolic representation and processing of conceptual structures

    NARCIS (Netherlands)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual

  17. Hypothesis testing in hydrology: Theory and practice

    Science.gov (United States)

    Kirchner, James; Pfister, Laurent

    2017-04-01

    Well-posed hypothesis tests have spurred major advances in hydrological theory. However, a random sample of recent research papers suggests that in hydrology, as in other fields, hypothesis formulation and testing rarely correspond to the idealized model of the scientific method. Practices such as "p-hacking" or "HARKing" (Hypothesizing After the Results are Known) are major obstacles to more rigorous hypothesis testing in hydrology, along with the well-known problem of confirmation bias - the tendency to value and trust confirmations more than refutations - among both researchers and reviewers. Hypothesis testing is not the only recipe for scientific progress, however: exploratory research, driven by innovations in measurement and observation, has also underlain many key advances. Further improvements in observation and measurement will be vital to both exploratory research and hypothesis testing, and thus to advancing the science of hydrology.

  18. Neural recording and modulation technologies

    Science.gov (United States)

    Chen, Ritchie; Canales, Andres; Anikeeva, Polina

    2017-01-01

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

  19. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  20. Synchronization and phonological skills: precise auditory timing hypothesis (PATH

    Directory of Open Access Journals (Sweden)

    Adam eTierney

    2014-11-01

    Full Text Available Phonological skills are enhanced by music training, but the mechanisms enabling this cross-domain enhancement remain unknown. To explain this cross-domain transfer, we propose a precise auditory timing hypothesis (PATH whereby entrainment practice is the core mechanism underlying enhanced phonological abilities in musicians. Both rhythmic synchronization and language skills such as consonant discrimination, detection of word and phrase boundaries, and conversational turn-taking rely on the perception of extremely fine-grained timing details in sound. Auditory-motor timing is an acoustic feature which meets all five of the pre-conditions necessary for cross-domain enhancement to occur (Patel 2011, 2012, 2014. There is overlap between the neural networks that process timing in the context of both music and language. Entrainment to music demands more precise timing sensitivity than does language processing. Moreover, auditory-motor timing integration captures the emotion of the trainee, is repeatedly practiced, and demands focused attention. The precise auditory timing hypothesis predicts that musical training emphasizing entrainment will be particularly effective in enhancing phonological skills.

  1. Aging affects neural precision of speech encoding.

    Science.gov (United States)

    Anderson, Samira; Parbery-Clark, Alexandra; White-Schwoch, Travis; Kraus, Nina

    2012-10-10

    Older adults frequently report they can hear what is said but cannot understand the meaning, especially in noise. This difficulty may arise from the inability to process rapidly changing elements of speech. Aging is accompanied by a general slowing of neural processing and decreased neural inhibition, both of which likely interfere with temporal processing in auditory and other sensory domains. Age-related reductions in inhibitory neurotransmitter levels and delayed neural recovery can contribute to decreases in the temporal precision of the auditory system. Decreased precision may lead to neural timing delays, reductions in neural response magnitude, and a disadvantage in processing the rapid acoustic changes in speech. The auditory brainstem response (ABR), a scalp-recorded electrical potential, is known for its ability to capture precise neural synchrony within subcortical auditory nuclei; therefore, we hypothesized that a loss of temporal precision results in subcortical timing delays and decreases in response consistency and magnitude. To assess this hypothesis, we recorded ABRs to the speech syllable /da/ in normal hearing younger (18-30 years old) and older (60-67 years old) adult humans. Older adults had delayed ABRs, especially in response to the rapidly changing formant transition, and greater response variability. We also found that older adults had decreased phase locking and smaller response magnitudes than younger adults. Together, our results support the theory that older adults have a loss of temporal precision in the subcortical encoding of sound, which may account, at least in part, for their difficulties with speech perception.

  2. A mechanistic hypothesis of the factors that enhance vulnerability to nicotine use in females

    OpenAIRE

    O'Dell, Laura E.; Torres, Oscar V.

    2013-01-01

    Women are particularly more vulnerable to tobacco use than men. This review proposes a unifying hypothesis that females experience greater rewarding effects of nicotine and more intense stress produced by withdrawal than males. We also provide a neural framework whereby estrogen promotes greater rewarding effects of nicotine in females via enhanced dopamine release in the nucleus accumbens (NAcc). During withdrawal, we suggest that corticotropin-releasing factor (CRF) stress systems are sensi...

  3. Reduced sensitivity to emotional prosody in congenital amusia rekindles the musical protolanguage hypothesis

    OpenAIRE

    Thompson, William Forde; Marin, Manuela M.; Stewart, Lauren

    2012-01-01

    A number of evolutionary theories assume that music and language have a common origin as an emotional protolanguage that remains evident in overlapping functions and shared neural circuitry. The most basic prediction of this hypothesis is that sensitivity to emotion in speech prosody derives from the capacity to process music. We examined sensitivity to emotion in speech prosody in a sample of individuals with congenital amusia, a neurodevelopmental disorder characterized by deficits in proce...

  4. Visual Complexity: A Review

    Science.gov (United States)

    Donderi, Don C.

    2006-01-01

    The idea of visual complexity, the history of its measurement, and its implications for behavior are reviewed, starting with structuralism and Gestalt psychology at the beginning of the 20th century and ending with visual complexity theory, perceptual learning theory, and neural circuit theory at the beginning of the 21st. Evidence is drawn from…

  5. The early anthropogenic hypothesis: Challenges and responses

    National Research Council Canada - National Science Library

    William F. Ruddiman

    2007-01-01

    .... Every aspect of this early anthropogenic hypothesis has been challenged: the timescale, the issue of stage 11 as a better analog, the ability of human activities to account for the gas anomalies, and the impact of the pandemics...

  6. HYPOTHESIS TESTING USING NUMEROUS APPROXIMATING FUNCTIONAL FORMS

    OpenAIRE

    Norwood, F. Bailey; Lusk, Jayson L.; Ferrier, Peyton Michael

    2001-01-01

    While the combination of several or more models is often found to improve forecasts (Brandt and Bessler, Min and Zellner, Norwood and Schroeder), hypothesis tests are typically conducted using a single model approach 1 . Hypothesis tests and forecasts have similar goals; they seek to define a range over which a parameter should lie within a degree of confidence. If it is true that, on average, composite forecasts are more accurate than a single model's forecast, it might also be true that hyp...

  7. Quantization of Prior Probabilities for Hypothesis Testing

    OpenAIRE

    Varshney, Kush R.; Varshney, Lav R.

    2008-01-01

    Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors.

  8. The discovered preference hypothesis - an empirical test

    DEFF Research Database (Denmark)

    Lundhede, Thomas; Ladenburg, Jacob; Olsen, Søren Bøye

    Using stated preference methods for valuation of non-market goods is known to be vulnerable to a range of biases. Some authors claim that these so-called anomalies in effect render the methods useless for the purpose. However, the Discovered Preference Hypothesis, as put forth by Plott [31], offers...... as respondents evaluate more and more choice sets. This finding supports the Discovered Preference Hypothesis interpretation and explanation of starting point bias....

  9. Robust Binary Hypothesis Testing Under Contaminated Likelihoods

    OpenAIRE

    Wei, Dennis; Varshney, Kush R.

    2014-01-01

    In hypothesis testing, the phenomenon of label noise, in which hypothesis labels are switched at random, contaminates the likelihood functions. In this paper, we develop a new method to determine the decision rule when we do not have knowledge of the uncontaminated likelihoods and contamination probabilities, but only have knowledge of the contaminated likelihoods. In particular we pose a minimax optimization problem that finds a decision rule robust against this lack of knowledge. The method...

  10. Is the amyloid hypothesis of Alzheimer's disease therapeutically relevant?

    Science.gov (United States)

    Teich, Andrew F; Arancio, Ottavio

    2012-09-01

    The conventional view of AD (Alzheimer's disease) is that much of the pathology is driven by an increased load of β-amyloid in the brain of AD patients (the 'Amyloid Hypothesis'). Yet, many therapeutic strategies based on lowering β-amyloid have so far failed in clinical trials. This failure of β-amyloid-lowering agents has caused many to question the Amyloid Hypothesis itself. However, AD is likely to be a complex disease driven by multiple factors. In addition, it is increasingly clear that β-amyloid processing involves many enzymes and signalling pathways that play a role in a diverse array of cellular processes. Thus the clinical failure of β-amyloid-lowering agents does not mean that the hypothesis itself is incorrect; it may simply mean that manipulating β-amyloid directly is an unrealistic strategy for therapeutic intervention, given the complex role of β-amyloid in neuronal physiology. Another possible problem may be that toxic β-amyloid levels have already caused irreversible damage to downstream cellular pathways by the time dementia sets in. We argue in the present review that a more direct (and possibly simpler) approach to AD therapeutics is to rescue synaptic dysfunction directly, by focusing on the mechanisms by which elevated levels of β-amyloid disrupt synaptic physiology.

  11. 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.

  12. Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach.

    Science.gov (United States)

    Okazaki, Ryoko; Takahashi, Tetsuya; Ueno, Kanji; Takahashi, Koichi; Ishitobi, Makoto; Kikuchi, Mitsuru; Higashima, Masato; Wada, Yuji

    2015-01-01

    Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE) can characterize the complexity inherent in electroencephalography (EEG) dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive-compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT). In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner that we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD.

  13. A model for integrating elementary neural functions into delayed-response behavior.

    Directory of Open Access Journals (Sweden)

    Thomas Gisiger

    2006-04-01

    Full Text Available It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning, and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task, or recalling from this image another one that has been associated with it during training (delayed-pair association task. The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

  14. 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.

  15. Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

    Science.gov (United States)

    Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C.; Zhou, Changsong

    2013-01-01

    The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy

  16. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.

    Directory of Open Access Journals (Sweden)

    Yuhan Chen

    Full Text Available The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter α, and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of α, resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of α values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real

  17. Aminoglycoside antibiotics and autism: a speculative hypothesis

    Directory of Open Access Journals (Sweden)

    Manev Hari

    2001-10-01

    Full Text Available Abstract Background Recently, it has been suspected that there is a relationship between therapy with some antibiotics and the onset of autism; but even more curious, some children benefited transiently from a subsequent treatment with a different antibiotic. Here, we speculate how aminoglycoside antibiotics might be associated with autism. Presentation We hypothesize that aminoglycoside antibiotics could a trigger the autism syndrome in susceptible infants by causing the stop codon readthrough, i.e., a misreading of the genetic code of a hypothetical critical gene, and/or b improve autism symptoms by correcting the premature stop codon mutation in a hypothetical polymorphic gene linked to autism. Testing Investigate, retrospectively, whether a link exists between aminoglycoside use (which is not extensive in children and the onset of autism symptoms (hypothesis "a", or between amino glycoside use and improvement of these symptoms (hypothesis "b". Whereas a prospective study to test hypothesis "a" is not ethically justifiable, a study could be designed to test hypothesis "b". Implications It should be stressed that at this stage no direct evidence supports our speculative hypothesis and that its main purpose is to initiate development of new ideas that, eventually, would improve our understanding of the pathobiology of autism.

  18. Genetic, epigenetic, and environmental contributions to neural tube closure.

    Science.gov (United States)

    Wilde, Jonathan J; Petersen, Juliette R; Niswander, Lee

    2014-01-01

    The formation of the embryonic brain and spinal cord begins as the neural plate bends to form the neural folds, which meet and adhere to close the neural tube. The neural ectoderm and surrounding tissues also coordinate proliferation, differentiation, and patterning. This highly orchestrated process is susceptible to disruption, leading to neural tube defects (NTDs), a common birth defect. Here, we highlight genetic and epigenetic contributions to neural tube closure. We describe an online database we created as a resource for researchers, geneticists, and clinicians. Neural tube closure is sensitive to environmental influences, and we discuss disruptive causes, preventative measures, and possible mechanisms. New technologies will move beyond candidate genes in small cohort studies toward unbiased discoveries in sporadic NTD cases. This will uncover the genetic complexity of NTDs and critical gene-gene interactions. Animal models can reveal the causative nature of genetic variants, the genetic interrelationships, and the mechanisms underlying environmental influences.

  19. A Projection Neural Network for Constrained Quadratic Minimax Optimization.

    Science.gov (United States)

    Liu, Qingshan; Wang, Jun

    2015-11-01

    This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.

  20. Tracing 'driver' versus 'modulator' information flow throughout large-scale, task-related neural circuitry.

    Science.gov (United States)

    Hermer-Vazquez, Linda

    2008-04-01

    PRIMARY OBJECTIVE: To determine the relative uses of neural action potential ('spike') data versus local field potentials (LFPs) for modeling information flow through complex brain networks. HYPOTHESIS: The common use of LFP data, which are continuous and therefore more mathematically suited for spectral information-flow modeling techniques such as Granger causality analysis, can lead to spurious inferences about whether a given brain area 'drives' the spiking in a downstream area. EXPERIMENT: We recorded spikes and LFPs from the forelimb motor cortex (M1) and the magnocellular red nucleus (mRN), which receives axon collaterals from M1 projection cells onto its distal dendrites, but not onto its perisomatic regions, as rats performed a skilled reaching task. RESULTS AND IMPLICATIONS: As predicted, Granger causality analysis on the LFPs-which are mainly composed of vector-summed dendritic currents-produced results that if conventionally interpreted would suggest that the M1 cells drove spike firing in the mRN, whereas analyses of spiking in the two recorded regions revealed no significant correlations. These results suggest that mathematical models of information flow should treat the sampled dendritic activity as more likely to reflect intrinsic dendritic and input-related processing in neural networks, whereas spikes are more likely to provide information about the output of neural network processing.

  1. The neural signature of emotional memories in serial crimes.

    Science.gov (United States)

    Chassy, Philippe

    2017-10-01

    Neural plasticity is the process whereby semantic information and emotional responses are stored in neural networks. It is hypothesized that the neural networks built over time to encode the sexual fantasies that motivate serial killers to act should display a unique, detectable activation pattern. The pathological neural watermark hypothesis posits that such networks comprise activation of brain sites that reflect four cognitive components: autobiographical memory, sexual arousal, aggression, and control over aggression. The neural sites performing these cognitive functions have been successfully identified by previous research. The key findings are reviewed to hypothesise the typical pattern of activity that serial killers should display. Through the integration of biological findings into one framework, the neural approach proposed in this paper is in stark contrast with the many theories accounting for serial killers that offer non-medical taxonomies. The pathological neural watermark hypothesis offers a new framework to understand and detect deviant individuals. The technical and legal issues are briefly discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Neuronal avalanches in complex networks

    Directory of Open Access Journals (Sweden)

    Victor Hernandez-Urbina

    2016-12-01

    Full Text Available Brain networks are neither regular nor random. Their structure allows for optimal information processing and transmission across the entire neural substrate of an organism. However, for topological features to be appropriately harnessed, brain networks should implement a dynamical regime which prevents phase-locked and chaotic behaviour. Critical neural dynamics refer to a dynamical regime in which the system is poised at the boundary between regularity and randomness. It has been reported that neural systems poised at this boundary achieve maximum computational power. In this paper, we review recent results regarding critical neural dynamics that emerge from systems whose underlying structure exhibits complex network properties.

  3. Testing competing forms of the Milankovitch hypothesis

    DEFF Research Database (Denmark)

    Kaufmann, Robert K.; Juselius, Katarina

    2016-01-01

    We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical...... that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: Internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land...... ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship...

  4. Pasture succession in the Neotropics: extending the nucleation hypothesis into a matrix discontinuity hypothesis.

    Science.gov (United States)

    Peterson, Chris J; Dosch, Jerald J; Carson, Walter P

    2014-08-01

    The nucleation hypothesis appears to explain widespread patterns of succession in tropical pastures, specifically the tendency for isolated trees to promote woody species recruitment. Still, the nucleation hypothesis has usually been tested explicitly for only short durations and in some cases isolated trees fail to promote woody recruitment. Moreover, at times, nucleation occurs in other key habitat patches. Thus, we propose an extension, the matrix discontinuity hypothesis: woody colonization will occur in focal patches that function to mitigate the herbaceous vegetation effects, thus providing safe sites or regeneration niches. We tested predictions of the classical nucleation hypothesis, the matrix discontinuity hypothesis, and a distance from forest edge hypothesis, in five abandoned pastures in Costa Rica, across the first 11 years of succession. Our findings confirmed the matrix discontinuity hypothesis: specifically, rotting logs and steep slopes significantly enhanced woody colonization. Surprisingly, isolated trees did not consistently significantly enhance recruitment; only larger trees did so. Finally, woody recruitment consistently decreased with distance from forest. Our results as well as results from others suggest that the nucleation hypothesis needs to be broadened beyond its historical focus on isolated trees or patches; the matrix discontinuity hypothesis focuses attention on a suite of key patch types or microsites that promote woody species recruitment. We argue that any habitat discontinuities that ameliorate the inhibition by dense graminoid layers will be foci for recruitment. Such patches could easily be manipulated to speed the transition of pastures to closed canopy forests.

  5. Response variability in Attention-Deficit/Hyperactivity Disorder: a neuronal and glial energetics hypothesis

    Directory of Open Access Journals (Sweden)

    Auerbach Judith G

    2006-08-01

    Full Text Available 1. Abstract Background Current concepts of Attention-Deficit/Hyperactivity Disorder (ADHD emphasize the role of higher-order cognitive functions and reinforcement processes attributed to structural and biochemical anomalies in cortical and limbic neural networks innervated by the monoamines, dopamine, noradrenaline and serotonin. However, these explanations do not account for the ubiquitous findings in ADHD of intra-individual performance variability, particularly on tasks that require continual responses to rapid, externally-paced stimuli. Nor do they consider attention as a temporal process dependent upon a continuous energy supply for efficient and consistent function. A consideration of this feature of intra-individual response variability, which is not unique to ADHD but is also found in other disorders, leads to a new perspective on the causes and potential remedies of specific aspects of ADHD. The hypothesis We propose that in ADHD, astrocyte function is insufficient, particularly in terms of its formation and supply of lactate. This insufficiency has implications both for performance and development: H1 In rapidly firing neurons there is deficient ATP production, slow restoration of ionic gradients across neuronal membranes and delayed neuronal firing; H2 In oligodendrocytes insufficient lactate supply impairs fatty acid synthesis and myelination of axons during development. These effects occur over vastly different time scales: those due to deficient ATP (H1 occur over milliseconds, whereas those due to deficient myelination (H2 occur over months and years. Collectively the neural outcomes of impaired astrocytic release of lactate manifest behaviourally as inefficient and inconsistent performance (variable response times across the lifespan, especially during activities that require sustained speeded responses and complex information processing. Testing the hypothesis Multi-level and multi-method approaches are required. These include

  6. The feeling of agency hypothesis: a critique

    DEFF Research Database (Denmark)

    Grünbaum, Thor

    2015-01-01

    A dominant view in contemporary cognitive neuroscience is that low-level, comparator-based mechanisms of motor control produce a distinctive experience often called the feeling of agency (the FoA-hypothesis). An opposing view is that comparator-based motor control is largely non-conscious and not......A dominant view in contemporary cognitive neuroscience is that low-level, comparator-based mechanisms of motor control produce a distinctive experience often called the feeling of agency (the FoA-hypothesis). An opposing view is that comparator-based motor control is largely non...

