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Sample records for neural efficiency hypothesis

  1. 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.

  2. 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

  3. 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

  4. 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…

  5. Isobars and the Efficient Market Hypothesis

    OpenAIRE

    Kristýna Ivanková

    2010-01-01

    Isobar surfaces, a method for describing the overall shape of multidimensional data, are estimated by nonparametric regression and used to evaluate the efficiency of selected markets based on returns of their stock market indices.

  6. 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

  7. The efficient market hypothesis and identification in structural VARs

    OpenAIRE

    Lucio Sarno; Daniel L. Thornton

    2004-01-01

    Structural vector autoregression (SVAR) models are commonly used to investigate the effect of structural shocks on economic variables. The identifying restrictions imposed in many of these exercises have been criticized in the literature. This paper extends this literature by showing that, if the SVAR includes one or more variables that are efficient in the strong form of the efficient market hypothesis, the identifying restrictions frequently imposed in SVARs cannot be satisfied. The authors...

  8. Nearly Efficient Likelihood Ratio Tests of the Unit Root Hypothesis

    DEFF Research Database (Denmark)

    Jansson, Michael; Nielsen, Morten Ørregaard

    Seemingly absent from the arsenal of currently available "nearly efficient" testing procedures for the unit root hypothesis, i.e. tests whose local asymptotic power functions are indistinguishable from the Gaussian power envelope, is a test admitting a (quasi-)likelihood ratio interpretation. We...

  9. 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.

  10. 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...

  11. An algorithm for testing the efficient market hypothesis.

    Science.gov (United States)

    Boboc, Ioana-Andreea; Dinică, Mihai-Cristian

    2013-01-01

    The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).

  12. An algorithm for testing the efficient market hypothesis.

    Directory of Open Access Journals (Sweden)

    Ioana-Andreea Boboc

    Full Text Available The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA, Moving Average Convergence Divergence (MACD, Relative Strength Index (RSI and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH.

  13. 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.

  14. Martingales, nonstationary increments, and the efficient market hypothesis

    Science.gov (United States)

    McCauley, Joseph L.; Bassler, Kevin E.; Gunaratne, Gemunu H.

    2008-06-01

    We discuss the deep connection between nonstationary increments, martingales, and the efficient market hypothesis for stochastic processes x(t) with arbitrary diffusion coefficients D(x,t). We explain why a test for a martingale is generally a test for uncorrelated increments. We explain why martingales look Markovian at the level of both simple averages and 2-point correlations. But while a Markovian market has no memory to exploit and cannot be beaten systematically, a martingale admits memory that might be exploitable in higher order correlations. We also use the analysis of this paper to correct a misstatement of the ‘fair game’ condition in terms of serial correlations in Fama’s paper on the EMH. We emphasize that the use of the log increment as a variable in data analysis generates spurious fat tails and spurious Hurst exponents.

  15. 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...

  16. 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.

  17. Martingales, detrending data, and the efficient market hypothesis

    Science.gov (United States)

    McCauley, Joseph L.; Bassler, Kevin E.; Gunaratne, Gemunu H.

    2008-01-01

    We discuss martingales, detrending data, and the efficient market hypothesis (EMH) for stochastic processes x( t) with arbitrary diffusion coefficients D( x, t). Beginning with x-independent drift coefficients R( t) we show that martingale stochastic processes generate uncorrelated, generally non-stationary increments. Generally, a test for a martingale is therefore a test for uncorrelated increments. A detrended process with an x-dependent drift coefficient is generally not a martingale, and so we extend our analysis to include the class of ( x, t)-dependent drift coefficients of interest in finance. We explain why martingales look Markovian at the level of both simple averages and 2-point correlations. And while a Markovian market has no memory to exploit and presumably cannot be beaten systematically, it has never been shown that martingale memory cannot be exploited in 3-point or higher correlations to beat the market. We generalize our Markov scaling solutions presented earlier, and also generalize the martingale formulation of the EMH to include ( x, t)-dependent drift in log returns. We also use the analysis of this paper to correct a misstatement of the ‘fair game’ condition in terms of serial correlations in Fama's paper on the EMH. We end with a discussion of Levy's characterization of Brownian motion and prove that an arbitrary martingale is topologically inequivalent to a Wiener process.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Efficient sensory encoding and Bayesian inference with heterogeneous neural populations.

    Science.gov (United States)

    Ganguli, Deep; Simoncelli, Eero P

    2014-10-01

    The efficient coding hypothesis posits that sensory systems maximize information transmitted to the brain about the environment. We develop a precise and testable form of this hypothesis in the context of encoding a sensory variable with a population of noisy neurons, each characterized by a tuning curve. We parameterize the population with two continuous functions that control the density and amplitude of the tuning curves, assuming that the tuning widths vary inversely with the cell density. This parameterization allows us to solve, in closed form, for the information-maximizing allocation of tuning curves as a function of the prior probability distribution of sensory variables. For the optimal population, the cell density is proportional to the prior, such that more cells with narrower tuning are allocated to encode higher-probability stimuli and that each cell transmits an equal portion of the stimulus probability mass. We also compute the stimulus discrimination capabilities of a perceptual system that relies on this neural representation and find that the best achievable discrimination thresholds are inversely proportional to the sensory prior. We examine how the prior information that is implicitly encoded in the tuning curves of the optimal population may be used for perceptual inference and derive a novel decoder, the Bayesian population vector, that closely approximates a Bayesian least-squares estimator that has explicit access to the prior. Finally, we generalize these results to sigmoidal tuning curves, correlated neural variability, and a broader class of objective functions. These results provide a principled embedding of sensory prior information in neural populations and yield predictions that are readily testable with environmental, physiological, and perceptual data.

  20. Neural efficiency as a function of task demands☆

    Science.gov (United States)

    Dunst, Beate; Benedek, Mathias; Jauk, Emanuel; Bergner, Sabine; Koschutnig, Karl; Sommer, Markus; Ischebeck, Anja; Spinath, Birgit; Arendasy, Martin; Bühner, Markus; Freudenthaler, Heribert; Neubauer, Aljoscha C.

    2014-01-01

    The neural efficiency hypothesis describes the phenomenon that brighter individuals show lower brain activation than less bright individuals when working on the same cognitive tasks. The present study investigated whether the brain activation–intelligence relationship still applies when more versus less intelligent individuals perform tasks with a comparable person-specific task difficulty. In an fMRI-study, 58 persons with lower (n = 28) or respectively higher (n = 30) intelligence worked on simple and difficult inductive reasoning tasks having the same person-specific task difficulty. Consequently, less bright individuals received sample-based easy and medium tasks, whereas bright subjects received sample-based medium and difficult tasks. This design also allowed a comparison of lower versus higher intelligent individuals when working on the same tasks (i.e. sample-based medium task difficulty). In line with expectations, differences in task performance and in brain activation were only found for the subset of tasks with the same sample-based task difficulty, but not when comparing tasks with the same person-specific task difficulty. These results suggest that neural efficiency reflects an (ability-dependent) adaption of brain activation to the respective task demands. PMID:24489416

  1. 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

  2. Efficient Market Hypothesis: Some Evidences from Emerging European Forex Markets

    National Research Council Canada - National Science Library

    Anoop S Kumar; Bandi Kamaiah

    2014-01-01

    This study attempts to analyze the presence of weak form efficiency in the forex markets of a set of select European emerging markets namely Bulgaria, Croatia, Czech Republic, Hungary Poland, Romania...

  3. Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences.

    Science.gov (United States)

    Schönbrodt, Felix D; Wagenmakers, Eric-Jan; Zehetleitner, Michael; Perugini, Marco

    2017-06-01

    Unplanned optional stopping rules have been criticized for inflating Type I error rates under the null hypothesis significance testing (NHST) paradigm. Despite these criticisms, this research practice is not uncommon, probably because it appeals to researcher's intuition to collect more data to push an indecisive result into a decisive region. In this contribution, we investigate the properties of a procedure for Bayesian hypothesis testing that allows optional stopping with unlimited multiple testing, even after each participant. In this procedure, which we call Sequential Bayes Factors (SBFs), Bayes factors are computed until an a priori defined level of evidence is reached. This allows flexible sampling plans and is not dependent upon correct effect size guesses in an a priori power analysis. We investigated the long-term rate of misleading evidence, the average expected sample sizes, and the biasedness of effect size estimates when an SBF design is applied to a test of mean differences between 2 groups. Compared with optimal NHST, the SBF design typically needs 50% to 70% smaller samples to reach a conclusion about the presence of an effect, while having the same or lower long-term rate of wrong inference. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  4. A Modern Approach to the Efficient-Market Hypothesis

    OpenAIRE

    Frahm, Gabriel

    2013-01-01

    Market efficiency at least requires the absence of weak arbitrage opportunities, but this is not sufficient to establish a situation where the market is sensitive, i.e., where it "fully reflects" or "rapidly adjusts to" some information flow including the evolution of asset prices. By contrast, No Weak Arbitrage together with market sensitivity is sufficient and necessary for a market to be informationally efficient.

  5. Efficient Market Hypothesis: Some Evidences from Emerging European Forex Markets

    Directory of Open Access Journals (Sweden)

    Anoop S Kumar

    2014-06-01

    Full Text Available This study attempts to analyze the presence of weak form efficiency in the forex markets of a set of select European emerging markets namely Bulgaria, Croatia, Czech Republic, Hungary Poland, Romania, Russia, Slovakia and Slovenia using the monthly NEER data ranging from jan-1994 to Dec-2013. We employ a two step comprehensive methodology where in the first place we test for weak form efficiency using a family of individual and joint variance ratio tests. The results show that while the markets of Croatia, Czech Republic and Bulgaria may be weak form efficient at a shorter lag, the other six markets are not informationally efficient. In the next stage, we estimate a measure of relative efficiency to show the extent to which a market is weak-form inefficient. From the results, it is found that the forex markets of Croatia, Czech Republic and Bulgaria are least weak form inefficient compared to others. The findings of the study are of relevance as it shows that even after roughly two decades of free market economic policies, majority of the forex markets in the area remains informationally inefficient.

  6. Experiential Learning of the Efficient Market Hypothesis: Two Trading Games

    Science.gov (United States)

    Park, Andreas

    2010-01-01

    In goods markets, an equilibrium price balances demand and supply. In a financial market, an equilibrium price also aggregates people's information to reveal the true value of a financial security. Although the underlying idea of informationally efficient markets is one of the centerpieces of capital market theory, students often have difficulties…

  7. 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.

  8. 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.

  9. Is the Economic andTesting the Efficient Markets Hypothesis on the Romanian Capital Market

    Directory of Open Access Journals (Sweden)

    Dragoș Mînjină

    2013-11-01

    Full Text Available Informational efficiency of capital markets has been the subject of numerous empirical studies. Intensive research of the field is justified by the important implications of the knowledge of the of informational efficiency level in the financial practice. Empirical studies that have tested the efficient markets hypothesis on the Romanian capital market revealed mostly that this market is not characterised by the weak form of the efficient markets hypothesis. However, recent empirical studies have obtained results for the weak form of the efficient markets hypothesis. The present decline period of the Romanian capital market, recorded on the background of adverse economic developments internally and externally, will be an important test for the continuation of recent positive developments, manifested the level of informational efficiency too.

  10. Investigating neural efficiency in the visuo-spatial domain: an FMRI study.

    Directory of Open Access Journals (Sweden)

    Ilona Lipp

    Full Text Available The neural efficiency hypothesis postulates an inverse relationship between intelligence and brain activation. Previous research suggests that gender and task modality represent two important moderators of the neural efficiency phenomenon. Since most of the existing studies on neural efficiency have used ERD in the EEG as a measure of brain activation, the central aim of this study was a more detailed analysis of this phenomenon by means of functional MRI. A sample of 20 males and 20 females, who had been screened for their visuo-spatial intelligence, was confronted with a mental rotation task employing an event-related approach. Results suggest that less intelligent individuals show a stronger deactivation of parts of the default mode network, as compared to more intelligent people. Furthermore, we found evidence of an interaction between task difficulty, intelligence and gender, indicating that more intelligent females show an increase in brain activation with an increase in task difficulty. These findings may contribute to a better understanding of the neural efficiency hypothesis, and possibly also of gender differences in the visuo-spatial domain.

  11. Are European Equity Style Indexes Mean Reverting? Testing the Validity of the Efficient Market Hypothesis

    OpenAIRE

    Berneburg, Marian

    2004-01-01

    The article tests for a random walk in European equity style indexes. After briefly introducing the efficient market hypothesis, equity styles in general and the used statistical techniques (Variance Ratio Test and modified Rescaled Range Test) it is shown that a random walk in European equity style indexes cannot be rejected. At least in the period since the mid 70s, for which this research has been conducted, the weak form efficient market hypothesis seems to hold.

  12. 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.

  13. Sex differences in neural efficiency: Are they due to the stereotype threat effect?

    Science.gov (United States)

    Dunst, Beate; Benedek, Mathias; Bergner, Sabine; Athenstaedt, Ursula; Neubauer, Aljoscha C

    2013-10-01

    The neural efficiency hypothesis postulates a more efficient use of brain resources in more intelligent people as compared to less intelligent ones. However, this relationship was found to be moderated by sex and task content. While the phenomenon of neural efficiency was previously supported for men when performing visuo-spatial tasks it occurred for women only when performing verbal tasks. One possible explanation for this finding could be provided by the well-studied phenomenon called stereotype threat. Stereotype threat arises when a negative stereotype of one's own group is made salient and can result in behavior that confirms the stereotype. Overall, 32 boys and 31 girls of varying intellectual ability were tested with a mental rotation task, either under a stereotype exposure or a no-stereotype exposure condition while measuring their EEG. The behavioral results show that an activated negative stereotype not necessarily hampers the performance of girls. Physiologically, a confirmation of the neural efficiency phenomenon was only obtained for boys working under a no-stereotype exposure condition. This result pattern replicates previous findings without threat and thus suggests that sex differences in neural efficiency during visuo-spatial tasks may not be due to the stereotype threat effect.

  14. 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

  15. 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.

  16. Computationally Efficient Neural Network Intrusion Security Awareness

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Milos Manic

    2009-08-01

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

  17. Analysis of fractal electrodes for efficient neural stimulation.

    Science.gov (United States)

    Golestanirad, Laleh; Elahi, Behzad; Molina, Alberto; Mosig, Juan R; Pollo, Claudio; Chen, Robert; Graham, Simon J

    2013-01-01

    Planar electrodes are increasingly used in therapeutic neural stimulation techniques such as functional electrical stimulation, epidural spinal cord stimulation (ESCS), and cortical stimulation. Recently, optimized electrode geometries have been shown to increase the efficiency of neural stimulation by increasing the variation of current density on the electrode surface. In the present work, a new family of modified fractal electrode geometries is developed to enhance the efficiency of neural stimulation. It is shown that a promising approach in increasing the neural activation function is to increase the "edginess" of the electrode surface, a concept that is explained and quantified by fractal mathematics. Rigorous finite element simulations were performed to compute electric potential produced by proposed modified fractal geometries. The activation of 256 model axons positioned around the electrodes was then quantified, showing that modified fractal geometries required a 22% less input power while maintaining the same level of neural activation. Preliminary in vivo experiments investigating muscle evoked potentials due to median nerve stimulation showed encouraging results, supporting the feasibility of increasing neural stimulation efficiency using modified fractal geometries.

  18. 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.

  19. Predictability of Exchange Rates in Sri Lanka: A Test of the Efficient Market Hypothesis

    OpenAIRE

    Guneratne B Wickremasinghe

    2007-01-01

    This study examined the validity of the weak and semi-strong forms of the efficient market hypothesis (EMH) for the foreign exchange market of Sri Lanka. Monthly exchange rates for four currencies during the floating exchange rate regime were used in the empirical tests. Using a battery of tests, empirical results indicate that the current values of the four exchange rates can be predicted from their past values. Further, the tests of semi-strong form efficiency indicate that exchange rate pa...

  20. 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.

  1. How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

    Science.gov (United States)

    Brinkman, Braden A W; Weber, Alison I; Rieke, Fred; Shea-Brown, Eric

    2016-10-01

    Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1) differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2) characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

  2. How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?

    Directory of Open Access Journals (Sweden)

    Braden A W Brinkman

    2016-10-01

    Full Text Available Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions. Here we systematically investigate how noise introduced at different stages of neural processing impacts optimal coding strategies. Using simulations and a flexible analytical approach, we show how these strategies depend on the strength of each noise source, revealing under what conditions the different noise sources have competing or complementary effects. We draw two primary conclusions: (1 differences in encoding strategies between sensory systems-or even adaptational changes in encoding properties within a given system-may be produced by changes in the structure or location of neural noise, and (2 characterization of both circuit nonlinearities as well as noise are necessary to evaluate whether a circuit is performing efficiently.

  3. Robust Approach to Verifying the Weak Form of the Efficient Market Hypothesis

    Science.gov (United States)

    Střelec, Luboš

    2011-09-01

    The weak form of the efficient markets hypothesis states that prices incorporate only past information about the asset. An implication of this form of the efficient markets hypothesis is that one cannot detect mispriced assets and consistently outperform the market through technical analysis of past prices. One of possible formulations of the efficient market hypothesis used for weak form tests is that share prices follow a random walk. It means that returns are realizations of IID sequence of random variables. Consequently, for verifying the weak form of the efficient market hypothesis, we can use distribution tests, among others, i.e. some tests of normality and/or some graphical methods. Many procedures for testing the normality of univariate samples have been proposed in the literature [7]. Today the most popular omnibus test of normality for a general use is the Shapiro-Wilk test. The Jarque-Bera test is the most widely adopted omnibus test of normality in econometrics and related fields. In particular, the Jarque-Bera test (i.e. test based on the classical measures of skewness and kurtosis) is frequently used when one is more concerned about heavy-tailed alternatives. As these measures are based on moments of the data, this test has a zero breakdown value [2]. In other words, a single outlier can make the test worthless. The reason so many classical procedures are nonrobust to outliers is that the parameters of the model are expressed in terms of moments, and their classical estimators are expressed in terms of sample moments, which are very sensitive to outliers. Another approach to robustness is to concentrate on the parameters of interest suggested by the problem under this study. Consequently, novel robust testing procedures of testing normality are presented in this paper to overcome shortcomings of classical normality tests in the field of financial data, which are typical with occurrence of remote data points and additional types of deviations from

  4. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Science.gov (United States)

    Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

    2012-01-01

    The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  5. Thermodynamic efficiency of learning a rule in neural networks

    Science.gov (United States)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  6. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

  7. On supertaskers and the neural basis of efficient multitasking.

    Science.gov (United States)

    Medeiros-Ward, Nathan; Watson, Jason M; Strayer, David L

    2015-06-01

    The present study used brain imaging to determine the neural basis of individual differences in multitasking, the ability to successfully perform at least two attention-demanding tasks at once. Multitasking is mentally taxing and, therefore, should recruit the prefrontal cortex to maintain task goals when coordinating attentional control and managing the cognitive load. To investigate this possibility, we used functional neuroimaging to assess neural activity in both extraordinary multitaskers (Supertaskers) and control subjects who were matched on working memory capacity. Participants performed a challenging dual N-back task in which auditory and visual stimuli were presented simultaneously, requiring independent and continuous maintenance, updating, and verification of the contents of verbal and spatial working memory. With the task requirements and considerable cognitive load that accompanied increasing N-back, relative to the controls, the multitasking of Supertaskers was characterized by more efficient recruitment of anterior cingulate and posterior frontopolar prefrontal cortices. Results are interpreted using neuropsychological and evolutionary perspectives on individual differences in multitasking ability and the neural correlates of attentional control.

  8. How to build VLSI-efficient neural chips

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-02-01

    This paper presents several upper and lower bounds for the number-of-bits required for solving a classification problem, as well as ways in which these bounds can be used to efficiently build neural network chips. The focus will be on complexity aspects pertaining to neural networks: (1) size complexity and depth (size) tradeoffs, and (2) precision of weights and thresholds as well as limited interconnectivity. They show difficult problems-exponential growth in either space (precision and size) and/or time (learning and depth)-when using neural networks for solving general classes of problems (particular cases may enjoy better performances). The bounds for the number-of-bits required for solving a classification problem represent the first step of a general class of constructive algorithms, by showing how the quantization of the input space could be done in O (m{sup 2}n) steps. Here m is the number of examples, while n is the number of dimensions. The second step of the algorithm finds its roots in the implementation of a class of Boolean functions using threshold gates. It is substantiated by mathematical proofs for the size O (mn/{Delta}), and the depth O [log(mn)/log{Delta}] of the resulting network (here {Delta} is the maximum fan in). Using the fan in as a parameter, a full class of solutions can be designed. The third step of the algorithm represents a reduction of the size and an increase of its generalization capabilities. Extensions by using analogue COMPARISONs, allows for real inputs, and increase the generalization capabilities at the expense of longer training times. Finally, several solutions which can lower the size of the resulting neural network are detailed. The interesting aspect is that they are obtained for limited, or even constant, fan-ins. In support of these claims many simulations have been performed and are called upon.

  9. Accuracy and Efficiency in Fixed-Point Neural ODE Solvers.

    Science.gov (United States)

    Hopkins, Michael; Furber, Steve

    2015-10-01

    Simulation of neural behavior on digital architectures often requires the solution of ordinary differential equations (ODEs) at each step of the simulation. For some neural models, this is a significant computational burden, so efficiency is important. Accuracy is also relevant because solutions can be sensitive to model parameterization and time step. These issues are emphasized on fixed-point processors like the ARM unit used in the SpiNNaker architecture. Using the Izhikevich neural model as an example, we explore some solution methods, showing how specific techniques can be used to find balanced solutions. We have investigated a number of important and related issues, such as introducing explicit solver reduction (ESR) for merging an explicit ODE solver and autonomous ODE into one algebraic formula, with benefits for both accuracy and speed; a simple, efficient mechanism for cancelling the cumulative lag in state variables caused by threshold crossing between time steps; an exact result for the membrane potential of the Izhikevich model with the other state variable held fixed. Parametric variations of the Izhikevich neuron show both similarities and differences in terms of algorithms and arithmetic types that perform well, making an overall best solution challenging to identify, but we show that particular cases can be improved significantly using the techniques described. Using a 1 ms simulation time step and 32-bit fixed-point arithmetic to promote real-time performance, one of the second-order Runge-Kutta methods looks to be the best compromise; Midpoint for speed or Trapezoid for accuracy. SpiNNaker offers an unusual combination of low energy use and real-time performance, so some compromises on accuracy might be expected. However, with a careful choice of approach, results comparable to those of general-purpose systems should be possible in many realistic cases.

  10. Efficient Market Hypothesis in South Africa: Evidence from Linear and Nonlinear Unit Root Tests

    Directory of Open Access Journals (Sweden)

    Andrew Phiri

    2015-12-01

    Full Text Available This study investigates the weak form efficient market hypothesis (EMH for five generalized stock indices in the Johannesburg Stock Exchange (JSE using weekly data collected from 31st January 2000 to 16th December 2014. In particular, we test for weak form market efficiency using a battery of linear and nonlinear unit root testing procedures comprising of the classical augmented Dickey-Fuller (ADF tests, the two-regime threshold autoregressive (TAR unit root tests described in Enders and Granger (1998 as well as the three-regime unit root tests described in Bec, Salem, and Carrasco (2004. Based on our empirical analysis, we are able to demonstrate that whilst the linear unit root tests advocate for unit roots within the time series, the nonlinear unit root tests suggest that most stock indices are threshold stationary processes. These results bridge two opposing contentions obtained from previous studies by concluding that under a linear framework the JSE stock indices offer support in favour of weak form market efficiency whereas when nonlinearity is accounted for, a majority of the indices violate the weak form EMH.

