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).

Associations between maternal negative affect and adolescent's neural response to peer evaluation

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Patricia Z. Tan

2014-04-01

Full Text Available Parenting is often implicated as a potential source of individual differences in youths’ emotional information processing. The present study examined whether parental affect is related to an important aspect of adolescent emotional development, response to peer evaluation. Specifically, we examined relations between maternal negative affect, observed during parent–adolescent discussion of an adolescent-nominated concern with which s/he wants parental support, and adolescent neural responses to peer evaluation in 40 emotionally healthy and depressed adolescents. We focused on a network of ventral brain regions involved in affective processing of social information: the amygdala, anterior insula, nucleus accumbens, and subgenual anterior cingulate, as well as the ventrolateral prefrontal cortex. Maternal negative affect was not associated with adolescent neural response to peer rejection. However, longer durations of maternal negative affect were associated with decreased responsivity to peer acceptance in the amygdala, left anterior insula, subgenual anterior cingulate, and left nucleus accumbens. These findings provide some of the first evidence that maternal negative affect is associated with adolescents’ neural processing of social rewards. Findings also suggest that maternal negative affect could contribute to alterations in affective processing, specifically, dampening the saliency and/or reward of peer interactions during adolescence.

Neural evidence reveals the rapid effects of reward history on selective attention.

Science.gov (United States)

MacLean, Mary H; Giesbrecht, Barry

2015-05-05

Selective attention is often framed as being primarily driven by two factors: task-relevance and physical salience. However, factors like selection and reward history, which are neither currently task-relevant nor physically salient, can reliably and persistently influence visual selective attention. The current study investigated the nature of the persistent effects of irrelevant, physically non-salient, reward-associated features. These features affected one of the earliest reliable neural indicators of visual selective attention in humans, the P1 event-related potential, measured one week after the reward associations were learned. However, the effects of reward history were moderated by current task demands. The modulation of visually evoked activity supports the hypothesis that reward history influences the innate salience of reward associated features, such that even when no longer relevant, nor physically salient, these features have a rapid, persistent, and robust effect on early visual selective attention. Copyright © 2015 Elsevier B.V. All rights reserved.

Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults.

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Sebastian, Catherine L; Fontaine, Nathalie M G; Bird, Geoffrey; Blakemore, Sarah-Jayne; Brito, Stephane A De; McCrory, Eamon J P; Viding, Essi

2012-01-01

Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11-16 years) and 15 adult (aged 24-40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing.

Project of neural network for steel grade selection with the assumed CCT diagram

OpenAIRE

S. Malara; L.A. Dobrzański; J. Trzaska

2008-01-01

Purpose: The aim of this paper was developing a project of neural network for selection of steel grade with the specified CCT diagram – structure and of harness after heat treatment.Design/methodology/approach: The goal has been achieved in the following stages: at the first stage characteristic points of CCT diagram have been determined. At the second stage neural network has been developed and optimized.Findings: The neural network was developed in this paper, that allowed selection of stee...

Neural networks underlying affective states in a multimodal virtual environment: contributions to boredom

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Krystyna Anna Mathiak

2013-11-01

Full Text Available The interaction of low perceptual stimulation or goal-directed behavior with a negative subjective evaluation may lead to boredom. This contribution to boredom may shed light on its neural correlates, which are poorly characterized so far. A video game served as simulation of free interactive behavior without interruption of the game’s narrative. Thirteen male German volunteers played a first-person shooter game (Tactical Ops: Assault on Terror during functional magnetic resonance imaging (fMRI. Two independent coders performed the time-based analysis of the audio-visual game content. Boredom was operationalized as interaction of prolonged absence of goal-directed behavior with lowered affect in the Positive and Negative Affect Schedule (PANAS.A decrease of positive affect correlated with response amplitudes in bilateral insular clusters extending into the amygdala to prolonged inactive phases in a game play and an increase in negative affect was associated with higher responses in bilateral ventromedial prefrontal cortex. Precuneus and hippocampus responses were negatively correlated with changes in negative affect.We describe for the first time neural contributions to boredom, using a video game as complex virtual environment. Further our study confirmed that positive and negative affect are separable constructs, reflected by distinct neural patterns. Positive affect may be associated with afferent limbic activity whereas negative affect with affective control.

Neural Networks for Target Selection in Direct Marketing

NARCIS (Netherlands)

R. Potharst (Rob); U. Kaymak (Uzay); W.H.L.M. Pijls (Wim)

2001-01-01

textabstractPartly due to a growing interest in direct marketing, it has become an important application field for data mining. Many techniques have been applied to select the targets in commercial applications, such as statistical regression, regression trees, neural computing, fuzzy clustering

The common and distinct neural bases of affect labeling and reappraisal in healthy adults

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Lisa Jane Burklund

2014-03-01

Full Text Available Emotion regulation is commonly characterized as involving conscious and intentional attempts to change felt emotions, such as, for example, through reappraisal whereby one intentionally decreases the intensity of one’s emotional response to a particular stimulus or situation by reinterpreting it in a less threatening way. However, there is growing evidence and appreciation that some types of emotion regulation are unintentional or incidental, meaning that affective modulation is a consequence but not an explicit goal. For example, affect labeling involves simply verbally labeling the emotional content of an external stimulus or one’s own affective responses without an intentional goal of altering emotional responses, yet has been associated with reduced affective responses at the neural and experiential levels. Although both intentional and incidental emotional regulation strategies have been associated with diminished limbic responses and self-reported distress, little previous research has directly compared their underlying neural mechanisms. In this study, we examined the extent to which incidental and intentional emotion regulation, namely, affect labeling and reappraisal, produced common and divergent neural and self-report responses to aversive images relative to an observe-only control condition in a sample of healthy older adults (N=39. Affect labeling and reappraisal produced common activations in several prefrontal regulatory regions, with affect labeling producing stronger responses in direct comparisons. Affect labeling and reappraisal were also associated with similar decreases in amygdala activity. Finally, affect labeling and reappraisal were associated with correlated reductions in self-reported distress. Together these results point to common neurocognitive mechanisms involved in affect labeling and reappraisal, supporting the idea that intentional and incidental emotion regulation may utilize overlapping neural processes.

Cotton genotypes selection through artificial neural networks.

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Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

2017-09-27

Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

An input feature selection method applied to fuzzy neural networks for signal esitmation

International Nuclear Information System (INIS)

Na, Man Gyun; Sim, Young Rok

2001-01-01

It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

Neural androgen receptors affect the number of surviving new neurones in the adult dentate gyrus of male mice.