  7. Ready for Retirement: The Gateway Drug Hypothesis.

    Science.gov (United States)

    Kleinig, John

    2015-01-01

    The psycho-social observation that the use of some psychoactive substances ("drugs") is often followed by the use of other and more problematic drugs has given rise to a cluster of so-called "gateway drug hypotheses," and such hypotheses have often played an important role in developing drug use policy. The current essay suggests that drug use policies that have drawn on versions of the hypothesis have involved an unjustified oversimplification of the dynamics of drug use, reflecting the interests of certain stakeholders rather than wise social policy. The hypothesis should be retired.

  8. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  9. A neural network simulation package in CLIPS

    Science.gov (United States)

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

    1990-01-01

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

  10. Neural prostheses and brain plasticity

    Science.gov (United States)

    Fallon, James B.; Irvine, Dexter R. F.; Shepherd, Robert K.

    2009-12-01

    The success of modern neural prostheses is dependent on a complex interplay between the devices' hardware and software and the dynamic environment in which the devices operate: the patient's body or 'wetware'. Over 120 000 severe/profoundly deaf individuals presently receive information enabling auditory awareness and speech perception from cochlear implants. The cochlear implant therefore provides a useful case study for a review of the complex interactions between hardware, software and wetware, and of the important role of the dynamic nature of wetware. In the case of neural prostheses, the most critical component of that wetware is the central nervous system. This paper will examine the evidence of changes in the central auditory system that contribute to changes in performance with a cochlear implant, and discuss how these changes relate to electrophysiological and functional imaging studies in humans. The relationship between the human data and evidence from animals of the remarkable capacity for plastic change of the central auditory system, even into adulthood, will then be examined. Finally, we will discuss the role of brain plasticity in neural prostheses in general.

  11. an assessment of the acoustic adaptation hypothesis

    African Journals Online (AJOL)

    Song is critical to territory defence, mate attraction, and both species and individual recognition. According to the Acoustic Adaptation Hypothesis (AAH), habitat structure may exercise a selective force on vocal evolution such that song evolves to minimise the degradation and attenuation of acoustic signals in the particular ...

  12. The (not so immortal strand hypothesis

    Directory of Open Access Journals (Sweden)

    Cristian Tomasetti

    2015-03-01

    Significance: Utilizing an approach that is fundamentally different from previous efforts to confirm or refute the immortal strand hypothesis, we provide evidence against non-random segregation of DNA during stem cell replication. Our results strongly suggest that parental DNA is passed randomly to stem cell daughters and provides new insight into the mechanism of DNA replication in stem cells.

  13. Exploring Braak's Hypothesis of Parkinson's Disease.

    Science.gov (United States)

    Rietdijk, Carmen D; Perez-Pardo, Paula; Garssen, Johan; van Wezel, Richard J A; Kraneveld, Aletta D

    2017-01-01

    Parkinson's disease (PD) is a neurodegenerative disorder for which there is no cure. Most patients suffer from sporadic PD, which is likely caused by a combination of genetic and environmental factors. Braak's hypothesis states that sporadic PD is caused by a pathogen that enters the body via the nasal cavity, and subsequently is swallowed and reaches the gut, initiating Lewy pathology (LP) in the nose and the digestive tract. A staging system describing the spread of LP from the peripheral to the central nervous system was also postulated by the same research group. There has been criticism to Braak's hypothesis, in part because not all patients follow the proposed staging system. Here, we review literature that either supports or criticizes Braak's hypothesis, focused on the enteric route, digestive problems in patients, the spread of LP on a tissue and a cellular level, and the toxicity of the protein αSynuclein (αSyn), which is the major constituent of LP. We conclude that Braak's hypothesis is supported by in vitro, in vivo, and clinical evidence. However, we also conclude that the staging system of Braak only describes a specific subset of patients with young onset and long duration of the disease.

  14. Revisiting the thinking-for-speaking hypothesis

    DEFF Research Database (Denmark)

    Wessel-Tolvig, Bjørn Nicola; Paggio, Patrizia

    2016-01-01

    Many studies try to explain thought processes based on verbal data alone and often take the linguistic variation between languages as evidence for cross-linguistic thought processes during speaking. We argue that looking at co-speech gestures might broaden the scope and shed new light on differen...... for the thinking part of the thinking-for-speaking hypothesis....

  15. Forty Years Later: Updating the Fossilization Hypothesis

    Science.gov (United States)

    Han, ZhaoHong

    2013-01-01

    A founding concept in second language acquisition (SLA) research, fossilization has been fundamental to understanding second language (L2) development. The Fossilization Hypothesis, introduced in Selinker's seminal text (1972), has thus been one of the most influential theories, guiding a significant bulk of SLA research for four decades; 2012…

  16. Multiple hypothesis clustering in radar plot extraction

    NARCIS (Netherlands)

    Huizing, A.G.; Theil, A.; Dorp, Ph. van; Ligthart, L.P.

    1995-01-01

    False plots and plots with inaccurate range and Doppler estimates may severely degrade the performance of tracking algorithms in radar systems. This paper describes how a multiple hypothesis clustering technique can be applied to mitigate the problems involved in plot extraction. The measures of

  17. Improving your Hypothesis Testing: Determining Sample Sizes.

    Science.gov (United States)

    Luftig, Jeffrey T.; Norton, Willis P.

    1982-01-01

    This article builds on an earlier discussion of the importance of the Type II error (beta) and power to the hypothesis testing process (CE 511 484), and illustrates the methods by which sample size calculations should be employed so as to improve the research process. (Author/CT)

  18. Commentary: Human papillomavirus and tar hypothesis for ...

    Indian Academy of Sciences (India)

    2010-08-09

    Aug 9, 2010 ... Home; Journals; Journal of Biosciences; Volume 35; Issue 3. Commentary: Human papillomavirus and tar hypothesis for squamous cell cervical cancer. Christina Bennett Allen E Kuhn Harry W Haverkos. Volume 35 Issue 3 September 2010 pp 331-337 ...

  19. Adaptive state multiple-hypothesis tracking

    NARCIS (Netherlands)

    Kleef, J. van; Kester, L.J.H.M.

    2006-01-01

    In tracking algorithms where measurements from various sensors are combined the track state representation is usually dependent on the type of sensor information that is received. When a multi-hypothesis tracking algorithm is used the probabilities of the different hypotheses containing tracks in

  20. Groupthink: Hypothesis in Need of Testing.

    Science.gov (United States)

    Moorhead, Gregory

    1982-01-01

    Reviews the major tenets of the groupthink hypothesis of Irving Janis, as well as the research on which it is based. Reviews previous research on group dynamics related to groupthink. Proposes guidelines for research to test the propositions of groupthink. (Author/RC)

  1. Television Exposure Measures and the Cultivation Hypothesis.

    Science.gov (United States)

    Potter, W. James; Chang, Ik Chin

    1990-01-01

    Describes study of students in grades 8 through 12 that was conducted to determine the degree to which television messages influence a person's construction of reality (the cultivation hypothesis). Research methodology that tests the effects of television exposure is examined with emphasis on the importance of demographic control variables. (38…

  2. On the Relationship Between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions

    Science.gov (United States)

    1994-01-01

    Radon measure on the Borel sets of Rk, G is a gaussian function with range in [0, V], the 2 ([nklnnl - In61/ symbol * stands for the convolution...I[fn,1] where A is a signed Radon measure on the Borel sets of Rh, G is a gaussian function with range [0, V], the Iemp[fn,i] • Iemp[fi] symbol ...Teoriya Veroyatnostei Ee Primenenzya, 26(3):543- [651 G. Pisier. Remarques sur un resultat non publiý 564, 1981. de B. Maurey. In Centre de Mathematique

  3. Genetic attack on neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  4. The Income Inequality Hypothesis Revisited : Assessing the Hypothesis Using Four Methodological Approaches

    NARCIS (Netherlands)

    Kragten, N.; Rözer, J.

    The income inequality hypothesis states that income inequality has a negative effect on individual’s health, partially because it reduces social trust. This article aims to critically assess the income inequality hypothesis by comparing several analytical strategies, namely OLS regression,

  5. A Dopamine Hypothesis of Autism Spectrum Disorder.

    Science.gov (United States)

    Pavăl, Denis

    2017-01-01

    Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by social deficits and stereotyped behaviors. While several theories have emerged, the pathogenesis of ASD remains unknown. Although studies report dopamine signaling abnormalities in autistic patients, a coherent dopamine hypothesis which could link neurobiology to behavior in ASD is currently lacking. In this paper, we present such a hypothesis by proposing that autistic behavior arises from dysfunctions in the midbrain dopaminergic system. We hypothesize that a dysfunction of the mesocorticolimbic circuit leads to social deficits, while a dysfunction of the nigrostriatal circuit leads to stereotyped behaviors. Furthermore, we discuss 2 key predictions of our hypothesis, with emphasis on clinical and therapeutic aspects. First, we argue that dopaminergic dysfunctions in the same circuits should associate with autistic-like behavior in nonautistic subjects. Concerning this, we discuss the case of PANDAS (pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections) which displays behaviors similar to those of ASD, presumed to arise from dopaminergic dysfunctions. Second, we argue that providing dopamine modulators to autistic subjects should lead to a behavioral improvement. Regarding this, we present clinical studies of dopamine antagonists which seem to have improving effects on autistic behavior. Furthermore, we explore the means of testing our hypothesis by using neuroreceptor imaging, which could provide comprehensive evidence for dopamine signaling dysfunctions in autistic subjects. Lastly, we discuss the limitations of our hypothesis. Along these lines, we aim to provide a dopaminergic model of ASD which might lead to a better understanding of the ASD pathogenesis. © 2017 S. Karger AG, Basel.

  6. Neurodevelopmental Hypothesis about the Etiology of Autism Spectrum Disorders

    Directory of Open Access Journals (Sweden)

    Toshio Inui

    2017-07-01

    Full Text Available Previous models or hypotheses of autism spectral disorder (ASD failed to take into full consideration the chronological and causal developmental trajectory, leading to the emergence of diverse phenotypes through a complex interaction between individual etiologies and environmental factors. Those phenotypes include persistent deficits in social communication and social interaction (criteria A in DSM-5, and restricted, repetitive patterns of behavior, interests, or activities (criteria B in DSM-5. In this article, we proposed a domain-general model that can explain criteria in DSM-5 based on the assumption that the same etiological mechanism would trigger the various phenotypes observed in different individuals with ASD. In the model, we assumed the following joint causes as the etiology of autism: (1 Hypoplasia of the pons in the brainstem, occurring immediately following neural tube closure; and (2 Deficiency in the GABA (γ-aminobutyric acid developmental switch during the perinatal period. Microstructural abnormalities of the pons directly affect both the structural and functional development of the brain areas strongly connected to it, especially amygdala. The impairment of GABA switch could not only lead to the deterioration of inhibitory processing in the neural network, but could also cause abnormal cytoarchitecture. We introduced a perspective that atypical development in both brain structure and function can give full explanation of diverse phenotypes and pathogenetic mechanism of ASD. Finally, we discussed about neural mechanisms underlying the phenotypic characteristics of ASD that are not described in DSM-5 but should be considered as important foundation: sleep, global precedence, categorical perception, intelligence, interoception and motor control.

  7. Complex movement patterns: modifiability and constraints.

    Science.gov (United States)

    Bout, R G

    1998-01-01

    Most behaviours involve complex morphological systems and vice versa morphological systems are used by the organism in many different ways. During evolution and ontogeny changes in kinematics and function of skeletal and muscular systems must be coordinated with changes in their neural control. Neuromotor patterns are sometimes believed to be conserved in evolution, leading to diversification at the level of musculoskeletal design. Vertebrate motor patterns used in feeding are reviewed to examine this hypothesis. Stereotyped behaviour is not necessarily the result of phylogenetic constraints but may also result from the functional demands imposed by the mechanics of the jaw apparatus and the nature of the task performed. Sensory feedback and descending control not only contribute to 'online' control of movement but also shape the development of motor patterns and learning behaviour and indicate a potentially large flexibility. The neural and sensory apparatus that produces this flexibility will be subject to evolutionary modification. In the absence of a demand for flexibility motor patterns may become stereotyped in some species, while they are very flexible in others. To the extent that morphological systems perform independent movements during different behaviours, separate basic motor patterns may be required, which may be coordinated in different ways.

  8. Complexity and valued landscapes

    Science.gov (United States)

    Michael M. McCarthy

    1979-01-01

    The variable "complexity," or "diversity," has received a great deal of attention in recent research efforts concerned with visual resource management, including the identification of complexity as one of the primary evaluation measures. This paper describes research efforts that support the hypothesis that the landscapes we value are those with...

  9. How organisms do the right thing: The attractor hypothesis

    Science.gov (United States)

    Emlen, J.M.; Freeman, D.C.; Mills, A.; Graham, J.H.

    1998-01-01

    Neo-Darwinian theory is highly successful at explaining the emergence of adaptive traits over successive generations. However, there are reasons to doubt its efficacy in explaining the observed, impressively detailed adaptive responses of organisms to day-to-day changes in their surroundings. Also, the theory lacks a clear mechanism to account for both plasticity and canalization. In effect, there is a growing sentiment that the neo-Darwinian paradigm is incomplete, that something more than genetic structure, mutation, genetic drift, and the action of natural selection is required to explain organismal behavior. In this paper we extend the view of organisms as complex self-organizing entities by arguing that basic physical laws, coupled with the acquisitive nature of organisms, makes adaptation all but tautological. That is, much adaptation is an unavoidable emergent property of organisms' complexity and, to some a significant degree, occurs quite independently of genomic changes wrought by natural selection. For reasons that will become obvious, we refer to this assertion as the attractor hypothesis. The arguments also clarify the concept of "adaptation." Adaptation across generations, by natural selection, equates to the (game theoretic) maximization of fitness (the success with which one individual produces more individuals), while self-organizing based adaptation, within generations, equates to energetic efficiency and the matching of intake and biosynthesis to need. Finally, we discuss implications of the attractor hypothesis for a wide variety of genetical and physiological phenomena, including genetic architecture, directed mutation, genetic imprinting, paramutation, hormesis, plasticity, optimality theory, genotype-phenotype linkage and puncuated equilibrium, and present suggestions for tests of the hypothesis. ?? 1998 American Institute of Physics.

  10. Neural art appraisal of painter: Dali or Picasso?

    Science.gov (United States)

    Yamamura, Hiromi; Sawahata, Yasuhito; Yamamoto, Miyuki; Kamitani, Yukiyasu

    2009-12-09

    One can infer an artist's identity from his or her artworks, but little is known about the neural representation of such elusive categorization. Here, we constructed a 'neural art appraiser' based on machine-learning methods that predicted the painter from the functional MRI activity pattern elicited by a painting. We found that Dali's and Picasso's artworks could be accurately classified based on brain activity alone, and that broadly distributed brain activity contributed to the neural prediction. Our approach provides a new means to probe into complex neural processes underlying art experiences.

  11. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  12. Hypothesis testing of scientific Monte Carlo calculations

    Science.gov (United States)

    Wallerberger, Markus; Gull, Emanuel

    2017-11-01

    The steadily increasing size of scientific Monte Carlo simulations and the desire for robust, correct, and reproducible results necessitates rigorous testing procedures for scientific simulations in order to detect numerical problems and programming bugs. However, the testing paradigms developed for deterministic algorithms have proven to be ill suited for stochastic algorithms. In this paper we demonstrate explicitly how the technique of statistical hypothesis testing, which is in wide use in other fields of science, can be used to devise automatic and reliable tests for Monte Carlo methods, and we show that these tests are able to detect some of the common problems encountered in stochastic scientific simulations. We argue that hypothesis testing should become part of the standard testing toolkit for scientific simulations.

  13. Eigenstate Thermalization Hypothesis and Quantum Thermodynamics

    Science.gov (United States)

    Olshanii, Maxim

    2009-03-01

    One of the open questions in quantum thermodynamics reads: how can linear quantum dynamics provide chaos necessary for thermalization of an isolated quantum system? To this end, we perform an ab initio numerical analysis of a system of hard-core bosons on a lattice and show [Marcos Rigol, Vanja Dunjko & Maxim Olshanii, Nature 452, 854 (2008)] that the above controversy can be resolved via the Eigenstate Thermalization Hypothesis suggested independently by Deutsch [J. M. Deutsch, Phys. Rev. A 43, 2046 (1991)] and Srednicki [M. Srednicki, Phys. Rev. E 50, 888 (1994)]. According to this hypothesis, in quantum systems thermalization happens in each individual eigenstate of the system separately, but it is hidden initially by coherences between them. In course of the time evolution the thermal properties become revealed through (linear) decoherence that needs not to be chaotic.

  14. Reverse hypothesis machine learning a practitioner's perspective

    CERN Document Server

    Kulkarni, Parag

    2017-01-01

    This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

  15. A reformulation of the hygiene hypothesis

    DEFF Research Database (Denmark)

    Hersoug, Lars-Georg

    2006-01-01

    Epidemiological studies have shown an inverse relationship between allergic respiratory diseases and the number of siblings. It was hypothesized that the lower prevalence of allergic respiratory diseases in large sibships was due to cross-infections between siblings. According to this hygiene hyp...... influence of the mother was overlooked. A new hypothesis is therefore proposed. Maternal exposure to infections induces immunological memory, which protects her children against allergic respiratory diseases.......Epidemiological studies have shown an inverse relationship between allergic respiratory diseases and the number of siblings. It was hypothesized that the lower prevalence of allergic respiratory diseases in large sibships was due to cross-infections between siblings. According to this hygiene...... hypothesis the increase in the prevalence of atopic diseases is caused by a decrease in the exposure to infections. It was believed that early infections were beneficial for health because of their contribution to the maturation of the immune system. However, in this interpretation a possible protective...

  16. Tests of the Giant Impact Hypothesis

    Science.gov (United States)

    Jones, J. H.

    1998-01-01

    The giant impact hypothesis has gained popularity as a means of explaining a volatile-depleted Moon that still has a chemical affinity to the Earth. As Taylor's Axiom decrees, the best models of lunar origin are testable, but this is difficult with the giant impact model. The energy associated with the impact would be sufficient to totally melt and partially vaporize the Earth. And this means that there should he no geological vestige of Barber times. Accordingly, it is important to devise tests that may be used to evaluate the giant impact hypothesis. Three such tests are discussed here. None of these is supportive of the giant impact model, but neither do they disprove it.