  11. Potential efficiency of antioxidants to prevent pressure ulcers. A neglected hypothesis.

    Science.gov (United States)

    Bonne, Claude

    2016-06-01

    Pressure ulcers are necrotic lesions mainly due to capillary hypoperfusion. It is well known that hypoxia and also subsequent oxygenation at reperfusion provoke the formation of reactive oxygen species (ROS) responsible for cell death. The hypothesis of their participation in the pathogenesis of pressure ulcers has already been tested; several antioxidants have the capacity to inhibit skin necrosis in animal models but their efficiency in preventing bedsores has never been demonstrated in patients. The failure of clinical trials to show the protective activity of some antioxidants does not rule out the involvement of ROS in ischemic ulcers and the potential efficacy of other antioxidants in preventing their formation remains possible. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  15. Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

    Science.gov (United States)

    Gold, Brian T; Kim, Chobok; Johnson, Nathan F; Kryscio, Richard J; Smith, Charles D

    2013-01-09

    Recent behavioral data have shown that lifelong bilingualism can maintain youthful cognitive control abilities in aging. Here, we provide the first direct evidence of a neural basis for the bilingual cognitive control boost in aging. Two experiments were conducted, using a perceptual task-switching paradigm, including a total of 110 participants. In Experiment 1, older adult bilinguals showed better perceptual switching performance than their monolingual peers. In Experiment 2, younger and older adult monolinguals and bilinguals completed the same perceptual task-switching experiment while functional magnetic resonance imaging (fMRI) was performed. Typical age-related performance reductions and fMRI activation increases were observed. However, like younger adults, bilingual older adults outperformed their monolingual peers while displaying decreased activation in left lateral frontal cortex and cingulate cortex. Critically, this attenuation of age-related over-recruitment associated with bilingualism was directly correlated with better task-switching performance. In addition, the lower blood oxygenation level-dependent response in frontal regions accounted for 82% of the variance in the bilingual task-switching reaction time advantage. These results suggest that lifelong bilingualism offsets age-related declines in the neural efficiency for cognitive control processes.

  16. Survivin Improves Reprogramming Efficiency of Human Neural Progenitors by Single Molecule OCT4

    Directory of Open Access Journals (Sweden)

    Shixin Zhou

    2016-01-01

    Full Text Available Induced pluripotent stem (iPS cells have been generated from human somatic cells by ectopic expression of four Yamanaka factors. Here, we report that Survivin, an apoptosis inhibitor, can enhance iPS cells generation from human neural progenitor cells (NPCs together with one factor OCT4 (1F-OCT4-Survivin. Compared with 1F-OCT4, Survivin accelerates the process of reprogramming from human NPCs. The neurocyte-originated induced pluripotent stem (NiPS cells generated from 1F-OCT4-Survivin resemble human embryonic stem (hES cells in morphology, surface markers, global gene expression profiling, and epigenetic status. Survivin keeps high expression in both iPS and ES cells. During the process of NiPS cell to neural cell differentiation, the expression of Survivin is rapidly decreased in protein level. The mechanism of Survivin promotion of reprogramming efficiency from NPCs may be associated with stabilization of β-catenin in WNT signaling pathway. This hypothesis is supported by experiments of RT-PCR, chromatin immune-precipitation, and Western blot in human ES cells. Our results showed overexpression of Survivin could improve the efficiency of reprogramming from NPCs to iPS cells by one factor OCT4 through stabilization of the key molecule, β-catenin.

  17. Efficiently passing messages in distributed spiking neural network simulation.

    Science.gov (United States)

    Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

    2013-01-01

    Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

  18. Efficient Pruning Method for Ensemble Self-Generating Neural Networks

    Directory of Open Access Journals (Sweden)

    Hirotaka Inoue

    2003-12-01

    Full Text Available Recently, multiple classifier systems (MCS have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.

  19. A SIMPLE BUT EFFICIENT SCHEME FOR COLOUR IMAGE RETRIEVAL BASED ON STATISTICAL TESTS OF HYPOTHESIS

    Directory of Open Access Journals (Sweden)

    K. Seetharaman

    2011-02-01

    Full Text Available This paper proposes a simple but efficient scheme for colour image retrieval, based on statistical tests of hypothesis, namely test for equality of variance, test for equality of mean. The test for equality of variance is performed to test the similarity of the query and target images. If the images pass the test, then the test for equality of mean is performed on the same images to examine whether the two images have the same attributes / characteristics. If the query and target images pass the tests then it is inferred that the two images belong to the same class i.e. both the images are same; otherwise, it is assumed that the images belong to different classes i.e. both the images are different. The obtained test statistic values are indexed in ascending order and the image corresponding to the least value is identified as same / similar images. The proposed system is invariant for translation, scaling, and rotation, since the proposed system adjusts itself and treats either the query image or the target image is sample of other. The proposed scheme provides cent percent accuracy if the query and target images are same, whereas there is a slight variation for similar, transformed.

  20. Evidence on the Efficient Market Hypothesis from 44 Global Financial Market Indexes

    Directory of Open Access Journals (Sweden)

    Huijian Dong

    2013-01-01

    Full Text Available This paper employs Granger causality tests to identify the impacts of historical information from global financial markets on their current levels in 30-day windows. The dataset consists primarily of the daily index levels of the (1 open, (2 closed, (3 intraday high, (4 intraday low, and (5 trading volume series for the world’s 37 most influential equity market indexes, two crude oil prices, a gold price, and four major money market prices in the United States are used as control groups. Our results indicate a persistent impact of historical information from global markets on their current levels, and this impact duplicates itself in a cyclical pattern consistently over decades. Such persistence in the patterns causes some market indexes to be upgraded to global or regional market leaders. These findings can be interpreted as constituting violations of the weak-form efficient market hypothesis. The results also reveal recursive impacts of information in these markets and the existence of an information digestion effect.

  1. Design of efficient and safe neural stimulators a multidisciplinary approach

    CERN Document Server

    van Dongen, Marijn

    2016-01-01

    This book discusses the design of neural stimulator systems which are used for the treatment of a wide variety of brain disorders such as Parkinson’s, depression and tinnitus. Whereas many existing books treating neural stimulation focus on one particular design aspect, such as the electrical design of the stimulator, this book uses a multidisciplinary approach: by combining the fields of neuroscience, electrophysiology and electrical engineering a thorough understanding of the complete neural stimulation chain is created (from the stimulation IC down to the neural cell). This multidisciplinary approach enables readers to gain new insights into stimulator design, while context is provided by presenting innovative design examples. Provides a single-source, multidisciplinary reference to the field of neural stimulation, bridging an important knowledge gap among the fields of bioelectricity, neuroscience, neuroengineering and microelectronics;Uses a top-down approach to understanding the neural activation proc...

  2. DANNP: an efficient artificial neural network pruning tool

    Directory of Open Access Journals (Sweden)

    Mona Alshahrani

    2017-11-01

    Full Text Available Background Artificial neural networks (ANNs are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge

  3. DANNP: an efficient artificial neural network pruning tool

    KAUST Repository

    Alshahrani, Mona

    2017-11-06

    Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly

  4. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  6. Energy efficient low-noise neural recording amplifier with enhanced noise efficiency factor.

    Science.gov (United States)

    Majidzadeh, V; Schmid, A; Leblebici, Y

    2011-06-01

    This paper presents a neural recording amplifier array suitable for large-scale integration with multielectrode arrays in very low-power microelectronic cortical implants. The proposed amplifier is one of the most energy-efficient structures reported to date, which theoretically achieves an effective noise efficiency factor (NEF) smaller than the limit that can be achieved by any existing amplifier topology, which utilizes a differential pair input stage. The proposed architecture, which is referred to as a partial operational transconductance amplifier sharing architecture, results in a significant reduction of power dissipation as well as silicon area, in addition to the very low NEF. The effect of mismatch on crosstalk between channels and the tradeoff between noise and crosstalk are theoretically analyzed. Moreover, a mathematical model of the nonlinearity of the amplifier is derived, and its accuracy is confirmed by simulations and measurements. For an array of four neural amplifiers, measurement results show a midband gain of 39.4 dB and a -3-dB bandwidth ranging from 10 Hz to 7.2 kHz. The input-referred noise integrated from 10 Hz to 100 kHz is measured at 3.5 μVrms and the power consumption is 7.92 μW from a 1.8-V supply, which corresponds to NEF = 3.35. The worst-case crosstalk and common-mode rejection ratio within the desired bandwidth are - 43.5 dB and 70.1 dB, respectively, and the active silicon area of each amplifier is 256 μm × 256 μm in 0.18-μm complementary metal-oxide semiconductor technology.

  7. Growth rate hypothesis and efficiency of protein synthesis under different sulphate concentrations in two green algae.

    Science.gov (United States)

    Giordano, Mario; Palmucci, Matteo; Raven, John A

    2015-11-01

    The growth rate hypothesis (GRH) predicts a positive correlation between growth rate and RNA content because growth depends upon the protein synthesis machinery. The application of this hypothesis to photoautotrophic organisms has been questioned. We tested the GRH on one prasinophycean, Tetraselmis suecica, and one chlorophycean, Dunaliella salina, grown at three sulphate concentrations. Sulphate was chosen because its concentration in the oceans increased through geological time and apparently had a role in the evolutionary trajectories of phytoplankton. Cell protein content and P quota were positively related to the RNA content (r = 0.62 and r = 0.74, respectively). The correlation of the RNA content with growth rates (r = 0.95) indicates that the GRH was valid for these species when growth rates were below 0.82 d(-1) . © 2015 John Wiley & Sons Ltd.

  8. Rethinking neural efficiency : Effects of controlling for strategy use

    NARCIS (Netherlands)

    Toffanin, Paolo; Johnson, Addie; de Jong, Ritske; Martens, Sander

    2007-01-01

    A sentence verification task (SVT) was used to test whether differences in neural activation patterns that have been attributed to IQ may actually depend on differential strategy use between IQ groups. Electroencephalograms were recorded from 14 low (89

  9. Design of efficient and safe neural stimulators : A multidisciplinary approach

    NARCIS (Netherlands)

    Van Dongen, M.N.

    2015-01-01

    Neural stimulation is an established treatment methodology for an increasing number of diseases. Electrical Stimulation injects a stimulation signal through electrodes that are implanted in the target area of the central or peripheral nervous system in order to evoke a specific neuronal response

  10. Prediction of adsorption efficiencies of Ni (II in aqueous solutions with perlite via artificial neural networks

    Directory of Open Access Journals (Sweden)

    Turp Sinan Mehmet

    2017-12-01

    Full Text Available This study investigates the estimated adsorption efficiency of artificial Nickel (II ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2, and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

  11. Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets

    Science.gov (United States)

    Eom, Cheoljun; Choi, Sunghoon; Oh, Gabjin; Jung, Woo-Sung

    2008-07-01

    We empirically investigated the relationships between the degree of efficiency and the predictability in financial time-series data. The Hurst exponent was used as the measurement of the degree of efficiency, and the hit rate calculated from the nearest-neighbor prediction method was used for the prediction of the directions of future price changes. We used 60 market indexes of various countries. We empirically discovered that the relationship between the degree of efficiency (the Hurst exponent) and the predictability (the hit rate) is strongly positive. That is, a market index with a higher Hurst exponent tends to have a higher hit rate. These results suggested that the Hurst exponent is useful for predicting future price changes. Furthermore, we also discovered that the Hurst exponent and the hit rate are useful as standards that can distinguish emerging capital markets from mature capital markets.

  12. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate......, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...

  13. A novel neural dynamical approach to convex quadratic program and its efficient applications.

    Science.gov (United States)

    Xia, Youshen; Sun, Changyin

    2009-12-01

    This paper proposes a novel neural dynamical approach to a class of convex quadratic programming problems where the number of variables is larger than the number of equality constraints. The proposed continuous-time and proposed discrete-time neural dynamical approach are guaranteed to be globally convergent to an optimal solution. Moreover, the number of its neurons is equal to the number of equality constraints. In contrast, the number of neurons in existing neural dynamical methods is at least the number of the variables. Therefore, the proposed neural dynamical approach has a low computational complexity. Compared with conventional numerical optimization methods, the proposed discrete-time neural dynamical approach reduces multiplication operation per iteration and has a large computational step length. Computational examples and two efficient applications to signal processing and robot control further confirm the good performance of the proposed approach.

  14. EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    F. Arce

    2017-09-01

    Full Text Available Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

  15. Efficient Lane Detection Based on Artificial Neural Networks

    Science.gov (United States)

    Arce, F.; Zamora, E.; Hernández, G.; Sossa, H.

    2017-09-01

    Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

  16. Efficient market hypothesis: is the Croatian stock market as (in)efficient as the U.S. market

    OpenAIRE

    Velimir Šonje; Denis Alajbeg; Zoran Bubaš

    2011-01-01

    Traditional statistical tests of serial independence of stock price changes often show that stock markets are ineffi cient. Our analysis on daily and monthly data confirms this finding for the Croatian and U.S. markets in the 2002-2010 period. However, this result seems to be mainly due to the impact of the crisis of 2008-2009. The observation of monthly data in the pre-crisis period suggests market efficiency in the U.S. and (rather surprisingly) in Croatia also. Daily data indicate a hig...

  17. Highly Efficient Neural Conversion of Human Pluripotent Stem Cells in Adherent and Animal-Free Conditions.

    Science.gov (United States)

    Lukovic, Dunja; Diez Lloret, Andrea; Stojkovic, Petra; Rodríguez-Martínez, Daniel; Perez Arago, Maria Amparo; Rodriguez-Jimenez, Francisco Javier; González-Rodríguez, Patricia; López-Barneo, José; Sykova, Eva; Jendelova, Pavla; Kostic, Jelena; Moreno-Manzano, Victoria; Stojkovic, Miodrag; Bhattacharya, Shomi S; Erceg, Slaven

    2017-04-01

    Neural differentiation of human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) can produce a valuable and robust source of human neural cell subtypes, holding great promise for the study of neurogenesis and development, and for treating neurological diseases. However, current hESCs and hiPSCs neural differentiation protocols require either animal factors or embryoid body formation, which decreases efficiency and yield, and strongly limits medical applications. Here we develop a simple, animal-free protocol for neural conversion of both hESCs and hiPSCs in adherent culture conditions. A simple medium formula including insulin induces the direct conversion of >98% of hESCs and hiPSCs into expandable, transplantable, and functional neural progenitors with neural rosette characteristics. Further differentiation of neural progenitors into dopaminergic and spinal motoneurons as well as astrocytes and oligodendrocytes indicates that these neural progenitors retain responsiveness to instructive cues revealing the robust applicability of the protocol in the treatment of different neurodegenerative diseases. The fact that this protocol includes animal-free medium and human extracellular matrix components avoiding embryoid bodies makes this protocol suitable for the use in clinic. Stem Cells Translational Medicine 2017;6:1217-1226. © 2017 The Authors Stem Cells Translational Medicine published by Wiley Periodicals, Inc. on behalf of AlphaMed Press.

  18. An efficient neural network based method for medical image segmentation.

    Science.gov (United States)

    Torbati, Nima; Ayatollahi, Ahmad; Kermani, Ali

    2014-01-01

    The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods. © 2013 Published by Elsevier Ltd.

  19. SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.

    Science.gov (United States)

    Ahmad, Jamil; Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

    2017-01-01

    Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches.

  20. SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.

    Directory of Open Access Journals (Sweden)

    Jamil Ahmad

    Full Text Available Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches.

  1. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    Directory of Open Access Journals (Sweden)

    Poramate eManoonpong

    2013-02-01

    Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  2. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    Science.gov (United States)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  3. Computationally efficient locally-recurrent neural networks for online signal processing

    CERN Document Server

    Hussain, A; Shim, I

    1999-01-01

    A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).

  4. Energy-efficient neural information processing in individual neurons and neuronal networks.

    Science.gov (United States)

    Yu, Lianchun; Yu, Yuguo

    2017-11-01

    Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  5. Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers.

    Science.gov (United States)

    Taheri, Mahboobeh; Mohebbi, Ali

    2008-08-30

    In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.

  6. 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.

  7. DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

    Science.gov (United States)

    Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

    2017-07-18

    Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

  8. The efficient market hypothesis of brazilian capital market, 2000-2010: an event study of distribution of dividends

    Directory of Open Access Journals (Sweden)

    Daniel Moreira Carvalho

    2013-11-01

    Full Text Available In the semi-strong form of the Efficient Markets Hypothesis - EMH, developed by Fama (1970, 1991, the prices reflect both the past and any information disclosed by companies, making impossible to an investor to get abnormal returns consistently, based on this type of information. In this paper we analyze the price behavior of common shares of 87 listed companies in the BM&FBovespa, in the announcements of 452 events of dividend distribution, occurred between January 2000 and September 2010, in order to identify the EMH in semi-strong form of Brazilian capital market. We used an event study, which evaluates abnormal returns of stocks relative to the market return (Ibovespa. The analysis of the abnormal return in the event window (10 days before and after the dividend distribution announcement showed an upward trend, with significant positive abnormal returns on days t-5, t-3, and t-1 to t+1. The results go in the direction of other studies of national literature and contribute to attest that the Brazilian capital market lacks the semi-strong form of informational efficiency.

  9. Prediction of Protein Thermostability by an Efficient Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Jalal Rezaeenour

    2016-10-01

    significantly improves the accuracy of ELM in prediction of thermostable enzymes. ELM tends to require more neurons in the hidden-layer than conventional tuning-based learning algorithms. To overcome these, the proposed approach uses a GA which optimizes the structure and the parameters of the ELM. In summary, optimization of ELM with GA results in an efficient prediction method; numerical experiments proved that our approach yields excellent results.

  10. Neural Efficiency in Expert Cognitive-Motor Performers During Affective Challenge.

    Science.gov (United States)

    Costanzo, Michelle E; VanMeter, John W; Janelle, Christopher M; Braun, Allen; Miller, Matthew W; Oldham, Jessica; Russell, Bartlett A H; Hatfield, Bradley D

    2016-01-01

    Skilled individuals demonstrate a spatially localized or relatively lower response in brain activity characterized as neural efficiency when performing within their domain of expertise. Elite athletes are experts in their chosen sport and thus must be not only adept in the motor domain but must be resilient to performing under the stress of high-level competition. Such stability of performance suggests this population processes emotion and mental stress in an adaptive and efficient manner. This study sought to determine if athletes with a history of successful performance under circumstances of mental stress demonstrate neural efficiency during affective challenges compared to age-matched controls. Using functional magnetic resonance imaging, the blood-oxygen level-dependent response was recorded during emotional challenge induced by sport-specific and general unpleasant images. The athletes demonstrated neural efficiency in brain regions critical to emotion regulation (prefrontal cortex) and affect (insula) independently of their domain of expertise, suggesting adaptive processing of negative events and less emotional reactivity to unpleasant stimuli.

  11. Extended passaging increases the efficiency of neural differentiation from induced pluripotent stem cells

    Directory of Open Access Journals (Sweden)

    Koehler Karl R

    2011-08-01

    Full Text Available Abstract Background The use of induced pluripotent stem cells (iPSCs for the functional replacement of damaged neurons and in vitro disease modeling is of great clinical relevance. Unfortunately, the capacity of iPSC lines to differentiate into neurons is highly variable, prompting the need for a reliable means of assessing the differentiation capacity of newly derived iPSC cell lines. Extended passaging is emerging as a method of ensuring faithful reprogramming. We adapted an established and efficient embryonic stem cell (ESC neural induction protocol to test whether iPSCs (1 have the competence to give rise to functional neurons with similar efficiency as ESCs and (2 whether the extent of neural differentiation could be altered or enhanced by increased passaging. Results Our gene expression and morphological analyses revealed that neural conversion was temporally delayed in iPSC lines and some iPSC lines did not properly form embryoid bodies during the first stage of differentiation. Notably, these deficits were corrected by continual passaging in an iPSC clone. iPSCs with greater than 20 passages (late-passage iPSCs expressed higher expression levels of pluripotency markers and formed larger embryoid bodies than iPSCs with fewer than 10 passages (early-passage iPSCs. Moreover, late-passage iPSCs started to express neural marker genes sooner than early-passage iPSCs after the initiation of neural induction. Furthermore, late-passage iPSC-derived neurons exhibited notably greater excitability and larger voltage-gated currents than early-passage iPSC-derived neurons, although these cells were morphologically indistinguishable. Conclusions These findings strongly suggest that the efficiency neuronal conversion depends on the complete reprogramming of iPSCs via extensive passaging.

  12. Acquiring Efficient Locomotion in a Simulated Quadruped through Evolving Random and Predefined Neural Networks

    DEFF Research Database (Denmark)

    Veenstra, Frank; Struck, Alexander; Krauledat, Matthias

    2015-01-01

    The acquisition and optimization of dynamically stable locomotion is important to engender fast and energy efficient locomotion in animals. Conventional optimization strategies tend to have difficulties in acquiring dynamically stable gaits in legged robots. In this paper, an evolving neural...... network (ENN) was implemented with the aim to optimize the locomotive behavior of a four-legged simulated robot. In the initial generation, individuals had neural networks (NNs) that were either predefined or randomly initialized. Additional investigations show that the efficiency of applying additional...... sensors to the simulated quadruped improved the performance of the ENN slightly. Promising results were seen in the evolutionary runs where the initial predefined NNs of the population contributed to slight movements of the limbs. This paper shows how a predefined ENNs linked to bio-inspired sensors can...

  13. Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

    Science.gov (United States)

    Latteri, Alberta; Arena, Paolo; Mazzone, Paolo

    2011-04-15

    Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort

  14. Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

    Science.gov (United States)

    Orhan, A Emin; Ma, Wei Ji

    2017-07-26

    Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.

  15. Affective personality differences in neural processing efficiency confirmed using fMRI.

    Science.gov (United States)

    Gray, Jeremy R; Burgess, Gregory C; Schaefer, Alexandre; Yarkoni, Tal; Larsen, Randy J; Braver, Todd S

    2005-06-01

    To test for a relation between individual differences in personality and neural-processing efficiency, we used functional magnetic resonance imaging (fMRI) to assess brain activity within regions associated with cognitive control during a demanding working memory task. Fifty-three participants completed both the self-report behavioral inhibition sensitivity (BIS) and behavioral approach sensitivity (BAS) personality scales and a standard measure of fluid intelligence (Raven's Advanced Progressive Matrices). They were then scanned as they performed a three-back working memory task. A mixed blocked/ event-related fMRI design enabled us to identify both sustained and transient neural activity. Higher BAS was negatively related to event-related activity in the dorsal anterior cingulate, the lateral prefrontal cortex, and parietal areas in regions of interest identified in previous work. These relationships were not explained by differences in either behavioral performance or fluid intelligence, consistent with greater neural efficiency. The results reveal the high specificity of the relationships among personality, cognition, and brain activity. The data confirm that affective dimensions of personality are independent of intelligence, yet also suggest that they might be interrelated in subtle ways, because they modulate activity in overlapping brain regions that appear to be critical for task performance.

  16. Learning Efficiency of Consciousness System for Robot Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Osama Shoubaky

    2014-12-01

    Full Text Available This paper presents learning efficiency of a consciousness system for robot using artificial neural network. The proposed conscious system consists of reason system, feeling system and association system. The three systems are modeled using Module of Nerves for Advanced Dynamics (ModNAD. Artificial neural network of the type of supervised learning with the back propagation is used to train the ModNAD. The reason system imitates behaviour and represents self-condition and other-condition. The feeling system represents sensation and emotion. The association system represents behaviour of self and determines whether self is comfortable or not. A robot is asked to perform cognition and tasks using the consciousness system. Learning converges to about 0.01 within about 900 orders for imitation, pain, solitude and the association modules. It converges to about 0.01 within about 400 orders for the comfort and discomfort modules. It can be concluded that learning in the ModNAD completed after a relatively small number of times because the learning efficiency of the ModNAD artificial neural network is good. The results also show that each ModNAD has a function to imitate and cognize emotion. The consciousness system presented in this paper may be considered as a fundamental step for developing a robot having consciousness and feelings similar to humans.

  17. Event-driven processing for hardware-efficient neural spike sorting.

    Science.gov (United States)

    Liu, Yan; L Pereira, João; Constandinou, Timothy

    2017-10-05

    The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope for large-scale integration of neural recording systems. In such systems the hardware resource, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can here provide a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous time level-crossing sampling for efficient data representation and subsequent spike processing. We first compare signals (using synthetic neural datasets) that are encoded using this technique against conventional sampling. It is observed that considerably lower data rates are achievable when utilising 7 bits or less to represent the signals, whilst maintaining the signal fidelity. We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. The proposed method is implemented in a low power FPGA platform to demonstrate the hardware viability. Results obtained using both MATLAB and reconfigurable logic (FPGA) hardware indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware cost. Creative Commons Attribution license.

  18. Mental workload and neural efficiency quantified in the prefrontal cortex using fNIRS.

    Science.gov (United States)

    Causse, Mickaël; Chua, Zarrin; Peysakhovich, Vsevolod; Del Campo, Natalia; Matton, Nadine

    2017-07-12

    An improved understanding of how the brain allocates mental resources as a function of task difficulty is critical for enhancing human performance. Functional near infrared spectroscopy (fNIRS) is a field-deployable optical brain monitoring technology that provides a direct measure of cerebral blood flow in response to cognitive activity. We found that fNIRS was sensitive to variations in task difficulty in both real-life (flight simulator) and laboratory settings (tests measuring executive functions), showing increased concentration of oxygenated hemoglobin (HbO2) and decreased concentration of deoxygenated hemoglobin (HHb) in the prefrontal cortex as the tasks became more complex. Intensity of prefrontal activation (HbO2 concentration) was not clearly correlated to task performance. Rather, activation intensity shed insight on the level of mental effort, i.e., how hard an individual was working to accomplish a task. When combined with performance, fNIRS provided an estimate of the participants' neural efficiency, and this efficiency was consistent across levels of difficulty of the same task. Overall, our data support the suitability of fNIRS to assess the mental effort related to human operations and represents a promising tool for the measurement of neural efficiency in other contexts such as training programs or the clinical setting.