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Swift-Gallant, A; Duarte-Guterman, P; Hamson, D K; Ibrahim, M; Monks, D A; Galea, L A M

2018-04-01

Adult hippocampal neurogenesis occurs in many mammalian species. In rats, the survival of new neurones within the hippocampus is modulated by the action of androgen via the androgen receptor (AR); however, it is not known whether this holds true in mice. Furthermore, the evidence is mixed regarding whether androgens act in neural tissue or via peripheral non-neural targets to promote new neurone survival in the hippocampus. We evaluated whether the action of androgen via AR underlies the survival of new neurones in mice, and investigated whether increasing AR selectively in neural tissue would increase new neurone survival in the hippocampus. We used the cre-loxP system to overexpress AR only in neural tissues (Nestin-AR). These males were compared with wild-type males, as well as control males with 1 of the 2 mutations required for overexpression. Mice were gonadectomised and injected with the DNA synthesis marker, bromodeoxyuridine (BrdU) and for 37 days (following BrdU injection), mice were treated with oil or dihydrotestosterone (DHT). Using immunohistochemistry, proliferation (Ki67) and survival (BrdU) of new neurones were both evaluated in the dorsal and ventral dentate gyrus. Dihydrotestosterone treatment increased the survival of new neurones in the entire hippocampus in wild-type mice and control mice that only have 1 of 2 necessary mutations for transgenic expression. However, DHT treatment did not increase the survival of new neurones in mice that overexpressed AR in neural tissue. Cell proliferation (Ki67) and cell death (pyknotic cells) were not affected by DHT treatment in wild-type or transgenic males. These results suggest that androgens act via neural AR to affect hippocampal neurogenesis by promoting cell survival; however, the relationship between androgen dose and new neurone survival is nonlinear. © 2018 British Society for Neuroendocrinology.

Selective attention on representations in working memory: cognitive and neural mechanisms.

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Ku, Yixuan

2018-01-01

Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

Neural underpinnings of the identifiable victim effect: affect shifts preferences for giving.

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Genevsky, Alexander; Västfjäll, Daniel; Slovic, Paul; Knutson, Brian

2013-10-23

The "identifiable victim effect" refers to peoples' tendency to preferentially give to identified versus anonymous victims of misfortune, and has been proposed to partly depend on affect. By soliciting charitable donations from human subjects during behavioral and neural (i.e., functional magnetic resonance imaging) experiments, we sought to determine whether and how affect might promote the identifiable victim effect. Behaviorally, subjects gave more to orphans depicted by photographs versus silhouettes, and their shift in preferences was mediated by photograph-induced feelings of positive arousal, but not negative arousal. Neurally, while photographs versus silhouettes elicited activity in widespread circuits associated with facial and affective processing, only nucleus accumbens activity predicted and could statistically account for increased donations. Together, these findings suggest that presenting evaluable identifiable information can recruit positive arousal, which then promotes giving. We propose that affect elicited by identifiable stimuli can compel people to give more to strangers, even despite costs to the self.

SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS

Directory of Open Access Journals (Sweden)

Marijana Zekić-Sušac

2012-07-01

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

Sample selection via angular distance in the space of the arguments of an artificial neural network

Science.gov (United States)

Fernández Jaramillo, J. M.; Mayerle, R.

2018-05-01

In the construction of an artificial neural network (ANN) a proper data splitting of the available samples plays a major role in the training process. This selection of subsets for training, testing and validation affects the generalization ability of the neural network. Also the number of samples has an impact in the time required for the design of the ANN and the training. This paper introduces an efficient and simple method for reducing the set of samples used for training a neural network. The method reduces the required time to calculate the network coefficients, while keeping the diversity and avoiding overtraining the ANN due the presence of similar samples. The proposed method is based on the calculation of the angle between two vectors, each one representing one input of the neural network. When the angle formed among samples is smaller than a defined threshold only one input is accepted for the training. The accepted inputs are scattered throughout the sample space. Tidal records are used to demonstrate the proposed method. The results of a cross-validation show that with few inputs the quality of the outputs is not accurate and depends on the selection of the first sample, but as the number of inputs increases the accuracy is improved and differences among the scenarios with a different starting sample have and important reduction. A comparison with the K-means clustering algorithm shows that for this application the proposed method with a smaller number of samples is producing a more accurate network.

Selective attention on representations in working memory: cognitive and neural mechanisms

Directory of Open Access Journals (Sweden)

Yixuan Ku

2018-04-01

Full Text Available Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

Selection of variables for neural network analysis. Comparisons of several methods with high energy physics data

International Nuclear Information System (INIS)

Proriol, J.

1994-01-01

Five different methods are compared for selecting the most important variables with a view to classifying high energy physics events with neural networks. The different methods are: the F-test, Principal Component Analysis (PCA), a decision tree method: CART, weight evaluation, and Optimal Cell Damage (OCD). The neural networks use the variables selected with the different methods. We compare the percentages of events properly classified by each neural network. The learning set and the test set are the same for all the neural networks. (author)

Aging differentially affects male and female neural stem cell neurogenic properties

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Jay Waldron

2010-09-01

Full Text Available Jay Waldron1, Althea McCourty1, Laurent Lecanu1,21The Research Institute of the McGill University Health Centre, Montreal, Canada; 2Department of Medicine, McGill University, Montreal, Quebec, CanadaPurpose: Neural stem cell transplantation as a brain repair strategy is a very promising technology. However, despite many attempts, the clinical success remains very deceiving. Despite clear evidence that sexual dimorphism rules many aspects of human biology, the occurrence of a sex difference in neural stem cell biology is largely understudied. Herein, we propose to determine whether gender is a dimension that drives the fate of neural stem cells through aging. Should it occur, we believe that neural stem cell sexual dimorphism and its variation during aging should be taken into account to refine clinical approaches of brain repair strategies.Methods: Neural stem cells were isolated from the subventricular zone of three- and 20-month-old male and female Long-Evans rats. Expression of the estrogen receptors, ERα and ERβ, progesterone receptor, androgen receptor, and glucocorticoid receptor was analyzed and quantified by Western blotting on undifferentiated neural stem cells. A second set of neural stem cells was treated with retinoic acid to trigger differentiation, and the expression of neuronal, astroglial, and oligodendroglial markers was determined using Western blotting.Conclusion: We provided in vitro evidence that the fate of neural stem cells is affected by sex and aging. Indeed, young male neural stem cells mainly expressed markers of neuronal and oligodendroglial fate, whereas young female neural stem cells underwent differentiation towards an astroglial phenotype. Aging resulted in a lessened capacity to express neuron and astrocyte markers. Undifferentiated neural stem cells displayed sexual dimorphism in the expression of steroid receptors, in particular ERα and ERβ, and the expression level of several steroid receptors increased

Emotion disrupts neural activity during selective attention in psychopathy.