  17. Lipofuscin Hypothesis of Alzheimer’s Disease

    Directory of Open Access Journals (Sweden)

    Giorgio Giaccone

    2011-09-01

    Full Text Available The primary culprit responsible for Alzheimer’s disease (AD remains unknown. Aβ protein has been identified as the main component of amyloid of senile plaques, the hallmark lesion of AD, but it is not definitively established whether the formation of extracellular Aβ deposits is the absolute harbinger of the series of pathological events that hit the brain in the course of sporadic AD. The aim of this paper is to draw attention to a relatively overlooked age-related product, lipofuscin, and advance the hypothesis that its release into the extracellular space following the death of neurons may substantially contribute to the formation of senile plaques. The presence of intraneuronal Aβ, similarities between AD and age-related macular degeneration, and the possible explanation of some of the unknown issues in AD suggest that this hypothesis should not be discarded out of hand.

  18. Exploring heterogeneous market hypothesis using realized volatility

    Science.gov (United States)

    Chin, Wen Cheong; Isa, Zaidi; Mohd Nor, Abu Hassan Shaari

    2013-04-01

    This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements.

  19. Testing the Markov hypothesis in fluid flows.

    Science.gov (United States)

    Meyer, Daniel W; Saggini, Frédéric

    2016-05-01

    Stochastic Markov processes are used very frequently to model, for example, processes in turbulence and subsurface flow and transport. Based on the weak Chapman-Kolmogorov equation and the strong Markov condition, we present methods to test the Markov hypothesis that is at the heart of these models. We demonstrate the capabilities of our methodology by testing the Markov hypothesis for fluid and inertial particles in turbulence, and fluid particles in the heterogeneous subsurface. In the context of subsurface macrodispersion, we find that depending on the heterogeneity level, Markov models work well above a certain scale of interest for media with different log-conductivity correlation structures. Moreover, we find surprising similarities in the velocity dynamics of the different media considered.

  20. Multi-agent sequential hypothesis testing

    KAUST Repository

    Kim, Kwang-Ki K.

    2014-12-15

    This paper considers multi-agent sequential hypothesis testing and presents a framework for strategic learning in sequential games with explicit consideration of both temporal and spatial coordination. The associated Bayes risk functions explicitly incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well-defined value functions with respect to (a) the belief states for the case of conditional independent private noisy measurements that are also assumed to be independent identically distributed over time, and (b) the information states for the case of correlated private noisy measurements. A sequential investment game of strategic coordination and delay is also discussed as an application of the proposed strategic learning rules.

  1. The Method of Hypothesis in Plato's Philosophy

    Directory of Open Access Journals (Sweden)

    Malihe Aboie Mehrizi

    2016-09-01

    Full Text Available The article deals with the examination of method of hypothesis in Plato's philosophy. This method, respectively, will be examined in three dialogues of Meno, Phaedon and Republic in which it is explicitly indicated. It will be shown the process of change of Plato’s attitude towards the position and usage of the method of hypothesis in his realm of philosophy. In Meno, considering the geometry, Plato attempts to introduce a method that can be used in the realm of philosophy. But, ultimately in Republic, Plato’s special attention to the method and its importance in the philosophical investigations, leads him to revise it. Here, finally Plato introduces the particular method of philosophy, i.e., the dialectic

  2. Conceptual framework for the etiology of alcoholism: a "kindling"/stress hypothesis.

    Science.gov (United States)

    Breese, George R; Overstreet, David H; Knapp, Darin J

    2005-04-01

    The rationale for proposing the "kindling"/stress hypothesis is to provide a conceptual basis for the insidious development and maintenance of alcohol abuse. An objective of the hypothesis is to emphasize how continued alcohol abuse is linked to progressive neural adaptation. Work has shown that repeated withdrawals from chronic low levels of alcohol sensitize ("kindle") anxiety-like behavior ("anxiety") in rats, a finding consistent with multiple withdrawal kindling of seizure activity. Additionally, stress substitutes for initial cycles of the multiple withdrawal protocol to sensitize withdrawal-induced anxiety, which is indicative that stress is capable of facilitating neuroadaptive processes related to withdrawal. The persistence of adaptation caused by stress and multiple withdrawals is revealed by the appearance of withdrawal-induced anxiety following a future re-exposure to a single 5-day period of alcohol. This persisting adaptation also permits stress to induce anxiety during a period of abstinence--a response not observed in animals without previous exposure to alcohol. Furthermore, stress interacts with repeated withdrawals to enhance voluntary alcohol drinking. Results of other preclinical and clinical studies reported in the literature are integrated with these investigations in support of the proposed hypothesis. The "kindling"/stress hypothesis is based on the premise that repeated withdrawals from cycles of chronic alcohol exposure contribute to a progressive development of persisting adaptive change that sensitizes withdrawal-induced anxiety and allows stress to evoke symptoms associated with negative affect during abstinence. Thus, these consequences of repeated withdrawals account for the evolution of major characteristics of alcoholism, which include worsened acute withdrawal symptoms and increased stress-induced negative affect during abstinence, both of which enhance the likelihood of relapse--and with relapse an inability to limit an abusive

  3. Sea otter health: Challenging a pet hypothesis

    Directory of Open Access Journals (Sweden)

    Kevin D. Lafferty

    2015-12-01

    Full Text Available A recent series of studies on tagged sea otters (Enhydra lutris nereis challenges the hypothesis that sea otters are sentinels of a dirty ocean, in particular, that pet cats are the main source of exposure to Toxoplasma gondii in central California. Counter to expectations, sea otters from unpopulated stretches of coastline are less healthy and more exposed to parasites than city-associated otters. Ironically, now it seems that spillover from wildlife, not pets, dominates spatial patterns of disease transmission.

  4. Kelvin on an old, celebrated hypothesis

    Science.gov (United States)

    Harrison, Edward

    1986-07-01

    Lord Kelvin in 1901 tested an ``old and celebrated hypothesis'' that if we could see far enough into space the whole sky would be occupied with stellar disks all of perhaps the same brightness as the Sun. Kelvin was the first to solve quantitatively and correctly the riddle of a dark night sky, a riddle that had been previously solved qualitatively by Edgar Allan Poe, and is now known as Olbers' paradox.

  5. Knudson's hypothesis revisited in Indian retinoblastoma patients.

    Science.gov (United States)

    Gaikwad, Namrata; Vanniarajan, Ayyasamy; Husain, Akram; Jeyaram, Illaiyaraja; Thirumalairaj, Kannan; Santhi, Radhakrishnan; Muthukkaruppan, Veerappan; Kim, Usha

    2015-12-01

    Retinoblastoma (RB) is the most common primary intraocular malignancy affecting children under 5 years of age. This study aims to correlate the clinical parameters with RB1 mutation in the light of Knudson's two-hit hypothesis in Indian RB patients. We analyzed the clinical details of 73 RB patients visiting Aravind Eye Hospital, Madurai, India, between January and October 2012. Data on gender, presenting age and sign, laterality, number of tumors in each eye and family history were collected. A semi log plot was derived based on Knudson's two-hit hypothesis. Genetic analysis of RB1 was carried out to identify the two hits. The mean age at diagnosis for unilateral and bilateral cases was 24.0 ± 15.1 and 9.8 ± 11.5 months, respectively. Familial RB was seen in 13 (17.8%) patients of whom 11 were bilateral. Multiple tumors were observed more frequently in bilateral than in unilateral cases. All unilateral and bilateral patients followed the two-hit and one-hit curves, respectively, confirming Knudson's hypothesis in Indian patients. Genetic analysis identified two somatic mutations in tumor samples of sporadic unilateral cases. Among the two bilateral patients, one received the first hit from her father and the other patient developed a de novo germline mutation during early development. The two-hit hypothesis has been reestablished in Indian patients. Genetic analysis of tumor samples has also complemented the statistical analysis to reaffirm the two hits in tumor development. © 2015 Wiley Publishing Asia Pty Ltd.

  6. Testing the single-state dominance hypothesis

    Energy Technology Data Exchange (ETDEWEB)

    Álvarez-Rodríguez, R. [Universidad Politécnica de Madrid, Avda. Juan Herrera 4, E-28040 Madrid (Spain); Moreno, O.; Moya de Guerra, E. [Universidad Complutense de Madrid, Avda. Complutense, E-28040 Madrid (Spain); Sarriguren, P. [Instituto de Estructura de la Materia (CSIC), Serrano 123, E-28006 Madrid (Spain); Šimkovic, F. [Comenius University, SK-842 15 Bratislava (Slovakia); Faessler, A. [University of Tübingen, D-72076 Tübingen (Germany)

    2013-12-30

    We present a theoretical analysis of the single-state dominance hypothesis for the two-neutrino double-beta decay process. The theoretical framework is a proton-neutron QRPA based on a deformed Hartree-Fock mean field with BCS pairing correlations. We focus on the decays of {sup 100}Mo, {sup 116}Cd and {sup 128}Te. We do not find clear evidences for single-state dominance within the present approach.

  7. Reflections on the Natural Rate Hypothesis

    OpenAIRE

    Joseph Stiglitz

    1997-01-01

    Does the deviation of unemployment from some natural rate provide a robust and useful way to predict changes in the inflation rate? Can economists explain why the NAIRU changes over time? Is the NAIRU a useful way to frame policy discussions despite the uncertainty surrounding its precise level? The NAIRU hypothesis passes all three tests. Recent research shows that the NAIRU has fallen dramatically in the last decade. This paper refutes the need for a highly restrictive bias in macroeconomic...

  8. Sea otter health: Challenging a pet hypothesis.

    Science.gov (United States)

    Lafferty, Kevin D

    2015-12-01

    A recent series of studies on tagged sea otters (Enhydra lutris nereis) challenges the hypothesis that sea otters are sentinels of a dirty ocean, in particular, that pet cats are the main source of exposure to Toxoplasma gondii in central California. Counter to expectations, sea otters from unpopulated stretches of coastline are less healthy and more exposed to parasites than city-associated otters. Ironically, now it seems that spillover from wildlife, not pets, dominates spatial patterns of disease transmission.

  9. Test of Taylor's Hypothesis with Distributed Temperature

    Science.gov (United States)

    Cheng, Y.; Gentine, P.; Sayde, C.; Tanner, E.; Ochsner, T. E.; Dong, J.

    2016-12-01

    Taylor's hypothesis[Taylor, 1938] assumes that mean wind speed carries the spatial pattern of turbulent motion past a fixed point in a "frozen" way, which has been widely used to relate streamwise wavenumber and angular frequency . Experiments[Fisher, 1964; Tong, 1996] have shown some deviation from Taylor's hypothesis at highly turbulent intensity flows and at high wavenumbers. However, the velocity or scalar measurements have always been fixed at a few spatial points rather than distributed in space. This experiment was designed for the first time to directly compare the time and spatial spectrum of temperature to test Taylor's hypothesis, measuring temperature with high resolution in both time and space by Distributed Temperature Sensing utilizing the attenuation difference of Raman scattering in the optic fiber at the MOISST site Oklahoma. The length of transact is 233 meters along the dominant wind direction. The temperature sampling distance is 0.127m and sampling time frequency is 1 Hz. The heights of the 4 fiber cables parallel to ground are 1m, 1.254m, 1.508m and 1.762m respectively. Also, eddy covariance instrument was set up near the Distributed Temperature Sensing as comparison for temperature data. The temperature spatial spectrum could be obtained with one fixed time point, while the temperature time spectrum could be obtained with one fixed spatial point in the middle of transact. The preliminary results would be presented in the AGU fall meeting. Reference Fisher, M. J., and Davies, P.O.A.L (1964), Correlation measurements in a non-frozen pattern of turbulence, Journal of fluid mechanics, 18(1), 97-116. Taylor, G. I. (1938), The spectrum of turbulence, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 164(919), 476-490. Tong, C. (1996), Taylor's Hypothesis and Two-point Coherence Measurements, Boundary-Layer Meteorology, 81(3), 399-410.

  10. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    In contemporary consciousness studies the phenomenon of neural plasticity has received little attention despite the fact that neural plasticity is of still increased interest in neuroscience. We will, however, argue that neural plasticity could be of great importance to consciousness studies....... If consciousness is related to neural processes it seems, at least prima facie, that the ability of the neural structures to change should be reflected in a theory of this relationship "Neural plasticity" refers to the fact that the brain can change due to its own activity. The brain is not static but rather...... a dynamic entity, which physical structure changes according to its use and environment. This change may take the form of growth of new neurons, the creation of new networks and structures, and change within network structures, that is, changes in synaptic strengths. Plasticity raises questions about...

  11. Isotopic niches support the resource breadth hypothesis.

    Science.gov (United States)

    Rader, Jonathan A; Newsome, Seth D; Sabat, Pablo; Chesser, R Terry; Dillon, Michael E; Martínez Del Rio, Carlos

    2017-03-01

    Because a broad spectrum of resource use allows species to persist in a wide range of habitat types, and thus permits them to occupy large geographical areas, and because broadly distributed species have access to more diverse resource bases, the resource breadth hypothesis posits that the diversity of resources used by organisms should be positively related with the extent of their geographic ranges. We investigated isotopic niche width in a small radiation of South American birds in the genus Cinclodes. We analysed feathers of 12 species of Cinclodes to test the isotopic version of the resource breadth hypothesis and to examine the correlation between isotopic niche breadth and morphology. We found a positive correlation between the widths of hydrogen and oxygen isotopic niches (which estimate breadth of elevational range) and widths of the carbon and nitrogen isotopic niches (which estimates the diversity of resources consumed, and hence of habitats used). We also found a positive correlation between broad isotopic niches and wing morphology. Our study not only supports the resource breadth hypothesis but it also highlights the usefulness of stable isotope analyses as tools in the exploration of ecological niches. It is an example of a macroecological application of stable isotopes. It also illustrates the importance of scientific collections in ecological studies. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

  12. Isotopic niches support the resource breadth hypothesis

    Science.gov (United States)

    Rader, Jonathan A.; Newsome, Seth D.; Sabat, Pablo; Chesser, R. Terry; Dillon, Michael E.; Martinez del Rio, Carlos

    2017-01-01

    Because a broad spectrum of resource use allows species to persist in a wide range of habitat types, and thus permits them to occupy large geographical areas, and because broadly distributed species have access to more diverse resource bases, the resource breadth hypothesis posits that the diversity of resources used by organisms should be positively related with the extent of their geographic ranges.We investigated isotopic niche width in a small radiation of South American birds in the genus Cinclodes. We analysed feathers of 12 species of Cinclodes to test the isotopic version of the resource breadth hypothesis and to examine the correlation between isotopic niche breadth and morphology.We found a positive correlation between the widths of hydrogen and oxygen isotopic niches (which estimate breadth of elevational range) and widths of the carbon and nitrogen isotopic niches (which estimates the diversity of resources consumed, and hence of habitats used). We also found a positive correlation between broad isotopic niches and wing morphology.Our study not only supports the resource breadth hypothesis but it also highlights the usefulness of stable isotope analyses as tools in the exploration of ecological niches. It is an example of a macroecological application of stable isotopes. It also illustrates the importance of scientific collections in ecological studies.

  13. Paleoindian demography and the extraterrestrial impact hypothesis.

    Science.gov (United States)

    Buchanan, Briggs; Collard, Mark; Edinborough, Kevan

    2008-08-19

    Recently it has been suggested that one or more large extraterrestrial (ET) objects struck northern North America 12,900 +/- 100 calendar years before present (calBP) [Firestone RB, et al. (2007) Proc Natl Acad Sci USA 104: 16016-16021]. This impact is claimed to have triggered the Younger Dryas major cooling event and resulted in the extinction of the North American megafauna. The impact is also claimed to have caused major cultural changes and population decline among the Paleoindians. Here, we report a study in which approximately 1,500 radiocarbon dates from archaeological sites in Canada and the United States were used to test the hypothesis that the ET resulted in population decline among the Paleoindians. Following recent studies [e.g., Gamble C, Davies W, Pettitt P, Hazelwood L, Richards M (2005) Camb Archaeol J 15:193-223), the summed probability distribution of the calibrated dates was used to identify probable changes in human population size between 15,000 and 9,000 calBP. Subsequently, potential biases were evaluated by modeling and spatial analysis of the dated occupations. The results of the analyses were not consistent with the predictions of extraterrestrial impact hypothesis. No evidence of a population decline among the Paleoindians at 12,900 +/- 100 calBP was found. Thus, minimally, the study suggests the extraterrestrial impact hypothesis should be amended.

  14. The GABAergic Hypothesis for Cognitive Disabilities in Down Syndrome.

    Science.gov (United States)

    Contestabile, Andrea; Magara, Salvatore; Cancedda, Laura

    2017-01-01

    Down syndrome (DS) is a genetic disorder caused by the presence of a third copy of chromosome 21. DS affects multiple organs, but it invariably results in altered brain development and diverse degrees of intellectual disability. A large body of evidence has shown that synaptic deficits and memory impairment are largely determined by altered GABAergic signaling in trisomic mouse models of DS. These alterations arise during brain development while extending into adulthood, and include genesis of GABAergic neurons, variation of the inhibitory drive and modifications in the control of neural-network excitability. Accordingly, different pharmacological interventions targeting GABAergic signaling have proven promising preclinical approaches to rescue cognitive impairment in DS mouse models. In this review, we will discuss recent data regarding the complex scenario of GABAergic dysfunctions in the trisomic brain of DS mice and patients, and we will evaluate the state of current clinical research targeting GABAergic signaling in individuals with DS.

  15. The GABAergic Hypothesis for Cognitive Disabilities in Down Syndrome

    Science.gov (United States)

    Contestabile, Andrea; Magara, Salvatore; Cancedda, Laura

    2017-01-01

    Down syndrome (DS) is a genetic disorder caused by the presence of a third copy of chromosome 21. DS affects multiple organs, but it invariably results in altered brain development and diverse degrees of intellectual disability. A large body of evidence has shown that synaptic deficits and memory impairment are largely determined by altered GABAergic signaling in trisomic mouse models of DS. These alterations arise during brain development while extending into adulthood, and include genesis of GABAergic neurons, variation of the inhibitory drive and modifications in the control of neural-network excitability. Accordingly, different pharmacological interventions targeting GABAergic signaling have proven promising preclinical approaches to rescue cognitive impairment in DS mouse models. In this review, we will discuss recent data regarding the complex scenario of GABAergic dysfunctions in the trisomic brain of DS mice and patients, and we will evaluate the state of current clinical research targeting GABAergic signaling in individuals with DS. PMID:28326014

  16. Finding the Answer in Space: The Mental Whiteboard Hypothesis on Serial Order in Working Memory

    Directory of Open Access Journals (Sweden)

    Elger eAbrahamse

    2014-11-01

    Full Text Available Various prominent models on serial order coding in working memory build on the notion that serial order is achieved by binding the various items to-be-maintained to fixed position markers. Despite being relatively successful in accounting for empirical observations and some recent neuro-imaging support, these models were largely formulated on theoretical grounds and few specifications have been provided with respect to the cognitive and/or neural nature of these position markers. Here we outline a hypothesis on a novel candidate mechanism to substantiate the notion of serial position markers. Specifically, we propose that serial order WM is grounded in the spatial attention system: (I The position markers that provide multi-item WM with a serial context should be understood as coordinates within an internal, spatially defined system, (II internal spatial attention is involved in searching through the resulting serial order representation, and (III retrieval corresponds to selection by spatial attention. We sketch the available empirical support and discuss how the hypothesis may provide a parsimonious framework from which to understand a broad range of observations across behavioral, neural and neuropsychological domains. Finally, we pinpoint what we believe are major questions for future research inspired by the hypothesis.