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

    Science.gov (United States)

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

    2014-02-01

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

  20. Synchronization in Array of Coupled Neural Networks with Unbounded Distributed Delay and Limited Transmission Efficiency

    Directory of Open Access Journals (Sweden)

    Xinsong Yang

    2013-01-01

    Full Text Available This paper investigates global synchronization in an array of coupled neural networks with time-varying delays and unbounded distributed delays. In the coupled neural networks, limited transmission efficiency between coupled nodes, which makes the model more practical, is considered. Based on a novel integral inequality and the Lyapunov functional method, sufficient synchronization criteria are derived. The derived synchronization criteria are formulated by linear matrix inequalities (LMIs and can be easily verified by using Matlab LMI Toolbox. It is displayed that, when some of the transmission efficiencies are limited, the dynamics of the synchronized state are different from those of the isolated node. Furthermore, the transmission efficiency and inner coupling matrices between nodes play important roles in the final synchronized state. The derivative of the time-varying delay can be any given value, and the time-varying delay can be unbounded. The outer-coupling matrices can be symmetric or asymmetric. Numerical simulations are finally given to demonstrate the effectiveness of the theoretical results.

  1. Sensor-Aided Localized Capsule-Cooling Using Neural Networks for Energy-Efficient Refrigeration

    Directory of Open Access Journals (Sweden)

    N. Banerjee

    2014-06-01

    Full Text Available Sensor-aided localized capsule-cooling technique is a unique refrigeration process where sensors precisely capsulate the location of an item(s on a shelf of a fridge and hence direct the governing artificial intelligence to take suitable action. Here the sensors are used to locate the objects and the designed smart system (neural network activates the corresponding ductlines to cool the object. Here neural network system opens the gate(s and tilts the angle to allow the flow of cool air through the ductlines. Then the orifices, which fall in the virtual “Hot Region”, the domain that the active sensors had created almost immediately on sensing an obstruction, are opened. The orifices and sensors are arranged in a series on the lower wall of the ductlines to allow flow of air in the downward direction. These open orifices facilitate the direct hitting of cool air on the target-item and hence create a cold block within a fridge, instead of cooling the entire fridge uniformly, to keep the singular item refrigerated. This mode of operation offering selective cooling, rather than the conventional uniform one, is useful in saving energy, as the region then needed to be cooled is reduced significantly. A detail structural and theoretical explanations along with graphical analysis clearly elucidate the effective working of this mechanism under practical circumstances is given here. In this paper neural network is used for capsule cooling for energy efficient refrigeration

  2. Altered Neural Efficiency of Decision Making During Temporal Reward Discounting in Anorexia Nervosa.

    Science.gov (United States)

    King, Joseph A; Geisler, Daniel; Bernardoni, Fabio; Ritschel, Franziska; Böhm, Ilka; Seidel, Maria; Mennigen, Eva; Ripke, Stephan; Smolka, Michael N; Roessner, Veit; Ehrlich, Stefan

    2016-11-01

    The ability of individuals with anorexia nervosa (AN) to resist hunger and restrict caloric intake is often believed to reflect an unusual amount of self-control. However, the underlying neural substrate is poorly understood, especially in adolescent patients. Functional magnetic resonance imaging was used during an intertemporal choice task to probe the hemodynamic correlates of a common measurement of self-control-delayed (monetary) reward discounting-in a sample of acutely ill, predominately adolescent female patients with AN (n = 31) and age-matched healthy controls (n = 31). Delayed discounting rates did not differ between the groups, but decision making was consistently faster in the AN group. Although no group differences in the neural correlates of reward valuation were evident, activation associated with decision making was decreased in the AN group, most notably in the lateral prefrontal and posterior parietal regions implicated in executive control. Follow-up analysis of difficult decisions showed decreased activation in the AN group in a region of the dorsal anterior cingulate cortex. Decreased activation in frontoparietal regions involved in decision making, but faster and more consistent choice behavior, suggests that the altered efficiency of neural resource allocation might underlie an increased level of self-control in AN. This pattern of neural activation and behavior might reflect an ingrained "habit" to sustain high-level proactive (anticipatory) cognitive control in AN, which in turn might compromise reactive control mechanisms needed to adapt to changing cognitive demands, such as when difficult decisions must be made. Copyright © 2016 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

  3. Plant disease severity assessment - How rater bias, assessment method and experimental design affect hypothesis testing and resource use efficiency

    Science.gov (United States)

    The impact of rater bias and assessment method on hypothesis testing was studied for different experimental designs for plant disease assessment using balanced and unbalanced data sets. Data sets with the same number of replicate estimates for each of two treatments are termed ‘balanced’, and those ...

  4. 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...

  5. Can computational efficiency alone drive the evolution of modularity in neural networks?

    Science.gov (United States)

    Tosh, Colin R

    2016-08-30

    Some biologists have abandoned the idea that computational efficiency in processing multipart tasks or input sets alone drives the evolution of modularity in biological networks. A recent study confirmed that small modular (neural) networks are relatively computationally-inefficient but large modular networks are slightly more efficient than non-modular ones. The present study determines whether these efficiency advantages with network size can drive the evolution of modularity in networks whose connective architecture can evolve. The answer is no, but the reason why is interesting. All simulations (run in a wide variety of parameter states) involving gradualistic connective evolution end in non-modular local attractors. Thus while a high performance modular attractor exists, such regions cannot be reached by gradualistic evolution. Non-gradualistic evolutionary simulations in which multi-modularity is obtained through duplication of existing architecture appear viable. Fundamentally, this study indicates that computational efficiency alone does not drive the evolution of modularity, even in large biological networks, but it may still be a viable mechanism when networks evolve by non-gradualistic means.

  6. Consequences of converting graded to action potentials upon neural information coding and energy efficiency.

    Directory of Open Access Journals (Sweden)

    Biswa Sengupta

    2014-01-01

    Full Text Available Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na(+ and K(+ channels, with generator potential and graded potential models lacking voltage-gated Na(+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1 the voltage-gated Na(+ channels necessary for action potential generation increase intrinsic noise and (2 introduce non-linearities, and (3 the finite duration of the action potential creates a 'footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by ∼50% in generator potentials, to ∼3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

  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. Efficient sequential Bayesian inference method for real-time detection and sorting of overlapped neural spikes.

    Science.gov (United States)

    Haga, Tatsuya; Fukayama, Osamu; Takayama, Yuzo; Hoshino, Takayuki; Mabuchi, Kunihiko

    2013-09-30

    Overlapping of extracellularly recorded neural spike waveforms causes the original spike waveforms to become hidden and merged, confounding the real-time detection and sorting of these spikes. Methods proposed for solving this problem include using a multi-trode or placing a restriction on the complexity of overlaps. In this paper, we propose a rapid sequential method for the robust detection and sorting of arbitrarily overlapped spikes recorded with arbitrary types of electrodes. In our method, the probabilities of possible spike trains, including those that are overlapping, are evaluated by sequential Bayesian inference based on probabilistic models of spike-train generation and extracellular voltage recording. To reduce the high computational cost inherent in an exhaustive evaluation, candidates with low probabilities are considered as impossible candidates and are abolished at each sampling time to limit the number of candidates in the next evaluation. In addition, the data from a few subsequent sampling times are considered and used to calculate the "look-ahead probability", resulting in improved calculation efficiency due to a more rapid elimination of candidates. These sufficiently reduce computational time to enable real-time calculation without impairing performance. We assessed the performance of our method using simulated neural signals and actual neural signals recorded in primary cortical neurons cultured on a multi-electrode array. Our results demonstrated that our computational method could be applied in real-time with a delay of less than 10 ms. The estimation accuracy was higher than that of a conventional spike sorting method, particularly for signals with multiple overlapping spikes. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. SKYNET: an efficient and robust neural network training tool for machine learning in astronomy

    Science.gov (United States)

    Graff, Philip; Feroz, Farhan; Hobson, Michael P.; Lasenby, Anthony

    2014-06-01

    We present the first public release of our generic neural network training algorithm, called SKYNET. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as regression, classification, density estimation, clustering and dimensionality reduction. SKYNET uses a `pre-training' method to obtain a set of network parameters that has empirically been shown to be close to a good solution, followed by further optimization using a regularized variant of Newton's method, where the level of regularization is determined and adjusted automatically; the latter uses second-order derivative information to improve convergence, but without the need to evaluate or store the full Hessian matrix, by using a fast approximate method to calculate Hessian-vector products. This combination of methods allows for the training of complicated networks that are difficult to optimize using standard backpropagation techniques. SKYNET employs convergence criteria that naturally prevent overfitting, and also includes a fast algorithm for estimating the accuracy of network outputs. The utility and flexibility of SKYNET are demonstrated by application to a number of toy problems, and to astronomical problems focusing on the recovery of structure from blurred and noisy images, the identification of gamma-ray bursters, and the compression and denoising of galaxy images. The SKYNET software, which is implemented in standard ANSI C and fully parallelized using MPI, is available at http://www.mrao.cam.ac.uk/software/skynet/.

  10. Efficient Simulation of Wing Modal Response: Application of 2nd Order Shape Sensitivities and Neural Networks

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    2000-01-01

    At the preliminary design stage of a wing structure, an efficient simulation, one needing little computation but yielding adequately accurate results for various response quantities, is essential in the search of optimal design in a vast design space. In the present paper, methods of using sensitivities up to 2nd order, and direct application of neural networks are explored. The example problem is how to decide the natural frequencies of a wing given the shape variables of the structure. It is shown that when sensitivities cannot be obtained analytically, the finite difference approach is usually more reliable than a semi-analytical approach provided an appropriate step size is used. The use of second order sensitivities is proved of being able to yield much better results than the case where only the first order sensitivities are used. When neural networks are trained to relate the wing natural frequencies to the shape variables, a negligible computation effort is needed to accurately determine the natural frequencies of a new design.

  11. Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Asis Mazumder

    2009-06-01

    Full Text Available The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive.

  12. Efficient Approach for RLS Type Learning in TSK Neural Fuzzy Systems.

    Science.gov (United States)

    Yeh, Jen-Wei; Su, Shun-Feng

    2017-09-01

    This paper presents an efficient approach for the use of recursive least square (RLS) learning algorithm in Takagi-Sugeno-Kang neural fuzzy systems. In the use of RLS, reduced covariance matrix, of which the off-diagonal blocks defining the correlation between rules are set to zeros, may be employed to reduce computational burden. However, as reported in the literature, the performance of such an approach is slightly worse than that of using the full covariance matrix. In this paper, we proposed a so-called enhanced local learning concept in which a threshold is considered to stop learning for those less fired rules. It can be found from our experiments that the proposed approach can have better performances than that of using the full covariance matrix. Enhanced local learning method can be more active on the structure learning phase. Thus, the method not only can stop the update for insufficiently fired rules to reduce disturbances in self-constructing neural fuzzy inference network but also raises the learning speed on structure learning phase by using a large backpropagation learning constant.

  13. A New Efficient Hybrid Intelligent Model for Biodegradation Process of DMP with Fuzzy Wavelet Neural Networks

    Science.gov (United States)

    Huang, Mingzhi; Zhang, Tao; Ruan, Jujun; Chen, Xiaohong

    2017-01-01

    A new efficient hybrid intelligent approach based on fuzzy wavelet neural network (FWNN) was proposed for effectively modeling and simulating biodegradation process of Dimethyl phthalate (DMP) in an anaerobic/anoxic/oxic (AAO) wastewater treatment process. With the self learning and memory abilities of neural networks (NN), handling uncertainty capacity of fuzzy logic (FL), analyzing local details superiority of wavelet transform (WT) and global search of genetic algorithm (GA), the proposed hybrid intelligent model can extract the dynamic behavior and complex interrelationships from various water quality variables. For finding the optimal values for parameters of the proposed FWNN, a hybrid learning algorithm integrating an improved genetic optimization and gradient descent algorithm is employed. The results show, compared with NN model (optimized by GA) and kinetic model, the proposed FWNN model have the quicker convergence speed, the higher prediction performance, and smaller RMSE (0.080), MSE (0.0064), MAPE (1.8158) and higher R2 (0.9851) values. which illustrates FWNN model simulates effluent DMP more accurately than the mechanism model.

  14. FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

    Directory of Open Access Journals (Sweden)

    T. Pasupathi

    2014-05-01

    Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

  15. 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...

  16. Improved biocompatibility and efficient labeling of neural stem cells with poly(L-lysine-coated maghemite nanoparticles

    Directory of Open Access Journals (Sweden)

    Igor M. Pongrac

    2016-06-01

    Full Text Available Background: Cell tracking is a powerful tool to understand cellular migration, dynamics, homing and function of stem cell transplants. Nanoparticles represent possible stem cell tracers, but they differ in cellular uptake and side effects. Their properties can be modified by coating with different biocompatible polymers. To test if a coating polymer, poly(L-lysine, can improve the biocompatibility of nanoparticles applied to neural stem cells, poly(L-lysine-coated maghemite nanoparticles were prepared and characterized. We evaluated their cellular uptake, the mechanism of internalization, cytotoxicity, viability and proliferation of neural stem cells, and compared them to the commercially available dextran-coated nanomag®-D-spio nanoparticles.Results: Light microscopy of Prussian blue staining revealed a concentration-dependent intracellular uptake of iron oxide in neural stem cells. The methyl thiazolyl tetrazolium assay and the calcein acetoxymethyl ester/propidium iodide assay demonstrated that poly(L-lysine-coated maghemite nanoparticles scored better than nanomag®-D-spio in cell labeling efficiency, viability and proliferation of neural stem cells. Cytochalasine D blocked the cellular uptake of nanoparticles indicating an actin-dependent process, such as macropinocytosis, to be the internalization mechanism for both nanoparticle types. Finally, immunocytochemistry analysis of neural stem cells after treatment with poly(L-lysine-coated maghemite and nanomag®-D-spio nanoparticles showed that they preserve their identity as neural stem cells and their potential to differentiate into all three major neural cell types (neurons, astrocytes and oligodendrocytes.Conclusion: Improved biocompatibility and efficient cell labeling makes poly(L-lysine-coated maghemite nanoparticles appropriate candidates for future neural stem cell in vivo tracking studies.

  17. Highly efficient differentiation of neural precursors from human embryonic stem cells and benefits of transplantation after ischemic stroke in mice.

    Science.gov (United States)

    Drury-Stewart, Danielle; Song, Mingke; Mohamad, Osama; Guo, Ying; Gu, Xiaohuan; Chen, Dongdong; Wei, Ling

    2013-08-08

    Ischemic stroke is a leading cause of death and disability, but treatment options are severely limited. Cell therapy offers an attractive strategy for regenerating lost tissues and enhancing the endogenous healing process. In this study, we investigated the use of human embryonic stem cell-derived neural precursors as a cell therapy in a murine stroke model. Neural precursors were derived from human embryonic stem cells by using a fully adherent SMAD inhibition protocol employing small molecules. The efficiency of neural induction and the ability of these cells to further differentiate into neurons were assessed by using immunocytochemistry. Whole-cell patch-clamp recording was used to demonstrate the electrophysiological activity of human embryonic stem cell-derived neurons. Neural precursors were transplanted into the core and penumbra regions of a focal ischemic stroke in the barrel cortex of mice. Animals received injections of bromodeoxyuridine to track regeneration. Neural differentiation of the transplanted cells and regenerative markers were measured by using immunohistochemistry. The adhesive removal test was used to determine functional improvement after stroke and intervention. After 11 days of neural induction by using the small-molecule protocol, over 95% of human embryonic stem-derived cells expressed at least one neural marker. Further in vitro differentiation yielded cells that stained for mature neuronal markers and exhibited high-amplitude, repetitive action potentials in response to depolarization. Neuronal differentiation also occurred after transplantation into the ischemic cortex. A greater level of bromodeoxyuridine co-localization with neurons was observed in the penumbra region of animals receiving cell transplantation. Transplantation also improved sensory recovery in transplant animals over that in control animals. Human embryonic stem cell-derived neural precursors derived by using a highly efficient small-molecule SMAD inhibition

  18. Hypothesis on skeletal muscle aging : mitochondrial adenine nucleotide translocator decreases reactive oxygen species production while preserving coupling efficiency

    Directory of Open Access Journals (Sweden)

    Philippe eDIOLEZ

    2015-12-01

    Full Text Available Mitochondrial membrane potential is the major regulator of mitochondrial functions, including coupling efficiency and production of reactive oxygen species (ROS. Both functions are crucial for cell bioenergetics. We previously presented evidences for a specific modulation of adenine nucleotide translocase (ANT appearing during aging that results in a decrease in membrane potential - and therefore ROS production – but surprisingly increases coupling efficiency under conditions of low ATP turnover. Careful study of the bioenergetic parameters (oxidation and phosphorylation rates, membrane potential of isolated mitochondria from skeletal muscles (gastrocnemius of aged and young rats revealed a remodeling at the level of the phosphorylation system, in the absence of alteration of the inner mitochondrial membrane (uncoupling or respiratory chain complexes regulation. We further observed a decrease in mitochondrial affinity for ADP in aged isolated mitochondria, and higher sensitivity of ANT to its specific inhibitor atractyloside. This age-induced modification of ANT results in an increase in the ADP concentration required to sustain the same ATP turnover as compared to young muscle, and therefore in a lower membrane potential under phosphorylating - in vivo - conditions. Thus, for equivalent ATP turnover (cellular ATP demand, coupling efficiency is even higher in aged muscle mitochondria, due to the down-regulation of inner membrane proton leak caused by the decrease in membrane potential. In the framework of the radical theory of aging, these modifications in ANT function may be the result of oxidative damage caused by intra mitochondrial ROS and may appear like a virtuous circle where ROS induce a mechanism that reduces their production, without causing uncoupling, and even leading in improved efficiency. Because of the importance of ROS as therapeutic targets, this new mechanism deserves further studies.

  19. Efficient multiple hypothesis track processing of boost-phase ballistic missiles using IMPULSE©-generated threat models

    OpenAIRE

    Rakdham, Bert

    2006-01-01

    In this thesis, a multiple hypotheses tracking (MHT) algorithm is developed to successfully track multiple ballistic missiles within the boost phase. The success of previous work on the MHT algorithm and its application in other scientific fields enables this study to realize an efficient form of the algorithm and examine its feasibility in tracking multiple crossing ballistic missiles even though various accelerations due to staging are present. A framework is developed for the MHT, whi...

  20. Dynamic frame resizing with convolutional neural network for efficient video compression

    Science.gov (United States)

    Kim, Jaehwan; Park, Youngo; Choi, Kwang Pyo; Lee, JongSeok; Jeon, Sunyoung; Park, JeongHoon

    2017-09-01

    In the past, video codecs such as vc-1 and H.263 used a technique to encode reduced-resolution video and restore original resolution from the decoder for improvement of coding efficiency. The techniques of vc-1 and H.263 Annex Q are called dynamic frame resizing and reduced-resolution update mode, respectively. However, these techniques have not been widely used due to limited performance improvements that operate well only under specific conditions. In this paper, video frame resizing (reduced/restore) technique based on machine learning is proposed for improvement of coding efficiency. The proposed method features video of low resolution made by convolutional neural network (CNN) in encoder and reconstruction of original resolution using CNN in decoder. The proposed method shows improved subjective performance over all the high resolution videos which are dominantly consumed recently. In order to assess subjective quality of the proposed method, Video Multi-method Assessment Fusion (VMAF) which showed high reliability among many subjective measurement tools was used as subjective metric. Moreover, to assess general performance, diverse bitrates are tested. Experimental results showed that BD-rate based on VMAF was improved by about 51% compare to conventional HEVC. Especially, VMAF values were significantly improved in low bitrate. Also, when the method is subjectively tested, it had better subjective visual quality in similar bit rate.

  1. A Hypothesis and Review of the Relationship between Selection for Improved Production Efficiency, Coping Behavior, and Domestication

    Directory of Open Access Journals (Sweden)

    Wendy M. Rauw

    2017-09-01

    Full Text Available Coping styles in response to stressors have been described both in humans and in other animal species. Because coping styles are directly related to individual fitness they are part of the life history strategy. Behavioral styles trade off with other life-history traits through the acquisition and allocation of resources. Domestication and subsequent artificial selection for production traits specifically focused on selection of individuals with energy sparing mechanisms for non-production traits. Domestication resulted in animals with low levels of aggression and activity, and a low hypothalamic–pituitary–adrenal (HPA axis reactivity. In the present work, we propose that, vice versa, selection for improved production efficiency may to some extent continue to favor docile domesticated phenotypes. It is hypothesized that both domestication and selection for improved production efficiency may result in the selection of reactive style animals. Both domesticated and reactive style animals are characterized by low levels of aggression and activity, and increased serotonin neurotransmitter levels. However, whereas domestication quite consistently results in a decrease in the functional state of the HPA axis, the reactive coping style is often found to be dominated by a high HPA response. This may suggest that fearfulness and coping behavior are two independent underlying dimensions to the coping response. Although it is generally proposed that animal welfare improves with selection for calmer animals that are less fearful and reactive to novelty, animals bred to be less sensitive with fewer desires may be undesirable from an ethical point of view.

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

    Science.gov (United States)

    Sengupta, Abhronil; Roy, Kaushik

    2017-12-01

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

  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. 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.

  5. Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

    KAUST Repository

    AlShahrani, Mona

    2015-05-24

    Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

  6. Efficient calculation of the Gauss-Newton approximation of the Hessian matrix in neural networks.

    Science.gov (United States)

    Fairbank, Michael; Alonso, Eduardo

    2012-03-01

    The Levenberg-Marquardt (LM) learning algorithm is a popular algorithm for training neural networks; however, for large neural networks, it becomes prohibitively expensive in terms of running time and memory requirements. The most time-critical step of the algorithm is the calculation of the Gauss-Newton matrix, which is formed by multiplying two large Jacobian matrices together. We propose a method that uses backpropagation to reduce the time of this matrix-matrix multiplication. This reduces the overall asymptotic running time of the LM algorithm by a factor of the order of the number of output nodes in the neural network.

  7. A Hardware-Efficient Scalable Spike Sorting Neural Signal Processor Module for Implantable High-Channel-Count Brain Machine Interfaces.

    Science.gov (United States)

    Yang, Yuning; Boling, Sam; Mason, Andrew J

    2017-08-01

    Next-generation brain machine interfaces demand a high-channel-count neural recording system to wirelessly monitor activities of thousands of neurons. A hardware efficient neural signal processor (NSP) is greatly desirable to ease the data bandwidth bottleneck for a fully implantable wireless neural recording system. This paper demonstrates a complete multichannel spike sorting NSP module that incorporates all of the necessary spike detector, feature extractor, and spike classifier blocks. To meet high-channel-count and implantability demands, each block was designed to be highly hardware efficient and scalable while sharing resources efficiently among multiple channels. To process multiple channels in parallel, scalability analysis was performed, and the utilization of each block was optimized according to its input data statistics and the power, area and/or speed of each block. Based on this analysis, a prototype 32-channel spike sorting NSP scalable module was designed and tested on an FPGA using synthesized datasets over a wide range of signal to noise ratios. The design was mapped to 130 nm CMOS to achieve 0.75 μW power and 0.023 mm2 area consumptions per channel based on post synthesis simulation results, which permits scalability of digital processing to 690 channels on a 4×4 mm2 electrode array.

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

    Science.gov (United States)

    James, Jason; Dagli, Cihan H.

    1995-04-01

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

  9. Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

    Science.gov (United States)

    Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  10. Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

    Directory of Open Access Journals (Sweden)

    Stefano Curcio

    2014-01-01

    Full Text Available The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  11. Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

    Science.gov (United States)

    Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

  12. Improved Neural Processing Efficiency in a Chronic Aphasia Patient following Melodic Intonation Therapy: A Neuropsychological and Functional MRI Study

    Directory of Open Access Journals (Sweden)

    Ken-Ichi Tabei

    2016-09-01

    Full Text Available Melodic intonation therapy (MIT is a treatment program for the rehabilitation of aphasic patients with speech production disorders. We report a case of severe chronic non-fluent aphasia unresponsive to several years of conventional therapy that showed a marked improvement following intensive nine-day training on the Japanese version of MIT (MIT-J. The purposes of this study were to verify the efficacy of MIT-J by functional assessment and examine associated changes in neural processing by functional magnetic resonance imaging. MIT improved language output and auditory comprehension, and decreased the response time for picture naming. Following MIT-J, an area of the right hemisphere was less activated on correct naming trials than compared to before training but similarly activated on incorrect trials. These results suggest that the aphasic symptoms of our patient were improved by increased neural processing efficiency and a concomitant decrease in cognitive load.