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Sadeh, Naomi; Spielberg, Jeffrey M; Heller, Wendy; Herrington, John D; Engels, Anna S; Warren, Stacie L; Crocker, Laura D; Sutton, Bradley P; Miller, Gregory A

2013-03-01

Dimensions of psychopathy are theorized to be associated with distinct cognitive and emotional abnormalities that may represent unique neurobiological risk factors for the disorder. This hypothesis was investigated by examining whether the psychopathic personality dimensions of fearless-dominance and impulsive-antisociality moderated neural activity and behavioral responses associated with selective attention and emotional processing during an emotion-word Stroop task in 49 adults. As predicted, the dimensions evidenced divergent selective-attention deficits and sensitivity to emotional distraction. Fearless-dominance was associated with disrupted attentional control to positive words, and activation in right superior frontal gyrus mediated the relationship between fearless-dominance and errors to positive words. In contrast, impulsive-antisociality evidenced increased behavioral interference to both positive and negative words and correlated positively with recruitment of regions associated with motivational salience (amygdala, orbitofrontal cortex, insula), emotion regulation (temporal cortex, superior frontal gyrus) and attentional control (dorsal anterior cingulate cortex). Individuals high on both dimensions had increased recruitment of regions related to attentional control (temporal cortex, rostral anterior cingulate cortex), response preparation (pre-/post-central gyri) and motivational value (orbitofrontal cortex) in response to negative words. These findings provide evidence that the psychopathy dimensions represent dual sets of risk factors characterized by divergent dysfunction in cognitive and affective processes.

The wandering mood: psychological and neural determinants of rest-related negative affect

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Michal eGruberger

2013-12-01

Full Text Available Rest related negative affect (RRNA has gained scientific interest in the past decade. However, it is mostly studied within the context of mind-wandering (MW, and the relevance of other psychological and neural aspects of the resting state to its' occurrence has never been studied. Several indications associate RRNA with internally directed attention, yet the nature of this relation remains largely unknown. Moreover, the role of neural networks associated with rest related phenomenology - the default mode (DMN, executive (EXE and salience (SAL networks, has not been studied in this context. To this end, we explored two 5- (baseline and 15-minute resting-state simultaneous fMRI-EEG scans of 29 participants. As vigilance has been shown to affect attention, and thus its availability for inward allocation, EEG-based vigilance levels were computed for each participant. Questionnaires for affective assessment were administered before and after scans, and retrospective reports of MW were additionally collected. Results revealed increased negative affect following rest, but only among participants who retained high vigilance levels. Among low-vigilance participants, changes in negative affect were negligible, despite reports of MW occurrence in both groups. In addition, in the high-vigilance group only, a significant increase in functional connectivity (FC levels was found between the DMN-related ventral anterior cingulate cortex (ACC,associated with emotional processing, and the EXE-related dorsal ACC, associated with monitoring of self and other's behavior. These heightened FC levels further correlated with reported negative affect among this group. Taken together, these results demonstrate that, rather than an unavoidable outcome of the resting state, RRNA depends on internal allocation of attention at rest. Results are discussed in terms of two rest-related possible scenarios which defer in mental and neural processing, and subsequently, in the

The wandering mood: psychological and neural determinants of rest-related negative affect.

Science.gov (United States)

Gruberger, Michal; Maron-Katz, Adi; Sharon, Haggai; Hendler, Talma; Ben-Simon, Eti

2013-01-01

Rest related negative affect (RRNA) has gained scientific interest in the past decade. However, it is mostly studied within the context of mind-wandering (MW), and the relevance of other psychological and neural aspects of the resting state to its' occurrence has never been studied. Several indications associate RRNA with internally directed attention, yet the nature of this relation remains largely unknown. Moreover, the role of neural networks associated with rest related phenomenology - the default mode (DMN), executive (EXE), and salience (SAL) networks, has not been studied in this context. To this end, we explored two 5 (baseline) and 15-minute resting-state simultaneous fMRI-EEG scans of 29 participants. As vigilance has been shown to affect attention, and thus its availability for inward allocation, EEG-based vigilance levels were computed for each participant. Questionnaires for affective assessment were administered before and after scans, and retrospective reports of MW were additionally collected. Results revealed increased negative affect following rest, but only among participants who retained high vigilance levels. Among low-vigilance participants, changes in negative affect were negligible, despite reports of MW occurrence in both groups. In addition, in the high-vigilance group only, a significant increase in functional connectivity (FC) levels was found between the DMN-related ventral anterior cingulate cortex (ACC), associated with emotional processing, and the EXE-related dorsal ACC, associated with monitoring of self and other's behavior. These heightened FC levels further correlated with reported negative affect among this group. Taken together, these results demonstrate that, rather than an unavoidable outcome of the resting state, RRNA depends on internal allocation of attention at rest. Results are discussed in terms of two rest-related possible scenarios which defer in mental and neural processing, and subsequently, in the occurrence of

A Pontine Region is a Neural Correlate of the Human Affective Processing Network

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Tatia M.C. Lee

2015-11-01

Full Text Available The in vivo neural activity of the pons during the perception of affective stimuli has not been studied despite the strong implications of its role in affective processing. To examine the activity of the pons during the viewing of affective stimuli, and to verify its functional and structural connectivity with other affective neural correlates, a multimodal magnetic resonance imaging methodology was employed in this study. We observed the in vivo activity of the pons when viewing affective stimuli. Furthermore, small-world connectivity indicated that the functional connectivity (FC between the pons and the cortico-limbic affective regions was meaningful, with the coefficient λ being positively associated with self-reported emotional reactivity. The FC between the pons and the cortico-limbic-striatal areas was related to self-reported negative affect. Corroborating this finding was the observation that the tract passing through the pons and the left hippocampus was negatively related to self-reported positive affect and positively correlated with emotional reactivity. Our findings support the framework that the pons works conjunctively with the distributed cortico-limbic-striatal systems in shaping individuals' affective states and reactivity. Our work paves the path for future research on the contribution of the pons to the precipitation and maintenance of affective disorders.

ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM

Institute of Scientific and Technical Information of China (English)

X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen

2003-01-01

An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

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Carlos A. Caceres

2017-06-01

Full Text Available Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Distracted and down: neural mechanisms of affective interference in subclinical depression.

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Kaiser, Roselinde H; Andrews-Hanna, Jessica R; Spielberg, Jeffrey M; Warren, Stacie L; Sutton, Bradley P; Miller, Gregory A; Heller, Wendy; Banich, Marie T

2015-05-01

Previous studies have shown that depressed individuals have difficulty directing attention away from negative distractors, a phenomenon known as affective interference. However, findings are mixed regarding the neural mechanisms and network dynamics of affective interference. The present study addressed these issues by comparing neural activation during emotion-word and color-word Stroop tasks in participants with varying levels of (primarily subclinical) depression. Depressive symptoms predicted increased activation to negative distractors in areas of dorsal anterior cingulate cortex (dACC) and posterior cingulate cortex (PCC), regions implicated in cognitive control and internally directed attention, respectively. Increased dACC activity was also observed in the group-average response to incongruent distractors, suggesting that dACC activity during affective interference is related to overtaxed cognitive control. In contrast, regions of PCC were deactivated across the group in response to incongruent distractors, suggesting that PCC activity during affective interference represents task-independent processing. A psychophysiological interaction emerged in which higher depression predicted more positively correlated activity between dACC and PCC during affective interference, i.e. greater connectivity between cognitive control and internal-attention systems. These findings suggest that, when individuals high in depression are confronted by negative material, increased attention to internal thoughts and difficulty shifting resources to the external world interfere with goal-directed behavior. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Selection in sugarcane families with artificial neural networks

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Bruno Portela Brasileiro

2015-04-01

Full Text Available The objective of this study was to evaluate Artificial Neural Networks (ANN applied in an selection process within sugarcane families. The best ANN model produced no mistake, but was able to classify all genotypes correctly, i.e., the network made the same selective choice as the breeder during the simulation individual best linear unbiased predictor (BLUPIS, demonstrating the ability of the ANN to learn from the inputs and outputs provided in the training and validation phases. Since the ANN-based selection facilitates the identification of the best plants and the development of a new selection strategy in the best families, to ensure that the best genotypes of the population are evaluated in the following stages of the breeding program, we recommend to rank families by BLUP, followed by selection of the best families and finally, select the seedlings by ANN, from information at the individual level in the best families.

Stress affects the neural ensemble for integrating new information and prior knowledge.

Science.gov (United States)

Vogel, Susanne; Kluen, Lisa Marieke; Fernández, Guillén; Schwabe, Lars

2018-06-01

Prior knowledge, represented as a schema, facilitates memory encoding. This schema-related learning is assumed to rely on the medial prefrontal cortex (mPFC) that rapidly integrates new information into the schema, whereas schema-incongruent or novel information is encoded by the hippocampus. Stress is a powerful modulator of prefrontal and hippocampal functioning and first studies suggest a stress-induced deficit of schema-related learning. However, the underlying neural mechanism is currently unknown. To investigate the neural basis of a stress-induced schema-related learning impairment, participants first acquired a schema. One day later, they underwent a stress induction or a control procedure before learning schema-related and novel information in the MRI scanner. In line with previous studies, learning schema-related compared to novel information activated the mPFC, angular gyrus, and precuneus. Stress, however, affected the neural ensemble activated during learning. Whereas the control group distinguished between sets of brain regions for related and novel information, stressed individuals engaged the hippocampus even when a relevant schema was present. Additionally, stressed participants displayed aberrant functional connectivity between brain regions involved in schema processing when encoding novel information. The failure to segregate functional connectivity patterns depending on the presence of prior knowledge was linked to impaired performance after stress. Our results show that stress affects the neural ensemble underlying the efficient use of schemas during learning. These findings may have relevant implications for clinical and educational settings. Copyright © 2018 Elsevier Inc. All rights reserved.

Ongoing neural development of affective theory of mind in adolescence.

Science.gov (United States)

Vetter, Nora C; Weigelt, Sarah; Döhnel, Katrin; Smolka, Michael N; Kliegel, Matthias

2014-07-01

Affective Theory of Mind (ToM), an important aspect of ToM, involves the understanding of affective mental states. This ability is critical in the developmental phase of adolescence, which is often related with socio-emotional problems. Using a developmentally sensitive behavioral task in combination with functional magnetic resonance imaging, the present study investigated the neural development of affective ToM throughout adolescence. Eighteen adolescent (ages 12-14 years) and 18 young adult women (aged 19-25 years) were scanned while evaluating complex affective mental states depicted by actors in video clips. The ventromedial prefrontal cortex (vmPFC) showed significantly stronger activation in adolescents in comparison to adults in the affective ToM condition. Current results indicate that the vmPFC might be involved in the development of affective ToM processing in adolescence. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Vector control of wind turbine on the basis of the fuzzy selective neural net*

Science.gov (United States)

Engel, E. A.; Kovalev, I. V.; Engel, N. E.

2016-04-01

An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

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

Science.gov (United States)

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

2014-02-01

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

Relationship between neural response and adaptation selectivity to form and color: an ERP study

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Ilias eRentzeperis

2012-04-01

Full Text Available Adaptation is widely used as a tool for studying selectivity to visual features. In these studies it is usually assumed that the loci of feature selective neural responses and adaptation coincide. We used an adaptation paradigm to investigate the relationship between response and adaptation selectivity in event-related potentials (ERP. ERPs were evoked by the presentation of colored Glass patterns in a form discrimination task. Response selectivities to form and, to some extent, color of the patterns were reflected in the C1 and N1 ERP components. Adaptation selectivity to color was reflected in N1 and was followed by a late (300-500 ms after stimulus onset effect of form adaptation. Thus for form, response and adaptation selectivity were manifested in non-overlapping intervals. These results indicate that adaptation and response selectivity can be associated with different processes. Therefore inferring selectivity from an adaptation paradigm requires analysis of both adaptation and neural response data.

A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

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Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

Directory of Open Access Journals (Sweden)

C. K. Kwong

2013-01-01

Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

Genetic search feature selection for affective modeling

DEFF Research Database (Denmark)

Martínez, Héctor P.; Yannakakis, Georgios N.

2010-01-01

Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

Applications of self-organizing neural networks in virtual screening and diversity selection.