  17. The ctenophore genome and the evolutionary origins of neural systems

    NARCIS (Netherlands)

    Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

    2014-01-01

    The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

  18. The biological sense of cancer: a hypothesis

    Directory of Open Access Journals (Sweden)

    Bustuoabad Oscar D

    2006-12-01

    Full Text Available Abstract Background Most theories about cancer proposed during the last century share a common denominator: cancer is believed to be a biological nonsense for the organism in which it originates, since cancer cells are believed to be ones evading the rules that control normal cell proliferation and differentiation. In this essay, we have challenged this interpretation on the basis that, throughout the animal kingdom, cancer seems to arise only in injured organs and tissues that display lost or diminished regenerative ability. Hypothesis According to our hypothesis, a tumor cell would be the only one able to respond to the demand to proliferate in the organ of origin. It would be surrounded by "normal" aged cells that cannot respond to that signal. According to this interpretation, cancer would have a profound biological sense: it would be the ultimate way to attempt to restore organ functions and structures that have been lost or altered by aging or noxious environmental agents. In this way, the features commonly associated with tumor cells could be reinterpreted as progressively acquired adaptations for responding to a permanent regenerative signal in the context of tissue injury. Analogously, several embryo developmental stages could be dependent on cellular damage and death, which together disrupt the field topography. However, unlike normal structures, cancer would have no physiological value, because the usually poor or non-functional nature of its cells would make their reparative task unattainable. Conclusion The hypothesis advanced in this essay might have significant practical implications. All conventional therapies against cancer attempt to kill all cancer cells. However, according to our hypothesis, the problem might not be solved even if all the tumor cells were eradicated. In effect, if the organ failure remained, new tumor cells would emerge and the tumor would reinitiate its progressive growth in response to the permanent

  19. A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations.

    Science.gov (United States)

    Xia, Youshen; Feng, Gang; Wang, Jun

    2004-09-01

    This paper presents a recurrent neural network for solving strict convex quadratic programming problems and related linear piecewise equations. Compared with the existing neural networks for quadratic program, the proposed neural network has a one-layer structure with a low model complexity. Moreover, the proposed neural network is shown to have a finite-time convergence and exponential convergence. Illustrative examples further show the good performance of the proposed neural network in real-time applications.

  20. What Is Neural Plasticity?

    Science.gov (United States)

    von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime

    2017-01-01

    "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.

  1. 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...

  2. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V......This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...

  3. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  4. Medical Text Classification using Convolutional Neural Networks

    OpenAIRE

    Hughes, Mark; Li, Irene; Kotoulas, Spyros; Suzumura, Toyotaro

    2017-01-01

    We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.

  5. Medical Text Classification Using Convolutional Neural Networks.

    Science.gov (United States)

    Hughes, Mark; Li, Irene; Kotoulas, Spyros; Suzumura, Toyotaro

    2017-01-01

    We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.

  6. Atypical Neural Self-Representation in Autism

    Science.gov (United States)

    Lombardo, Michael V.; Chakrabarti, Bhismadev; Bullmore, Edward T.; Sadek, Susan A.; Pasco, Greg; Wheelwright, Sally J.; Suckling, John; Baron-Cohen, Simon

    2010-01-01

    The "self" is a complex multidimensional construct deeply embedded and in many ways defined by our relations with the social world. Individuals with autism are impaired in both self-referential and other-referential social cognitive processing. Atypical neural representation of the self may be a key to understanding the nature of such impairments.…

  7. Neural modeling of prefrontal executive function

    Energy Technology Data Exchange (ETDEWEB)

    Levine, D.S. [Univ. of Texas, Arlington, TX (United States)

    1996-12-31

    Brain executive function is based in a distributed system whereby prefrontal cortex is interconnected with other cortical. and subcortical loci. Executive function is divided roughly into three interacting parts: affective guidance of responses; linkage among working memory representations; and forming complex behavioral schemata. Neural network models of each of these parts are reviewed and fit into a preliminary theoretical framework.

  8. Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

    Directory of Open Access Journals (Sweden)

    Dr. Hanan A.R. Akkar

    2015-08-01

    Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.

  9. An efficient neural network approach to dynamic robot motion planning.

    Science.gov (United States)

    Yang, S X; Meng, M

    2000-03-01

    In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

  10. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

    Science.gov (United States)

    Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli

    2016-01-01

    In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423

  11. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

    Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. The Laplacian spectrum of neural networks

    Science.gov (United States)

    de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

    2014-01-01

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

  13. Pansharpening by Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Giuseppe Masi

    2016-07-01

    Full Text Available A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

  14. Parallelization of Neural Network Training for NLP with Hogwild!

    Directory of Open Access Journals (Sweden)

    Deyringer Valentin

    2017-10-01

    Full Text Available Neural Networks are prevalent in todays NLP research. Despite their success for different tasks, training time is relatively long. We use Hogwild! to counteract this phenomenon and show that it is a suitable method to speed up training Neural Networks of different architectures and complexity. For POS tagging and translation we report considerable speedups of training, especially for the latter. We show that Hogwild! can be an important tool for training complex NLP architectures.

  15. The role of chlorophyll b in photosynthesis: Hypothesis

    Directory of Open Access Journals (Sweden)

    Park Hyoungshin

    2001-10-01

    Full Text Available Abstract Background The physico-chemical properties of chlorophylls b and c have been known for decades. Yet the mechanisms by which these secondary chlorophylls support assembly and accumulation of light-harvesting complexes in vivo have not been resolved. Presentation Biosynthetic modifications that introduce electronegative groups on the periphery of the chlorophyll molecule withdraw electrons from the pyrrole nitrogens and thus reduce their basicity. Consequently, the tendency of the central Mg to form coordination bonds with electron pairs in exogenous ligands, a reflection of its Lewis acid properties, is increased. Our hypothesis states that the stronger coordination bonds between the Mg atom in chlorophyll b and chlorophyll c and amino acid sidechain ligands in chlorophyll a/b- and a/c-binding apoproteins, respectively, enhance their import into the chloroplast and assembly of light-harvesting complexes. Testing Several apoproteins of light-harvesting complexes, in particular, the major protein Lhcb1, are not detectable in leaves of chlorophyll b-less plants. A direct test of the hypothesis – with positive selection – is expression, in mutant plants that synthesize only chlorophyll a, of forms of Lhcb1 in which weak ligands are replaced with stronger Lewis bases. Implications The mechanistic explanation for the effects of deficiencies in chlorophyll b or c points to the need for further research on manipulation of coordination bonds between these chlorophylls and chlorophyll-binding proteins. Understanding these interactions will possibly lead to engineering plants to expand their light-harvesting antenna and ultimately their productivity.

  16. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  17. Hypothesis Testing as an Act of Rationality

    Science.gov (United States)

    Nearing, Grey

    2017-04-01

    Statistical hypothesis testing is ad hoc in two ways. First, setting probabilistic rejection criteria is, as Neyman (1957) put it, an act of will rather than an act of rationality. Second, physical theories like conservation laws do not inherently admit probabilistic predictions, and so we must use what are called epistemic bridge principles to connect model predictions with the actual methods of hypothesis testing. In practice, these bridge principles are likelihood functions, error functions, or performance metrics. I propose that the reason we are faced with these problems is because we have historically failed to account for a fundamental component of basic logic - namely the portion of logic that explains how epistemic states evolve in the presence of empirical data. This component of Cox' (1946) calculitic logic is called information theory (Knuth, 2005), and adding information theory our hypothetico-deductive account of science yields straightforward solutions to both of the above problems. This also yields a straightforward method for dealing with Popper's (1963) problem of verisimilitude by facilitating a quantitative approach to measuring process isomorphism. In practice, this involves data assimilation. Finally, information theory allows us to reliably bound measures of epistemic uncertainty, thereby avoiding the problem of Bayesian incoherency under misspecified priors (Grünwald, 2006). I therefore propose solutions to four of the fundamental problems inherent in both hypothetico-deductive and/or Bayesian hypothesis testing. - Neyman (1957) Inductive Behavior as a Basic Concept of Philosophy of Science. - Cox (1946) Probability, Frequency and Reasonable Expectation. - Knuth (2005) Lattice Duality: The Origin of Probability and Entropy. - Grünwald (2006). Bayesian Inconsistency under Misspecification. - Popper (1963) Conjectures and Refutations: The Growth of Scientific Knowledge.

  18. Set theory and the continuum hypothesis

    CERN Document Server

    Cohen, Paul J

    2008-01-01

    This exploration of a notorious mathematical problem is the work of the man who discovered the solution. The independence of the continuum hypothesis is the focus of this study by Paul J. Cohen. It presents not only an accessible technical explanation of the author's landmark proof but also a fine introduction to mathematical logic. An emeritus professor of mathematics at Stanford University, Dr. Cohen won two of the most prestigious awards in mathematics: in 1964, he was awarded the American Mathematical Society's Bôcher Prize for analysis; and in 1966, he received the Fields Medal for Logic.

  19. Null hypothesis significance testing: a short tutorial

    Science.gov (United States)

    Pernet, Cyril

    2016-01-01

    Although thoroughly criticized, null hypothesis significance testing (NHST) remains the statistical method of choice used to provide evidence for an effect, in biological, biomedical and social sciences. In this short tutorial, I first summarize the concepts behind the method, distinguishing test of significance (Fisher) and test of acceptance (Newman-Pearson) and point to common interpretation errors regarding the p-value. I then present the related concepts of confidence intervals and again point to common interpretation errors. Finally, I discuss what should be reported in which context. The goal is to clarify concepts to avoid interpretation errors and propose reporting practices. PMID:29067159

  20. Statistical hypothesis testing with SAS and R

    CERN Document Server

    Taeger, Dirk

    2014-01-01

    A comprehensive guide to statistical hypothesis testing with examples in SAS and R When analyzing datasets the following questions often arise:Is there a short hand procedure for a statistical test available in SAS or R?If so, how do I use it?If not, how do I program the test myself? This book answers these questions and provides an overview of the most commonstatistical test problems in a comprehensive way, making it easy to find and performan appropriate statistical test. A general summary of statistical test theory is presented, along with a basicdescription for each test, including the

  1. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  2. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

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

  3. [Examination of the hypothesis 'the factors and mechanisms of superiority'].

    Science.gov (United States)

    Sierra-Fitzgerald, O; Quevedo-Caicedo, J; López-Calderón, M G

    INTRODUCTION. The hypothesis of Geschwind and Galaburda suggests that specific cognitive superiority arises as a result of an alteration in development of the nervous system. In this article we review the co existence of superiority and inferiority . PATIENTS AND METHODS. A study was made of six children aged between 6 and 8 years old at the Instituto de Belles Artes Antonio Maria Valencia in Cali,Columbia with an educational level between second and third grade at a primary school and of medium low socio economic status. The children were considered to have superior musical ability by music experts, which is the way in which the concept of superiority was to be tested. The concept of inferiority was tested by neuropsychological tests = 1.5 DE below normal for the same age. We estimated the perinatal neurological risk in each case. Subsequently the children s general intelligence and specific cognitive abilities were evaluated. In the first case the WISC R and MSCA were used. The neuropsychological profiles were obtained by broad evaluation using a verbal fluency test, a test using counters, Boston vocabulary test, the Wechster memory scale, sequential verbal memory test, super imposed figures test, Piaget Head battery, Rey Osterrieth complex figure and the Wisconsin card classification test. The RESULTS showed slight/moderate deficits in practical construction ability and mild defects of memory and concept abilities. In general the results supported the hypothesis tested. The mechanisms of superiority proposed in the classical hypothesis mainly involve the contralateral hemisphere: in this study the ipsilateral mechanism was more important.

  4. Why Does REM Sleep Occur? A Wake-up Hypothesis

    Directory of Open Access Journals (Sweden)

    Dr. W. R. eKlemm

    2011-09-01

    Full Text Available Brain activity differs in the various sleep stages and in conscious wakefulness. Awakening from sleep requires restoration of the complex nerve impulse patterns in neuronal network assemblies necessary to re-create and sustain conscious wakefulness. Herein I propose that the brain uses REM to help wake itself up after it has had a sufficient amount of sleep. Evidence suggesting this hypothesis includes the facts that, 1 when first going to sleep, the brain plunges into Stage N3 (formerly called Stage IV, a deep abyss of sleep, and, as the night progresses, the sleep is punctuated by episodes of REM that become longer and more frequent toward morning, 2 conscious-like dreams are a reliable component of the REM state in which the dreamer is an active mental observer or agent in the dream, 3 the last awakening during a night’s sleep usually occurs in a REM episode during or at the end of a dream, 4 both REM and awake consciousness seem to arise out of a similar brainstem ascending arousal system 5 N3 is a functionally perturbed state that eventually must be corrected so that embodied brain can direct adaptive behavior, and 6 corticofugal projections to brainstem arousal areas provide a way to trigger increased cortical activity in REM to progressively raise the sleeping brain to the threshold required for wakefulness. This paper shows how the hypothesis conforms to common experience and has substantial predictive and explanatory power regarding the phenomenology of sleep in terms of ontogeny, aging, phylogeny, abnormal/disease states, cognition, and behavioral physiology. That broad range of consistency is not matched by competing theories, which are summarized herein. Specific ways to test this wake-up hypothesis are suggested. Such research could lead to a better understanding of awake consciousness.

  5. A hypothesis on a role of oxytocin in the social mechanisms of speech and vocal learning.

    Science.gov (United States)

    Theofanopoulou, Constantina; Boeckx, Cedric; Jarvis, Erich D

    2017-08-30

    Language acquisition in humans and song learning in songbirds naturally happen as a social learning experience, providing an excellent opportunity to reveal social motivation and reward mechanisms that boost sensorimotor learning. Our knowledge about the molecules and circuits that control these social mechanisms for vocal learning and language is limited. Here we propose a hypothesis of a role for oxytocin (OT) in the social motivation and evolution of vocal learning and language. Building upon existing evidence, we suggest specific neural pathways and mechanisms through which OT might modulate vocal learning circuits in specific developmental stages. © 2017 The Authors.

  6. Gaussian Hypothesis Testing and Quantum Illumination.

    Science.gov (United States)

    Wilde, Mark M; Tomamichel, Marco; Lloyd, Seth; Berta, Mario

    2017-09-22

    Quantum hypothesis testing is one of the most basic tasks in quantum information theory and has fundamental links with quantum communication and estimation theory. In this paper, we establish a formula that characterizes the decay rate of the minimal type-II error probability in a quantum hypothesis test of two Gaussian states given a fixed constraint on the type-I error probability. This formula is a direct function of the mean vectors and covariance matrices of the quantum Gaussian states in question. We give an application to quantum illumination, which is the task of determining whether there is a low-reflectivity object embedded in a target region with a bright thermal-noise bath. For the asymmetric-error setting, we find that a quantum illumination transmitter can achieve an error probability exponent stronger than a coherent-state transmitter of the same mean photon number, and furthermore, that it requires far fewer trials to do so. This occurs when the background thermal noise is either low or bright, which means that a quantum advantage is even easier to witness than in the symmetric-error setting because it occurs for a larger range of parameters. Going forward from here, we expect our formula to have applications in settings well beyond those considered in this paper, especially to quantum communication tasks involving quantum Gaussian channels.

  7. Urbanization and the more-individuals hypothesis.

    Science.gov (United States)

    Chiari, Claudia; Dinetti, Marco; Licciardello, Cinzia; Licitra, Gaetano; Pautasso, Marco

    2010-03-01

    1. Urbanization is a landscape process affecting biodiversity world-wide. Despite many urban-rural studies of bird assemblages, it is still unclear whether more species-rich communities have more individuals, regardless of the level of urbanization. The more-individuals hypothesis assumes that species-rich communities have larger populations, thus reducing the chance of local extinctions. 2. Using newly collated avian distribution data for 1 km(2) grid cells across Florence, Italy, we show a significantly positive relationship between species richness and assemblage abundance for the whole urban area. This richness-abundance relationship persists for the 1 km(2) grid cells with less than 50% of urbanized territory, as well as for the remaining grid cells, with no significant difference in the slope of the relationship. These results support the more-individuals hypothesis as an explanation of patterns in species richness, also in human modified and fragmented habitats. 3. However, the intercept of the species richness-abundance relationship is significantly lower for highly urbanized grid cells. Our study confirms that urban communities have lower species richness but counters the common notion that assemblages in densely urbanized ecosystems have more individuals. In Florence, highly inhabited areas show fewer species and lower assemblage abundance. 4. Urbanized ecosystems are an ongoing large-scale natural experiment which can be used to test ecological theories empirically.

  8. Hypothesis-driven physical examination curriculum.

    Science.gov (United States)

    Allen, Sharon; Olson, Andrew; Menk, Jeremiah; Nixon, James

    2017-12-01

    Medical students traditionally learn physical examination skills as a rote list of manoeuvres. Alternatives like hypothesis-driven physical examination (HDPE) may promote students' understanding of the contribution of physical examination to diagnostic reasoning. We sought to determine whether first-year medical students can effectively learn to perform a physical examination using an HDPE approach, and then tailor the examination to specific clinical scenarios. Medical students traditionally learn physical examination skills as a rote list of manoeuvres CONTEXT: First-year medical students at the University of Minnesota were taught both traditional and HDPE approaches during a required 17-week clinical skills course in their first semester. The end-of-course evaluation assessed HDPE skills: students were assigned one of two cardiopulmonary cases. Each case included two diagnostic hypotheses. During an interaction with a standardised patient, students were asked to select physical examination manoeuvres in order to make a final diagnosis. Items were weighted and selection order was recorded. First-year students with minimal pathophysiology performed well. All students selected the correct diagnosis. Importantly, students varied the order when selecting examination manoeuvres depending on the diagnoses under consideration, demonstrating early clinical decision-making skills. An early introduction to HDPE may reinforce physical examination skills for hypothesis generation and testing, and can foster early clinical decision-making skills. This has important implications for further research in physical examination instruction. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

  9. Gaussian Hypothesis Testing and Quantum Illumination

    Science.gov (United States)

    Wilde, Mark M.; Tomamichel, Marco; Lloyd, Seth; Berta, Mario

    2017-09-01

    Quantum hypothesis testing is one of the most basic tasks in quantum information theory and has fundamental links with quantum communication and estimation theory. In this paper, we establish a formula that characterizes the decay rate of the minimal type-II error probability in a quantum hypothesis test of two Gaussian states given a fixed constraint on the type-I error probability. This formula is a direct function of the mean vectors and covariance matrices of the quantum Gaussian states in question. We give an application to quantum illumination, which is the task of determining whether there is a low-reflectivity object embedded in a target region with a bright thermal-noise bath. For the asymmetric-error setting, we find that a quantum illumination transmitter can achieve an error probability exponent stronger than a coherent-state transmitter of the same mean photon number, and furthermore, that it requires far fewer trials to do so. This occurs when the background thermal noise is either low or bright, which means that a quantum advantage is even easier to witness than in the symmetric-error setting because it occurs for a larger range of parameters. Going forward from here, we expect our formula to have applications in settings well beyond those considered in this paper, especially to quantum communication tasks involving quantum Gaussian channels.