  13. An Efficient Neural Network Based Modeling Method for Automotive EMC Simulation

    Science.gov (United States)

    Frank, Florian; Weigel, Robert

    2011-09-01

    This paper presents a newly developed methodology for VHDL-AMS model integration into SPICE-based EMC simulations. To this end the VHDL-AMS model, which is available in a compiled version only, is characterized under typical loading conditions, and afterwards a neural network based technique is applied to convert characteristic voltage and current data into an equivalent circuit in SPICE syntax. After the explanation of the whole method and the presentation of a newly developed switched state space dynamic neural network model, the entire analysis process is demonstrated using a typical application from automotive industry.

  14. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    K.L. Groenland (Koen); S.M. Bohte (Sander)

    2016-01-01

    textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order

  15. Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation

    Directory of Open Access Journals (Sweden)

    M. Agatonović

    2012-12-01

    Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.

  16. Thymidine Kinase-Negative Herpes Simplex Virus 1 Can Efficiently Establish Persistent Infection in Neural Tissues of Nude Mice.

    Science.gov (United States)

    Huang, Chih-Yu; Yao, Hui-Wen; Wang, Li-Chiu; Shen, Fang-Hsiu; Hsu, Sheng-Min; Chen, Shun-Hua

    2017-02-15

    patients with persistent infection. However, answers to the questions as to whether TK-negative (TK-) HSV-1 can establish persistent infection in brains of immunocompromised hosts and whether neurons in vivo are permissive for TK- HSV-1 remain elusive. Using three genetically engineered HSV-1 TK- mutants and two strains of nude mice deficient in T cells, we found that all three HSV-1 TK- mutants can efficiently establish persistent infection in the brain stem and trigeminal ganglion and detected glycoprotein C, a true late viral antigen, in brainstem neurons. Our study provides evidence that TK- HSV-1 can persist in neural tissues and replicate in brain neurons of immunocompromised hosts. Copyright © 2017 American Society for Microbiology.

  17. Big Multidimensional Datasets Visualization Using Neural Networks – Efficient Decision Support

    Directory of Open Access Journals (Sweden)

    Gintautas Dzemyda

    2016-04-01

    Full Text Available Nowadays business information systems are thought of as decision-oriented systems supported by different types of subsystems. Multidimensional data visualization is an essential part of such systems. As datasets tend to be increasingly large, more effective ways are required to display, analyze and interpret information they contain. Most of the classical visualization methods are unsuitable for large datasets. This paper focuses on the artificial neural networks-based methods for visualization of big multidimensional datasets; namely,  on the approaches for the faster obtaining of visual results. The new strategy, which is identified by the decreased number of cycles of data reviews (passes of training data up to the only one, when training neural networks, is proposed. To test this strategy, the results of experiments, using two unsupervised learning methods on benchmark data, are briefly presented.

  18. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  19. A Power-Efficient Multichannel Neural Stimulator Using High-Frequency Pulsed Excitation From an Unfiltered Dynamic Supply.

    Science.gov (United States)

    van Dongen, Marijn N; Serdijn, Wouter A

    2016-02-01

    This paper presents a neural stimulator system that employs a fundamentally different way of stimulating neural tissue compared to classical constant current stimulation. A stimulation pulse is composed of a sequence of current pulses injected at a frequency of 1 MHz for which the duty cycle is used to control the stimulation intensity. The system features 8 independent channels that connect to any of the 16 electrodes at the output. A sophisticated control system allows for individual control of each channel's stimulation and timing parameters. This flexibility makes the system suitable for complex electrode configurations and current steering applications. Simultaneous multichannel stimulation is implemented using a high frequency alternating technique, which reduces the amount of electrode switches by a factor 8. The system has the advantage of requiring a single inductor as its only external component. Furthermore it offers a high power efficiency, which is nearly independent on both the voltage over the load as well as on the number of simultaneously operated channels. Measurements confirm this: in multichannel mode the power efficiency can be increased for specific cases to 40% compared to 20% that is achieved by state-of-the-art classical constant current stimulators with adaptive power supply.

  20. TESTING THE SEMI-STRONG FORM OF THE EFFICIENT MARKET HYPOTHESIS ON PUBLIC OFFERS FOR ACQUISITION/TAKEOVER IN THE PHARMACEUTICAL AND THE ALUMINIUM SECTORS OF THE ROMANIAN CAPITAL MARKET

    OpenAIRE

    Dragos Ioan Minjina; Oana Resceanu

    2008-01-01

    The efficiency of the capital market is one of the most studied hypotheses, having an important impact, both in financial modelling and real economy. The research paper is focused on the announcements for acquisitions and takeover public offers for companies acting in the pharmaceutical and the aluminium sectors listed on the Romanian capital market. After testing the semi-strong form of the efficient market hypothesis in Romania, using the event studies technique for these announcements, the...

  1. A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors.

    Science.gov (United States)

    Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L; Nicolau, Alex; Veidenbaum, Alexander V

    2009-01-01

    Neural network simulators that take into account the spiking behavior of neurons are useful for studying brain mechanisms and for various neural engineering applications. Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures. Alternatively, Compute Unified Device Architecture (CUDA) Graphics Processing Units (GPUs) can provide a low-cost, programmable, and high-performance computing platform for simulation of SNNs. In this paper we demonstrate an efficient, biologically realistic, large-scale SNN simulator that runs on a single GPU. The SNN model includes Izhikevich spiking neurons, detailed models of synaptic plasticity and variable axonal delay. We allow user-defined configuration of the GPU-SNN model by means of a high-level programming interface written in C++ but similar to the PyNN programming interface specification. PyNN is a common programming interface developed by the neuronal simulation community to allow a single script to run on various simulators. The GPU implementation (on NVIDIA GTX-280 with 1 GB of memory) is up to 26 times faster than a CPU version for the simulation of 100K neurons with 50 Million synaptic connections, firing at an average rate of 7 Hz. For simulation of 10 Million synaptic connections and 100K neurons, the GPU SNN model is only 1.5 times slower than real-time. Further, we present a collection of new techniques related to parallelism extraction, mapping of irregular communication, and network representation for effective simulation of SNNs on GPUs. The fidelity of the simulation results was validated on CPU simulations using firing rate, synaptic weight distribution, and inter-spike interval analysis. Our simulator is publicly available to the modeling community so that researchers will have easy access to large-scale SNN simulations.

  2. Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices

    Directory of Open Access Journals (Sweden)

    S.C. Nayak

    2017-09-01

    Full Text Available The underlying system models of time series prediction are complex and not known a priori, hence, accurate and unbiased estimation cannot be always achieved using well known linear techniques. The estimation process requires more advanced prediction algorithms, such as multilayer perceptrons (MLPs. This paper presents an artificial chemical reaction neural network (ACRNN, which uses artificial chemical reaction optimization (ACRO to train the MLP models for forecasting the stock market indices. The underlying motivation for using ACRO is the ability to overcome the issues of convergence, parameter setting and overfitting and to accurately forecast financial time series data even when the underlying system processes are typically nonlinear. Historical data of seven different stock indices have been collected for 15 years to test the performance of the ACRNN approach. After extensive experimentation, it is observed that the ACRNN technique demonstrates significant improvements in prediction accuracy over the MLP approach.

  3. Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil.

    Science.gov (United States)

    Maran, J Prakash; Priya, B

    2015-03-01

    The present study is to evaluate and compare the prediction and simulating efficiencies of response surface methodology (RSM) and artificial neural network (ANN) based models on fatty acid methyl esters (FAME) yield achieved from muskmelon oil (MMO) under ultrasonication by two step in situ process. In first in situ process, free fatty acid content of MMO was reduced from 6.43% to 0.91% using H2SO4 as acid catalyst and organic phase in the first step was subjected to second reaction by adding KOH in methanol as basic catalyst. The influence of process variables (methanol to oil molar ratio, catalyst concentration, reaction temperature and reaction time) on conversion of FAME (second step) was investigated by central composite rotatable design (CCRD) of RSM and Multi-Layer Perceptron (MLP) neural network with the topology of 4-7-1. Both (RSM and ANN) were statistically compared by the coefficient of determination, root mean square error and absolute average deviation, based on the validation data set. The coefficient of determination (R(2)) calculated from the validation data for RSM and ANN models were 0.869 and 0.991 respectively. While both models showed good predictions in this study. But, the ANN model was more precise compared to the RSM model and it showed that, ANN is to be a powerful tool for modeling and optimizing FAME production. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Efficiency of neural transmission as a function of synaptic noise, threshold, and source characteristics.

    Science.gov (United States)

    Paprocki, Bartosz; Szczepanski, Janusz

    2011-07-01

    There has been a growing interest in the estimation of information carried by a single neuron and multiple single units or population of neurons to specific stimuli. In this paper we analyze, inspired by article of Levy and Baxter (2002), the efficiency of a neuronal communication by considering dendrosomatic summation as a Shannon-type channel (1948) and by considering such uncertain synaptic transmission as part of the dendrosomatic computation. Specifically, we study Mutual Information between input and output signals for different types of neuronal network architectures by applying efficient entropy estimators. We analyze the influence of the following quantities affecting transmission abilities of neurons: synaptic failure, activation threshold, firing rate and type of the input source. We observed a number of surprising non-intuitive effects. It turns out that, especially for lower activation thresholds, significant synaptic noise can lead even to twofold increase of the transmission efficiency. Moreover, the efficiency turns out to be a non-monotonic function of the activation threshold. We find a universal value of threshold for which a local maximum of Mutual Information is achieved for most of the neuronal architectures, regardless of the type of the source (correlated and non-correlated). Additionally, to reach the global maximum the optimal firing rates must increase with the threshold. This effect is particularly visible for lower firing rates. For higher firing rates the influence of synaptic noise on the transmission efficiency is more advantageous. Noise is an inherent component of communication in biological systems, hence, based on our analysis, we conjecture that the neuronal architecture was adjusted to make more effective use of this attribute. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  5. Investigating the Neural Bases for Intra-Subject Cognitive Efficiency Using Functional Magnetic Resonance Imaging

    Directory of Open Access Journals (Sweden)

    Neena K. Rao

    2014-10-01

    Full Text Available Several fMRI studies have examined brain regions mediating inter-subject variability in cognitive efficiency, but none have examined regions mediating intra-subject variability in efficiency. Thus, the present study was designed to identify brain regions involved in intra-subject variability in cognitive efficiency via participant-level correlations between trial-level reaction time (RT and trial-level fMRI BOLD percent signal change on a processing speed task. On each trial, participants indicated whether a digit-symbol probe-pair was present or absent in an array of nine digit-symbol probe-pairs while fMRI data were collected. Deconvolution analyses, using RT time-series models (derived from the proportional scaling of an event-related hemodynamic response function model by trial-level RT, were used to evaluate relationships between trial-level RTs and BOLD percent signal change. Although task-related patterns of activation and deactivation were observed in regions including bilateral occipital, bilateral parietal, portions of the medial wall such as the precuneus, default mode network regions including anterior cingulate, posterior cingulate, bilateral temporal, right cerebellum, and right cuneus, RT-BOLD correlations were observed in a more circumscribed set of regions. Positive RT-related patterns, or RT-BOLD correlations where fast RTs were associated with lower BOLD percent signal change, were observed in regions including bilateral occipital, bilateral parietal, and the precuneus. RT-BOLD correlations were not observed in the default mode network indicating a smaller set of regions associated with intra-subject variability in cognitive efficiency. The results are discussed in terms of a distributed area of regions that mediate variability in the cognitive efficiency that might underlie processing speed differences between individuals.

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

    Science.gov (United States)

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

    2008-09-01

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

  7. 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.

  8. An integrated multichannel neural recording analog front-end ASIC with area-efficient driven right leg circuit.

    Science.gov (United States)

    Tao Tang; Wang Ling Goh; Lei Yao; Jia Hao Cheong; Yuan Gao

    2017-07-01

    This paper describes an integrated multichannel neural recording analog front end (AFE) with a novel area-efficient driven right leg (DRL) circuit to improve the system common mode rejection ratio (CMRR). The proposed AFE consists of an AC-coupled low-noise programmable-gain amplifier, an area-efficient DRL block and a 10-bit SAR ADC. Compared to conventional DRL circuit, the proposed capacitor-less DRL design achieves 90% chip area reduction with enhanced CMRR performance, making it ideal for multichannel biomedical recording applications. The AFE circuit has been designed in a standard 0.18-μm CMOS process. Post-layout simulation results show that the AFE provides two gain settings of 54dB/60dB while consuming 1 μA per channel under a supply voltage of 1 V. The input-referred noise of the AFE integrated from 1 Hz to 10k Hz is only 4 μVrms and the CMRR is 110 dB.

  9. Efficient derivation of multipotent neural stem/progenitor cells from non-human primate embryonic stem cells.

    Directory of Open Access Journals (Sweden)

    Hiroko Shimada

    Full Text Available The common marmoset (Callithrix jacchus is a small New World primate that has been used as a non-human primate model for various biomedical studies. We previously demonstrated that transplantation of neural stem/progenitor cells (NS/PCs derived from mouse and human embryonic stem cells (ESCs and induced pluripotent stem cells (iPSCs promote functional locomotor recovery of mouse spinal cord injury models. However, for the clinical application of such a therapeutic approach, we need to evaluate the efficacy and safety of pluripotent stem cell-derived NS/PCs not only by xenotransplantation, but also allotransplantation using non-human primate models to assess immunological rejection and tumorigenicity. In the present study, we established a culture method to efficiently derive NS/PCs as neurospheres from common marmoset ESCs. Marmoset ESC-derived neurospheres could be passaged repeatedly and showed sequential generation of neurons and astrocytes, similar to that of mouse ESC-derived NS/PCs, and gave rise to functional neurons as indicated by calcium imaging. Although marmoset ESC-derived NS/PCs could not differentiate into oligodendrocytes under default culture conditions, these cells could abundantly generate oligodendrocytes by incorporating additional signals that recapitulate in vivo neural development. Moreover, principal component analysis of microarray data demonstrated that marmoset ESC-derived NS/PCs acquired similar gene expression profiles to those of fetal brain-derived NS/PCs by repeated passaging. Therefore, marmoset ESC-derived NS/PCs may be useful not only for accurate evaluation by allotransplantation of NS/PCs into non-human primate models, but are also applicable to analysis of iPSCs established from transgenic disease model marmosets.

  10. TESTING THE HYPOTHESIS OF MARKET EFFICIENCY THROUGH ARTIFICIAL NEURAL NETWORKS: A CASE STUDY WITH THE TEN MAJOR IBOVESPA SHARES IN THE FIRST QUARTER OF 2011

    Directory of Open Access Journals (Sweden)

    Luiz Henrique Herling

    2013-01-01

    Full Text Available Fuel market is facing political, economic, social and environmental problems that are fuzzing the future of fossil energy sources and in face of these facts, countries are looking for hybrid and electric vehicles as part of solution in transportation sector due to the fact of electric vehicles use few or no fossil fuel. The objective in this article was to identify options until 2020 to introduce electric vehicle in the urban traffic of São Paulo city and to develop this study the method of literature review in secondary sources was used to present electric vehicle technologies and to identify parameters that were assessed through morphological analysis technique. In morphological analysis, sets of values were defined by the author for these parameters, possible combinations were structured, clearly impractical deployment options before 2020 were discarded and some viable solutions were analyzed in details. These analyses concluded that there are viable options for actual days in São Paulo city, but important requirements regarding technology, politic, market, infrastructure and innovation in products and services still need to be addressed and it is the main reason of electric vehicle remain unnoticed by consumers as an viable option. The challenges are great and the actors who are willing to solve them will find a promising market to explore.

  11. Experimental evaluation of heat transfer efficiency of nanofluid in a double pipe heat exchanger and prediction of experimental results using artificial neural networks

    Science.gov (United States)

    Maddah, Heydar; Ghasemi, Nahid

    2017-12-01

    In this study, heat transfer efficiency of water and iron oxide nanofluid in a double pipe heat exchanger equipped with a typical twisted tape is experimentally investigated and impacts of the concentration of nanofluid and twisted tape on the heat transfer efficiency are also studied. Experiments were conducted under the laminar and turbulent flow for Reynolds numbers in the range of 1000 to 6000 and the concentration of nanofluid was 0.01, 0.02 and 0.03 wt%. In order to model and predict the heat transfer efficiency, an artificial neural network was used. The temperature of the hot fluid (nanofluid), the temperature of the cold fluid (water), mass flow rate of hot fluid (nanofluid), mass flow rate of cold fluid (water), the concentration of nanofluid and twist ratio are input data in artificial neural network and heat transfer is output or target. Heat transfer efficiency in the presence of 0.03 wt% nanofluid increases by 30% while using both the 0.03 wt% nanofluid and twisted tape with twist ratio 2 increases the heat transfer efficiency by 60%. Implementation of various structures of neural network with different number of neurons in the middle layer showed that 1-10-6 arrangement with the correlation coefficient 0.99181 and normal root mean square error 0.001621 is suggested as a desirable arrangement. The above structure has been successful in predicting 72% to 97%of variation in heat transfer efficiency characteristics based on the independent variables changes. In total, comparing the predicted results in this study with other studies and also the statistical measures shows the efficiency of artificial neural network.

  12. Anxiety and cognitive efficiency: differential modulation of transient and sustained neural activity during a working memory task.

    Science.gov (United States)

    Fales, C L; Barch, D M; Burgess, G C; Schaefer, A; Mennin, D S; Gray, J R; Braver, T S

    2008-09-01

    According to the processing-efficiency hypothesis (Eysenck, Derakshan, Santos, & Calvo, 2007), anxious individuals are thought to require greater activation of brain systems supporting cognitive control (e.g.,dorsolateral prefrontal cortex; DLPFC) in order to maintain equivalent performance to nonanxious subjects. A recent theory of cognitive control (Braver, Gray, & Burgess, 2007) has proposed that reduced cognitive efficiency might occur as a result of changes in the temporal dynamics of DLPFC recruitment. In this study, we used a mixed blocked/ event-related fMRI design to track transient and sustained activity in DLPFC while high- and low-anxious participants performed a working memory task. The task was performed after the participants viewed videos designed to induce neutral or anxiety-related moods. After the neutral video, the high-anxious participants had reduced sustained but increased transient activation in working memory areas, in comparison with low-anxious participants. The high-anxious group also showed extensive reductions in sustained activation of "default-network" areas (possible deactivation). After the negative video,the low-anxiety group shifted their activation dynamics in cognitive control regions to resemble those of the high-anxious group. These results suggest that reduced cognitive control in anxiety might be due to a transient, rather than sustained, pattern of working memory recruitment. Supplementary information for this study may be found at www.psychonomic.org/archive.

  13. Rules and mechanisms for efficient two-stage learning in neural circuits.

    Science.gov (United States)

    Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

    2017-04-04

    Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

  14. Efficient CRISPR/Cas9-assisted gene targeting enables rapid and precise genetic manipulation of mammalian neural stem cells.

    Science.gov (United States)

    Bressan, Raul Bardini; Dewari, Pooran Singh; Kalantzaki, Maria; Gangoso, Ester; Matjusaitis, Mantas; Garcia-Diaz, Claudia; Blin, Carla; Grant, Vivien; Bulstrode, Harry; Gogolok, Sabine; Skarnes, William C; Pollard, Steven M

    2017-02-15

    Mammalian neural stem cell (NSC) lines provide a tractable model for discovery across stem cell and developmental biology, regenerative medicine and neuroscience. They can be derived from foetal or adult germinal tissues and continuously propagated in vitro as adherent monolayers. NSCs are clonally expandable, genetically stable, and easily transfectable - experimental attributes compatible with targeted genetic manipulations. However, gene targeting, which is crucial for functional studies of embryonic stem cells, has not been exploited to date in NSC lines. Here, we deploy CRISPR/Cas9 technology to demonstrate a variety of sophisticated genetic modifications via gene targeting in both mouse and human NSC lines, including: (1) efficient targeted transgene insertion at safe harbour loci (Rosa26 and AAVS1); (2) biallelic knockout of neurodevelopmental transcription factor genes; (3) simple knock-in of epitope tags and fluorescent reporters (e.g. Sox2-V5 and Sox2-mCherry); and (4) engineering of glioma mutations (TP53 deletion; H3F3A point mutations). These resources and optimised methods enable facile and scalable genome editing in mammalian NSCs, providing significant new opportunities for functional genetic analysis. © 2017. Published by The Company of Biologists Ltd.

  15. 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.

  16. Efficient Market Hypothesis and Comovement Among Emerging Markets = Etkin Piyasa Hipotezi ve Gelişmekte Olan Piyasaların Birlikte Hareketi

    Directory of Open Access Journals (Sweden)

    Oktay TAŞ

    2010-06-01

    Full Text Available The main purpose of this study is to investigate stock market cointegration from the market efficiency perspective. Therefore, eleven emerging stock market indices are tested by using weekly data for the period of January 1998-December 2008 and for the sub period of January 2002-December 2008. Comovement among the emerging market countries was analyzed through Johansen cointegration test. The existence of two cointegrating vectors has been found at 5% significance level. However, the firm evidence against the market efficiency could not be established because of the low explanatory power of the results generated from the vector error correction model.

  17. Efficient genome editing of genes involved in neural crest development using the CRISPR/Cas9 system in Xenopus embryos.

    Science.gov (United States)

    Liu, Zhongzhen; Cheng, Tina Tsz Kwan; Shi, Zhaoying; Liu, Ziran; Lei, Yong; Wang, Chengdong; Shi, Weili; Chen, Xiongfeng; Qi, Xufeng; Cai, Dongqing; Feng, Bo; Deng, Yi; Chen, Yonglong; Zhao, Hui

    2016-01-01

    The RNA guided CRISPR/Cas9 nucleases have been proven to be effective for gene disruption in various animal models including Xenopus tropicalis. The neural crest (NC) is a transient cell population during embryonic development and contributes to a large variety of tissues. Currently, loss-of-function studies on NC development in X. tropicalis are largely based on morpholino antisense oligonucleotide. It is worthwhile establishing targeted gene knockout X. tropicails line using CRISPR/Cas9 system to study NC development. We utilized CRISPR/Cas9 to disrupt genes that are involved in NC formation in X. tropicalis embryos. A single sgRNA and Cas9 mRNA synthesized in vitro, were co-injected into X. tropicalis embryos at one-cell stage to induce single gene disruption. We also induced duplex mutations, large segmental deletions and inversions in X. tropicalis by injecting Cas9 and a pair of sgRNAs. The specificity of CRISPR/Cas9 was assessed in X. tropicalis embryos and the Cas9 nickase was used to reduce the off-target cleavages. Finally, we crossed the G0 mosaic frogs with targeted mutations to wild type frogs and obtained the germline transmission. Total 16 target sites in 15 genes were targeted by CRISPR/Cas9 and resulted in successful indel mutations at 14 loci with disruption efficiencies in a range from 9.3 to 57.8 %. Furthermore, we demonstrated the feasibility of generation of duplex mutations, large segmental deletions and inversions by using Cas9 and a pair of sgRNAs. We observed that CRISPR/Cas9 displays obvious off-target effects at some loci in X. tropicalis embryos. Such off-target cleavages was reduced by using the D10A Cas9 nickase. Finally, the Cas9 induced indel mutations were efficiently passed to G1 offspring. Our study proved that CRISPR/Cas9 could mediate targeted gene mutation in X. tropicalis with high efficiency. This study expands the application of CRISPR/Cas9 platform in X. tropicalis and set a basis for studying NC development using genetic

  18. 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 ...

  19. Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

    Science.gov (United States)

    Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

    2017-12-01

    The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction(v/v%) 0.05.

  20. Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control.

    Science.gov (United States)

    Liu, Xilin; Zhang, Milin; Richardson, Andrew G; Lucas, Timothy H; Van der Spiegel, Jan

    2017-08-01

    This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 μ m CMOS technology, occupying a silicon area of 3.7 mm 2 . The chip dissipates 56 μW/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.

  1. Complementary fMRI and EEG evidence for more efficient neural processing of rhythmic vs. unpredictably timed sounds

    NARCIS (Netherlands)

    van Atteveldt, N.M.; Musacchia, G.; Zion-Golumbic, E.; Sehatpour, P.; Javitt, D.C.; Schroeder, C.E.