Science.gov (United States)

Selzer, Paul; Ertl, Peter

2006-01-01

Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

Improved Selectivity From a Wavelength Addressable Device for Wireless Stimulation of Neural Tissue

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Elif Ç. Seymour

2014-02-01

Full Text Available Electrical neural stimulation with micro electrodes is a promising technique for restoring lost functions in the central nervous system as a result of injury or disease. One of the problems related to current neural stimulators is the tissue response due to the connecting wires and the presence of a rigid electrode inside soft neural tissue. We have developed a novel, optically activated, microscale photovoltaic neurostimulator based on a custom layered compound semiconductor heterostructure that is both wireless and has a comparatively small volume. Optical activation provides a wireless means of energy transfer to the neurostimulator, eliminating wires and the associated complications. This neurostimulator was shown to evoke action potentials and a functional motor response in the rat spinal cord. In this work, we extend our design to include wavelength selectivity and thus allowing independent activation of devices. As a proof of concept, we fabricated two different microscale devices with different spectral responsivities in the near-infrared region. We assessed the improved addressability of individual devices via wavelength selectivity as compared to spatial selectivity alone through on-bench optical measurements of the devices in combination with an in vivo light intensity profile in the rat cortex obtained in a previous study. We show that wavelength selectivity improves the individual addressability of the floating stimulators, thus increasing the number of devices that can be implanted in close proximity to each other.

Deep convolutional neural network based antenna selection in multiple-input multiple-output system

Science.gov (United States)

Cai, Jiaxin; Li, Yan; Hu, Ying

2018-03-01

Antenna selection of wireless communication system has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity in large-scale Multiple-Input MultipleOutput antenna systems. Recently, deep learning based methods have achieved promising performance for large-scale data processing and analysis in many application fields. This paper is the first attempt to introduce the deep learning technique into the field of Multiple-Input Multiple-Output antenna selection in wireless communications. First, the label of attenuation coefficients channel matrix is generated by minimizing the key performance indicator of training antenna systems. Then, a deep convolutional neural network that explicitly exploits the massive latent cues of attenuation coefficients is learned on the training antenna systems. Finally, we use the adopted deep convolutional neural network to classify the channel matrix labels of test antennas and select the optimal antenna subset. Simulation experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based wireless antenna selection.

Neural correlates of emotion regulation in patients with schizophrenia and non-affected siblings.

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Lisette van der Meer

Full Text Available BACKGROUND: Patients with schizophrenia often experience problems regulating their emotions. Non-affected relatives show similar difficulties, although to a lesser extent, and the neural basis of such difficulties remains to be elucidated. In the current paper we investigated whether schizophrenia patients, non-affected siblings and healthy controls (HC exhibit differences in brain activation during emotion regulation. METHODS: All subjects (n = 20 per group performed an emotion regulation task while they were in an fMRI scanner. The task contained two experimental conditions for the down-regulation of emotions (reappraise and suppress, in which IAPS pictures were used to generate a negative affect. We also assessed whether the groups differed in emotion regulation strategies used in daily life by means of the emotion regulation questionnaire (ERQ. RESULTS: Though the overall negative affect was higher for patients as well as for siblings compared to HC for all conditions, all groups reported decreased negative affect after both regulation conditions. Nonetheless, neuroimaging results showed hypoactivation relative to HC in VLPFC, insula, middle temporal gyrus, caudate and thalamus for patients when reappraising negative pictures. In siblings, the same pattern was evident as in patients, but only in cortical areas. CONCLUSIONS: Given that all groups performed similarly on the emotion regulation task, but differed in overall negative affect ratings and brain activation, our findings suggest reduced levels of emotion regulation processing in neural circuits in patients with schizophrenia. Notably, this also holds for siblings, albeit to a lesser extent, indicating that it may be part and parcel of a vulnerability for psychosis.

Selective estrogen receptor modulators as brain therapeutic agents

OpenAIRE

Arévalo, María Ángeles; Santos-Galindo, María; Lagunas, Natalia; Azcoitia, I.; García-Segura, Luis M.

2011-01-01

Selective estrogen receptor modulators (SERMs), used for the treatment of breast cancer, osteoporosis, and menopausal symptoms, affect the nervous system. Some SERMs trigger neuroprotective mechanisms and reduce neural damage in different experimental models of neural trauma, brain inflammation, neurodegenerative diseases, cognitive impairment, and affective disorders. New SERMs with specific actions on neurons and glial cells may represent promising therapeutic tools for the brain. © 2011 So...

Spontaneous neural tube defects in splotch mice supplemented with selected micronutrients

International Nuclear Information System (INIS)

Wlodarczyk, Bogdan J.; Tang, Louisa S.; Triplett, Aleata; Aleman, Frank; Finnell, Richard H.

2006-01-01

Splotch (Sp/Sp) mice homozygous for a mutation in the Pax3 gene inevitably present with neural tube defects (NTDs), along with other associated congenital anomalies. The affected mutant embryos usually die by gestation days (E) 12-13. In the present study, the effect of modifier genes from a new genetic background (CXL-Sp) and periconceptional supplementation with selected micronutrients (folic acid, 5-formyltetrahydrofolate, 5-methyltetrahydrofolate, methionine, myoinositol, thiamine, thymidine, and α-tocopherol) was determined with respect to the incidence of NTDs. In order to explore how different exposure parameters (time, dose, and route of compound administration) modulate the beneficial effects of micronutrient supplementation, female mice received either short- or long-term nutrient supplements via enteral or parenteral routes. Embryos were collected on E12.5 and examined for the presence of anterior or posterior NTDs. Additionally, whole mount in situ hybridization studies were conducted in order to reveal/confirm normal expression patterns of the Pax3 gene during neurulation in the wild-type and Sp/Sp homozygous mutant mouse embryos utilized in this study. A strong Pax3 signal was demonstrated in CXL-Sp embryos during neural tube closure (E9.5 to E10.5). The intensity and spatial pattern of expression were similar to other Splotch mutant mice. Of all the micronutrients tested, only supplementation with folic acid or 5-methyltetrahydrofolate rescued the normal phenotype in Sp/Sp embryos. When the folate supplementation dose was increased to 200 mg/kg in the diet, the incidence of rescued splotch homozygotes reached 30%; however, this was accompanied by six-fold increased resorption rate

Neural mechanism for judging the appropriateness of facial affect.