  10. The Alliance Hypothesis for Human Friendship

    Science.gov (United States)

    DeScioli, Peter; Kurzban, Robert

    2009-01-01

    Background Exploration of the cognitive systems underlying human friendship will be advanced by identifying the evolved functions these systems perform. Here we propose that human friendship is caused, in part, by cognitive mechanisms designed to assemble support groups for potential conflicts. We use game theory to identify computations about friends that can increase performance in multi-agent conflicts. This analysis suggests that people would benefit from: 1) ranking friends, 2) hiding friend-ranking, and 3) ranking friends according to their own position in partners' rankings. These possible tactics motivate the hypotheses that people possess egocentric and allocentric representations of the social world, that people are motivated to conceal this information, and that egocentric friend-ranking is determined by allocentric representations of partners' friend-rankings (more than others' traits). Methodology/Principal Findings We report results from three studies that confirm predictions derived from the alliance hypothesis. Our main empirical finding, replicated in three studies, was that people's rankings of their ten closest friends were predicted by their own perceived rank among their partners' other friends. This relationship remained strong after controlling for a variety of factors such as perceived similarity, familiarity, and benefits. Conclusions/Significance Our results suggest that the alliance hypothesis merits further attention as a candidate explanation for human friendship. PMID:19492066

  11. The alliance hypothesis for human friendship.

    Directory of Open Access Journals (Sweden)

    Peter DeScioli

    Full Text Available BACKGROUND: Exploration of the cognitive systems underlying human friendship will be advanced by identifying the evolved functions these systems perform. Here we propose that human friendship is caused, in part, by cognitive mechanisms designed to assemble support groups for potential conflicts. We use game theory to identify computations about friends that can increase performance in multi-agent conflicts. This analysis suggests that people would benefit from: 1 ranking friends, 2 hiding friend-ranking, and 3 ranking friends according to their own position in partners' rankings. These possible tactics motivate the hypotheses that people possess egocentric and allocentric representations of the social world, that people are motivated to conceal this information, and that egocentric friend-ranking is determined by allocentric representations of partners' friend-rankings (more than others' traits. METHODOLOGY/PRINCIPAL FINDINGS: We report results from three studies that confirm predictions derived from the alliance hypothesis. Our main empirical finding, replicated in three studies, was that people's rankings of their ten closest friends were predicted by their own perceived rank among their partners' other friends. This relationship remained strong after controlling for a variety of factors such as perceived similarity, familiarity, and benefits. CONCLUSIONS/SIGNIFICANCE: Our results suggest that the alliance hypothesis merits further attention as a candidate explanation for human friendship.

  12. [The Morbidity Compression Hypothesis and its Alternatives].

    Science.gov (United States)

    Geyer, S

    2015-06-01

    Fries' hypothesis of morbidity compression asserts that the length of lifetime spent in states of chronic disease and disability is decreasing. This can be explained by improved living and working conditions and by successful primary prevention. Using the available studies on morbidity compression it is examined whether the lengths of periods spent in states of morbidity have changed in the last decades. For multimorbidity, chronic diseases, cognitive impairment, and for subjective health the developments are in favour of the morbidity compression hypothesis. The conclusions are nevertheless dependent on the type of health impairment considered. There is evidence that morbidity compression has taken place in the last decades. Depending on the disease, morbidity expansion and dynamic equilibrium may also have occurred. A comprehensive assessment of the development of morbidities is only possible if more diseases are considered. In addition, there is evidence that outside of Europe and the USA morbidity patterns may also develop in other directions. © Georg Thieme Verlag KG Stuttgart · New York.

  13. Neuroticism, intelligence, and intra-individual variability in elementary cognitive tasks: testing the mental noise hypothesis.

    Science.gov (United States)

    Colom, Roberto; Quiroga, Ma Angeles

    2009-08-01

    Some studies show positive correlations between intraindividual variability in elementary speed measures (reflecting processing efficiency) and individual differences in neuroticism (reflecting instability in behaviour). The so-called neural noise hypothesis assumes that higher levels of noise are related both to smaller indices of processing efficiency and greater levels of neuroticism. Here, we test this hypothesis measuring mental speed by means of three elementary cognitive tasks tapping similar basic processes but varying systematically their content (verbal, numerical, and spatial). Neuroticism and intelligence are also measured. The sample comprised 196 undergraduate psychology students. The results show that (1) processing efficiency is generally unrelated to individual differences in neuroticism, (2) processing speed and efficiency correlate with intelligence, and (3) only the efficiency index is genuinely related to intelligence when the colinearity between speed and efficiency is controlled.

  14. Neural simulations on multi-core architectures

    Directory of Open Access Journals (Sweden)

    Hubert Eichner

    2009-07-01

    Full Text Available Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i. e. user-transparent load balancing.

  15. Artificial neural network in cosmic landscape

    Science.gov (United States)

    Liu, Junyu

    2017-12-01

    In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.

  16. The Younger Dryas impact hypothesis: A requiem

    Science.gov (United States)

    Pinter, Nicholas; Scott, Andrew C.; Daulton, Tyrone L.; Podoll, Andrew; Koeberl, Christian; Anderson, R. Scott; Ishman, Scott E.

    2011-06-01

    The Younger Dryas (YD) impact hypothesis is a recent theory that suggests that a cometary or meteoritic body or bodies hit and/or exploded over North America 12,900 years ago, causing the YD climate episode, extinction of Pleistocene megafauna, demise of the Clovis archeological culture, and a range of other effects. Since gaining widespread attention in 2007, substantial research has focused on testing the 12 main signatures presented as evidence of a catastrophic extraterrestrial event 12,900 years ago. Here we present a review of the impact hypothesis, including its evolution and current variants, and of efforts to test and corroborate the hypothesis. The physical evidence interpreted as signatures of an impact event can be separated into two groups. The first group consists of evidence that has been largely rejected by the scientific community and is no longer in widespread discussion, including: particle tracks in archeological chert; magnetic nodules in Pleistocene bones; impact origin of the Carolina Bays; and elevated concentrations of radioactivity, iridium, and fullerenes enriched in 3He. The second group consists of evidence that has been active in recent research and discussions: carbon spheres and elongates, magnetic grains and magnetic spherules, byproducts of catastrophic wildfire, and nanodiamonds. Over time, however, these signatures have also seen contrary evidence rather than support. Recent studies have shown that carbon spheres and elongates do not represent extraterrestrial carbon nor impact-induced megafires, but are indistinguishable from fungal sclerotia and arthropod fecal material that are a small but common component of many terrestrial deposits. Magnetic grains and spherules are heterogeneously distributed in sediments, but reported measurements of unique peaks in concentrations at the YD onset have yet to be reproduced. The magnetic grains are certainly just iron-rich detrital grains, whereas reported YD magnetic spherules are

  17. Adenosine hypothesis of schizophrenia –opportunities for pharmacotherapy

    Science.gov (United States)

    Boison, Detlev; Singer, Philipp; Shen, Hai-Ying; Feldon, Joram; Yee, Benjamin K.

    2011-01-01

    Pharmacotherapy of schizophrenia based on the dopamine hypothesis remains unsatisfactory for the negative and cognitive symptoms of the disease. Enhancing N-methyl-d-aspartate receptors (NMDAR) function is expected to alleviate such persistent symptoms, but successful development of novel clinically effective compounds remains challenging. Adenosine is a homeostatic bioenergetic network modulator that is able to affect complex networks synergistically at different levels (receptor dependent pathways, biochemistry, bioenergetics, and epigenetics). By affecting brain dopamine and glutamate activities it represents a promising candidate for restoring the functional imbalance in these neurotransmitter systems believed to underlie the genesis of schizophrenia symptoms, as well as restoring homeostasis of bioenergetics. Suggestion of an adenosine hypothesis of schizophrenia further posits that adenosinergic dysfunction might contribute to the emergence of multiple neurotransmitter dysfunctionscharacteristic of schizophrenia via diverse mechanisms. Given the importance of adenosine in early brain development and regulation of brain immune response, it also bears direct relevance to the aetiology of schizophrenia. Here, we provide an overview of the rationale and evidence in support of the therapeutic potential of multiple adenosinergic targets, including the high-affinity adenosine receptors (A1R and A2AR), and the regulatory enzyme adenosine kinase (ADK). Key preliminary clinical data and preclinical findings are reviewed. PMID:21315743

  18. The minimotif synthesis hypothesis for the origin of life.

    Science.gov (United States)

    Schiller, Martin R

    2016-01-01

    Several theories for the origin of life have gained widespread acceptance, led by primordial soup, chemical evolution, metabolism first, and the RNA world. However, while new and existing theories often address a key step, there is less focus on a comprehensive abiogenic continuum leading to the last universal common ancestor. Herein, I present the "minimotif synthesis" hypothesis unifying select origin of life theories with new and revised steps. The hypothesis is based on first principles, on the concept of selection over long time scales, and on a stepwise progression toward complexity. The major steps are the thermodynamically-driven origination of extant molecular specificity emerging from primordial soup leading to the rise of peptide catalysts, and a cyclic feed-forward catalytic diversification of compound and peptides in the primordial soup. This is followed by degenerate, semi-partially conservative peptide replication to pass on catalytic knowledge to progeny protocells. At some point during this progression, the emergence of RNA and selection could drive the separation of catalytic and genetic functions, allowing peptides and proteins to permeate the catalytic space, and RNA to encode higher fidelity information transfer. Translation may have emerged from RNA template driven organization and successive ligation of activated amino acids as a predecessor to translation.

  19. Testing the reproductive groundplan hypothesis in ants (Hymenoptera: Formicidae).

    Science.gov (United States)

    Pamminger, Tobias; Hughes, William O H

    2017-01-01

    The evolution of complex societies with obligate reproductive division of labor represents one of the major transitions in evolution. In such societies, functionally sterile individuals (workers) perform many of fitness-relevant behaviors including allomaternal ones, without getting any direct fitness benefits. The question of how such worker division of labor has evolved remains controversial. The reproductive groundplan hypothesis (RGPH) offers a powerful proximate explanation for this evolutionary leap. The RGPH argues that the conserved genetic and endocrinological networks regulating fitness-relevant behavior (e g. foraging and brood care) in their solitary ancestors have become decoupled from actual reproduction in the worker caste and now generate worker behavioral phenotypes. However, the empirical support for this hypothesis remains limited to a handful of species making its general validity uncertain. In this study, we combine data from the literature with targeted sampling of key species and apply phylogenetically controlled comparative analysis to investigate if the key prediction of the RGPH, namely an association between allomaternal behavior and an allomaternal physiological state holds in the largest and most species-rich clade of social insects, the ants. Our findings clearly support the RPGH as a general framework to understand the evolution of the worker caste and shed light on one of the major transition in evolutionary history. © 2016 The Author(s). Evolution © 2016 The Society for the Study of Evolution.

  20. The efficient market hypothesis: problems with interpretations of empirical tests

    Directory of Open Access Journals (Sweden)

    Denis Alajbeg

    2012-03-01

    Full Text Available Despite many “refutations” in empirical tests, the efficient market hypothesis (EMH remains the central concept of financial economics. The EMH’s resistance to the results of empirical testing emerges from the fact that the EMH is not a falsifiable theory. Its axiomatic definition shows how asset prices would behave under assumed conditions. Testing for this price behavior does not make much sense as the conditions in the financial markets are much more complex than the simplified conditions of perfect competition, zero transaction costs and free information used in the formulation of the EMH. Some recent developments within the tradition of the adaptive market hypothesis are promising regarding development of a falsifiable theory of price formation in financial markets, but are far from giving assurance that we are approaching a new formulation. The most that can be done in the meantime is to be very cautious while interpreting the empirical evidence that is presented as “testing” the EMH.

  1. Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research

    Directory of Open Access Journals (Sweden)

    Facio Flavia M

    2012-10-01

    Full Text Available Abstract Background Massively-parallel sequencing (MPS technologies create challenges for informed consent of research participants given the enormous scale of the data and the wide range of potential results. Discussion We propose that the consent process in these studies be based on whether they use MPS to test a hypothesis or to generate hypotheses. To demonstrate the differences in these approaches to informed consent, we describe the consent processes for two MPS studies. The purpose of our hypothesis-testing study is to elucidate the etiology of rare phenotypes using MPS. The purpose of our hypothesis-generating study is to test the feasibility of using MPS to generate clinical hypotheses, and to approach the return of results as an experimental manipulation. Issues to consider in both designs include: volume and nature of the potential results, primary versus secondary results, return of individual results, duty to warn, length of interaction, target population, and privacy and confidentiality. Summary The categorization of MPS studies as hypothesis-testing versus hypothesis-generating can help to clarify the issue of so-called incidental or secondary results for the consent process, and aid the communication of the research goals to study participants.

  2. Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research.

    Science.gov (United States)

    Facio, Flavia M; Sapp, Julie C; Linn, Amy; Biesecker, Leslie G

    2012-10-10

    Massively-parallel sequencing (MPS) technologies create challenges for informed consent of research participants given the enormous scale of the data and the wide range of potential results. We propose that the consent process in these studies be based on whether they use MPS to test a hypothesis or to generate hypotheses. To demonstrate the differences in these approaches to informed consent, we describe the consent processes for two MPS studies. The purpose of our hypothesis-testing study is to elucidate the etiology of rare phenotypes using MPS. The purpose of our hypothesis-generating study is to test the feasibility of using MPS to generate clinical hypotheses, and to approach the return of results as an experimental manipulation. Issues to consider in both designs include: volume and nature of the potential results, primary versus secondary results, return of individual results, duty to warn, length of interaction, target population, and privacy and confidentiality. The categorization of MPS studies as hypothesis-testing versus hypothesis-generating can help to clarify the issue of so-called incidental or secondary results for the consent process, and aid the communication of the research goals to study participants.

  3. The neural legacy of a single concussion.

    Science.gov (United States)

    Kraus, Nina; Lindley, Tory; Colegrove, Danielle; Krizman, Jennifer; Otto-Meyer, Sebastian; Thompson, Elaine C; White-Schwoch, Travis

    2017-04-12

    It has been hypothesized that concussions impart lasting brain damage, even after a patient has ostensibly recovered. This hypothesis is based largely upon neuropathological studies in deceased athletes, however, leaving open the question of whether it can be detected in vivo. We measured neural responses to speech in collegiate student-athletes with a history of a single concussion from which they had recovered. These student-athletes had weaker responses to speech than age- and position-matched peers. This group difference suggests that concussions engender small, but detectable, changes in brain function prior to the emergence of frank behavioral indications. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Atypical neural synchronization to speech envelope modulations in dyslexia.

    Science.gov (United States)

    De Vos, Astrid; Vanvooren, Sophie; Vanderauwera, Jolijn; Ghesquière, Pol; Wouters, Jan

    2017-01-01

    A fundamental deficit in the synchronization of neural oscillations to temporal information in speech could underlie phonological processing problems in dyslexia. In this study, the hypothesis of a neural synchronization impairment is investigated more specifically as a function of different neural oscillatory bands and temporal information rates in speech. Auditory steady-state responses to 4, 10, 20 and 40Hz modulations were recorded in normal reading and dyslexic adolescents to measure neural synchronization of theta, alpha, beta and low-gamma oscillations to syllabic and phonemic rate information. In comparison to normal readers, dyslexic readers showed reduced non-synchronized theta activity, reduced synchronized alpha activity and enhanced synchronized beta activity. Positive correlations between alpha synchronization and phonological skills were found in normal readers, but were absent in dyslexic readers. In contrast, dyslexic readers exhibited positive correlations between beta synchronization and phonological skills. Together, these results suggest that auditory neural synchronization of alpha and beta oscillations is atypical in dyslexia, indicating deviant neural processing of both syllabic and phonemic rate information. Impaired synchronization of alpha oscillations in particular demonstrated to be the most prominent neural anomaly possibly hampering speech and phonological processing in dyslexic readers. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Multitask Classification Hypothesis Space With Improved Generalization Bounds.

    Science.gov (United States)

    Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2015-07-01

    This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived bounds and validate this paper's analysis.

  6. Buffet hypothesis for microbial nutrition at the rhizosphere

    Science.gov (United States)

    López-Guerrero, Martha G.; Ormeño-Orrillo, Ernesto; Rosenblueth, Mónica; Martinez-Romero, Julio; Martïnez-Romero, Esperanza

    2013-01-01

    An emphasis is made on the diversity of nutrients that rhizosphere bacteria may encounter derived from roots, soil, decaying organic matter, seeds, or the microbial community. This nutrient diversity may be considered analogous to a buffet and is contrasting to the hypothesis of oligotrophy at the rhizosphere. Different rhizosphere bacteria may have preferences for some substrates and this would allow a complex community to be established at the rhizosphere. To profit from diverse nutrients, root-associated bacteria should have large degrading capabilities and many transporters (seemingly inducible) that may be encoded in a significant proportion of the large genomes that root-associated bacteria have. Rhizosphere microbes may have a tendency to evolve toward generalists. We propose that many genes with unknown function may encode enzymes that participate in degrading diverse rhizosphere substrates. Knowledge of bacterial genes required for nutrition at the rhizosphere will help to make better use of bacteria as plant-growth promoters in agriculture. PMID:23785373

  7. Buffet hypothesis for microbial nutrition at the rhizosphere

    Directory of Open Access Journals (Sweden)

    Martha eLopez-Guerrero

    2013-06-01

    Full Text Available An emphasis is made on the diversity of nutrients that rhizosphere bacteria may encounter derived from roots, soil, decaying organic matter, seeds or the microbial community. This nutrient diversity may be considered analogous to a buffet and is contrasting to the hypothesis of oligotrophy at the rhizosphere. Different rhizosphere bacteria may have preferences for some substrates and this would allow a complex community to be established at the rhizosphere. To profit from diverse nutrients, root associated bacteria should have large degrading capabilities and many transporters (seemingly inducible that may be encoded in a significant proportion of the large genomes that root associated bacteria have. Rhizosphere microbes may have a tendency to evolve towards generalists. We propose that enzymes encoded by many genes with unknown function may participate in degrading diverse rhizosphere substrates. Knowledge of bacterial genes required for nutrition at the rhizosphere will help to better make use of bacteria as plant-growth promoters in agriculture.

  8. Imaging of Ventricular Fibrillation and Defibrillation: The Virtual Electrode Hypothesis.

    Science.gov (United States)

    Boukens, Bastiaan J; Gutbrod, Sarah R; Efimov, Igor R

    2015-01-01

    Ventricular fibrillation is the major underlying cause of sudden cardiac death. Understanding the complex activation patterns that give rise to ventricular fibrillation requires high resolution mapping of localized activation. The use of multi-electrode mapping unraveled re-entrant activation patterns that underlie ventricular fibrillation. However, optical mapping contributed critically to understanding the mechanism of defibrillation, where multi-electrode recordings could not measure activation patterns during and immediately after a shock. In addition, optical mapping visualizes the virtual electrodes that are generated during stimulation and defibrillation pulses, which contributed to the formulation of the virtual electrode hypothesis. The generation of virtual electrode induced phase singularities during defibrillation is arrhythmogenic and may lead to the induction of fibrillation subsequent to defibrillation. Defibrillating with low energy may circumvent this problem. Therefore, the current challenge is to use the knowledge provided by optical mapping to develop a low energy approach of defibrillation, which may lead to more successful defibrillation.