    2015-01-01

    The brain's fascinating ability to adapt its internal neural dynamics to the temporal structure of the sensory environment is becoming increasingly clear. It is thought to be metabolically beneficial to align ongoing oscillatory activity to the relevant inputs in a predictable stream, so that they

  2. 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...

  3. 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

  4. 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.

  5. 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

  6. Efficient and Fast Differentiation of Human Neural Stem Cells from Human Embryonic Stem Cells for Cell Therapy

    Directory of Open Access Journals (Sweden)

    Xinxin Han

    2017-01-01

    Full Text Available Stem cell-based therapies have been used for repairing damaged brain tissue and helping functional recovery after brain injury. Aberrance neurogenesis is related with brain injury, and multipotential neural stem cells from human embryonic stem (hES cells provide a great promise for cell replacement therapies. Optimized protocols for neural differentiation are necessary to produce functional human neural stem cells (hNSCs for cell therapy. However, the qualified procedure is scarce and detailed features of hNSCs originated from hES cells are still unclear. In this study, we developed a method to obtain hNSCs from hES cells, by which we could harvest abundant hNSCs in a relatively short time. Then, we examined the expression of pluripotent and multipotent marker genes through immunostaining and confirmed differentiation potential of the differentiated hNSCs. Furthermore, we analyzed the mitotic activity of these hNSCs. In this report, we provided comprehensive features of hNSCs and delivered the knowledge about how to obtain more high-quality hNSCs from hES cells which may help to accelerate the NSC-based therapies in brain injury treatment.

  7. 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.

  8. 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.

  9. A CMOS power-efficient low-noise current-mode front-end amplifier for neural signal recording.

    Science.gov (United States)

    Wu, Chung-Yu; Chen, Wei-Ming; Kuo, Liang-Ting

    2013-04-01

    In this paper, a new current-mode front-end amplifier (CMFEA) for neural signal recording systems is proposed. In the proposed CMFEA, a current-mode preamplifier with an active feedback loop operated at very low frequency is designed as the first gain stage to bypass any dc offset current generated by the electrode-tissue interface and to achieve a low high-pass cutoff frequency below 0.5 Hz. No reset signal or ultra-large pseudo resistor is required. The current-mode preamplifier has low dc operation current to enhance low-noise performance and decrease power consumption. A programmable current gain stage is adopted to provide adjustable gain for adaptive signal scaling. A following current-mode filter is designed to adjust the low-pass cutoff frequency for different neural signals. The proposed CMFEA is designed and fabricated in 0.18-μm CMOS technology and the area of the core circuit is 0.076 mm(2). The measured high-pass cutoff frequency is as low as 0.3 Hz and the low-pass cutoff frequency is adjustable from 1 kHz to 10 kHz. The measured maximum current gain is 55.9 dB. The measured input-referred current noise density is 153 fA /√Hz , and the power consumption is 13 μW at 1-V power supply. The fabricated CMFEA has been successfully applied to the animal test for recording the seizure ECoG of Long-Evan rats.

  10. Safe and efficient method for cryopreservation of human induced pluripotent stem cell-derived neural stem and progenitor cells by a programmed freezer with a magnetic field.

    Science.gov (United States)

    Nishiyama, Yuichiro; Iwanami, Akio; Kohyama, Jun; Itakura, Go; Kawabata, Soya; Sugai, Keiko; Nishimura, Soraya; Kashiwagi, Rei; Yasutake, Kaori; Isoda, Miho; Matsumoto, Morio; Nakamura, Masaya; Okano, Hideyuki

    2016-06-01

    Stem cells represent a potential cellular resource in the development of regenerative medicine approaches to the treatment of pathologies in which specific cells are degenerated or damaged by genetic abnormality, disease, or injury. Securing sufficient supplies of cells suited to the demands of cell transplantation, however, remains challenging, and the establishment of safe and efficient cell banking procedures is an important goal. Cryopreservation allows the storage of stem cells for prolonged time periods while maintaining them in adequate condition for use in clinical settings. Conventional cryopreservation systems include slow-freezing and vitrification both have advantages and disadvantages in terms of cell viability and/or scalability. In the present study, we developed an advanced slow-freezing technique using a programmed freezer with a magnetic field called Cells Alive System (CAS) and examined its effectiveness on human induced pluripotent stem cell-derived neural stem/progenitor cells (hiPSC-NS/PCs). This system significantly increased cell viability after thawing and had less impact on cellular proliferation and differentiation. We further found that frozen-thawed hiPSC-NS/PCs were comparable with non-frozen ones at the transcriptome level. Given these findings, we suggest that the CAS is useful for hiPSC-NS/PCs banking for clinical uses involving neural disorders and may open new avenues for future regenerative medicine. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  11. Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion

    Science.gov (United States)

    Hansen, T. M.; Cordua, K. S.

    2017-12-01

    Probabilistically formulated inverse problems can be solved using Monte Carlo-based sampling methods. In principle, both advanced prior information, based on for example, complex geostatistical models and non-linear forward models can be considered using such methods. However, Monte Carlo methods may be associated with huge computational costs that, in practice, limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical forward response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival traveltime inversion of crosshole ground penetrating radar data. An accurate forward model, based on 2-D full-waveform modeling followed by automatic traveltime picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the accurate and computationally expensive forward model, and also considerably faster and more accurate (i.e. with better resolution), than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of non-linear and non-Gaussian inverse problems that have to be solved using Monte Carlo sampling techniques.

  12. 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...

  13. 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...

  14. Histopathological examination of nerve samples from pure neural leprosy patients: obtaining maximum information to improve diagnostic efficiency.

    Science.gov (United States)

    Antunes, Sérgio Luiz Gomes; Chimelli, Leila; Jardim, Márcia Rodrigues; Vital, Robson Teixeira; Nery, José Augusto da Costa; Corte-Real, Suzana; Hacker, Mariana Andréa Vilas Boas; Sarno, Euzenir Nunes

    2012-03-01

    Nerve biopsy examination is an important auxiliary procedure for diagnosing pure neural leprosy (PNL). When acid-fast bacilli (AFB) are not detected in the nerve sample, the value of other nonspecific histological alterations should be considered along with pertinent clinical, electroneuromyographical and laboratory data (the detection of Mycobacterium leprae DNA with polymerase chain reaction and the detection of serum anti-phenolic glycolipid 1 antibodies) to support a possible or probable PNL diagnosis. Three hundred forty nerve samples [144 from PNL patients and 196 from patients with non-leprosy peripheral neuropathies (NLN)] were examined. Both AFB-negative and AFB-positive PNL samples had more frequent histopathological alterations (epithelioid granulomas, mononuclear infiltrates, fibrosis, perineurial and subperineurial oedema and decreased numbers of myelinated fibres) than the NLN group. Multivariate analysis revealed that independently, mononuclear infiltrate and perineurial fibrosis were more common in the PNL group and were able to correctly classify AFB-negative PNL samples. These results indicate that even in the absence of AFB, these histopathological nerve alterations may justify a PNL diagnosis when observed in conjunction with pertinent clinical, epidemiological and laboratory data.

  15. 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

  16. 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 ...

  17. 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.

  18. Eating breakfast enhances the efficiency of neural networks engaged during mental arithmetic in school-aged children.

    Science.gov (United States)

    Pivik, R T; Tennal, Kevin B; Chapman, Stephen D; Gu, Yuyuan

    2012-06-25

    To determine the influence of a morning meal on complex mental functions in children (8-11 y), time-frequency analyses were applied to electroencephalographic (EEG) activity recorded while children solved simple addition problems after an overnight fast and again after having either eaten or skipped breakfast. Power of low frequency EEG activity [2 Hertz (Hz) bands in the 2-12 Hz range] was determined from recordings over frontal and parietal brain regions associated with mathematical thinking during mental calculation of correctly answered problems. Analyses were adjusted for background variables known to influence or reflect the development of mathematical skills, i.e., age and measures of math competence and math fluency. Relative to fed children, those who continued to fast showed greater power increases in upper theta (6-8 Hz) and both alpha bands (8-10 Hz; 10-12 Hz) across sites. Increased theta suggests greater demands on working memory. Increased alpha may facilitate task-essential activity by suppressing non-task-essential activity. Fasting children also had greater delta (2-4 Hz) and greater lower-theta (4-6 Hz) power in left frontal recordings-indicating a region-specific emphasis on both working memory for mental calculation (theta) and activation of processes that suppress interfering activity (delta). Fed children also showed a significant increase in correct responses while children who continued to fast did not. Taken together the findings suggest that neural network activity involved in processing numerical information is functionally enhanced and performance is improved in children who have eaten breakfast, whereas greater mental effort is required for this mathematical thinking in children who skip breakfast. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. FNS: an event-driven spiking neural network framework for efficient simulations of large-scale brain models

    OpenAIRE

    Susi, Gianluca; Garces, Pilar; Cristini, Alessandro; Paracone, Emanuele; Salerno, Mario; Maestu, Fernando; Pereda, Ernesto

    2018-01-01

    Limitations in processing capabilities and memory of today's computers make spiking neuron-based (human) whole-brain simulations inevitably characterized by a compromise between bio-plausibility and computational cost. It translates into brain models composed of a reduced number of neurons and a simplified neuron's mathematical model. Taking advantage of the sparse character of brain-like computation, eventdriven technique allows us to carry out efficient simulation of large-scale Spiking Neu...

  20. 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...

  1. 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.

  2. 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.

  3. Modelling changes in the energy efficiency of buildings using neural networks on the example of Zielona Góra

    Directory of Open Access Journals (Sweden)

    Łączak Andrzej

    2016-01-01

    Full Text Available The objective of this article is to find a way to pursue optimum spatial policy on the local level to meet the assumptions of the energy policy of the European Union. One of the possible ways of developing energy efficient civil engineering is varied town policy and programmes supporting energy efficient buildings. And the second is the use of renewable energy sources as a factor improving the energy safety of built areas and reducing the emission of greenhouse gases. And the third is the optimization of expenditure on these goals in towns. Although our current research and estimations based on it are limited to a medium-sized town in the west of Poland, the observations included in this article may be important for other regions that are interested in reducing energy consumption in buildings, residential areas and towns. Taking into account the geographical context, it is especially important for these regions of Europe that are obtaining financial aid from the European Union in the perspective for the years 2014-2020.

  4. 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.

  5. 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

  6. An Efficient Approach for Lipase-Catalyzed Synthesis of Retinyl Laurate Nutraceutical by Combining Ultrasound Assistance and Artificial Neural Network Optimization.

    Science.gov (United States)

    Huang, Shang-Ming; Li, Hsin-Ju; Liu, Yung-Chuan; Kuo, Chia-Hung; Shieh, Chwen-Jen

    2017-11-15

    Although retinol is an important nutrient, retinol is highly sensitive to oxidation. At present, some ester forms of retinol are generally used in nutritional supplements because of its stability and bioavailability. However, such esters are commonly synthesized by chemical procedures which are harmful to the environment. Thus, this study utilized a green method using lipase as a catalyst with sonication assistance to produce a retinol derivative named retinyl laurate. Moreover, the process was optimized by an artificial neural network (ANN). First, a three-level-four-factor central composite design (CCD) was employed to design 27 experiments, which the highest relative conversion was 82.64%. Further, the optimal architecture of the CCD-employing ANN was developed, including the learning Levenberg-Marquardt algorithm, the transfer function (hyperbolic tangent), iterations (10,000), and the nodes of the hidden layer (6). The best performance of the ANN was evaluated by the root mean squared error (RMSE) and the coefficient of determination (R²) from predicting and observed data, which displayed a good data-fitting property. Finally, the process performed with optimal parameters actually obtained a relative conversion of 88.31% without long-term reactions, and the lipase showed great reusability for biosynthesis. Thus, this study utilizes green technology to efficiently produce retinyl laurate, and the bioprocess is well established by ANN-mediated modeling and optimization.

  7. An Efficient Approach for Lipase-Catalyzed Synthesis of Retinyl Laurate Nutraceutical by Combining Ultrasound Assistance and Artificial Neural Network Optimization

    Directory of Open Access Journals (Sweden)

    Shang-Ming Huang

    2017-11-01

    Full Text Available Although retinol is an important nutrient, retinol is highly sensitive to oxidation. At present, some ester forms of retinol are generally used in nutritional supplements because of its stability and bioavailability. However, such esters are commonly synthesized by chemical procedures which are harmful to the environment. Thus, this study utilized a green method using lipase as a catalyst with sonication assistance to produce a retinol derivative named retinyl laurate. Moreover, the process was optimized by an artificial neural network (ANN. First, a three-level-four-factor central composite design (CCD was employed to design 27 experiments, which the highest relative conversion was 82.64%. Further, the optimal architecture of the CCD-employing ANN was developed, including the learning Levenberg-Marquardt algorithm, the transfer function (hyperbolic tangent, iterations (10,000, and the nodes of the hidden layer (6. The best performance of the ANN was evaluated by the root mean squared error (RMSE and the coefficient of determination (R2 from predicting and observed data, which displayed a good data-fitting property. Finally, the process performed with optimal parameters actually obtained a relative conversion of 88.31% without long-term reactions, and the lipase showed great reusability for biosynthesis. Thus, this study utilizes green technology to efficiently produce retinyl laurate, and the bioprocess is well established by ANN-mediated modeling and optimization.

  8. 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....

  9. 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.

  10. 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...

  11. 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

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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…

  18. 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

  19. 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…

  20. 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...

  1. Intermittent reductions in respiratory neural activity elicit spinal TNF-α-independent, atypical PKC-dependent inactivity-induced phrenic motor facilitation

    Science.gov (United States)

    Baertsch, Nathan A.

    2015-01-01

    In many neural networks, mechanisms of compensatory plasticity respond to prolonged reductions in neural activity by increasing cellular excitability or synaptic strength. In the respiratory control system, a prolonged reduction in synaptic inputs to the phrenic motor pool elicits a TNF-α- and atypical PKC-dependent form of spinal plasticity known as inactivity-induced phrenic motor facilitation (iPMF). Although iPMF may be elicited by a prolonged reduction in respiratory neural activity, iPMF is more efficiently induced when reduced respiratory neural activity (neural apnea) occurs intermittently. Mechanisms giving rise to iPMF following intermittent neural apnea are unknown. The purpose of this study was to test the hypothesis that iPMF following intermittent reductions in respiratory neural activity requires spinal TNF-α and aPKC. Phrenic motor output was recorded in anesthetized and ventilated rats exposed to brief intermittent (5, ∼1.25 min), brief sustained (∼6.25 min), or prolonged sustained (30 min) neural apnea. iPMF was elicited following brief intermittent and prolonged sustained neural apnea, but not following brief sustained neural apnea. Unlike iPMF following prolonged neural apnea, spinal TNF-α was not required to initiate iPMF during intermittent neural apnea; however, aPKC was still required for its stabilization. These results suggest that different patterns of respiratory neural activity induce iPMF through distinct cellular mechanisms but ultimately converge on a similar downstream pathway. Understanding the diverse cellular mechanisms that give rise to inactivity-induced respiratory plasticity may lead to development of novel therapeutic strategies to treat devastating respiratory control disorders when endogenous compensatory mechanisms fail. PMID:25673781

  2. 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.

  3. [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.

  4. 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.

  5. 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.

  6. 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....

  7. Efficient Transduction of Feline Neural Progenitor Cells for Delivery of Glial Cell Line-Derived Neurotrophic Factor Using a Feline Immunodeficiency Virus-Based Lentiviral Construct

    Directory of Open Access Journals (Sweden)

    X. Joann You

    2011-01-01

    Full Text Available Work has shown that stem cell transplantation can rescue or replace neurons in models of retinal degenerative disease. Neural progenitor cells (NPCs modified to overexpress neurotrophic factors are one means of providing sustained delivery of therapeutic gene products in vivo. To develop a nonrodent animal model of this therapeutic strategy, we previously derived NPCs from the fetal cat brain (cNPCs. Here we use bicistronic feline lentiviral vectors to transduce cNPCs with glial cell-derived neurotrophic factor (GDNF together with a GFP reporter gene. Transduction efficacy is assessed, together with transgene expression level and stability during induction of cellular differentiation, together with the influence of GDNF transduction on growth and gene expression profile. We show that GDNF overexpressing cNPCs expand in vitro, coexpress GFP, and secrete high levels of GDNF protein—before and after differentiation—all qualities advantageous for use as a cell-based approach in feline models of neural degenerative disease.

  8. 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

  9. 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

  10. Structural and functional correlates for language efficiency in auditory word processing.

    Science.gov (United States)

    Jung, JeYoung; Kim, Sunmi; Cho, Hyesuk; Nam, Kichun

    2017-01-01

    This study aims to provide convergent understanding of the neural basis of auditory word processing efficiency using a multimodal imaging. We investigated the structural and functional correlates of word processing efficiency in healthy individuals. We acquired two structural imaging (T1-weighted imaging and diffusion tensor imaging) and functional magnetic resonance imaging (fMRI) during auditory word processing (phonological and semantic tasks). Our results showed that better phonological performance was predicted by the greater thalamus activity. In contrary, better semantic performance was associated with the less activation in the left posterior middle temporal gyrus (pMTG), supporting the neural efficiency hypothesis that better task performance requires less brain activation. Furthermore, our network analysis revealed the semantic network including the left anterior temporal lobe (ATL), dorsolateral prefrontal cortex (DLPFC) and pMTG was correlated with the semantic efficiency. Especially, this network acted as a neural efficient manner during auditory word processing. Structurally, DLPFC and cingulum contributed to the word processing efficiency. Also, the parietal cortex showed a significate association with the word processing efficiency. Our results demonstrated that two features of word processing efficiency, phonology and semantics, can be supported in different brain regions and, importantly, the way serving it in each region was different according to the feature of word processing. Our findings suggest that word processing efficiency can be achieved by in collaboration of multiple brain regions involved in language and general cognitive function structurally and functionally.

  11. New Evidence for the Darwinian Hypothesis of Heterostyly: Breeding Systems and Pollinators in Narcissus Sect. Apodanthi

    National Research Council Canada - National Science Library

    Rocio Pérez-Barrales; Pablo Vargas; Juan Arroyo

    2006-01-01

    ... in pollinator proficiency or breeding system variation (Darwinian hypothesis). • We studied pollinators, pollen-transfer efficiency, and incompatibility systems in all seven species of Narcissus sect...

  12. 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.

  13. 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.

  14. 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...

  15. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

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

    1990-01-01

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

  16. 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.

  17. The biological sense of cancer: a hypothesis

    Directory of Open Access Journals (Sweden)

    Bustuoabad Oscar D

    2006-12-01

    regenerative signal of the non-restored organ. Therefore, efficient anti-cancer therapy should combine an attack against the tumor cells themselves with the correction of the organ failure, which, according to this hypothesis, is fundamental to the origin of the cancer.

  18. Testing the efficiency market hypothesis for the Colombian stock market

    Directory of Open Access Journals (Sweden)

    Juan Benjamín Duarte-Duarte

    2014-01-01

    Full Text Available Uno de los supuestos básicos de los modelos de valoración de activos (CAPM y APT, es la eficiencia de los mercados. El presente trabajo busca comprobar este requisito en su forma débil, tanto al Índice General de la Bolsa de Valores de Colombia como a las acciones más representativas del mercado colombiano. Para tal fin se comprueba por diferentes métodos estadísticos que las series bursátiles no siguen el patrón de una distribución normal. Además, al indagar sobre la eficiencia del mercado colombiano, mediante los test de Rachas, BDS, LB y Bartlett, se evidencia no aleatoriedad en los principales activos financieros con excepción de Ecopetrol, mientras que para el IGBC se observa una mejora en la eficiencia del mercado del 2008 a 2010, periodo que coincide con el inicio de la crisis económica mundial.

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. Initialization of multilayer forecasting artifical neural networks

    OpenAIRE

    Bochkarev, Vladimir V.; Maslennikova, Yulia S.

    2014-01-01

    In this paper, a new method was developed for initialising artificial neural networks predicting dynamics of time series. Initial weighting coefficients were determined for neurons analogously to the case of a linear prediction filter. Moreover, to improve the accuracy of the initialization method for a multilayer neural network, some variants of decomposition of the transformation matrix corresponding to the linear prediction filter were suggested. The efficiency of the proposed neural netwo...

  4. 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

  5. Does Effeciency Wage Hypothesis Hold in Tanzanian Labour Market?

    African Journals Online (AJOL)

    The primary objective of this paper is to test the hypothesis of efficiency wage in the context of Tanzania labour market. The test is facilitated via estimating the correlation between firm level productivity and firm level weighted average wage in Tanzania manufacturing enterprises. The study uses panel dimension of the data ...

  6. Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

    Directory of Open Access Journals (Sweden)

    Yang Xu

    2017-01-01

    Full Text Available Integrating wireless sensor network (WSN into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS, has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC-based extreme learning machine (ELM algorithm (ELM-RCC. Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square

  7. Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme.

    Science.gov (United States)

    Xu, Yang; Luo, Xiong; Wang, Weiping; Zhao, Wenbing

    2017-01-12

    Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE

  8. Robust estimation and hypothesis testing

    CERN Document Server

    Tiku, Moti L

    2004-01-01

    In statistical theory and practice, a certain distribution is usually assumed and then optimal solutions sought. Since deviations from an assumed distribution are very common, one cannot feel comfortable with assuming a particular distribution and believing it to be exactly correct. That brings the robustness issue in focus. In this book, we have given statistical procedures which are robust to plausible deviations from an assumed mode. The method of modified maximum likelihood estimation is used in formulating these procedures. The modified maximum likelihood estimators are explicit functions of sample observations and are easy to compute. They are asymptotically fully efficient and are as efficient as the maximum likelihood estimators for small sample sizes. The maximum likelihood estimators have computational problems and are, therefore, elusive. A broad range of topics are covered in this book. Solutions are given which are easy to implement and are efficient. The solutions are also robust to data anomali...

  9. A 12-Week Physical and Cognitive Exercise Program Can Improve Cognitive Function and Neural Efficiency in Community-Dwelling Older Adults: A Randomized Controlled Trial.

    Science.gov (United States)

    Nishiguchi, Shu; Yamada, Minoru; Tanigawa, Takanori; Sekiyama, Kaoru; Kawagoe, Toshikazu; Suzuki, Maki; Yoshikawa, Sakiko; Abe, Nobuhito; Otsuka, Yuki; Nakai, Ryusuke; Aoyama, Tomoki; Tsuboyama, Tadao

    2015-07-01

    To investigate whether a 12-week physical and cognitive exercise program can improve cognitive function and brain activation efficiency in community-dwelling older adults. Randomized controlled trial. Kyoto, Japan. Community-dwelling older adults (N = 48) were randomized into an exercise group (n = 24) and a control group (n = 24). Exercise group participants received a weekly dual task-based multimodal exercise class in combination with pedometer-based daily walking exercise during the 12-week intervention phase. Control group participants did not receive any intervention and were instructed to spend their time as usual during the intervention phase. The outcome measures were global cognitive function, memory function, executive function, and brain activation (measured using functional magnetic resonance imaging) associated with visual short-term memory. Exercise group participants had significantly greater postintervention improvement in memory and executive functions than the control group (P brain regions associated with visual short-term memory, including the prefrontal cortex, in the exercise group (P physical and cognitive exercise program can improve the efficiency of brain activation during cognitive tasks in older adults, which is associated with improvements in memory and executive function. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

  10. 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…

  11. 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

  12. 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.

  13. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural ...... the uncertainty in a latent path, like a state space model, we improve the state of the art results on the Blizzard and TIMIT speech modeling data sets by a large margin, while achieving comparable performances to competing methods on polyphonic music modeling....

  14. 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.

  15. 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.

  16. 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...

  17. 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,…

  18. A double-blind, placebo-controlled study on the effects of lutein and zeaxanthin on neural processing speed and efficiency.