Science.gov (United States)

Kim, Ji-Woong; Kim, Jae-Jin; Jeong, Bum Seok; Ki, Seon Wan; Im, Dong-Mi; Lee, Soo Jung; Lee, Hong Shick

2005-12-01

Questions regarding the appropriateness of facial expressions in particular situations arise ubiquitously in everyday social interactions. To determine the appropriateness of facial affect, first of all, we should represent our own or the other's emotional state as induced by the social situation. Then, based on these representations, we should infer the possible affective response of the other person. In this study, we identified the brain mechanism mediating special types of social evaluative judgments of facial affect in which the internal reference is related to theory of mind (ToM) processing. Many previous ToM studies have used non-emotional stimuli, but, because so much valuable social information is conveyed through nonverbal emotional channels, this investigation used emotionally salient visual materials to tap ToM. Fourteen right-handed healthy subjects volunteered for our study. We used functional magnetic resonance imaging to examine brain activation during the judgmental task for the appropriateness of facial affects as opposed to gender matching tasks. We identified activation of a brain network, which includes both medial frontal cortex, left temporal pole, left inferior frontal gyrus, and left thalamus during the judgmental task for appropriateness of facial affect compared to the gender matching task. The results of this study suggest that the brain system involved in ToM plays a key role in judging the appropriateness of facial affect in an emotionally laden situation. In addition, our result supports that common neural substrates are involved in performing diverse kinds of ToM tasks irrespective of perceptual modalities and the emotional salience of test materials.

Like or dislike? Affective preference modulates neural response to others' gains and losses.

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Yang Wang

Full Text Available Previous studies have demonstrated that the brain responds differentially to others' gains and losses relative to one's own, moderated by social context factors such as competition and interpersonal relationships. In the current study, we tested the hypothesis that the neural response to others' outcomes could be modulated by a short-term induced affective preference. We engaged 17 men and 18 women in a social-exchange game, in which two confederates played fairly or unfairly. Both men and women rated the fair player as likable and the unfair players as unlikable. Afterwards, ERPs were recorded while participants observed each confederates playing a gambling game individually. This study examines feedback related negativity (FRN, an ERP component sensitive to negative feedback. ANOVA showed a significant interaction in which females but not males displayed stronger FRNs when observing likable players' outcomes compared to unlikable ones'. However, males did not respond differently under either circumstance. These findings suggest that, at least in females, the neural response is influenced by a short-term induced affective preference.

Pushing the Limits: Cognitive, Affective, & Neural Plasticity Revealed by an Intensive Multifaceted Intervention

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Michael David Mrazek

2016-03-01

Full Text Available Scientific understanding of how much the adult brain can be shaped by experience requires examination of how multiple influences combine to elicit cognitive, affective, and neural plasticity. Using an intensive multifaceted intervention, we discovered that substantial and enduring improvements can occur in parallel across multiple cognitive and neuroimaging measures in healthy young adults. The intervention elicited substantial improvements in physical health, working memory, standardized test performance, mood, self-esteem, self-efficacy, mindfulness, and life satisfaction. Improvements in mindfulness were associated with increased degree centrality of the insula, greater functional connectivity between insula and somatosensory cortex, and reduced functional connectivity between posterior cingulate cortex and somatosensory cortex. Improvements in working memory and reading comprehension were associated with increased degree centrality of a region within the middle temporal gyrus that was extensively and predominately integrated with the executive control network. The scope and magnitude of the observed improvements represent the most extensive demonstration to date of the considerable human capacity for change. These findings point to higher limits for rapid and concurrent cognitive, affective, and neural plasticity than is widely assumed.

5-HTTLPR polymorphism is linked to neural mechanisms of selective attention in preschoolers from lower socioeconomic status backgrounds

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Elif Isbell

2016-12-01

Full Text Available While a growing body of research has identified experiential factors associated with differences in selective attention, relatively little is known about the contribution of genetic factors to the skill of sustained selective attention, especially in early childhood. Here, we assessed the association between the serotonin transporter linked polymorphic region (5-HTTLPR genotypes and the neural mechanisms of selective attention in young children from lower socioeconomic status (SES backgrounds. Event-related potentials (ERPs were recorded during a dichotic listening task from 121 children (76 females, aged 40–67 months, who were also genotyped for the short and long allele of 5-HTTLPR. The effect of selective attention was measured as the difference in ERP mean amplitudes elicited by identical probe stimuli embedded in stories when they were attended versus unattended. Compared to children homozygous for the long allele, children who carried at least one copy of the short allele showed larger effects of selective attention on neural processing. These findings link the short allele of the 5-HTTLPR to enhanced neural mechanisms of selective attention and lay the groundwork for future studies of gene-by-environment interactions in the context of key cognitive skills.

NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

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Sergey A. Sannikov

2017-03-01

Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

Readout from iconic memory and selective spatial attention involve similar neural processes.

Science.gov (United States)

Ruff, Christian C; Kristjánsson, Arni; Driver, Jon

2007-10-01

Iconic memory and spatial attention are often considered separately, but they may have functional similarities. Here we provide functional magnetic resonance imaging evidence for some common underlying neural effects. Subjects judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (precue, spatial attention) or shortly after the array (postcue, iconic memory). Pre- and postcues led to similar activity modulations in lateral occipital cortex contralateral to the cued side. This finding indicates that readout from iconic memory can have some neural effects similar to those of spatial attention. We also found common bilateral activation of a fronto-parietal network for postcue and precue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found; compared with precues, postcues led to higher activity in the right middle frontal gyrus.

Neural bases of selective attention in action video game players

OpenAIRE

Bavelier, D; Achtman, RL; Mani, M; Föcker, J

2011-01-01

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 et al., 2010). The present study used brain imaging to test this hypothes...

Neural Cognition and Affective Computing on Cyber Language.

Science.gov (United States)

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

2015-01-01

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

Neural Cognition and Affective Computing on Cyber Language

Directory of Open Access Journals (Sweden)

Shuang Huang

2015-01-01

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

The neural bases of host plant selection in a Neuroecology framework

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Carolina E Reisenman

2015-08-01

Full Text Available Understanding how animals make use of environmental information to guide behavior is a fundamental problem in the field of neuroscience. Similarly, the field of ecology seeks to understand the role of behavior in shaping interactions between organisms at various levels of organization, including population-, community- and even ecosystem-level scales. Together, the newly emerged field of Neuroecology seeks to unravel this fundamental question by studying both the function of neurons at many levels of the sensory pathway and the interactions between organisms and their natural environment. The interactions between herbivorous insects and their host plants are ideal examples of Neuroecology given the strong ecological and evolutionary forces and the underlying physiological and behavioral mechanisms that shaped these interactions. In this review we focus on an exemplary herbivorous insect within the Lepidoptera, the giant sphinx moth Manduca sexta, as much is known about the natural behaviors related to hostplant selection and the involved neurons at several level of the sensory pathway. We also discuss how herbivore-induced plant odorants and secondary metabolites in floral nectar in turn can affect moth behavior, and the underlying neural mechanisms.