  9. 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

  10. 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

  11. Behavioral and neural properties of social reinforcement learning.

    Science.gov (United States)

    Jones, Rebecca M; Somerville, Leah H; Li, Jian; Ruberry, Erika J; Libby, Victoria; Glover, Gary; Voss, Henning U; Ballon, Douglas J; Casey, B J

    2011-09-14

    Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based on work in nonhuman primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging. Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis--social preferences, response latencies, and modeling neural responses--are consistent with reinforcement learning theory and nonhuman primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one's peers in altering subsequent behavior.

  12. Behavioral and neural properties of social reinforcement learning

    Science.gov (United States)

    Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Libby, Victoria; Glover, Gary; Voss, Henning U.; Ballon, Douglas J.; Casey, BJ

    2011-01-01

    Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based upon work in non-human primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging (fMRI). Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis - social preferences, response latencies and modeling neural responses – are consistent with reinforcement learning theory and non-human primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one’s peers in altering subsequent behavior. PMID:21917787

  13. A novel hypothesis splitting method implementation for multi-hypothesis filters

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Andersen, Nils Axel

    2013-01-01

    The paper presents a multi-hypothesis filter library featuring a novel method for splitting Gaussians into ones with smaller variances. The library is written in C++ for high performance and the source code is open and free1. The multi-hypothesis filters commonly approximate the distribution...... transformations better, if the covariances of the individual hypotheses are sufficiently small. We propose a look-up table based method to calculate a set of Gaussian hypotheses approximating a wider Gaussian in order to improve the filter approximation. Python bindings for the library are also provided for fast...

  14. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  15. Large numbers hypothesis. II - Electromagnetic radiation

    Science.gov (United States)

    Adams, P. J.

    1983-01-01

    This paper develops the theory of electromagnetic radiation in the units covariant formalism incorporating Dirac's large numbers hypothesis (LNH). A direct field-to-particle technique is used to obtain the photon propagation equation which explicitly involves the photon replication rate. This replication rate is fixed uniquely by requiring that the form of a free-photon distribution function be preserved, as required by the 2.7 K cosmic radiation. One finds that with this particular photon replication rate the units covariant formalism developed in Paper I actually predicts that the ratio of photon number to proton number in the universe varies as t to the 1/4, precisely in accord with LNH. The cosmological red-shift law is also derived and it is shown to differ considerably from the standard form of (nu)(R) - const.

  16. Extra dimensions hypothesis in high energy physics

    Directory of Open Access Journals (Sweden)

    Volobuev Igor

    2017-01-01

    Full Text Available We discuss the history of the extra dimensions hypothesis and the physics and phenomenology of models with large extra dimensions with an emphasis on the Randall- Sundrum (RS model with two branes. We argue that the Standard Model extension based on the RS model with two branes is phenomenologically acceptable only if the inter-brane distance is stabilized. Within such an extension of the Standard Model, we study the influence of the infinite Kaluza-Klein (KK towers of the bulk fields on collider processes. In particular, we discuss the modification of the scalar sector of the theory, the Higgs-radion mixing due to the coupling of the Higgs boson to the radion and its KK tower, and the experimental restrictions on the mass of the radion-dominated states.

  17. Reversing cell polarity: evidence and hypothesis.

    Science.gov (United States)

    Kaiser, Dale; Yu, Rosa

    2005-04-01

    The long, rod-shaped cells of myxobacteria are polarized by their gliding engines. At the rear, A-engines push while pili pull the front end forward. An hypothesis is developed whereby both engines are partially dis-assembled, then re-assembled at the opposite pole when cells reverse their movement direction. Reversals are induced by an Mgl G-protein switch that controls engine polarity. The switch is driven by an oscillatory circuit of Frizzy proteins. In growing cells, the circuit gives rise to an occasional reversal that makes swarming possible. Then, as myxobacteria begin fruiting body development, a rising level of C-signal input drives the oscillator and changes the reversal pattern. Cells reverse regularly every eight minutes in traveling waves, the reversal period is then prolonged enabling cells to form streams that enlarge tiny random aggregates into fruiting bodies.

  18. A critical examination of the bioplasma hypothesis.

    Science.gov (United States)

    Quickenden, T I; Tilbury, R N

    1986-01-01

    The hypothesis of Zon (Physiol. Chem. and Physics 11, 501-506 (1979); 12, 357-364 (1980] that regions of semiconduction within living organisms may exhibit plasma behaviour is shown to be most unlikely. Although charge carrier concentrations may be acceptable, calculated Debye lengths are shown to be only marginally acceptable and calculated plasma frequencies are not sufficiently high to ensure that charge carrier motions are governed by electrical and magnetic forces rather than hydrodynamic considerations. For the latter reason, conventional semiconductors do not exhibit plasma behaviour except close to absolute zero and if they are free from impurities and lattice disorder. The experimental evidences presented for the existence of biological plasma (bioplasma) from the areas of Kirlian photography, mitogenetic radiation, acupuncture and studies of biological fields, are largely explainable in conventional terms without invoking the existence of biological plasma.

  19. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  20. On the immunostimulatory hypothesis of cancer

    Directory of Open Access Journals (Sweden)

    Juan Bruzzo

    2011-12-01

    Full Text Available There is a rather generalized belief that the worst possible outcome for the application of immunological therapies against cancer is a null effect on tumor growth. However, a significant body of evidence summarized in the immunostimulatory hypothesis of cancer suggests that, upon certain circumstances, the growth of incipient and established tumors can be accelerated rather than inhibited by the immune response supposedly mounted to limit tumor growth. In order to provide more compelling evidence of this proposition, we have explored the growth behavior characteristics of twelve murine tumors -most of them of spontaneous origin- arisen in the colony of our laboratory, in putatively immunized and control mice. Using classical immunization procedures, 8 out of 12 tumors were actually stimulated in "immunized" mice while the remaining 4 were neither inhibited nor stimulated. Further, even these apparently non-antigenic tumors could reveal some antigenicity if more stringent than classical immunization procedures were used. This possibility was suggested by the results obtained with one of these four apparently non-antigenic tumors: the LB lymphoma. In effect, upon these stringent immunization pretreatments, LB was slightly inhibited or stimulated, depending on the titer of the immune reaction mounted against the tumor, with higher titers rendering inhibition and lower titers rendering tumor stimulation. All the above results are consistent with the immunostimulatory hypothesis that entails the important therapeutic implications -contrary to the orthodoxy- that, anti-tumor vaccines may run a real risk of doing harm if the vaccine-induced immunity is too weak to move the reaction into the inhibitory part of the immune response curve and that, a slight and prolonged immunodepression -rather than an immunostimulation- might interfere with the progression of some tumors and thus be an aid to cytotoxic therapies.

  1. The redox stress hypothesis of aging.

    Science.gov (United States)

    Sohal, Rajindar S; Orr, William C

    2012-02-01

    The main objective of this review is to examine the role of endogenous reactive oxygen/nitrogen species (ROS) in the aging process. Until relatively recently, ROS were considered to be potentially toxic by-products of aerobic metabolism, which, if not eliminated, may inflict structural damage on various macromolecules. Accrual of such damage over time was postulated to be responsible for the physiological deterioration in the postreproductive phase of life and eventually the death of the organism. This "structural damage-based oxidative stress" hypothesis has received support from the age-associated increases in the rate of ROS production and the steady-state amounts of oxidized macromolecules; however, there are increasing indications that structural damage alone is insufficient to satisfactorily explain the age-associated functional losses. The level of oxidative damage accrued during aging often does not match the magnitude of functional losses. Although experimental augmentation of antioxidant defenses tends to enhance resistance to induced oxidative stress, such manipulations are generally ineffective in the extension of life span of long-lived strains of animals. More recently, in a major conceptual shift, ROS have been found to be physiologically vital for signal transduction, gene regulation, and redox regulation, among others, implying that their complete elimination would be harmful. An alternative notion, advocated here, termed the "redox stress hypothesis," proposes that aging-associated functional losses are primarily caused by a progressive pro-oxidizing shift in the redox state of the cells, which leads to the overoxidation of redox-sensitive protein thiols and the consequent disruption of the redox-regulated signaling mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

  2. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-07-01

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

  3. Foetal ECG recovery using dynamic neural networks.

    Science.gov (United States)

    Camps-Valls, Gustavo; Martínez-Sober, Marcelino; Soria-Olivas, Emilio; Magdalena-Benedito, Rafael; Calpe-Maravilla, Javier; Guerrero-Martínez, Juan

    2004-07-01

    Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both

  4. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-02-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  5. Representations in neural network based empirical potentials

    Science.gov (United States)

    Cubuk, Ekin D.; Malone, Brad D.; Onat, Berk; Waterland, Amos; Kaxiras, Efthimios

    2017-07-01

    Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a "black box" tool. This lack of understanding is not desirable especially for applications of neural networks in scientific inquiry. We argue that machine learning models trained on physical systems can be used as more than just approximations since they had to "learn" physical concepts in order to reproduce the labels they were trained on. We use dimensionality reduction techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model atomic interactions.

  6. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  7. After Six Decades: Applying the U-Curve Hypothesis to the Adjustment of International Postgraduate Students

    Science.gov (United States)

    Chien, Yu-Yi Grace

    2016-01-01

    The research described in this article concludes that the widely cited U-curve hypothesis is no longer supported by research data because the adjustment of international postgraduate students is a complex phenomenon that does not fit easily with attempts to define and categorize it. Methodological issues, different internal and external factors,…

  8. Human centromedian-parafascicular complex signals sensory cues for goal-oriented behavior selection.

    Science.gov (United States)

    Schepers, Inga M; Beck, Anne-Kathrin; Bräuer, Susann; Schwabe, Kerstin; Abdallat, Mahmoud; Sandmann, Pascale; Dengler, Reinhard; Rieger, Jochem W; Krauss, Joachim K

    2017-05-15

    Experimental research has shown that the centromedian-parafascicular complex (CM-Pf) of the intralaminar thalamus is activated in attentional orienting and processing of behaviorally relevant stimuli. These observations resulted in the hypothesis that the CM-Pf plays a pivotal role in goal-oriented behavior selection. We here set out to test this hypothesis with electrophysiological recordings from patients with electrodes implanted in CM-Pf for deep brain stimulation (DBS) treatment of chronic neuropathic pain. Six patients participated in (1) an auditory three-class oddball experiment, which required a button press to target tones, but not to standard and deviant tones and in (2) a multi-speaker experiment with a target word that required attention selection and a target image that required response selection. Subjects showed transient neural responses (8-15Hz) to the target tone and the target word. Two subjects additionally showed transient neural responses (15-25Hz) to the target image. All sensory target stimuli were related to an internal goal and required a behavior selection (attention selection, response selection). In group analyses, neural responses were greater to target tones than deviant and standard tones and to target words than other task-relevant words that did not require attention selection. The transient neural responses occurred after the target stimuli but prior to the overt behavioral response. Our results demonstrate that in human subjects the CM-Pf is involved in signaling sensory inputs related to goal-oriented selection of behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  10. [Neural codes for perception].

    Science.gov (United States)

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

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

  11. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  12. Can nonlinguistic musical training change the way the brain processes speech? The expanded OPERA hypothesis.

    Science.gov (United States)

    Patel, Aniruddh D

    2014-02-01

    A growing body of research suggests that musical training has a beneficial impact on speech processing (e.g., hearing of speech in noise and prosody perception). As this research moves forward two key questions need to be addressed: 1) Can purely instrumental musical training have such effects? 2) If so, how and why would such effects occur? The current paper offers a conceptual framework for understanding such effects based on mechanisms of neural plasticity. The expanded OPERA hypothesis proposes that when music and speech share sensory or cognitive processing mechanisms in the brain, and music places higher demands on these mechanisms than speech does, this sets the stage for musical training to enhance speech processing. When these higher demands are combined with the emotional rewards of music, the frequent repetition that musical training engenders, and the focused attention that it requires, neural plasticity is activated and makes lasting changes in brain structure and function which impact speech processing. Initial data from a new study motivated by the OPERA hypothesis is presented, focusing on the impact of musical training on speech perception in cochlear-implant users. Suggestions for the development of animal models to test OPERA are also presented, to help motivate neurophysiological studies of how auditory training using non-biological sounds can impact the brain's perceptual processing of species-specific vocalizations. This article is part of a Special Issue entitled brain>. Copyright © 2013 Elsevier B.V. All rights reserved.

  13. Hypothesis of demodicidosis rosacea flushing etiopathogenesis.

    Science.gov (United States)

    Robledo, Mary Ann; Orduz, Mariana

    2015-04-01

    Most of the patients with erythematotelangiectatic rosacea are characterized by flushing, oedema and telangiectasia. The etiopathogenesis of the flushing in rosacea patients is unknown. Clinically the flushing in rosacea is similar to the "Asian flushing syndrome". Most Asians have an overactive alcohol dehydrogenase (ADH) that tends to break down alcohol into acetaldehyde faster. People with "Asians flushing syndrome" have a genetic disorder with the Aldehyde Dehydrogenase 2(∗)2 (ALDH2(∗)2) allele. This is the reason why they do not metabolize very well the acetaldehyde that comes from the alcohol, which means that acetaldehyde takes much longer to clear from their blood. ALDH2 enzyme is primarily responsible for oxidation of acetaldehyde derived from ethanol metabolism, as well as oxidation of various other endogenous and exogenous aldehydes. Acetaldehyde produces the vasodilatation in the "Asian flushing syndrome". The antibodies against the GroEl chaperonin protein, a 62-kDa heat shock protein were found in the Bacillus oleronius isolated from Demodex mites, in rosacea patients. The GroEl chaperonin protein is a protein that plays a key role in normal folding of ALDH2. If the GroEl chaperonin antibodies found in patients with rosacea, cross react with the human GroEl chaperonin protein, they will not fold normally the ALDH2, and then the enzyme will not metabolize the acetaldehyde. Many of the patients with rosacea have a concomitant infection with Helicobacter pylori in their stomach. The H.pylori produces high amounts of acetaldehyde, which comes from their metabolism of ethanol or carbohydrates. As a result, high amounts of acetaldehyde will circulate for longer time in the blood, until the liver CYP2E1(p450) enzyme system finally metabilizes the acetaldehyde, during that period of time the patients will experience a flushing as well as the people with the "Asian flushing syndrome" suffer when they drink ethanol. To prove the hypothesis it is necessary

  14. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  15. Neural cryptography with feedback

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  16. Neural network applications

    Science.gov (United States)

    Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.

    1993-01-01

    A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

  17. Building Neural Net Software

    OpenAIRE

    Neto, João Pedro; Costa, José Félix

    1999-01-01

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

  18. NEMEFO: NEural MEteorological FOrecast

    Energy Technology Data Exchange (ETDEWEB)

    Pasero, E.; Moniaci, W.; Meindl, T.; Montuori, A. [Polytechnic of Turin (Italy). Dept. of Electronics

    2004-07-01

    Artificial Neural Systems are a well-known technique used to classify and recognize objects. Introducing the time dimension they can be used to forecast numerical series. NEMEFO is a ''nowcasting'' tool, which uses both statistical and neural systems to forecast meteorological data in a restricted area close to a meteorological weather station in a short time range (3 hours). Ice, fog, rain are typical events which can be anticipated by NEMEFO. (orig.)

  19. Upregulation of the Nr2f1-A830082K12Rik gene pair in murine neural crest cells results in a complex phenotype reminiscent of Waardenburg syndrome type 4.

    Science.gov (United States)

    Bergeron, Karl-F; Nguyen, Chloé M A; Cardinal, Tatiana; Charrier, Baptiste; Silversides, David W; Pilon, Nicolas

    2016-11-01

    Waardenburg syndrome is a neurocristopathy characterized by a combination of skin and hair depigmentation, and inner ear defects. In the type 4 form, these defects show comorbidity with Hirschsprung disease, a disorder marked by an absence of neural ganglia in the distal colon, triggering functional intestinal obstruction. Here, we report that the Spot mouse line - obtained through an insertional mutagenesis screen for genes involved in neural crest cell (NCC) development - is a model for Waardenburg syndrome type 4. We found that the Spot insertional mutation causes overexpression of an overlapping gene pair composed of the transcription-factor-encoding Nr2f1 and the antisense long non-coding RNA A830082K12Rik in NCCs through a mechanism involving relief of repression of these genes. Consistent with the previously described role of Nr2f1 in promoting gliogenesis in the central nervous system, we further found that NCC-derived progenitors of the enteric nervous system fail to fully colonize Spot embryonic guts owing to their premature differentiation in glial cells. Taken together, our data thus identify silencer elements of the Nr2f1-A830082K12Rik gene pair as new candidate loci for Waardenburg syndrome type 4. © 2016. Published by The Company of Biologists Ltd.

  20. Upregulation of the Nr2f1-A830082K12Rik gene pair in murine neural crest cells results in a complex phenotype reminiscent of Waardenburg syndrome type 4

    Directory of Open Access Journals (Sweden)

    Karl-F. Bergeron

    2016-11-01

    Full Text Available Waardenburg syndrome is a neurocristopathy characterized by a combination of skin and hair depigmentation, and inner ear defects. In the type 4 form, these defects show comorbidity with Hirschsprung disease, a disorder marked by an absence of neural ganglia in the distal colon, triggering functional intestinal obstruction. Here, we report that the Spot mouse line – obtained through an insertional mutagenesis screen for genes involved in neural crest cell (NCC development – is a model for Waardenburg syndrome type 4. We found that the Spot insertional mutation causes overexpression of an overlapping gene pair composed of the transcription-factor-encoding Nr2f1 and the antisense long non-coding RNA A830082K12Rik in NCCs through a mechanism involving relief of repression of these genes. Consistent with the previously described role of Nr2f1 in promoting gliogenesis in the central nervous system, we further found that NCC-derived progenitors of the enteric nervous system fail to fully colonize Spot embryonic guts owing to their premature differentiation in glial cells. Taken together, our data thus identify silencer elements of the Nr2f1-A830082K12Rik gene pair as new candidate loci for Waardenburg syndrome type 4.

  1. Neural tube closure: cellular, molecular and biomechanical mechanisms.

    Science.gov (United States)

    Nikolopoulou, Evanthia; Galea, Gabriel L; Rolo, Ana; Greene, Nicholas D E; Copp, Andrew J

    2017-02-15

    Neural tube closure has been studied for many decades, across a range of vertebrates, as a paradigm of embryonic morphogenesis. Neurulation is of particular interest in view of the severe congenital malformations - 'neural tube defects' - that result when closure fails. The process of neural tube closure is complex and involves cellular events such as convergent extension, apical constriction and interkinetic nuclear migration, as well as precise molecular control via the non-canonical Wnt/planar cell polarity pathway, Shh/BMP signalling, and the transcription factors Grhl2/3, Pax3, Cdx2 and Zic2. More recently, biomechanical inputs into neural tube morphogenesis have also been identified. Here, we review these cellular, molecular and biomechanical mechanisms involved in neural tube closure, based on studies of various vertebrate species, focusing on the most recent advances in the field. © 2017. Published by The Company of Biologists Ltd.