    Directory of Open Access Journals (Sweden)

    Emily R Bovier

    Full Text Available Lutein and zeaxanthin are major carotenoids in the eye but are also found in post-receptoral visual pathways. It has been hypothesized that these pigments influence the processing of visual signals within and post-retina, and that increasing lutein and zeaxanthin levels within the visual system will lead to increased visual processing speeds. To test this, we measured macular pigment density (as a biomarker of lutein and zeaxanthin levels in brain, critical flicker fusion (CFF thresholds, and visual motor reaction time in young healthy subjects (n = 92. Changes in these outcome variables were also assessed after four months of supplementation with either placebo (n = 10, zeaxanthin only (20 mg/day; n = 29 or a mixed formulation containing 26 mg/day zeaxanthin, 8 mg/day lutein, and 190 mg/day mixed omega-3 fatty acids (n = 25. Significant correlations were found between retinal lutein and zeaxanthin (macular pigment and CFF thresholds (p<0.01 and visual motor performance (overall p<0.01. Supplementation with zeaxanthin and the mixed formulation (considered together produced significant (p<0.01 increases in CFF thresholds (∼12% and visual motor reaction time (∼10% compared to placebo. In general, increasing macular pigment density through supplementation (average increase of about 0.09 log units resulted in significant improvements in visual processing speed, even when testing young, healthy individuals who tend to be at peak efficiency.

  19. Direct evidence for two different neural mechanisms for reading familiar and unfamiliar words: an intra-cerebral EEG study

    Directory of Open Access Journals (Sweden)

    Alexandra eJuphard

    2011-09-01

    Full Text Available After intensive practice, unfamiliar letter strings become familiar words and reading speed increases strikingly from a slow processing to a fast and with more global recognition of words. While this effect has been well documented at the behavioral level, its neural underpinnings are still unclear. The question is how the brain modulates the activity of the reading network according to the novelty of the items. Several models have proposed that familiar and unfamiliar words are not processed by separate networks but rather by common regions operating differently according to familiarity. This hypothesis has proved difficult to test at the neural level because the effects of familiarity and length on reading occur (a on a millisecond scale, shorter than the resolution of fMRI and (b in regions which cannot be isolated with non-invasive EEG or MEG. We overcame these limitations by using invasive intra-cerebral EEG recording in epileptic patients. Neural activity (gamma-band responses, GBR, between 50 Hz and 150 Hz was measured in three major nodes of reading network – left inferior frontal, supramarginal and inferior temporo-occipital cortices - while patients silently read familiar (words and unfamiliar (pseudo-words items of two lengths (short composed of one-syllable vs. long composed of three syllables. While all items elicited strong neural responses in the three regions, we found that the duration of the neural response increases with length only for pseudo-words, in direct relation to grapheme-to-phoneme conversion. Our results validate at the neural level the hypothesis that all words are processed by a common network operating more or less efficiently depending on words’ novelty.

  20. 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.

  1. Highly efficient simultaneous ultrasonic assisted adsorption of brilliant green and eosin B onto ZnS nanoparticles loaded activated carbon: Artificial neural network modeling and central composite design optimization

    Science.gov (United States)

    Jamshidi, M.; Ghaedi, M.; Dashtian, K.; Ghaedi, A. M.; Hajati, S.; Goudarzi, A.; Alipanahpour, E.

    2016-01-01

    In this work, central composite design (CCD) combined with response surface methodology (RSM) and desirability function approach (DFA) gives useful information about operational condition and also to obtain useful information about interaction and main effect of variables concerned to simultaneous ultrasound-assisted removal of brilliant green (BG) and eosin B (EB) by zinc sulfide nanoparticles loaded on activated carbon (ZnS-NPs-AC). Spectra overlap between BG and EB dyes was extensively reduced and/or omitted by derivative spectrophotometric method, while multi-layer artificial neural network (ML-ANN) model learned with Levenberg-Marquardt (LM) algorithm was used for building up a predictive model and prediction of the BG and EB removal. The ANN efficiently was able to forecast the simultaneous BG and EB removal that was confirmed by reasonable numerical value i.e. MSE of 0.0021 and R2 of 0.9589 and MSE of 0.0022 and R2 of 0.9455 for testing data set, respectively. The results reveal acceptable agreement among experimental data and ANN predicted results. Langmuir as the best model for fitting experimental data relevant to BG and EB removal indicates high, economic and profitable adsorption capacity (258.7 and 222.2 mg g- 1) that supports and confirms its applicability for wastewater treatment.

  2. Vectorized algorithms for spiking neural network simulation.

    Science.gov (United States)

    Brette, Romain; Goodman, Dan F M

    2011-06-01

    High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.

  3. Subjective duration distortions mirror neural repetition suppression.

    Directory of Open Access Journals (Sweden)

    Vani Pariyadath

    Full Text Available Subjective duration is strongly influenced by repetition and novelty, such that an oddball stimulus in a stream of repeated stimuli appears to last longer in duration in comparison. We hypothesize that this duration illusion, called the temporal oddball effect, is a result of the difference in expectation between the oddball and the repeated stimuli. Specifically, we conjecture that the repeated stimuli contract in duration as a result of increased predictability; these duration contractions, we suggest, result from decreased neural response amplitude with repetition, known as repetition suppression.Participants viewed trials consisting of lines presented at a particular orientation (standard stimuli followed by a line presented at a different orientation (oddball stimulus. We found that the size of the oddball effect correlates with the number of repetitions of the standard stimulus as well as the amount of deviance from the oddball stimulus; both of these results are consistent with a repetition suppression hypothesis. Further, we find that the temporal oddball effect is sensitive to experimental context--that is, the size of the oddball effect for a particular experimental trial is influenced by the range of duration distortions seen in preceding trials.Our data suggest that the repetition-related duration contractions causing the oddball effect are a result of neural repetition suppression. More generally, subjective duration may reflect the prediction error associated with a stimulus and, consequently, the efficiency of encoding that stimulus. Additionally, we emphasize that experimental context effects need to be taken into consideration when designing duration-related tasks.

  4. 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 ...

  5. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-03-01

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

  7. Social learning and evolution: the cultural intelligence hypothesis

    Science.gov (United States)

    van Schaik, Carel P.; Burkart, Judith M.

    2011-01-01

    If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer. PMID:21357223

  8. 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....

  9. 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.

  10. Multiprocessor Neural Network in Healthcare.

    Science.gov (United States)

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

    2015-01-01

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

  11. [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.

  12. 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

  13. 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

  14. 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 ...

  15. 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…

  16. 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,

  17. 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…

  18. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  19. 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.

  20. Neural scaling laws for an uncertain world

    CERN Document Server

    Howard, Marc W

    2016-01-01

    The Weber-Fechner law describes the form of psychological space in many behavioral experiments involving perception of one-dimensional physical quantities. If the physical quantity is expressed using multiple neural receptors, then placing receptive fields evenly along a logarithmic scale naturally leads to the psychological Weber-Fechner law. In the visual system, the spacing and width of extrafoveal receptive fields are consistent with logarithmic scaling. Other sets of neural "receptors" appear to show the same qualitative properties, suggesting that this form of neural scaling reflects a solution to a very general problem. This paper argues that these neural scaling laws enable the brain to represent information about the world efficiently without making any assumptions about the statistics of the world. This analysis suggests that the organization of neural scales to represent one-dimensional quantities, including more abstract quantities such as numerosity, time, and allocentric space, should have a uni...

  1. 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.

  2. A computational neural model of orientation detection based on multiple guesses: comparison of geometrical and algebraic models.

    Science.gov (United States)

    Wei, Hui; Ren, Yuan; Wang, Zi Yan

    2013-10-01

    The implementation of Hubel-Wiesel hypothesis that orientation selectivity of a simple cell is based on ordered arrangement of its afferent cells has some difficulties. It requires the receptive fields (RFs) of those ganglion cells (GCs) and LGN cells to be similar in size and sub-structure and highly arranged in a perfect order. It also requires an adequate number of regularly distributed simple cells to match ubiquitous edges. However, the anatomical and electrophysiological evidence is not strong enough to support this geometry-based model. These strict regularities also make the model very uneconomical in both evolution and neural computation. We propose a new neural model based on an algebraic method to estimate orientations. This approach synthesizes the guesses made by multiple GCs or LGN cells and calculates local orientation information subject to a group of constraints. This algebraic model need not obey the constraints of Hubel-Wiesel hypothesis, and is easily implemented with a neural network. By using the idea of a satisfiability problem with constraints, we also prove that the precision and efficiency of this model are mathematically practicable. The proposed model makes clear several major questions which Hubel-Wiesel model does not account for. Image-rebuilding experiments are conducted to check whether this model misses any important boundary in the visual field because of the estimation strategy. This study is significant in terms of explaining the neural mechanism of orientation detection, and finding the circuit structure and computational route in neural networks. For engineering applications, our model can be used in orientation detection and as a simulation platform for cell-to-cell communications to develop bio-inspired eye chips.

  3. 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.

  4. 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.

  5. 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...

  6. 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...

  7. 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...

  8. Performance sustaining intracortical neural prostheses

    Science.gov (United States)

    Nuyujukian, Paul; Kao, Jonathan C.; Fan, Joline M.; Stavisky, Sergey D.; Ryu, Stephen I.; Shenoy, Krishna V.

    2014-12-01

    Objective. Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr-1. In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days’ experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder

  9. 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...

  10. 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.

  11. 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....

  12. 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...

  13. 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.

  14. Automated Modeling of Microwave Structures by Enhanced Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

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

  15. Radial glial cells play a key role in echinoderm neural regeneration

    Science.gov (United States)

    2013-01-01

    Background Unlike the mammalian central nervous system (CNS), the CNS of echinoderms is capable of fast and efficient regeneration following injury and constitutes one of the most promising model systems that can provide important insights into evolution of the cellular and molecular events involved in neural repair in deuterostomes. So far, the cellular mechanisms of neural regeneration in echinoderm remained obscure. In this study we show that radial glial cells are the main source of new cells in the regenerating radial nerve cord in these animals. Results We demonstrate that radial glial cells of the sea cucumber Holothuria glaberrima react to injury by dedifferentiation. Both glia and neurons undergo programmed cell death in the lesioned CNS, but it is the dedifferentiated glial subpopulation in the vicinity of the injury that accounts for the vast majority of cell divisions. Glial outgrowth leads to formation of a tubular scaffold at the growing tip, which is later populated by neural elements. Most importantly, radial glial cells themselves give rise to new neurons. At least some of the newly produced neurons survive for more than 4 months and express neuronal markers typical of the mature echinoderm CNS. Conclusions A hypothesis is formulated that CNS regeneration via activation of radial glial cells may represent a common capacity of the Deuterostomia, which is not invoked spontaneously in higher vertebrates, whose adult CNS does not retain radial glial cells. Potential implications for biomedical research aimed at finding the cure for human CNS injuries are discussed. PMID:23597108

  16. Age related neural adaptation following short term resistance training in women.

    Science.gov (United States)

    Bemben, M G; Murphy, R E

    2001-09-01

    This study examined the influence of age on neural facilitation and neural cross-education following short term unilateral dynamic resistance training with the hypothesis that older women may have a diminished ability for adaptation. This was a prospective, repeated measures design. The non-dominant left arm served as a control limb and follow-up testing was performed two weeks after pretesting. Testing was conducted in the Neuromuscular Research Laboratory at the University of Oklahoma. 20 females (n=10, young (YF) 20.8+/-0.1 yrs; n=10, older (OF) 58.1+/-0.14) were assessed. 14 days of training of the right elbow flexors only. On each day, subjects performed four sets of ten repetitions using 70 percent of maximal strength of the biceps brachii. The following variables in both right and left elbow flexor muscle groups were evaluated; isometric strength (IMS), efficiency of electrical activity (EEA) and estimated upper arm cross-sectional area (CSA). There were significant increases (peffects. Short term unilateral dynamic resistance training is a sufficient stimulus to induce significant strength increases in both trained and untrained contralateral limbs and that a neural mechanism is responsible for the muscular adaptation in both young and older women. Implication exists for unilateral stroke victims, individuals with single hip or knee replacements, or single limb casts.

  17. 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.

  18. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-12-31

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

  19. Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells

    DEFF Research Database (Denmark)

    Chandrasekaran, Abinaya; Avci, Hasan; Ochalek, Anna

    2017-01-01

    Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency...

  20. 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.

  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. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  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. Neural bases of selective attention in action video game players.

    Science.gov (United States)

    Bavelier, D; Achtman, R L; Mani, M; Föcker, J

    2012-05-15

    Over the past few years, the very act of playing action video games has been shown to enhance several different aspects of visual selective attention, yet little is known about the neural mechanisms that mediate such attentional benefits. A review of the aspects of attention enhanced in action game players suggests there are changes in the mechanisms that control attention allocation and its efficiency (Hubert-Wallander, Green, & Bavelier, 2010). The present study used brain imaging to test this hypothesis by comparing attentional network recruitment and distractor processing in action gamers versus non-gamers as attentional demands increased. Moving distractors were found to elicit lesser activation of the visual motion-sensitive area (MT/MST) in gamers as compared to non-gamers, suggestive of a better early filtering of irrelevant information in gamers. As expected, a fronto-parietal network of areas showed greater recruitment as attentional demands increased in non-gamers. In contrast, gamers barely engaged this network as attentional demands increased. This reduced activity in the fronto-parietal network that is hypothesized to control the flexible allocation of top-down attention is compatible with the proposal that action game players may allocate attentional resources more automatically, possibly allowing more efficient early filtering of irrelevant information. Copyright © 2011 Elsevier Ltd. All rights reserved.

  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. Optimal neural computations require analog processors

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

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

  9. 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.

  10. Neural Networks Methodology and Applications

    CERN Document Server

    Dreyfus, Gérard

    2005-01-01

    Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...

  11. Energy coding in neural network with inhibitory neurons.

    Science.gov (United States)

    Wang, Ziyin; Wang, Rubin; Fang, Ruiyan

    2015-04-01

    This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.

  12. 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 ...

  13. 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.

  14. 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.

  15. 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....

  16. 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…

  17. 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

  18. 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)

  19. 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 ...

  20. 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

  1. 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)

  2. 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…

  3. 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,

  4. 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.

  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. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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 ...

  12. 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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...

  4. 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.

  5. 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.

  6. 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...

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Is protection against florivory consistent with the optimal defense hypothesis?

    Science.gov (United States)

    Godschalx, Adrienne L; Stady, Lauren; Watzig, Benjamin; Ballhorn, Daniel J

    2016-01-28

    Plant defense traits require resources and energy that plants may otherwise use for growth and reproduction. In order to most efficiently protect plant tissues from herbivory, one widely accepted assumption of the optimal defense hypothesis states that plants protect tissues most relevant to fitness. Reproductive organs directly determining plant fitness, including flowers and immature fruit, as well as young, productive leaf tissue thus should be particularly well-defended. To test this hypothesis, we quantified the cyanogenic potential (HCNp)-a direct, chemical defense-systemically expressed in vegetative and reproductive organs in lima bean (Phaseolus lunatus), and we tested susceptibility of these organs in bioassays with a generalist insect herbivore, the Large Yellow Underwing (Noctuidae: Noctua pronuba). To determine the actual impact of either florivory (herbivory on flowers) or folivory on seed production as a measure of maternal fitness, we removed varying percentages of total flowers or young leaf tissue and quantified developing fruit, seeds, and seed viability. We found extremely low HCNp in flowers (8.66 ± 2.19 μmol CN(-) g(-1) FW in young, white flowers, 6.23 ± 1.25 μmol CN(-) g(-1) FW in mature, yellow flowers) and in pods (ranging from 32.05 ± 7.08 to 0.09 ± 0.08 μmol CN(-) g(-1) FW in young to mature pods, respectively) whereas young leaves showed high levels of defense (67.35 ± 3.15 μmol CN(-) g(-1) FW). Correspondingly, herbivores consumed more flowers than any other tissue, which, when taken alone, appears to contradict the optimal defense hypothesis. However, experimentally removing flowers did not significantly impact fitness, while leaf tissue removal significantly reduced production of viable seeds. Even though flowers were the least defended and most consumed, our results support the optimal defense hypothesis due to i) the lack of flower removal effects on fitness and ii) the high defense investment in

  12. 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.

  13. Recent advances in neural recording microsystems.

    Science.gov (United States)

    Gosselin, Benoit

    2011-01-01

    The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  14. Recent Advances in Neural Recording Microsystems

    Directory of Open Access Journals (Sweden)

    Benoit Gosselin

    2011-04-01

    Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  15. 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.

  16. 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...

  17. One pass learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2016-01-01

    Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright

  18. 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.

  19. Functional neural anatomy of talent.

    Science.gov (United States)

    Kalbfleisch, M Layne

    2004-03-01

    The terms gifted, talented, and intelligent all have meanings that suggest an individual's highly proficient or exceptional performance in one or more specific areas of strength. Other than Spearman's g, which theorizes about a general elevated level of potential or ability, more contemporary theories of intelligence are based on theoretical models that define ability or intelligence according to a priori categories of specific performance. Recent studies in cognitive neuroscience report on the neural basis of g from various perspectives such as the neural speed theory and the efficiency of prefrontal function. Exceptional talent is the result of interactions between goal-directed behavior and nonvolitional perceptual processes in the brain that have yet to be fully characterized and understood by the fields of psychology and cognitive neuroscience. Some developmental studies report differences in region-specific neural activation, recruitment patterns, and reaction times in subjects who are identified with high IQ scores according to traditional scales of assessment such as the WISC-III or Stanford-Binet. Although as cases of savants and prodigies illustrate, talent is not synonymous with high IQ. This review synthesizes information from the fields of psychometrics and gifted education, with findings from the neurosciences on the neural basis of intelligence, creativity, profiles of expert performers, cognitive function, and plasticity to suggest a paradigm for investigating talent as the maximal and productive use of either or both of one's high level of general intelligence or domain-specific ability. Anat Rec (Part B: New Anat) 277B:21-36, 2004. Copyright 2004 Wiley-Liss, Inc.

  20. 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

  1. 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.

  2. 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.

  3. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  4. 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.

  5. 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

  6. 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

  7. 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…

  8. 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....

  9. 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.

  10. EyeTribe Tracker Data Accuracy Evaluation and Its Interconnection with Hypothesis Software for Cartographic Purposes

    Directory of Open Access Journals (Sweden)

    Stanislav Popelka

    2016-01-01

    Full Text Available The mixed research design is a progressive methodological discourse that combines the advantages of quantitative and qualitative methods. Its possibilities of application are, however, dependent on the efficiency with which the particular research techniques are used and combined. The aim of the paper is to introduce the possible combination of Hypothesis with EyeTribe tracker. The Hypothesis is intended for quantitative data acquisition and the EyeTribe is intended for qualitative (eye-tracking data recording. In the first part of the paper, Hypothesis software is described. The Hypothesis platform provides an environment for web-based computerized experiment design and mass data collection. Then, evaluation of the accuracy of data recorded by EyeTribe tracker was performed with the use of concurrent recording together with the SMI RED 250 eye-tracker. Both qualitative and quantitative results showed that data accuracy is sufficient for cartographic research. In the third part of the paper, a system for connecting EyeTribe tracker and Hypothesis software is presented. The interconnection was performed with the help of developed web application HypOgama. The created system uses open-source software OGAMA for recording the eye-movements of participants together with quantitative data from Hypothesis. The final part of the paper describes the integrated research system combining Hypothesis and EyeTribe.

  11. EyeTribe Tracker Data Accuracy Evaluation and Its Interconnection with Hypothesis Software for Cartographic Purposes.

    Science.gov (United States)

    Popelka, Stanislav; Stachoň, Zdeněk; Šašinka, Čeněk; Doležalová, Jitka

    2016-01-01

    The mixed research design is a progressive methodological discourse that combines the advantages of quantitative and qualitative methods. Its possibilities of application are, however, dependent on the efficiency with which the particular research techniques are used and combined. The aim of the paper is to introduce the possible combination of Hypothesis with EyeTribe tracker. The Hypothesis is intended for quantitative data acquisition and the EyeTribe is intended for qualitative (eye-tracking) data recording. In the first part of the paper, Hypothesis software is described. The Hypothesis platform provides an environment for web-based computerized experiment design and mass data collection. Then, evaluation of the accuracy of data recorded by EyeTribe tracker was performed with the use of concurrent recording together with the SMI RED 250 eye-tracker. Both qualitative and quantitative results showed that data accuracy is sufficient for cartographic research. In the third part of the paper, a system for connecting EyeTribe tracker and Hypothesis software is presented. The interconnection was performed with the help of developed web application HypOgama. The created system uses open-source software OGAMA for recording the eye-movements of participants together with quantitative data from Hypothesis. The final part of the paper describes the integrated research system combining Hypothesis and EyeTribe.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  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. 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

  18. 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.

  19. [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.

  20. A Test of the Adaptive Market Hypothesis using a Time-Varying AR Model in Japan

    OpenAIRE

    Akihiko Noda

    2012-01-01

    This study examines the adaptive market hypothesis (AMH) in Japanese stock markets (TOPIX and TSE2). In particular, we measure the degree of market efficiency by using a time-varying model approach. The empirical results show that (1) the degree of market efficiency changes over time in the two markets, (2) the level of market efficiency of the TSE2 is lower than that of the TOPIX in most periods, and (3) the market efficiency of the TOPIX has evolved, but that of the TSE2 has not. We conclud...

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. EQUITY EVALUATION OF PADDY IRRIGATION WATER DISTRIBUTION BY SOCIETY-JUSTICE-WATER DISTRIBUTION RULE HYPOTHESIS

    Science.gov (United States)

    Tanji, Hajime; Kiri, Hirohide; Kobayashi, Shintaro

    When total supply is smaller than total demand, it is difficult to apply the paddy irrigation water distribution rule. The gap must be narrowed by decreasing demand. Historically, the upstream served rule, rotation schedule, or central schedule weight to irrigated area was adopted. This paper proposes the hypothesis that these rules are dependent on social justice, a hypothesis called the "Society-Justice-Water Distribution Rule Hypothesis". Justice, which means a balance of efficiency and equity of distribution, is discussed under the political philosophy of utilitarianism, liberalism (Rawls), libertarianism, and communitarianism. The upstream served rule can be derived from libertarianism. The rotation schedule and central schedule can be derived from communitarianism. Liberalism can provide arranged schedule to adjust supply and demand based on "the Difference Principle". The authors conclude that to achieve efficiency and equity, liberalism may provide the best solution after modernization.

  7. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Applying Artificial Neural Networks for Face Recognition

    Directory of Open Access Journals (Sweden)

    Thai Hoang Le

    2011-01-01

    Full Text Available This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.

  19. 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.

  20. 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...

  1. [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.

  2. 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.

  3. 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.

  4. Facilitating memory for novel characters by reducing neural repetition suppression in the left fusiform cortex.

    Science.gov (United States)

    Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell A; Dong, Qi

    2010-10-06

    The left midfusiform and adjacent regions have been implicated in processing and memorizing familiar words, yet its role in memorizing novel characters has not been well understood. Using functional MRI, the present study examined the hypothesis that the left midfusiform is also involved in memorizing novel characters and spaced learning could enhance the memory by enhancing the left midfusiform activity during learning. Nineteen native Chinese readers were scanned while memorizing the visual form of 120 Korean characters that were novel to the subjects. Each character was repeated four times during learning. Repetition suppression was manipulated by using two different repetition schedules: massed learning and spaced learning, pseudo-randomly mixed within the same scanning session. Under the massed learning condition, the four repetitions were consecutive (with a jittered inter-repetition interval to improve the design efficiency). Under the spaced learning condition, the four repetitions were interleaved with a minimal inter-repetition lag of 6 stimuli. Spaced learning significantly improved participants' performance during the recognition memory test administered one hour after the scan. Stronger left midfusiform and inferior temporal gyrus activities during learning (summed across four repetitions) were associated with better memory of the characters, based on both within- and cross-subjects analyses. Compared to massed learning, spaced learning significantly reduced neural repetition suppression and increased the overall activities in these regions, which were associated with better memory for novel characters. These results demonstrated a strong link between cortical activity in the left midfusiform and memory for novel characters, and thus challenge the visual word form area (VWFA) hypothesis. Our results also shed light on the neural mechanisms of the spacing effect in memorizing novel characters.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.)

  10. Predicting the parameters of energy installations with laser ignition: Neural network models

    Directory of Open Access Journals (Sweden)

    Alexey A. Pastukhov

    2015-06-01

    Full Text Available This article considers the possibility of using artificial neural networks for predicting the parameters of the model energy installation with laser ignition. The main stages of creating a prognostic model based on an artificial neural network have been presented. Input data were analyzed by principal component method. The synthesized neural network was designed to predict the parameter value of the model in question. The artificial neural network was trained by a back-propagation algorithm. The efficiency of the artificial neural networks and their applicability to predicting parameter values of various rocket engine elements were demonstrated.

  11. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

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

  12. 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:…

  13. 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

  14. [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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Sequential Analysis: Hypothesis Testing and Changepoint Detection

    Science.gov (United States)

    2014-07-11

    markets, detection of signals with unknown arrival time in seismology, navigation, radar and sonar signal processing, speech segmentation, and the...are less critical than for onset detection algorithms. A false alarm for the detection of an imminent tsunami obviously has severe and costly... detection (Part II). In Part III, we briefly describe certain important applications where theoretical results can be used efficiently, perhaps with

  6. 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.