The neural bases of host plant selection in a Neuroecology framework.

Science.gov (United States)

Reisenman, Carolina E; Riffell, Jeffrey A

2015-01-01

Understanding how animals make use of environmental information to guide behavior is a fundamental problem in the field of neuroscience. Similarly, the field of ecology seeks to understand the role of behavior in shaping interactions between organisms at various levels of organization, including population-, community- and even ecosystem-level scales. Together, the newly emerged field of "Neuroecology" seeks to unravel this fundamental question by studying both the function of neurons at many levels of the sensory pathway and the interactions between organisms and their natural environment. The interactions between herbivorous insects and their host plants are ideal examples of Neuroecology given the strong ecological and evolutionary forces and the underlying physiological and behavioral mechanisms that shaped these interactions. In this review we focus on an exemplary herbivorous insect within the Lepidoptera, the giant sphinx moth Manduca sexta, as much is known about the natural behaviors related to host plant selection and the involved neurons at several level of the sensory pathway. We also discuss how herbivore-induced plant odorants and secondary metabolites in floral nectar in turn can affect moth behavior, and the underlying neural mechanisms.

5-HTTLPR polymorphism is linked to neural mechanisms of selective attention in preschoolers from lower socioeconomic status backgrounds.

Science.gov (United States)

Isbell, Elif; Stevens, Courtney; Hampton Wray, Amanda; Bell, Theodore; Neville, Helen J

2016-12-01

While a growing body of research has identified experiential factors associated with differences in selective attention, relatively little is known about the contribution of genetic factors to the skill of sustained selective attention, especially in early childhood. Here, we assessed the association between the serotonin transporter linked polymorphic region (5-HTTLPR) genotypes and the neural mechanisms of selective attention in young children from lower socioeconomic status (SES) backgrounds. Event-related potentials (ERPs) were recorded during a dichotic listening task from 121 children (76 females, aged 40-67 months), who were also genotyped for the short and long allele of 5-HTTLPR. The effect of selective attention was measured as the difference in ERP mean amplitudes elicited by identical probe stimuli embedded in stories when they were attended versus unattended. Compared to children homozygous for the long allele, children who carried at least one copy of the short allele showed larger effects of selective attention on neural processing. These findings link the short allele of the 5-HTTLPR to enhanced neural mechanisms of selective attention and lay the groundwork for future studies of gene-by-environment interactions in the context of key cognitive skills. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Behavioral and neural indices of affective coloring for neutral social stimuli

Science.gov (United States)

Schaefer, Stacey M; Lapate, Regina C; Schoen, Andrew J; Gresham, Lauren K; Mumford, Jeanette A; Davidson, Richard J

2018-01-01

Abstract Emotional processing often continues beyond the presentation of emotionally evocative stimuli, which can result in affective biasing or coloring of subsequently encountered events. Here, we describe neural correlates of affective coloring and examine how individual differences in affective style impact the magnitude of affective coloring. We conducted functional magnetic resonance imaging in 117 adults who passively viewed negative, neutral and positive pictures presented 2 s prior to neutral faces. Brain responses to neutral faces were modulated by the valence of preceding pictures, with greater activation for faces following negative (vs positive) pictures in the amygdala, dorsomedial and lateral prefrontal cortex, ventral visual cortices, posterior superior temporal sulcus, and angular gyrus. Three days after the magnetic resonance imaging scan, participants rated their memory and liking of previously encountered neutral faces. Individuals higher in trait positive affect and emotional reappraisal rated faces as more likable when preceded by emotionally arousing (negative or positive) pictures. In addition, greater amygdala responses to neutral faces preceded by positively valenced pictures were associated with greater memory for these faces 3 days later. Collectively, these results reveal individual differences in how emotions spill over onto the processing of unrelated social stimuli, resulting in persistent and affectively biased evaluations of such stimuli. PMID:29447377

Selection of hadronic W-decays in DELPHI with feed forward neural networks - An update

CERN Document Server

Becks, K H; Müller, U; Wahlen, H

2003-01-01

Since 1998 feed forward neural networks have been successfully applied to select candidates of hadronic W-decays measured at different center of mass-energies by the DELPHI collaboration at the Large Electron Positron collider at CERN. To prepare the final publication, the neural network was adapted to all center of mass- energies. Detailed studies were performed concerning the level of preselection, the choice of network parameters and especially of the network architecture. The number of hidden nodes was optimized by testing different pruning methods. All studies and results will be discussed.

Selection of hadronic W-decays in DELPHI with feed forward neural networks - an update

International Nuclear Information System (INIS)

Becks, K.-H.; Drees, J.; Mueller, U.; Wahlen, H.

2003-01-01

Since 1998 feed forward neural networks have been successfully applied to select candidates of hadronic W-decays measured at different center of mass-energies by the DELPHI collaboration at the Large Electron Positron collider at CERN. To prepare the final publication, the neural network was adapted to all center of mass-energies. Detailed studies were performed concerning the level of preselection, the choice of network parameters and especially of the network architecture. The number of hidden nodes was optimized by testing different pruning methods. All studies and results will be discussed

The method of educational assessment affects children's neural processing and performance: behavioural and fMRI Evidence

Science.gov (United States)

Howard, Steven J.; Burianová, Hana; Calleia, Alysha; Fynes-Clinton, Samuel; Kervin, Lisa; Bokosmaty, Sahar

2017-08-01

Standardised educational assessments are now widespread, yet their development has given comparatively more consideration to what to assess than how to optimally assess students' competencies. Existing evidence from behavioural studies with children and neuroscience studies with adults suggest that the method of assessment may affect neural processing and performance, but current evidence remains limited. To investigate the impact of assessment methods on neural processing and performance in young children, we used functional magnetic resonance imaging to identify and quantify the neural correlates during performance across a range of current approaches to standardised spelling assessment. Results indicated that children's test performance declined as the cognitive load of assessment method increased. Activation of neural nodes associated with working memory further suggests that this performance decline may be a consequence of a higher cognitive load, rather than the complexity of the content. These findings provide insights into principles of assessment (re)design, to ensure assessment results are an accurate reflection of students' true levels of competency.