  2. Neural network for constrained nonsmooth optimization using Tikhonov regularization.

    Science.gov (United States)

    Qin, Sitian; Fan, Dejun; Wu, Guangxi; Zhao, Lijun

    2015-03-01

    This paper presents a one-layer neural network to solve nonsmooth convex optimization problems based on the Tikhonov regularization method. Firstly, it is shown that the optimal solution of the original problem can be approximated by the optimal solution of a strongly convex optimization problems. Then, it is proved that for any initial point, the state of the proposed neural network enters the equality feasible region in finite time, and is globally convergent to the unique optimal solution of the related strongly convex optimization problems. Compared with the existing neural networks, the proposed neural network has lower model complexity and does not need penalty parameters. In the end, some numerical examples and application are given to illustrate the effectiveness and improvement of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Why Traditional Expository Teaching-Learning Approaches May Founder? An Experimental Examination of Neural Networks in Biology Learning

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-01-01

    Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed:…

  4. Intergenerational transmission of homeownership in Europe : Revisiting the socialisation hypothesis

    NARCIS (Netherlands)

    Lersch, P.M.; Luijkx, R.

    2015-01-01

    Socialisation towards homeownership during childhood has been proposed as one transmission channel of homeownership across generations in previous literature, but tests of this socialisation hypothesis are scarce. This study presents the yet most rigorous test of the socialisation hypothesis using

  5. Hypothesis on the dual origin of the mammalian subplate

    Directory of Open Access Journals (Sweden)

    Juan F Montiel

    2011-04-01

    Full Text Available The development of the mammalian neocortex relies heavily on subplate. The proportion of this cell population varies considerably in different mammalian species. Subplate is almost undetectable in marsupials, forms a thin, but distinct layer in mouse and rat, a larger layer in carnivores and big-brained mammals as pig and a highly developed embryonic structure in human and non-human primates. The evolutionary origin of subplate neurons is the subject of current debate. Some hypothesize that subplate represents the ancestral cortex of sauropsids, while others consider it to be an increasingly complex phylogenetic novelty of the mammalian neocortex. Here we review recent work on expression of several genes that were originally identified in rodent as highly and differentially expressed in subplate. We relate these observations to cellular morphology, birthdating and hodology in the dorsal cortex/dorsal pallium of several amniote species. Based on this reviewed evidence we argue for a third hypothesis according to which subplate contains both ancestral and newly derived cell populations. We propose that the mammalian subplate originally derived from a phylogenetically ancient structure in the dorsal pallium of stem amniotes, but subsequently expanded with additional cell populations in the synapsid lineage to support an increasingly complex cortical plate development. Further understanding of the detailed molecular taxonomy, somatodendritic morphology and connectivity of subplate in a comparative context should contribute to the identification of the ancestral and newly evolved populations of subplate neurons.

  6. Handedness in man: The energy availability hypothesis.

    Science.gov (United States)

    Chan, Yoo Kuen; Loh, Pui San

    2016-09-01

    More than 90% of the human species are right handed. Although outwardly our body appears symmetrical, a 50/50% lateralization in handedness never occurs. Neither have we seen more than 50% left handedness in any subset of the human population. By 12-15weeks of intrauterine life, as many as 6 times more fetuses are noted by ultrasound studies to be sucking on their right thumbs. Distinct difference in oxygenation leading to dissimilar energy availability between right and left subclavian arteries in place by week 9 of life may hold the clue to the lateralization of hand function and eventually, the same in the brain. We know there is a higher incidence of left handedness in males, twins, premature babies and those born to mothers who smoke. They may represent a subset with less distinct difference in oxygenation between the 2 subclavian arteries during the fetal stage. This hypothesis if correct not only closes the gap in understanding human handedness and lateralization but also opens a vista for new research to focus on in utero tissue energy availability and its impact on outcome in life. Copyright © 2016. Published by Elsevier Ltd.

  7. Impulse Control Disorders - The Continuum Hypothesis.

    Science.gov (United States)

    Stenberg, Georg

    2016-01-01

    The group Parkinson Inside Out is composed of health professionals and academic researchers who have been diagnosed with Parkinson's Disease. In our discussions we try to make use of both our inside perspective as patients, and our outside perspective as professionals. In this paper, we apply the two perspectives to the Impulse Control Disorders. These impulsive behaviour patterns are thought to be relatively uncommon side effects of some of the medication used in dopamine replacement therapy. The phenomenon is usually described as relatively rare (impulses is a very common experience for patients undergoing dopamine replacement therapy. They result from difficulties in decision making engendered by variations in dopamine accessibility in the reward centre of the brain. Only in a minority do the consequences grow to the damaging proportions of a disorder, but most patients are probably affected to some degree. Seeing, and measuring, decision difficulties as a continuous dimension, rather than as a discrete category, brings increased possibilities for early detection and continuous monitoring. With reliable measures of the propensity for impulsive decision making, it may become possible to both reap the benefits and avoid the dangers of the dopamine agonists. We point to ways of empirically testing our continuity hypothesis.

  8. Bayesian Hypothesis Testing for Planet Finding

    Science.gov (United States)

    Braems, I.; Kasdin, N. J.

    2003-12-01

    One of the most important performance metrics of any space planet finding system is integration time. The time needed to make a positive detection of an extrasolar planet determines the number of systems we can observe for the life of the mission and the stability requirements of the spacecraft and optical control systems. Most astronomical detection approaches rely on fairly simple signal-to-noise calculations and a threshold determined by the ability of the human eye to extract the planet image from the background (usually a signal-to-noise ratio of five). In this paper we present an alternative approach to detection using Bayesian hypothesis testing. This optimal approach provides a quantitative measure of the probability of detection under various conditions and integration times (such as known or unknown background levels) and under different prior assumptions. We also show how the technique allows for a much higher probability of detection for shorter integration times than the previous photometric approaches. We gratefully acknowledge the support of the Jet Propulsion Laboratory of the National Aeronautics and Space Administration for this work and Institut National de Recherche en Informatique et Automatique (INRIA) for its support of Ms. Braems.

  9. A HYPOTHESIS-DRIVEN FRAMEWORK FOR ASSESSING ...

    Science.gov (United States)

    Understanding how climate change will alter the availability of coastal final ecosystem goods and services (FEGS; such as food provisioning from fisheries, property protection, and recreation) has significant implications for coastal planning and the development of adaptive management strategies to maximize sustainability of natural resources. The dynamic social and physical settings of these important resources means that there is not a “one-size-fits-all” model to predict the specific changes in coastal FEGS that will occur as a result of climate change. Instead, we propose a hypothesis-driven approach that builds on available literature to understand the likely effects of climate change on FEGS across coastal regions of the United States. We present an analysis for three FEGS: food provisioning from fisheries, recreation, and property protection. Hypotheses were restricted to changes precipitated by four prominent climate stressors projected in coastal areas: 1) sea-level rise, 2) ocean acidification, 3) increased temperatures, and 4) intensification of coastal storms. Our approach identified links between these stressors and the ecological processes that produce the FEGS, with the capacity to incorporate regional differences in FEGS availability. Linkages were first presented in a logic model to conceptualize the framework. For each region, we developed hypotheses regarding the effects of climate stressors on FEGS by examining case studies For example, w

  10. Evolutionary hypothesis for Chiari type I malformation.

    Science.gov (United States)

    Fernandes, Yvens Barbosa; Ramina, Ricardo; Campos-Herrera, Cynthia Resende; Borges, Guilherme

    2013-10-01

    Chiari I malformation (CM-I) is classically defined as a cerebellar tonsillar herniation (≥5 mm) through the foramen magnum. A decreased posterior fossa volume, mainly due to basioccipital hypoplasia and sometimes platybasia, leads to posterior fossa overcrowding and consequently cerebellar herniation. Regardless of radiological findings, embryological genetic hypothesis or any other postulations, the real cause behind this malformation is yet not well-elucidated and remains largely unknown. The aim of this paper is to approach CM-I under a broader and new perspective, conjoining anthropology, genetics and neurosurgery, with special focus on the substantial changes that have occurred in the posterior cranial base through human evolution. Important evolutionary allometric changes occurred during brain expansion and genetics studies of human evolution demonstrated an unexpected high rate of gene flow interchange and possibly interbreeding during this process. Based upon this review we hypothesize that CM-I may be the result of an evolutionary anthropological imprint, caused by evolving species populations that eventually met each other and mingled in the last 1.7 million years. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. The Stem Cell Hypothesis of Aging

    Directory of Open Access Journals (Sweden)

    Anna Meiliana

    2010-04-01

    Full Text Available BACKGROUND: There is probably no single way to age. Indeed, so far there is no single accepted explanation or mechanisms of aging (although more than 300 theories have been proposed. There is an overall decline in tissue regenerative potential with age, and the question arises as to whether this is due to the intrinsic aging of stem cells or rather to the impairment of stem cell function in the aged tissue environment. CONTENT: Recent data suggest that we age, in part, because our self-renewing stem cells grow old as a result of heritable intrinsic events, such as DNA damage, as well as extrinsic forces, such as changes in their supporting niches. Mechanisms that suppress the development of cancer, such as senescence and apoptosis, which rely on telomere shortening and the activities of p53 and p16INK4a may also induce an unwanted consequence: a decline in the replicative function of certain stem cells types with advancing age. This decrease regenerative capacity appears to pointing to the stem cell hypothesis of aging. SUMMARY: Recent evidence suggested that we grow old partly because of our stem cells grow old as a result of mechanisms that suppress the development of cancer over a lifetime. We believe that a further, more precise mechanistic understanding of this process will be required before this knowledge can be translated into human anti-aging therapies. KEYWORDS: stem cells, senescence, telomere, DNA damage, epigenetic, aging.

  12. Marginal contrasts and the Contrastivist Hypothesis

    Directory of Open Access Journals (Sweden)

    Daniel Currie Hall

    2016-12-01

    Full Text Available The Contrastivist Hypothesis (CH; Hall 2007; Dresher 2009 holds that the only features that can be phonologically active in any language are those that serve to distinguish phonemes, which presupposes that phonemic status is categorical. Many researchers, however, demonstrate the existence of gradient relations. For instance, Hall (2009 quantifies these using the information-theoretic measure of entropy (unpredictability of distribution and shows that a pair of sounds may have an entropy between 0 (totally predictable and 1 (totally unpredictable. We argue that the existence of such intermediate degrees of contrastiveness does not make the CH untenable, but rather offers insight into contrastive hierarchies. The existence of a continuum does not preclude categorical distinctions: a categorical line can be drawn between zero entropy (entirely predictable, and thus by the CH phonologically inactive and non-zero entropy (at least partially contrastive, and thus potentially phonologically active. But this does not mean that intermediate degrees of surface contrastiveness are entirely irrelevant to the CH; rather, we argue, they can shed light on how deeply ingrained a phonemic distinction is in the phonological system. As an example, we provide a case study from Pulaar [ATR] harmony, which has previously been claimed to be problematic for the CH.

  13. Confabulation: Developing the 'emotion dysregulation' hypothesis.

    Science.gov (United States)

    Turnbull, Oliver H; Salas, Christian E

    2017-02-01

    Confabulations offer unique opportunities for establishing the neurobiological basis of delusional thinking. As regards causal factors, a review of the confabulation literature suggests that neither amnesia nor executive impairment can be the sole (or perhaps even the primary) cause of all delusional beliefs - though they may act in concert with other factors. A key perspective in the modern literature is that many delusions have an emotionally positive or 'wishful' element, that may serve to modulate or manage emotional experience. Some authors have referred to this perspective as the 'emotion dysregulation' hypothesis. In this article we review the theoretical underpinnings of this approach, and develop the idea by suggesting that the positive aspects of confabulatory states may have a role in perpetuating the imbalance between cognitive control and emotion. We draw on existing evidence from fields outside neuropsychology, to argue for three main causal factors: that positive emotions are related to more global or schematic forms of cognitive processing; that positive emotions influence the accuracy of memory recollection; and that positive emotions make people more susceptible to false memories. These findings suggest that the emotions that we want to feel (or do not want to feel) can influence the way we reconstruct past experiences and generate a sense of self - a proposition that bears on a unified theory of delusional belief states. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

  14. Oscillatory power decreases and long-term memory: The information via desynchronization hypothesis

    Directory of Open Access Journals (Sweden)

    Simon eHanslmayr

    2012-04-01

    Full Text Available The traditional belief is that brain oscillations are important for human long-term memory, because they induce synchronized firing between cell assemblies which shapes synaptic plasticity. Therefore, most prior studies focused on the role of synchronization for episodic memory, as reflected in theta (~5 Hz and gamma (>40 Hz power increases. These studies, however, neglect the role that is played by neural desynchronization, which is usually reflected in power decreases in the alpha and beta frequency band (8-30 Hz. In this paper we present a first idea, derived from information theory that gives a mechanistic explanation of how neural desynchronization aids human memory encoding and retrieval. Thereby we will review current studies investigating the role of alpha and beta power decreases during long-term memory tasks and show that alpha and beta power decreases play an important and active role for human memory. Applying mathematical models of information theory, we demonstrate that neural desynchronization is positively related to the richness of information represented in the brain, thereby enabling encoding and retrieval of long-term memories. This information via desynchronization hypothesis makes several predictions, which can be tested in future experiments.

  15. The neural correlates of beauty comparison.

    Science.gov (United States)

    Kedia, Gayannée; Mussweiler, Thomas; Mullins, Paul; Linden, David E J

    2014-05-01

    Beauty is in the eye of the beholder. How attractive someone is perceived to be depends on the individual or cultural standards to which this person is compared. But although comparisons play a central role in the way people judge the appearance of others, the brain processes underlying attractiveness comparisons remain unknown. In the present experiment, we tested the hypothesis that attractiveness comparisons rely on the same cognitive and neural mechanisms as comparisons of simple nonsocial magnitudes such as size. We recorded brain activity with functional magnetic resonance imaging (fMRI) while participants compared the beauty or height of two women or two dogs. Our data support the hypothesis of a common process underlying these different types of comparisons. First, we demonstrate that the distance effect characteristic of nonsocial comparisons also holds for attractiveness comparisons. Behavioral results indicated, for all our comparisons, longer response times for near than far distances. Second, the neural correlates of these distance effects overlapped in a frontoparietal network known for its involvement in processing simple nonsocial quantities. These results provide evidence for overlapping processes in the comparison of physical attractiveness and nonsocial magnitudes.

  16. The neural correlates of beauty comparison

    Science.gov (United States)

    Mussweiler, Thomas; Mullins, Paul; Linden, David E. J.

    2014-01-01

    Beauty is in the eye of the beholder. How attractive someone is perceived to be depends on the individual or cultural standards to which this person is compared. But although comparisons play a central role in the way people judge the appearance of others, the brain processes underlying attractiveness comparisons remain unknown. In the present experiment, we tested the hypothesis that attractiveness comparisons rely on the same cognitive and neural mechanisms as comparisons of simple nonsocial magnitudes such as size. We recorded brain activity with functional magnetic resonance imaging (fMRI) while participants compared the beauty or height of two women or two dogs. Our data support the hypothesis of a common process underlying these different types of comparisons. First, we demonstrate that the distance effect characteristic of nonsocial comparisons also holds for attractiveness comparisons. Behavioral results indicated, for all our comparisons, longer response times for near than far distances. Second, the neural correlates of these distance effects overlapped in a frontoparietal network known for its involvement in processing simple nonsocial quantities. These results provide evidence for overlapping processes in the comparison of physical attractiveness and nonsocial magnitudes. PMID:23508477

  17. Models of Hopfield-type quaternion neural networks and their energy functions.

    Science.gov (United States)

    Yoshida, Mitsuo; Kuroe, Yasuaki; Mori, Takehiro

    2005-01-01

    Recently models of neural networks that can directly deal with complex numbers, complex-valued neural networks, have been proposed and several studies on their abilities of information processing have been done. Furthermore models of neural networks that can deal with quaternion numbers, which is the extension of complex numbers, have also been proposed. However they are all multilayer quaternion neural networks. This paper proposes models of fully connected recurrent quaternion neural networks, Hopfield-type quaternion neural networks. Since quaternion numbers are non-commutative on multiplication, some different models can be considered. We investigate dynamics of these proposed models from the point of view of the existence of an energy function and derive their conditions for existence.

  18. In Defense of the Play-Creativity Hypothesis

    Science.gov (United States)

    Silverman, Irwin W.

    2016-01-01

    The hypothesis that pretend play facilitates the creative thought process in children has received a great deal of attention. In a literature review, Lillard et al. (2013, p. 8) concluded that the evidence for this hypothesis was "not convincing." This article focuses on experimental and training studies that have tested this hypothesis.…

  19. Childhood attachment and schizophrenia: the "attachment-developmental-cognitive" (ADC) hypothesis.

    Science.gov (United States)

    Rajkumar, Ravi Philip

    2014-09-01

    Schizophrenia is a complex psychiatric syndrome whose exact causes remain unclear. However, current scientific consensus has highlighted the importance of neurodevelopmental and neurocognitive processes in the development of schizophrenic symptoms. Research over the past three decades, motivated by the findings of the World Health Organization's large-scale studies, has highlighted the importance of psychosocial adversities - including childhood abuse and neglect - in this disorder. In this paper, I propose a hypothesis based on John Bowlby's framework of attachment theory, which I have termed the attachment-developmental-cognitive (ADC) hypothesis. The ADC hypothesis integrates recent developments related to (1) existing models of schizophrenia, (2) studies examining the effect of attachment on brain biology and cognitive development, and (3) various known facts about the course and outcome of this disorder. In doing so, it explains how disturbed childhood attachment leads to core psychological and neurochemical abnormalities which are implicated in the genesis of schizophrenia and also affect its outcome. The ADC hypothesis compasses and expands on earlier formulations, such as the "social defeat" and "traumagenic" models, and has important implications regarding the prevention and treatment of schizophrenia. Ways of testing and refining this hypothesis are outlined as avenues for future research. Though provisional, the ADC hypothesis is entirely consistent with both biological and psychosocial research into the origins of schizophrenia. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

    Science.gov (United States)

    Li, Hong; Ding, Xue

    2017-03-01

    This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

  1. Functional imaging of brain responses to different outcomes of hypothesis testing: revealed in a category induction task.