  7. 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.

  8. 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

  9. Neural reactivity to reward in school-age offspring of depressed mothers.

    Science.gov (United States)

    Wiggins, Jillian Lee; Schwartz, Karen T G; Kryza-Lacombe, Maria; Spechler, Philip A; Blankenship, Sarah L; Dougherty, Lea R

    2017-05-01

    Identifying neural profiles predictive of future psychopathology in at-risk individuals is important to efficiently direct preventive care. Alterations in reward processing may be a risk factor for depression. The current study characterized neural substrates of reward processing in children at low- and high-risk for psychopathology due to maternal depression status. Children with (n=27) and without (n=19) maternal depression (ages 5.9-9.6 years) performed a monetary incentive delay task in which they received rewards, if they successfully hit a target, or no reward regardless of performance, during fMRI acquisition. Multiple dorsal prefrontal, temporal, and striatal regions showed significant Group (high- vs. low-risk)×Performance (hit vs. miss)×Condition (no reward vs. reward) interactions in a whole-brain analysis. All regions exhibited similar patterns, whereby the high-risk group showed blunted activation differences between trials with vs. without rewards when participants hit the target. Moreover, high-risk children showed activation differences between trials with vs. without rewards in the opposite direction, compared to the low-risk group, when they missed the target. This study had a modest sample size, though larger than existing studies. Children with maternal depression are at elevated risk for future psychopathology, yet not all experience clinically significant symptoms; longitudinal research is necessary to fully track the pathway from risk to disorder. Children of depressed mothers exhibited attenuated neural activation differences and activation patterns opposite to children without depressed mothers. Our findings may provide targets for hypothesis-driven preventive interventions and lead to earlier identification of individuals at risk. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Neural network real time event selection for the DIRAC experiment

    CERN Document Server

    Kokkas, P; Tauscher, Ludwig; Vlachos, S

    2001-01-01

    The neural network first level trigger for the DIRAC experiment at CERN is presented. Both the neural network algorithm used and its actual hardware implementation are described. The system uses the fast plastic scintillator information of the DIRAC spectrometer. In 210 ns it selects events with two particles having low relative momentum. Such events are selected with an efficiency of more than 0.94. The corresponding rate reduction for background events is a factor of 2.5. (10 refs).

  11. Recurrent neural networks training with stable bounding ellipsoid algorithm.

    Science.gov (United States)

    Yu, Wen; de Jesús Rubio, José

    2009-06-01

    Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

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

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2001-12-01

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

  13. 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.…

  14. 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.

  15. 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...

  16. 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

  17. 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.

  18. 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.

  19. [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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. [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.

  5. Age-Related Changes in 1/f Neural Electrophysiological Noise.

    Science.gov (United States)

    Voytek, Bradley; Kramer, Mark A; Case, John; Lepage, Kyle Q; Tempesta, Zechari R; Knight, Robert T; Gazzaley, Adam

    2015-09-23

    Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15-53 years) and scalp EEG data from healthy younger (20-30 years) and older (60-70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often noise (defined as χ = 0) with increasing task difficulty. We observed, in both electrophysiological datasets, that aging is associated with a flatter (more noisy) 1/f power spectral density, even at rest, and that visual cortical 1/f noise statistically mediates age-related impairments in visual working memory. These results provide electrophysiological support for the neural noise hypothesis of aging. Significance statement: Understanding the neurobiological origins of age-related cognitive decline is of critical scientific, medical, and public health importance, especially considering the rapid aging of the world's population. We find, in two separate human studies, that 1/f electrophysiological noise increases with aging. In addition, we observe that this age-related 1/f noise statistically mediates age-related working memory decline. These results significantly add to this understanding and contextualize a long-standing problem in cognition by encapsulating age-related cognitive decline within a neurocomputational model of 1/f noise-induced deficits in neural communication. Copyright © 2015 the authors 0270-6474/15/3513257-09$15.00/0.

  6. 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

  7. 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.

  8. Analysis of the Effect of the CaCl2 Mass Fraction on the Efficiency of a Heat Pump Integrated Heat-Source Tower Using an Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Xiaoqing Wei

    2016-04-01

    Full Text Available An existing idle cooling tower can be reversibly used as a heat-source tower (HST to drive a heat pump (HP in cold seasons, with calcium chloride (CaCl2 aqueous solution commonly selected as the secondary working fluid in an indirect system due to its good thermo-physical properties. This study analyzed the effect of CaCl2 mass fraction on the effectiveness (ε of a closed HST and the coefficient of performance (COP of a HP heating system using an artificial neural network (ANN technique. CaCl2 aqueous solutions with five different mass fractions, viz. 3%, 9%, 15%, 21%, and 27%, were chosen as the secondary working fluids for the HSTHP heating system. In order to collect enough measured data, extensive field tests were conducted on an experimental test rig in Changsha, China which experiences hot summer and cold winter weather. After back-propagation (BP training, the three-layer (4-9-2 ANN model with a tangent sigmoid transfer function at the hidden layer and a linear transfer function at the output layer was developed for predicting the tower effectiveness and the COP of the HP under different inlet air dry-/wet-bulb temperatures, hot water inlet temperatures and CaCl2 mass fractions. The correlation coefficient (R, mean relative error (MRE and root mean squared error (RMSE were adopted to evaluate the prediction accuracy of the ANN model. The results showed that the R, MRE, and RMSE between the training values and the experimental values of ε (COP were 0.995 (0.996, 2.09% (1.89%, and 0.005 (0.060, respectively, which indicated that the ANN model was reliable and robust in predicting the performance of the HP. The findings of this paper indicated that in order to guarantee normal operation of the system, the freezing point temperature of the CaCl2 aqueous solution should be sufficiently (3–5 K below its lowest operating temperature or lower than the normal operating temperature by about 10 K. The tower effectiveness increased with

  9. Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris.

    Directory of Open Access Journals (Sweden)

    Martin F Strube-Bloss

    Full Text Available To trigger innate behavior, sensory neural networks are pre-tuned to extract biologically relevant stimuli. Many male-female or insect-plant interactions depend on this phenomenon. Especially communication among individuals within social groups depends on innate behaviors. One example is the efficient recruitment of nest mates by successful bumblebee foragers. Returning foragers release a recruitment pheromone in the nest while they perform a 'dance' behavior to activate unemployed nest mates. A major component of this pheromone is the sesquiterpenoid farnesol. How farnesol is processed and perceived by the olfactory system, has not yet been identified. It is much likely that processing farnesol involves an innate mechanism for the extraction of relevant information to trigger a fast and reliable behavioral response. To test this hypothesis, we used population response analyses of 100 antennal lobe (AL neurons recorded in alive bumblebee workers under repeated stimulation with four behaviorally different, but chemically related odorants (geraniol, citronellol, citronellal and farnesol. The analysis identified a unique neural representation of the recruitment pheromone component compared to the other odorants that are predominantly emitted by flowers. The farnesol induced population activity in the AL allowed a reliable separation of farnesol from all other chemically related odor stimuli we tested. We conclude that the farnesol induced population activity may reflect a predetermined representation within the AL-neural network allowing efficient and fast extraction of a behaviorally relevant stimulus. Furthermore, the results show that population response analyses of multiple single AL-units may provide a powerful tool to identify distinct representations of behaviorally relevant odors.

  10. Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris.

    Science.gov (United States)

    Strube-Bloss, Martin F; Brown, Austin; Spaethe, Johannes; Schmitt, Thomas; Rössler, Wolfgang

    2015-01-01

    To trigger innate behavior, sensory neural networks are pre-tuned to extract biologically relevant stimuli. Many male-female or insect-plant interactions depend on this phenomenon. Especially communication among individuals within social groups depends on innate behaviors. One example is the efficient recruitment of nest mates by successful bumblebee foragers. Returning foragers release a recruitment pheromone in the nest while they perform a 'dance' behavior to activate unemployed nest mates. A major component of this pheromone is the sesquiterpenoid farnesol. How farnesol is processed and perceived by the olfactory system, has not yet been identified. It is much likely that processing farnesol involves an innate mechanism for the extraction of relevant information to trigger a fast and reliable behavioral response. To test this hypothesis, we used population response analyses of 100 antennal lobe (AL) neurons recorded in alive bumblebee workers under repeated stimulation with four behaviorally different, but chemically related odorants (geraniol, citronellol, citronellal and farnesol). The analysis identified a unique neural representation of the recruitment pheromone component compared to the other odorants that are predominantly emitted by flowers. The farnesol induced population activity in the AL allowed a reliable separation of farnesol from all other chemically related odor stimuli we tested. We conclude that the farnesol induced population activity may reflect a predetermined representation within the AL-neural network allowing efficient and fast extraction of a behaviorally relevant stimulus. Furthermore, the results show that population response analyses of multiple single AL-units may provide a powerful tool to identify distinct representations of behaviorally relevant odors.

  11. Attempting to Unravel the Australian Megatsunami Hypothesis

    Science.gov (United States)

    Goff, J. R.

    2008-12-01

    Nearly two decades of information report apparent megatsunamis along the SE coast of Australia and yet these interpretations are still highly controversial. This work has proven to be particularly influential in guiding more recent megatsunami researchers, and yet it has never been critically evaluated in the light of recent advances in tsunami research. Much of the controversy hinges upon the nature of the original observations, event chronologies, and source identification. The most recent incarnation of the megatsunami hypothesis is indicative of the controversy. A supposed impact crater to the SW of New Zealand is linked with abandoned Maori settlements, Maori legends, and high elevation beach sand deposits in New Zealand, and apparent megatsunami evidence in eastern Australia and on Lord Howe Island. A date of around AD1500 is proposed. There are two key issues here. First, is there currently any evidence for contemporaneous trans Tasman palaeotsunamis (or megatsunamis) in the Holocene? Second, how reliable is the evidence? The first issue was addressed by comparing Holocene events from the Australian and New Zealand palaeotsunami databases. Up to five possible contemporaneous events were identified, but at the same time flaws in the underpinning data were highlighted. To start with, there is no consistent approach to the interpretation of chronological information comprising the databases. A consistent recalibration of all available radiocarbon data was carried out for both databases. This was based upon information contained in the relevant original papers. No clusters of radiocarbon ages were found for apparent megatsunami deposits along the SE coast of Australia. Clusters were found however, in New Zealand for inferred local and regional events. Next, the nature and extent of physical evidence used to determine tsunami emplacement were found to be highly variable. A preliminary reassessment of the physical evidence casts doubt upon the interpretation of

  12. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  13. Dual adaptive dynamic control of mobile robots using neural networks.

    Science.gov (United States)

    Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

    2009-02-01

    This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

  14. Marketing actions can modulate neural representations of experienced pleasantness.

    Science.gov (United States)

    Plassmann, Hilke; O'Doherty, John; Shiv, Baba; Rangel, Antonio

    2008-01-22

    Despite the importance and pervasiveness of marketing, almost nothing is known about the neural mechanisms through which it affects decisions made by individuals. We propose that marketing actions, such as changes in the price of a product, can affect neural representations of experienced pleasantness. We tested this hypothesis by scanning human subjects using functional MRI while they tasted wines that, contrary to reality, they believed to be different and sold at different prices. Our results show that increasing the price of a wine increases subjective reports of flavor pleasantness as well as blood-oxygen-level-dependent activity in medial orbitofrontal cortex, an area that is widely thought to encode for experienced pleasantness during experiential tasks. The paper provides evidence for the ability of marketing actions to modulate neural correlates of experienced pleasantness and for the mechanisms through which the effect operates.

  15. Market Efficiency in Indian Stock Market

    OpenAIRE

    Sahani, Rishi

    2009-01-01

    In this era, efficient market hypothesis has become a very important theory for all the investors who wish to hold or plan to have an international diversified portfolio. As today, all the world economies and markets are globally getting connected, and investors have all the opportunities to invest internationally, so the understanding of market efficiency concept is gaining greater importance for all kinds of investors. In this research I have test the weak form hypothesis and random walk hy...

  16. 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.

  17. Neural Semantic Encoders.

    Science.gov (United States)

    Munkhdalai, Tsendsuren; Yu, Hong

    2017-04-01

    We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

  18. The neural crest and neural crest cells: discovery and significance ...

    Indian Academy of Sciences (India)

    In this paper I provide a brief overview of the major phases of investigation into the neural crest and the major players involved, discuss how the origin of the neural crest relates to the origin of the nervous system in vertebrate embryos, discuss the impact on the germ-layer theory of the discovery of the neural crest and of ...

  19. 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.

  20. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  1. Localizing Tortoise Nests by Neural Networks.

    Directory of Open Access Journals (Sweden)

    Roberto Barbuti

    Full Text Available The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating. Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN. We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours, the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

  2. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  3. Does pressure cause liver cirrhosis? The sinusoidal pressure hypothesis.

    Science.gov (United States)

    Mueller, Sebastian

    2016-12-28

    Independent of their etiology, all chronic liver diseases ultimately lead to liver cirrhosis, which is a major health problem worldwide. The underlying molecular mechanisms are still poorly understood and no efficient treatment strategies are available. This paper introduces the sinusoidal pressure hypothesis (SPH), which identifies an elevated sinusoidal pressure (SP) as cause of fibrosis. SPH has been mainly derived from recent studies on liver stiffness. So far, pressure changes have been exclusively seen as a consequence of cirrhosis. According to the SPH, however, an elevated SP is the major upstream event that initiates fibrosis via biomechanic signaling by stretching of perisinusoidal cells such as hepatic stellate cells or fibroblasts (SPH part I: initiation). Fibrosis progression is determined by the degree and time of elevated SP. The SPH predicts that the degree of extracellular matrix eventually matches SP with critical thresholds > 12 mmHg and > 4 wk. Elevated arterial flow and final arterialization of the cirrhotic liver represents the self-perpetuating key event exposing the low-pressure-organ to pathologically high pressures (SPH part II: perpetuation). It also defines the "point of no return" where fibrosis progression becomes irreversible. The SPH is able to explain the macroscopic changes of cirrhotic livers and the uniform fibrotic response to various etiologies. It also opens up new views on the role of fat and disease mechanisms in other organs. The novel concept will hopefully stimulate the search for new treatment strategies.

  4. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  5. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  6. Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning.

    Science.gov (United States)

    Sousa, Emanuel; Erlhagen, Wolfram; Ferreira, Flora; Bicho, Estela

    2015-12-01

    There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Is the stock market efficient?

    Science.gov (United States)

    Malkiel, B G

    1989-03-10

    A stock market is said to be efficient if it accurately reflects all relevant information in determining security prices. Critics have asserted that share prices are far too volatile to be explained by changes in objective economic events-the October 1987 crash being a case in point. Although the evidence is not unambiguous, reports of the death of the efficient market hypothesis appear premature.

  8. The Market Efficiency of the Stock Market in India

    OpenAIRE

    Rahman, Sahnawaz

    2011-01-01

    The greatest and engendering event in the Twenty first century is capital and financial market revolution and reformation especially for India. Efficient Market Hypothesis has attracted numbers of studies in empirical finance particularly in determining the market efficiency of an emerging financial market which produced conflicting and inconclusive outcomes. This paper tests the efficiency of the Indian Capital Market in its semi-strong form and weak form of Efficient Market Hypothesis (EMH)...

  9. The Alzheimer's Disease Mitochondrial Cascade Hypothesis: Progress and Perspectives

    Science.gov (United States)

    Swerdlow, Russell H.; Burns, Jeffrey M.; Khan, Shaharyar M.

    2013-01-01

    Ten years ago we first proposed the Alzheimer's disease (AD) mitochondrial cascade hypothesis. This hypothesis maintains gene inheritance defines an individual's baseline mitochondrial function; inherited and environmental factors determine rates at which mitochondrial function changes over time; and baseline mitochondrial function and mitochondrial change rates influence AD chronology. Our hypothesis unequivocally states in sporadic, late-onset AD, mitochondrial function affects amyloid precursor protein (APP) expression, APP processing, or beta amyloid (Aβ) accumulation and argues if an amyloid cascade truly exists, mitochondrial function triggers it. We now review the state of the mitochondrial cascade hypothesis, and discuss it in the context of recent AD biomarker studies, diagnostic criteria, and clinical trials. Our hypothesis predicts biomarker changes reflect brain aging, new AD definitions clinically stage brain aging, and removing brain Aβ at any point will marginally impact cognitive trajectories. Our hypothesis, therefore, offers unique perspective into what sporadic, late-onset AD is and how to best treat it. PMID:24071439

  10. The Alzheimer's disease mitochondrial cascade hypothesis: progress and perspectives.

    Science.gov (United States)

    Swerdlow, Russell H; Burns, Jeffrey M; Khan, Shaharyar M

    2014-08-01

    Ten years ago we first proposed the Alzheimer's disease (AD) mitochondrial cascade hypothesis. This hypothesis maintains that gene inheritance defines an individual's baseline mitochondrial function; inherited and environmental factors determine rates at which mitochondrial function changes over time; and baseline mitochondrial function and mitochondrial change rates influence AD chronology. Our hypothesis unequivocally states in sporadic, late-onset AD, mitochondrial function affects amyloid precursor protein (APP) expression, APP processing, or beta amyloid (Aβ) accumulation and argues if an amyloid cascade truly exists, mitochondrial function triggers it. We now review the state of the mitochondrial cascade hypothesis, and discuss it in the context of recent AD biomarker studies, diagnostic criteria, and clinical trials. Our hypothesis predicts that biomarker changes reflect brain aging, new AD definitions clinically stage brain aging, and removing brain Aβ at any point will marginally impact cognitive trajectories. Our hypothesis, therefore, offers unique perspective into what sporadic, late-onset AD is and how to best treat it. © 2013.

  11. Stock returns predictability and the adaptive market hypothesis in emerging markets: evidence from India.

    Science.gov (United States)

    Hiremath, Gourishankar S; Kumari, Jyoti

    2014-01-01

    This study addresses the question of whether the adaptive market hypothesis provides a better description of the behaviour of emerging stock market like India. We employed linear and nonlinear methods to evaluate the hypothesis empirically. The linear tests show a cyclical pattern in linear dependence suggesting that the Indian stock market switched between periods of efficiency and inefficiency. In contrast, the results from nonlinear tests reveal a strong evidence of nonlinearity in returns throughout the sample period with a sign of tapering magnitude of nonlinear dependence in the recent period. The findings suggest that Indian stock market is moving towards efficiency. The results provide additional insights on association between financial crises, foreign portfolio investments and inefficiency. G14; G12; C12.

  12. Expression patterns of neural genes in Euperipatoides kanangrensis suggest divergent evolution of onychophoran and euarthropod neurogenesis.

    Science.gov (United States)

    Eriksson, Bo Joakim; Stollewerk, Angelika

    2010-12-28

    One of the controversial debates on euarthropod relationships centers on the question as to whether insects, crustaceans, and myriapods (Mandibulata) share a common ancestor or whether myriapods group with the chelicerates (Myriochelata). The debate was stimulated recently by studies in chelicerates and myriapods that show that neural precursor groups (NPGs) segregate from the neuroectoderm generating the nervous system, whereas in insects and crustaceans the nervous tissue is produced by stem cells. Do the shared neural characters of myriapods and chelicerates represent derived characters that support the Myriochelata grouping? Or do they rather reflect the ancestral pattern? Analyses of neurogenesis in a group closely related to euarthropods, the onychophorans, show that, similar to insects and crustaceans, single neural precursors are formed in the neuroectoderm, potentially supporting the Myriochelata hypothesis. Here we show that the nature and the selection of onychophoran neural precursors are distinct from euarthropods. The onychophoran nervous system is generated by the massive irregular segregation of single neural precursors, contrasting with the limited number and stereotyped arrangement of NPGs/stem cells in euarthropods. Furthermore, neural genes do not show the spatiotemporal pattern that sets up the precise position of neural precursors as in euarthropods. We conclude that neurogenesis in onychophorans largely does not reflect the ancestral pattern of euarthropod neurogenesis, but shows a mixture of derived characters and ancestral characters that have been modified in the euarthropod lineage. Based on these data and additional evidence, we suggest an evolutionary sequence of arthropod neurogenesis that is in line with the Mandibulata hypothesis.

  13. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

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

  14. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  15. Neural Tube Defects

    Science.gov (United States)

    ... pregnancies each year in the United States. A baby’s neural tube normally develops into the brain and spinal cord. ... fluid in the brain. This is called hydrocephalus. Babies with this condition are treated with surgery to insert a tube (called a shunt) into the brain. The shunt ...

  16. Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells

    DEFF Research Database (Denmark)

    Chandrasekaran, Abinaya; Avci, Hasan; Ochalek, Anna

    2017-01-01

    Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency......), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells...... and the derived neurons exhibited longer neurites. In contrast, 2D neural induction resulted in more SOX1 positive cells. While 2D monolayer induction resulted in slightly less mature neurons, at an early stage of differentiation, the patch clamp analysis failed to reveal any significant differences between...

  17. Introducing the refined gravity hypothesis of extreme sexual size dimorphism.

    Science.gov (United States)

    Corcobado, Guadalupe; Rodríguez-Gironés, Miguel A; De Mas, Eva; Moya-Laraño, Jordi

    2010-08-03

    Explanations for the evolution of female-biased, extreme Sexual Size Dimorphism (SSD), which has puzzled researchers since Darwin, are still controversial. Here we propose an extension of the Gravity Hypothesis (i.e., the GH, which postulates a climbing advantage for small males) that in conjunction with the fecundity hypothesis appears to have the most general power to explain the evolution of SSD in spiders so far. In this "Bridging GH" we propose that bridging locomotion (i.e., walking upside-down under own-made silk bridges) may be behind the evolution of extreme SSD. A biomechanical model shows that there is a physical constraint for large spiders to bridge. This should lead to a trade-off between other traits and dispersal in which bridging would favor smaller sizes and other selective forces (e.g. fecundity selection in females) would favor larger sizes. If bridging allows faster dispersal, small males would have a selective advantage by enjoying more mating opportunities. We predicted that both large males and females would show a lower propensity to bridge, and that SSD would be negatively correlated with sexual dimorphism in bridging propensity. To test these hypotheses we experimentally induced bridging in males and females of 13 species of spiders belonging to the two clades in which bridging locomotion has evolved independently and in which most of the cases of extreme SSD in spiders are found. We found that 1) as the degree of SSD increased and females became larger, females tended to bridge less relative to males, and that 2) smaller males and females show a higher propensity to bridge. Physical constraints make bridging inefficient for large spiders. Thus, in species where bridging is a very common mode of locomotion, small males, by being more efficient at bridging, will be competitively superior and enjoy more mating opportunities. This "Bridging GH" helps to solve the controversial question of what keeps males small and also contributes to

  18. Introducing the refined gravity hypothesis of extreme sexual size dimorphism

    Directory of Open Access Journals (Sweden)

    Corcobado Guadalupe

    2010-08-01

    Full Text Available Abstract Background Explanations for the evolution of female-biased, extreme Sexual Size Dimorphism (SSD, which has puzzled researchers since Darwin, are still controversial. Here we propose an extension of the Gravity Hypothesis (i.e., the GH, which postulates a climbing advantage for small males that in conjunction with the fecundity hypothesis appears to have the most general power to explain the evolution of SSD in spiders so far. In this "Bridging GH" we propose that bridging locomotion (i.e., walking upside-down under own-made silk bridges may be behind the evolution of extreme SSD. A biomechanical model shows that there is a physical constraint for large spiders to bridge. This should lead to a trade-off between other traits and dispersal in which bridging would favor smaller sizes and other selective forces (e.g. fecundity selection in females would favor larger sizes. If bridging allows faster dispersal, small males would have a selective advantage by enjoying more mating opportunities. We predicted that both large males and females would show a lower propensity to bridge, and that SSD would be negatively correlated with sexual dimorphism in bridging propensity. To test these hypotheses we experimentally induced bridging in males and females of 13 species of spiders belonging to the two clades in which bridging locomotion has evolved independently and in which most of the cases of extreme SSD in spiders are found. Results We found that 1 as the degree of SSD increased and females became larger, females tended to bridge less relative to males, and that 2 smaller males and females show a higher propensity to bridge. Conclusions Physical constraints make bridging inefficient for large spiders. Thus, in species where bridging is a very common mode of locomotion, small males, by being more efficient at bridging, will be competitively superior and enjoy more mating opportunities. This "Bridging GH" helps to solve the controversial question of

  19. Rapid three dimensional two photon neural population scanning.

    Science.gov (United States)

    Schuck, Renaud; Quicke, Peter; Copeland, Caroline; Garasto, Stefania; Annecchino, Luca A; Hwang, June Kyu; Schultz, Simon R

    2015-08-01

    Recording the activity of neural populations at high sampling rates is a fundamental requirement for understanding computation in neural circuits. Two photon microscopy provides one promising approach towards this. However, neural circuits are three dimensional, and functional imaging in two dimensions fails to capture the 3D nature of neural dynamics. Electrically tunable lenses (ETLs) provide a simple and cheap method to extend laser scanning microscopy into the relatively unexploited third dimension. We have therefore incorporated them into our Adaptive Spiral Scanning (SSA) algorithm, which calculates kinematically efficient scanning strategies using radially modulated spiral paths. We characterised the response of the ETL, incorporated its dynamics using MATLAB models of the SSA algorithm and tested the models on populations of Izhikevich neurons of varying size and density. From this, we show that our algorithms can theoretically at least achieve sampling rates of 36.2Hz compared to 21.6Hz previously reported for 3D scanning techniques.