    Science.gov (United States)

    Li, Fuhong; Cao, Bihua; Luo, Yuejia; Lei, Yi; Li, Hong

    2013-02-01

    Functional magnetic resonance imaging (fMRI) was used to examine differences in brain activation that occur when a person receives the different outcomes of hypothesis testing (HT). Participants were provided with a series of images of batteries and were asked to learn a rule governing what kinds of batteries were charged. Within each trial, the first two charged batteries were sequentially displayed, and participants would generate a preliminary hypothesis based on the perceptual comparison. Next, a third battery that served to strengthen, reject, or was irrelevant to the preliminary hypothesis was displayed. The fMRI results revealed that (1) no significant differences in brain activation were found between the 2 hypothesis-maintain conditions (i.e., strengthen and irrelevant conditions); and (2) compared with the hypothesis-maintain conditions, the hypothesis-reject condition activated the left medial frontal cortex, bilateral putamen, left parietal cortex, and right cerebellum. These findings are discussed in terms of the neural correlates of the subcomponents of HT and working memory manipulation. Copyright © 2012 Elsevier Inc. All rights reserved.

  2. Introduction to neural networks in high energy physics

    Science.gov (United States)

    Therhaag, Jan

    2013-07-01

    Artificial neural networks are a well established tool in high energy physics, playing an important role in both online and offline data analysis. Nevertheless they are often perceived as black boxes which perform obscure operations beyond the control of the user, resulting in a skepticism against any results that may be obtained using them. The situation is not helped by common explanations which try to draw analogies between artificial neural networks and the human brain, for the brain is an even more complex black box itself. In this introductory text, I will take a problem-oriented approach to neural network techniques, showing how the fundamental concepts arise naturally from the demand to solve classification tasks which are frequently encountered in high energy physics. Particular attention is devoted to the question how probability theory can be used to control the complexity of neural networks.

  3. Introduction to neural networks in high energy physics

    Directory of Open Access Journals (Sweden)

    Therhaag Jan

    2013-07-01

    Full Text Available Artificial neural networks are a well established tool in high energy physics, playing an important role in both online and offline data analysis. Nevertheless they are often perceived as black boxes which perform obscure operations beyond the control of the user, resulting in a skepticism against any results that may be obtained using them. The situation is not helped by common explanations which try to draw analogies between artificial neural networks and the human brain, for the brain is an even more complex black box itself. In this introductory text, I will take a problem-oriented approach to neural network techniques, showing how the fundamental concepts arise naturally from the demand to solve classification tasks which are frequently encountered in high energy physics. Particular attention is devoted to the question how probability theory can be used to control the complexity of neural networks.

  4. Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research

    OpenAIRE

    Facio Flavia M; Sapp Julie C; Linn Amy; Biesecker Leslie G

    2012-01-01

    Abstract Background Massively-parallel sequencing (MPS) technologies create challenges for informed consent of research participants given the enormous scale of the data and the wide range of potential results. Discussion We propose that the consent process in these studies be based on whether they use MPS to test a hypothesis or to generate hypotheses. To demonstrate the differences in these approaches to informed consent, we describe the consent processes for two MPS studies. The purpose of...

  5. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  6. The three principles of action: a Pavlovian-instrumental transfer hypothesis

    Science.gov (United States)

    Cartoni, Emilio; Puglisi-Allegra, Stefano; Baldassarre, Gianluca

    2013-01-01

    Pavlovian conditioned stimuli can influence instrumental responding, an effect called Pavlovian-instrumental transfer (PIT). During the last decade, PIT has been subdivided into two types: specific PIT and general PIT, each having its own neural substrates. Specific PIT happens when a conditioned stimulus (CS) associated with a reward enhances an instrumental response directed to the same reward. Under general PIT, instead, the CS enhances a response directed to a different reward. While important progress has been made into identifying the neural substrates, the function of specific and general PIT and how they interact with instrumental responses are still not clear. In the experimental paradigm that distinguishes specific and general PIT an effect of PIT inhibition has also been observed and is waiting for an explanation. Here we propose an hypothesis that links these three PIT effects (specific PIT, general PIT and PIT inhibition) to three aspects of action evaluation. These three aspects, which we call “principles of action”, are: context, efficacy, and utility. In goal-directed behavior, an agent has to evaluate if the context is suitable to accomplish the goal, the efficacy of his action in getting the goal, and the utility of the goal itself: we suggest that each of the three PIT effects is related to one of these aspects of action evaluation. In particular, we link specific PIT with the estimation of efficacy, general PIT with the evaluation of utility, and PIT inhibition with the adequacy of context. We also provide a latent cause Bayesian computational model that exemplifies this hypothesis. This hypothesis and the model provide a new framework and new predictions to advance knowledge about PIT functioning and its role in animal adaptation. PMID:24312025

  7. The three principles of action: a Pavlovian-instrumental transfer hypothesis

    Directory of Open Access Journals (Sweden)

    Emilio eCartoni

    2013-11-01

    Full Text Available Pavlovian conditioned stimuli can influence instrumental responding, an effect called Pavlovian-instrumental transfer (PIT.During the last decade, PIT has been subdivided into two types: specific PIT and general PIT, each having its own neural substrates.Specific PIT happens when a conditioned stimulus (CS associated with a reward enhances an instrumental response directed to the same reward.Under general PIT instead, the CS enhances a response directed to a different reward.While important progress has been made into identifying the neural substrates, the function of specific and general PIT and how they interact with instrumental responses, are still not clear.In the experimental paradigm that distinguishes specific and general PIT an effect of PIT inhibition has also been observed and is waiting for an explanation.Here we propose an hypothesis that links these three PIT effects (specific PIT, general PIT and PIT inhibition to three aspects of action evaluation.These three aspects, which we call "principles of action" are: context, efficacy, and utility.In goal-directed behavior, an agent has to evaluate if the context is suitable to accomplish the goal, the efficacy of his action in getting the goal and the utility of the goal itself:we suggest that each of the three PIT effects is related to one of these aspects of action evaluation.In particular, we link specific PIT with the estimation of efficacy, general PIT with the evaluation of utility and PIT inhibition with the adequacy of context.We also provide a latent cause Bayesian computational model that exemplifies this hypothesis.This hypothesis and the model provide a new framework and new predictions to advance knowledge about PIT functioning and its role in animal adaptation.

  8. Refining the perfusion-diffusion mismatch hypothesis.

    Science.gov (United States)

    Butcher, K S; Parsons, M; MacGregor, L; Barber, P A; Chalk, J; Bladin, C; Levi, C; Kimber, T; Schultz, D; Fink, J; Tress, B; Donnan, G; Davis, S

    2005-06-01

    The Echoplanar Imaging Thrombolysis Evaluation Trial (EPITHET) tests the hypothesis that perfusion-weighted imaging (PWI)-diffusion-weighted imaging (DWI) mismatch predicts the response to thrombolysis. There is no accepted standardized definition of PWI-DWI mismatch. We compared common mismatch definitions in the initial 40 EPITHET patients. Raw perfusion images were used to generate maps of time to peak (TTP), mean transit time (MTT), time to peak of the impulse response (Tmax) and first moment transit time (FMT). DWI, apparent diffusion coefficient (ADC), and PWI volumes were measured with planimetric and thresholding techniques. Correlations between mismatch volume (PWIvol-DWIvol) and DWI expansion (T2(Day 90-vol)-DWI(Acute-vol)) were also assessed. Mean age was 68+/-11, time to MRI 4.5+/-0.7 hours, and median National Institutes of Health Stroke Scale (NIHSS) score 11 (range 4 to 23). Tmax and MTT hypoperfusion volumes were significantly lower than those calculated with TTP and FMT maps (P or =20% was observed in 89% (Tmax) to 92% (TTP/FMT/MTT) of patients. Application of a +4s (relative to the contralateral hemisphere) PWI threshold reduced the frequency of positive mismatch volumes (TTP 73%/FMT 68%/Tmax 54%/MTT 43%). Mismatch was not significantly different when assessed with ADC maps. Mismatch volume, calculated with all parameters and thresholds, was not significantly correlated with DWI expansion. In contrast, reperfusion was correlated inversely with infarct growth (R=-0.51; P=0.009). Deconvolution and application of PWI thresholds provide more conservative estimates of tissue at risk and decrease the frequency of mismatch accordingly. The precise definition may not be critical; however, because reperfusion alters tissue fate irrespective of mismatch.

  9. [Psychodynamic hypothesis about suicidality in elderly men].

    Science.gov (United States)

    Lindner, Reinhard

    2010-08-01

    Old men are overrepresented in the whole of all suicides. In contrast, only very few elderly men find their way to specialised treatment facilities. Elderly accept psychotherapy more rarely than younger persons. Therefore presentations on the psychodynamics of suicidality in old men are rare and mostly casuistical. By means of a stepwise reconstructable qualitative case comparison of five randomly chosen elderly suicidal men with ideal types of suicidal (younger) men concerning biography, suicidal symptoms and transference, psychodynamic hypothesis of suicidality in elderly men are developed. All patients came into psychotherapy in a specialised academic out-patient clinic for psychodynamic treatment of acute and chronic suicidality. The five elderly suicidal men predominantly were living in long-term, conflictuous sexual relationships and also had ambivalent relationships to their children. Suicidality in old age refers to lifelong existing intrapsychic conflicts, concerning (male) identity, self-esteem and a core conflict between fusion and separation wishes. The body gets a central role in suicidal experiences, being a defensive instance modified by age and/or physical illness, which brings up to consciousness aggressive and envious impulses, but also feelings of emptiness and insecurity, which have to be warded off again by projection into the body. In transference relationships there are on the one hand the regular transference, on the other hand an age specific turned around transference, with their counter transference reactions. The chosen methodological approach serves the systematic finding of hypotheses with a higher degree in evidence than hypotheses generated from single case studies. Georg Thieme Verlag KG Stuttgart - New York.

  10. Limb apraxia and the 'affordance competition hypothesis'

    Directory of Open Access Journals (Sweden)

    Elisabeth eRounis

    2015-07-01

    Full Text Available Limb apraxia, a disorder of higher order motor control, has long been a challenge for clinical assessment and understanding (Leiguarda and Marsden 2000.The deficits originally described in limb apraxia (Liepmann 1908 have been classified by the nature of the errors made by the patients leading to, namely, ideational and ideomotor apraxia. The dual stream hypothesis (Goodale and Milner 1992 has been used to explain these categories: ideational apraxia is thought to relate to a deficit in the concept of a movement (coded in the ventral stream, whereas ideomotor apraxia, is thought to arise from problems in the accurate implementation of movements within the dorsal stream. One of the limitations on understanding apraxia is the failure by the clinical literature to draw on knowledge of the factors determining actions in the environment. Here we emphasize the role of affordance. There is much recent work indicating that our responses to stimuli are strongly influenced by the actions that the objects ‘afford’, based on their physical properties and the intentions of the actor (e.g, Ellis & Tucker, 1998; Humphreys et al., 2010. The concept of affordance, originally suggested by Gibson (1979 has been incorporated in a recent model of interactive behaviour that draws from findings in non-human primates, namely the ‘affordance competition hypothesis’ (Cisek 2007. This postulates that interactive behaviour arises by a process of competition between possible actions elicited by the environment. In this paper we argue that ‘affordance competition’ may play a role in apraxia. We review evidence that at least some aspects of apraxia may reflect an abnormal sensitivity to competition when multiple affordances are present (Riddoch et al., 1998 and/or a poor ability to exert cognitive control over this competition when it occurs. This framework suggests a new way of conceptualising deficits in apraxia which invites further investigations in the field.

  11. Presynaptic quantal plasticity: Katz's original hypothesis revisited.

    Science.gov (United States)

    Vautrin, Jean; Barker, Jeffery L

    2003-03-01

    Changes in the amplitudes of signals conveyed at synaptic contacts between neurons underlie many brain functions and pathologies. Here we review the possible determinants of the amplitude and plasticity of the elementary postsynaptic signal, the miniature. In the absence of a definite understanding of the molecular mechanism releasing transmitters, we investigated a possible alternative interpretation. Classically, both the quantal theory and the vesicle theory predict that the amount of transmitter producing a miniature is determined presynaptically prior to release and that rapid changes in miniature amplitude reflect essentially postsynaptic alterations. However, recent data indicates that short-term and long-lasting changes in miniature amplitude are in large part due to changes in the amount of transmitter in individual released packets that show no evidence of preformation. Current representations of transmitter release derive from basic properties of neuromuscular transmission and endocrine secretion. Reexamination of overlooked properties of these two systems indicate that the amplitude of miniatures may depend as much, if not more, on the Ca(2+) signals in the presynaptic terminal than on the number of postsynaptic receptors available or on vesicle's contents. Rapid recycling of transmitter and its possible adsorption at plasma and vesicle lumenal membrane surfaces suggest that exocytosis may reflect membrane traffic rather than actual transmitter release. This led us to reconsider the disregarded hypothesis introduced by Fatt and Katz (1952; J Physiol 117:109-128) that the excitability of the release site may account for the "quantal effect" in fast synaptic transmission. In this case, changes in excitability of release sites would contribute to the presynaptic quantal plasticity that is often recorded. Copyright 2002 Wiley-Liss, Inc.

  12. The oxidative damage initiation hypothesis for meiosis.

    Science.gov (United States)

    Hörandl, Elvira; Hadacek, Franz

    2013-12-01

    The maintenance of sexual reproduction in eukaryotes is still a major enigma in evolutionary biology. Meiosis represents the only common feature of sex in all eukaryotic kingdoms, and thus, we regard it a key issue for discussing its function. Almost all asexuality modes maintain meiosis either in a modified form or as an alternative pathway, and facultatively apomictic plants increase frequencies of sexuality relative to apomixis after abiotic stress. On the physiological level, abiotic stress causes oxidative stress. We hypothesize that repair of oxidative damage on nuclear DNA could be a major driving force in the evolution of meiosis. We present a hypothetical model for the possible redox chemistry that underlies the binding of the meiosis-specific protein Spo11 to DNA. During prophase of meiosis I, oxidized sites at the DNA molecule are being targeted by the catalytic tyrosine moieties of Spo11 protein, which acts like an antioxidant reducing the oxidized target. The oxidized tyrosine residues, tyrosyl radicals, attack the phosphodiester bonds of the DNA backbone causing DNA double strand breaks that can be repaired by various mechanisms. Polyploidy in apomictic plants could mitigate oxidative DNA damage and decrease Spo11 activation. Our hypothesis may contribute to explaining various enigmatic phenomena: first, DSB formation outnumbers crossovers and, thus, effective recombination events by far because the target of meiosis may be the removal of oxidative lesions; second, it offers an argument for why expression of sexuality is responsive to stress in many eukaryotes; and third, repair of oxidative DNA damage turns meiosis into an essential characteristic of eukaryotic reproduction.

  13. Beyond Pattern Recognition With Neural Nets

    Science.gov (United States)

    Arsenault, Henri H.; Macukow, Bohdan

    1989-02-01

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

  14. Modelling Framework of a Neural Object Recognition

    Directory of Open Access Journals (Sweden)

    Aswathy K S

    2016-02-01

    Full Text Available In many industrial, medical and scientific image processing applications, various feature and pattern recognition techniques are used to match specific features in an image with a known template. Despite the capabilities of these techniques, some applications require simultaneous analysis of multiple, complex, and irregular features within an image as in semiconductor wafer inspection. In wafer inspection discovered defects are often complex and irregular and demand more human-like inspection techniques to recognize irregularities. By incorporating neural network techniques such image processing systems with much number of images can be trained until the system eventually learns to recognize irregularities. The aim of this project is to develop a framework of a machine-learning system that can classify objects of different category. The framework utilizes the toolboxes in the Matlab such as Computer Vision Toolbox, Neural Network Toolbox etc.

  15. Neural representation of probabilities for Bayesian inference.

    Science.gov (United States)

    Rich, Dylan; Cazettes, Fanny; Wang, Yunyan; Peña, José Luis; Fischer, Brian J

    2015-04-01

    Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system. Here we consider three models: a non-uniform population code where the stimulus-driven activity and distribution of preferred stimuli in the population represent a likelihood function and a prior, respectively; the sampling hypothesis which proposes that the stimulus-driven activity over time represents a posterior probability and that the spontaneous activity represents a prior; and the class of models which propose that a population of neurons represents a posterior probability in a distributed code. It has been shown that the non-uniform population code model matches the representation of auditory space generated in the owl's external nucleus of the inferior colliculus (ICx). However, the alternative models have not been tested, nor have the three models been directly compared in any system. Here we tested the three models in the owl's ICx. We found that spontaneous firing rate and the average stimulus-driven response of these neurons were not consistent with predictions of the sampling hypothesis. We also found that neural activity in ICx under varying levels of sensory noise did not reflect a posterior probability. On the other hand, the responses of ICx neurons were consistent with the non-uniform population code model. We further show that Bayesian inference can be implemented in the non-uniform population code model using one spike per neuron when the population is large and is thus able to support the rapid inference that is necessary for sound localization.

  16. Application of artificial neural networks (ANNs) in wine technology.

    Science.gov (United States)

    Baykal, Halil; Yildirim, Hatice Kalkan

    2013-01-01

    In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.

  17. Neural reactivation links unconscious thought to decision-making performance.

    Science.gov (United States)

    Creswell, John David; Bursley, James K; Satpute, Ajay B

    2013-12-01

    Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.

  18. [GADV]-protein world hypothesis on the origin of life.

    Science.gov (United States)

    Ikehara, Kenji

    2014-12-01

    RNA world hypothesis is widely accepted still now, as an idea by which the origin of life might be explained. But, there are many weak points in the hypothesis. In contrast, I have proposed a more reasonable [GADV]-protein world hypothesis or GADV hypothesis, suggesting that life originated from the protein world, which was formed by pseudo-replication of [GADV]-proteins. In this communication, I will discuss about the origin of life from the point of view of the GADV hypothesis.

  19. Polystochastic Models for Complexity

    CERN Document Server

    Iordache, Octavian

    2010-01-01

    This book is devoted to complexity understanding and management, considered as the main source of efficiency and prosperity for the next decades. Divided into six chapters, the book begins with a presentation of basic concepts as complexity, emergence and closure. The second chapter looks to methods and introduces polystochastic models, the wave equation, possibilities and entropy. The third chapter focusing on physical and chemical systems analyzes flow-sheet synthesis, cyclic operations of separation, drug delivery systems and entropy production. Biomimetic systems represent the main objective of the fourth chapter. Case studies refer to bio-inspired calculation methods, to the role of artificial genetic codes, neural networks and neural codes for evolutionary calculus and for evolvable circuits as biomimetic devices. The fifth chapter, taking its inspiration from systems sciences and cognitive sciences looks to engineering design, case base reasoning methods, failure analysis, and multi-agent manufacturing...

  20. Rekonsiliasi Perseteruan antara Efficient Market Hypothesis dan Behavioral Finance melalui Perspektif Neuroeconomics

    Directory of Open Access Journals (Sweden)

    Satia Nur Maharani

    2014-08-01

    Full Text Available Behavioral finance evaluation on Efficient Market Hypothesis causes debates among scientists supporting both theories. This article describes a comprehensive debate between rational behavior perspective on the Efficient Market Hypothesis with irrational behavior on behavioral finance, and how neuroeconomics shed some light on these two perspectives. This article gives a wider range of colors to represent investors behavior that is very complex, and encourage the growth of new generations of related theory of capital markets through interdisciplinary collaboration. Findings indicated that neuroeconomics perspective identified economic behavior through psychological functions.