  20. 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...

  1. Neural basis of nonanalytical reasoning expertise during clinical evaluation.

    Science.gov (United States)

    Durning, Steven J; Costanzo, Michelle E; Artino, Anthony R; Graner, John; van der Vleuten, Cees; Beckman, Thomas J; Wittich, Christopher M; Roy, Michael J; Holmboe, Eric S; Schuwirth, Lambert

    2015-03-01

    Understanding clinical reasoning is essential for patient care and medical education. Dual-processing theory suggests that nonanalytic reasoning is an essential aspect of expertise; however, assessing nonanalytic reasoning is challenging because it is believed to occur on the subconscious level. This assumption makes concurrent verbal protocols less reliable assessment tools. Functional magnetic resonance imaging was used to explore the neural basis of nonanalytic reasoning in internal medicine interns (novices) and board-certified staff internists (experts) while completing United States Medical Licensing Examination and American Board of Internal Medicine multiple-choice questions. The results demonstrated that novices and experts share a common neural network in addition to nonoverlapping neural resources. However, experts manifested greater neural processing efficiency in regions such as the prefrontal cortex during nonanalytical reasoning. These findings reveal a multinetwork system that supports the dual-process mode of expert clinical reasoning during medical evaluation.

  2. The 'late' reflex responses to muscle stretch: the 'resonance hypothesis' versus the 'long-loop hypothesis'.

    Science.gov (United States)

    Eklund, G; Hagbarth, K E; Hägglund, J V; Wallin, E U

    1982-05-01

    1. Experiments were performed to check the validity of previous claims concerning the ;long-loop' aetiology of ;late' reflex electromyogram (e.m.g.) responses to muscle stretch in man. The primary aim was to investigate whether observations previously presented in favour of the ;long-loop hypothesis' are explicable also in terms of the ;resonance hypothesis', according to which the ;late' reflex components represent spinal, short-latency responses to intramuscular oscillations initiated by the impact.2. The contracting wrist flexors of healthy subjects were exposed to trains of recurrent 25-50 Hz stretch stimuli (wrist torque pulses). Each of the initial two or three pulses in the train was followed by e.m.g. peaks with a latency of 20-25 msec. The e.m.g. peaks driven in this way had the following characteristics in common with the successive two or three e.m.g. peaks which were induced by single ramp stretches or tendon taps. (a) Changes in stimulus parameters which altered the strength of the initial e.m.g. peak often had an opposite effect on the strength of the succeeding peak(s). Muscle vibration which attenuated the initial peak often enchanced the succeeding one(s). (b) The initial e.m.g. peak was less affected than the succeeding peak(s) by the subjects' attempts to respond with rapid ;resist' or ;let go' reactions.3. Intramuscular oscillations (monitored by a needle accelerometer) and e.m.g. responses evoked by single ramp stretches and/or tendon taps were also studied in the long thumb flexor, the calf muscles and the masseter muscle. In the thumb flexor, the initial accelerometer deflexion was only rarely succeeded by a short latency e.m.g. peak, but the succeeding wave in the needle accelerogram was followed by such a peak, appearing about 40 msec after stimulus application. By contrast, the calf muscles and the jaw elevators exhibited a high amplitude, short-latency e.m.g. response to the first but only rarely to the second intramuscular oscillation

  3. Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks

    NARCIS (Netherlands)

    Kooi, T.; Karssemeijer, N.

    2017-01-01

    Neural networks, in particular deep Convolutional Neural Networks (CNN), have recently gone through a renaissance sparked by the introduction of more efficient training procedures and massive amounts of raw annotated data. Barring a handful of modalities, medical images are typically too large to

  4. A 41 μW real-time adaptive neural spike classifier

    NARCIS (Netherlands)

    Zjajo, A.; van Leuken, T.G.R.M.

    2016-01-01

    Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike

  5. Tampa Electric Neural Network Sootblowing

    Energy Technology Data Exchange (ETDEWEB)

    Mark A. Rhode

    2003-12-31

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NO{sub x} formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent soot-blowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat

  6. Tampa Electric Neural Network Sootblowing

    Energy Technology Data Exchange (ETDEWEB)

    Mark A. Rhode

    2004-09-30

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing cofunding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate

  7. Tampa Electric Neural Network Sootblowing

    Energy Technology Data Exchange (ETDEWEB)

    Mark A. Rhode

    2004-03-31

    Boiler combustion dynamics change continuously due to several factors including coal quality, boiler loading, ambient conditions, changes in slag/soot deposits and the condition of plant equipment. NOx formation, Particulate Matter (PM) emissions, and boiler thermal performance are directly affected by the sootblowing practices on a unit. As part of its Power Plant Improvement Initiative program, the US DOE is providing co-funding (DE-FC26-02NT41425) and NETL is the managing agency for this project at Tampa Electric's Big Bend Station. This program serves to co-fund projects that have the potential to increase thermal efficiency and reduce emissions from coal-fired utility boilers. A review of the Big Bend units helped identify intelligent sootblowing as a suitable application to achieve the desired objectives. The existing sootblower control philosophy uses sequential schemes, whose frequency is either dictated by the control room operator or is timed based. The intent of this project is to implement a neural network based intelligent sootblowing system, in conjunction with state-of-the-art controls and instrumentation, to optimize the operation of a utility boiler and systematically control boiler fouling. Utilizing unique, on-line, adaptive technology, operation of the sootblowers can be dynamically controlled based on real-time events and conditions within the boiler. This could be an extremely cost-effective technology, which has the ability to be readily and easily adapted to virtually any pulverized coal fired boiler. Through unique on-line adaptive technology, Neural Network-based systems optimize the boiler operation by accommodating equipment performance changes due to wear and maintenance activities, adjusting to fluctuations in fuel quality, and improving operating flexibility. The system dynamically adjusts combustion setpoints and bias settings in closed-loop supervisory control to simultaneously reduce NO{sub x} emissions and improve heat rate

  8. [Distinguishing the voice of self from others: the self-monitoring hypothesis of auditory hallucination].

    Science.gov (United States)

    Asai, Tomohisa; Tanno, Yoshihiko

    2010-08-01

    Auditory hallucinations (AH), a psychopathological phenomenon where a person hears non-existent voices, commonly occur in schizophrenia. Recent cognitive and neuroscience studies suggest that AH may be the misattribution of one's own inner speech. Self-monitoring through neural feedback mechanisms allows individuals to distinguish between their own and others' actions, including speech. AH maybe the results of an individual's inability to discriminate between their own speech and that of others. The present paper tries to integrate the three theories (behavioral, brain, and model approaches) proposed to explain the self-monitoring hypothesis of AH. In addition, we investigate the lateralization of self-other representation in the brain, as suggested by recent studies, and discuss future research directions.

  9. Neural Markers of Performance States in an Olympic Athlete: An EEG Case Study in Air-Pistol Shooting

    Directory of Open Access Journals (Sweden)

    Selenia di Fronso, Claudio Robazza, Edson Filho, Laura Bortoli, Silvia Comani, Maurizio Bertollo

    2016-06-01

    Full Text Available This study focused on identifying the neural markers underlying optimal and suboptimal performance experiences of an elite air-pistol shooter, based on the tenets of the multi-action plan (MAP model. According to the MAP model’s assumptions, skilled athletes’ cortical patterns are expected to differ among optimal/automatic (Type 1, optimal/controlled (Type 2, suboptimal/controlled (Type 3, and suboptimal/automatic (Type 4 performance experiences. We collected performance (target pistol shots, cognitive-affective (perceived control, accuracy, and hedonic tone, and cortical activity data (32-channel EEG of an elite shooter. Idiosyncratic descriptive analyses revealed differences in perceived accuracy in regard to optimal and suboptimal performance states. Event-Related Desynchronization/Synchronization analysis supported the notion that optimal-automatic performance experiences (Type 1 were characterized by a global synchronization of cortical arousal associated with the shooting task, whereas suboptimal controlled states (Type 3 were underpinned by high cortical activity levels in the attentional brain network. Results are addressed in light of the neural efficiency hypothesis and reinvestment theory. Perceptual training recommendations aimed at restoring optimal performance levels are discussed.

  10. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

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

    2018-01-01

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

  11. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  12. Implementing Signature Neural Networks with Spiking Neurons

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the

  13. Early history of the Riemann Hypothesis in positive characteristic

    NARCIS (Netherlands)

    Oort, F.; Schappacher, Norbert

    2016-01-01

    The classical Riemann Hypothesis RH is among the most prominent unsolved problems in modern mathematics. The development of Number Theory in the 19th century spawned an arithmetic theory of polynomials over finite fields in which an analogue of the Riemann Hypothesis suggested itself. We describe

  14. Assess the Critical Period Hypothesis in Second Language Acquisition

    Science.gov (United States)

    Du, Lihong

    2010-01-01

    The Critical Period Hypothesis aims to investigate the reason for significant difference between first language acquisition and second language acquisition. Over the past few decades, researchers carried out a series of studies to test the validity of the hypothesis. Although there were certain limitations in these studies, most of their results…

  15. Moving beyond traditional null hypothesis testing: evaluating expectations directly

    NARCIS (Netherlands)

    van de Schoot, R.; Hoijtink, H.J.A.; Romeijn, J.W.

    2011-01-01

    This mini-review illustrates that testing the traditional null hypothesis is not always the appropriate strategy. Half in jest, we discuss Aristotle's scientific investigations into the shape of the earth in the context of evaluating the traditional null hypothesis. We conclude that Aristotle was

  16. A new 'hidden colour hypothesis' in hadron physics

    Indian Academy of Sciences (India)

    A new `hidden colour hypothesis' within the framework of QCD, as an extension of and in keeping with the spirit of the `colour singlet hypothesis' is hereby proposed. As such it should play a role in a consistent description of exotic hadrons, such as diquonia, pentaquarks, dibaryons etc. How these exotic hadrons are ...

  17. Teaching Hypothesis Testing by Debunking a Demonstration of Telepathy.

    Science.gov (United States)

    Bates, John A.

    1991-01-01

    Discusses a lesson designed to demonstrate hypothesis testing to introductory college psychology students. Explains that a psychology instructor demonstrated apparent psychic abilities to students. Reports that students attempted to explain the instructor's demonstrations through hypothesis testing and revision. Provides instructions on performing…

  18. Inflation uncertainty and a test of the Friedman hypothesis

    OpenAIRE

    Hafer, R.W.

    1985-01-01

    This paper tests Friedman's (1977) hypothesis that increases in inflation uncertainty, ceteris paribus, may yield higher levels of unemployment. Tests are made using quarterly measures of inflation uncertainty taken from the ASA-NBER survey. Using the 1972-1984 period, we find general support for the hypothesis.

  19. Teacher Satisfaction and Dissatisfaction: Herzberg's 'Two-Factor' Hypothesis Revisited.

    Science.gov (United States)

    Nias, Jennifer

    1981-01-01

    Discusses a study undertaken to evaluate perceptions of job satisfaction and dissatisfaction among 100 graduates trained to teach in primary schools. Weighs findings in light of a hypothesis (Herzberg's two-factor hypothesis) which states that causes of job satisfaction are substantially independent of those determining job dissatisfaction.…

  20. The GABA Hypothesis in Essential Tremor: Lights and Shadows.

    Science.gov (United States)

    Gironell, Alexandre

    2014-01-01

    The gamma-aminobutyric acid (GABA) hypothesis in essential tremor (ET) implies a disturbance of the GABAergic system, especially involving the cerebellum. This review examines the evidence of the GABA hypothesis. The review is based on published data about GABA dysfunction in ET, taking into account studies on cerebrospinal fluid, pathology, electrophysiology, genetics, neuroimaging, experimental animal models, and human drug therapies. Findings from several studies support the GABA hypothesis in ET. The hypothesis follows four steps: 1) cerebellar neurodegeneration with Purkinje cell loss; 2) a decrease in GABA system activity in deep cerebellar neurons; 3) disinhibition in output deep cerebellar neurons with pacemaker activity; and 4) an increase in rhythmic activity of the thalamus and thalamo-cortical circuit, contributing to the generation of tremor. Doubts have been cast on this hypothesis, however, by the fact that it is based on relatively few works, controversial post-mortem findings, and negative genetic studies on the GABA system. Furthermore, GABAergic drug efficacy is low and some GABAergic drugs do not have antitremoric efficacy. The GABA hypothesis continues to be the most robust pathophysiological hypothesis to explain ET. There is light in all GABA hypothesis steps, but a number of shadows cannot be overlooked. We need more studies to clarify the neurodegenerative nature of the disease, to confirm the decrease of GABA activity in the cerebellum, and to test more therapies that enhance the GABA transmission specifically in the cerebellum area.

  1. Export-Led Growth Hypothesis: Evidence from Agricultural Exports ...

    African Journals Online (AJOL)

    The Granger causality test results revealed no any support of the export-led growth (ELG) hypothesis for Tanzania. However, the growth-led exports (GLE) hypothesis for Tanzania was supported by the results of this study, implying that the government of Tanzania needs to promote growth in order to generate exports.

  2. Seeking health information on the web: positive hypothesis testing.

    Science.gov (United States)

    Kayhan, Varol Onur

    2013-04-01

    The goal of this study is to investigate positive hypothesis testing among consumers of health information when they search the Web. After demonstrating the extent of positive hypothesis testing using Experiment 1, we conduct Experiment 2 to test the effectiveness of two debiasing techniques. A total of 60 undergraduate students searched a tightly controlled online database developed by the authors to test the validity of a hypothesis. The database had four abstracts that confirmed the hypothesis and three abstracts that disconfirmed it. Findings of Experiment 1 showed that majority of participants (85%) exhibited positive hypothesis testing. In Experiment 2, we found that the recommendation technique was not effective in reducing positive hypothesis testing since none of the participants assigned to this server could retrieve disconfirming evidence. Experiment 2 also showed that the incorporation technique successfully reduced positive hypothesis testing since 75% of the participants could retrieve disconfirming evidence. Positive hypothesis testing on the Web is an understudied topic. More studies are needed to validate the effectiveness of the debiasing techniques discussed in this study and develop new techniques. Search engine developers should consider developing new options for users so that both confirming and disconfirming evidence can be presented in search results as users test hypotheses using search engines. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  3. The Role of Hypothesis in Constructive Design Research

    DEFF Research Database (Denmark)

    Bang, Anne Louise; Krogh, Peter; Ludvigsen, Martin

    2012-01-01

    and solid perspective on how to keep constructive design research on track, this paper offers a model for understanding the role of hypothesis in constructive design research. The model allows for understanding the hypothesis’s relation to research motivation, questions, experiments, evaluation...... position of the hypothesis as a key-governing element even in artistic led research processes....

  4. A re-examination of the exchange rate overshooting hypothesis ...

    African Journals Online (AJOL)

    Dornbusch's exchange rate overshooting hypothesis has guided monetary policy conduct for many years, despite the fact that empirical evidence on its validity is mixed. This study re-examines the validity of the overshooting hypothesis by using the autoregressive distributed lag (ARDL) procedure. Specifi cally, the study ...

  5. An Exercise for Illustrating the Logic of Hypothesis Testing

    Science.gov (United States)

    Lawton, Leigh

    2009-01-01

    Hypothesis testing is one of the more difficult concepts for students to master in a basic, undergraduate statistics course. Students often are puzzled as to why statisticians simply don't calculate the probability that a hypothesis is true. This article presents an exercise that forces students to lay out on their own a procedure for testing a…

  6. An Individual Differences Analysis of the Self-Teaching Hypothesis

    Science.gov (United States)

    Conners, Frances A.; Loveall, Susan J.; Moore, Marie S.; Hume, Laura E.; Maddox, Christopher D.

    2011-01-01

    The self-teaching hypothesis suggests that children learn orthographic structure of words through the experience of phonologically recoding them. The current study is an individual differences analysis of the self-teaching hypothesis. A total of 40 children in Grades 2 and 3 (7-9 years of age) completed tests of phonological recoding, word…

  7. Testing the Double-Deficit Hypothesis in an Adult Sample

    Science.gov (United States)

    Miller, Carlin J.; Miller, Scott R.; Bloom, Juliana S.; Jones, Lauren; Lindstrom, William; Craggs, Jason; Garcia-Barrera, Mauricio; Semrud-Clikeman, Margaret; Gilger, Jeffrey W.; Hynd, George W.

    2006-01-01

    The double-deficit hypothesis of dyslexia posits that reading deficits are more severe in individuals with weaknesses in phonological awareness and rapid naming than in individuals with deficits in only one of these reading composite skills. In this study, the hypothesis was tested in an adult sample as a model of reading achievement. Participants…

  8. A default Bayesian hypothesis test for ANOVA designs

    NARCIS (Netherlands)

    Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.

    2012-01-01

    This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA

  9. The Sapir-Whorf hypothesis and inference under uncertainty.

    Science.gov (United States)

    Regier, Terry; Xu, Yang

    2017-11-01

    The Sapir-Whorf hypothesis holds that human thought is shaped by language, leading speakers of different languages to think differently. This hypothesis has sparked both enthusiasm and controversy, but despite its prominence it has only occasionally been addressed in computational terms. Recent developments support a view of the Sapir-Whorf hypothesis in terms of probabilistic inference. This view may resolve some of the controversy surrounding the Sapir-Whorf hypothesis, and may help to normalize the hypothesis by linking it to established principles that also explain other phenomena. On this view, effects of language on nonlinguistic cognition or perception reflect standard principles of inference under uncertainty. WIREs Cogn Sci 2017, 8:e1440. doi: 10.1002/wcs.1440 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

  10. Concerns regarding a call for pluralism of information theory and hypothesis testing

    Science.gov (United States)

    Lukacs, P.M.; Thompson, W.L.; Kendall, W.L.; Gould, W.R.; Doherty, P.F.; Burnham, K.P.; Anderson, D.R.

    2007-01-01

    1. Stephens et al . (2005) argue for `pluralism? in statistical analysis, combining null hypothesis testing and information-theoretic (I-T) methods. We show that I-T methods are more informative even in single variable problems and we provide an ecological example. 2. I-T methods allow inferences to be made from multiple models simultaneously. We believe multimodel inference is the future of data analysis, which cannot be achieved with null hypothesis-testing approaches. 3. We argue for a stronger emphasis on critical thinking in science in general and less reliance on exploratory data analysis and data dredging. Deriving alternative hypotheses is central to science; deriving a single interesting science hypothesis and then comparing it to a default null hypothesis (e.g. `no difference?) is not an efficient strategy for gaining knowledge. We think this single-hypothesis strategy has been relied upon too often in the past. 4. We clarify misconceptions presented by Stephens et al . (2005). 5. We think inference should be made about models, directly linked to scientific hypotheses, and their parameters conditioned on data, Prob(Hj| data). I-T methods provide a basis for this inference. Null hypothesis testing merely provides a probability statement about the data conditioned on a null model, Prob(data |H0). 6. Synthesis and applications. I-T methods provide a more informative approach to inference. I-T methods provide a direct measure of evidence for or against hypotheses and a means to consider simultaneously multiple hypotheses as a basis for rigorous inference. Progress in our science can be accelerated if modern methods can be used intelligently; this includes various I-T and Bayesian methods.

  11. The impact of letter spacing on reading: a test of the bigram coding hypothesis.

    Science.gov (United States)

    Vinckier, Fabien; Qiao, Emilie; Pallier, Christophe; Dehaene, Stanislas; Cohen, Laurent

    2011-05-12

    Identifying letters and their relative positions is the basis of reading in literate adults. The Local Combinations Detector model hypothesizes that this ability results from the general organization of the visual system, whereby object encoding proceeds through a hierarchy of neural detectors that, in the case of reading, would be tuned to letters, bigrams, or other letter combinations. Given the increase of receptive fields by a factor of 2 to 3 from one neural level to the next, detectors should integrate information only for letters separated by at most 2 other characters. We test this prediction by measuring the impact of letter spacing on reading, purifying this effect from confounding variables. We establish that performance deteriorates non-linearly whenever letters are separated by at least 2 blank spaces, with the concomitant emergence of a word length effect. We then show that this cannot be reduced to an effect of physical size nor of visual eccentricity. Finally, we demonstrate that the threshold of about 2 spaces is constant across variations in font size. Those results support the hypothesis that the fast recognition of combinations of nearby letters plays a central role in the coding of words, such that interfering with this representation prevents the parallel analysis of letter strings.

  12. Neural network technologies

    Science.gov (United States)

    Villarreal, James A.

    1991-01-01

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

  13. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  14. Neural tube defects

    Directory of Open Access Journals (Sweden)

    M.E. Marshall

    1981-09-01

    Full Text Available Neural tube defects refer to any defect in the morphogenesis of the neural tube, the most common types being spina bifida and anencephaly. Spina bifida has been recognised in skeletons found in north-eastern Morocco and estimated to have an age of almost 12 000 years. It was also known to the ancient Greek and Arabian physicians who thought that the bony defect was due to the tumour. The term spina bifida was first used by Professor Nicolai Tulp of Amsterdam in 1652. Many other terms have been used to describe this defect, but spina bifida remains the most useful general term, as it describes the separation of the vertebral elements in the midline.

  15. 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.

  16. 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. .

  17. Neurally-mediated sincope.

    Science.gov (United States)

    Can, I; Cytron, J; Jhanjee, R; Nguyen, J; Benditt, D G

    2009-08-01

    Syncope is a syndrome characterized by a relatively sudden, temporary and self-terminating loss of consciousness; the causes may vary, but they have in common a temporary inadequacy of cerebral nutrient flow, usually due to a fall in systemic arterial pressure. However, while syncope is a common problem, it is only one explanation for episodic transient loss of consciousness (TLOC). Consequently, diagnostic evaluation should start with a broad consideration of real or seemingly real TLOC. Among those patients in whom TLOC is deemed to be due to ''true syncope'', the focus may then reasonably turn to assessing the various possible causes; in this regard, the neurally-mediated syncope syndromes are among the most frequently encountered. There are three common variations: vasovagal syncope (often termed the ''common'' faint), carotid sinus syndrome, and the so-called ''situational faints''. Defining whether the cause is due to a neurally-mediated reflex relies heavily on careful history taking and selected testing (e.g., tilt-test, carotid massage). These steps are important. Despite the fact that neurally-mediated faints are usually relatively benign from a mortality perspective, they are nevertheless only infrequently an isolated event; neurally-mediated syncope tends to recur, and physical injury resulting from falls or accidents, diminished quality-of-life, and possible restriction from employment or avocation are real concerns. Consequently, defining the specific form and developing an effective treatment strategy are crucial. In every case the goal should be to determine the cause of syncope with sufficient confidence to provide patients and family members with a reliable assessment of prognosis, recurrence risk, and treatment options.

  18. The Neural Noisy Channel

    OpenAIRE

    Yu, Lei; Blunsom, Phil; Dyer, Chris; Grefenstette, Edward; Kocisky, Tomas

    2016-01-01

    We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control ...

  19. Mechanisms of Developmental Regression in Autism and the Broader Phenotype: A Neural Network Modeling Approach

    Science.gov (United States)

    Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette

    2011-01-01

    Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…

  20. Asyntactic Thematic Role Assignment by Mandarin Aphasics: A Test of the Trace-Deletion Hypothesis and the Double Dependency Hypothesis

    Science.gov (United States)

    Su, Yi-ching.; Lee, Shu-er; Chung, Yuh-mei

    2007-01-01

    This study examines the comprehension patterns of various sentence types by Mandarin-speaking aphasic patients and evaluates the validity of the predictions from the Trace-Deletion Hypothesis (TDH) and the Double Dependency Hypothesis (DDH). Like English, the canonical word order in Mandarin is SVO, but the two languages differ in that the head…