Controlling selective stimulations below a spinal cord hemisection using brain recordings with a neural interface system approach

Science.gov (United States)

Panetsos, Fivos; Sanchez-Jimenez, Abel; Torets, Carlos; Largo, Carla; Micera, Silvestro

2011-08-01

In this work we address the use of realtime cortical recordings for the generation of coherent, reliable and robust motor activity in spinal-lesioned animals through selective intraspinal microstimulation (ISMS). The spinal cord of adult rats was hemisectioned and groups of multielectrodes were implanted in both the central nervous system (CNS) and the spinal cord below the lesion level to establish a neural system interface (NSI). To test the reliability of this new NSI connection, highly repeatable neural responses recorded from the CNS were used as a pattern generator of an open-loop control strategy for selective ISMS of the spinal motoneurons. Our experimental procedure avoided the spontaneous non-controlled and non-repeatable neural activity that could have generated spurious ISMS and the consequent undesired muscle contractions. Combinations of complex CNS patterns generated precisely coordinated, reliable and robust motor actions.

Selection of hidden layer nodes in neural networks by statistical tests

International Nuclear Information System (INIS)

Ciftcioglu, Ozer

1992-05-01

A statistical methodology for selection of the number of hidden layer nodes in feedforward neural networks is described. The method considers the network as an empirical model for the experimental data set subject to pattern classification so that the selection process becomes a model estimation through parameter identification. The solution is performed for an overdetermined estimation problem for identification using nonlinear least squares minimization technique. The number of the hidden layer nodes is determined as result of hypothesis testing. Accordingly the redundant network structure with respect to the number of parameters is avoided and the classification error being kept to a minimum. (author). 11 refs.; 4 figs.; 1 tab

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

Science.gov (United States)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Neural and psychological underpinnings of gambling disorder

DEFF Research Database (Denmark)

Grant, Jon E; Odlaug, Brian L; Chamberlain, Samuel R

2016-01-01

Gambling disorder affects 0.4 to 1.6% of adults worldwide, and is highly comorbid with other mental health disorders. This article provides a concise primer on the neural and psychological underpinnings of gambling disorder based on a selective review of the literature. Gambling disorder is assoc......Gambling disorder affects 0.4 to 1.6% of adults worldwide, and is highly comorbid with other mental health disorders. This article provides a concise primer on the neural and psychological underpinnings of gambling disorder based on a selective review of the literature. Gambling disorder...... is associated with dysfunction across multiple cognitive domains which can be considered in terms of impulsivity and compulsivity. Neuroimaging data suggest structural and functional abnormalities of networks involved in reward processing and top-down control. Gambling disorder shows 50-60% heritability...... is required to evaluate whether cognitive dysfunction and personality aspects influence the longitudinal course and treatment outcome for gambling disorder. It is hoped that improved understanding of the biological and psychological components of gambling disorder, and their interactions, may lead to improved...

Orientation selective neural network for cosmic muon identification

International Nuclear Information System (INIS)

Abramowicz, H.; Tel Aviv Univ.; Horn, D.; Naftaly, U.; Sahar-Pikielny, C.

1997-01-01

We discuss a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Motivated by principles employed by the visual cortex, we construct orientation selective neurons in a neural network that performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons that leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices. (orig.)

Neural systems supporting cognitive-affective interactions in adolescence: The role of puberty and implications for affective disorders

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Cecile D. Ladouceur

2012-08-01

Full Text Available Evidence from longitudinal studies suggests that adolescence may represent a period of vulnerability that, in the context of adverse events, could contribute to developmental trajectories toward behavioral and emotional health problems, including affective disorders. Adolescence is also a sensitive period for the development of neural systems supporting cognitive-affective processes, which have been implicated in the pathophysiology of affective disorders such as anxiety and mood disorders. In particular, the onset of puberty brings about a cascade of physical, hormonal, psychological, and social changes that contribute in complex ways to the development of these systems. This article provides a brief overview of neuroimaging research pertaining to the development of cognitive-affective processes in adolescence. It also includes a brief review of evidence from animal and human neuroimaging studies suggesting that sex steroids influence the connectivity between prefrontal cortical and subcortical limbic regions in ways that contribute to increased reactivity to emotionally salient stimuli. We integrate these findings in the context of a developmental affective neuroscience framework suggesting that the impact of rising levels of sex steroids during puberty on fronto-limbic connectivity may be even greater in the context of protracted development of prefrontal cortical regions in adolescence. We conclude by discussing the implications of these findings for future research aimed at identifying neurodevelopmental markers of risk for future onset of affective disorders.

Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce

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Alcinei Mistico Azevedo

2015-12-01

Full Text Available The efficiency of artificial neural networks (ANN to model complex problems may enable the prediction of characteristics that are hard to measure, providing better results than the traditional indirect selection. Thus, this study aimed to investigate the potential of using artificial neural networks (ANN for indirect selection against early flowering in lettuce, identify the influence of genotype by environment interaction in this strategy and compare your results with the traditional indirect selection. The number of days to anthesis were used as the desired output and the information of six characteristics (fresh weight of shoots, mass of marketable fresh matter of shoots, commercial dry matter of shoots, average diameter of the head, head circumference and leaf number as input file for the training of the ANN-MLP (Perceptron Multi-Layer. The use of ANN has great potential adjustment for indirect selection for genetic improvement of lettuce against early flowering. The selection based on the predicted values by network provided estimates of gain selection largest that traditional indirect selection. The ANN trained with data from an experiment have low power extrapolation to another experiment, due to effect of interaction genotype by environment. The ANNs trained simultaneously with data from different experiments presented greater predictive power and extrapolation.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

Science.gov (United States)

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Lying about the valence of affective pictures: an fMRI study.

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Tatia M C Lee

Full Text Available The neural correlates of lying about affective information were studied using a functional magnetic resonance imaging (fMRI methodology. Specifically, 13 healthy right-handed Chinese men were instructed to lie about the valence, positive or negative, of pictures selected from the International Affective Picture System (IAPS while their brain activity was scanned by a 3T Philip Achieva scanner. The key finding is that the neural activity associated with deception is valence-related. Comparing to telling the truth, deception about the valence of the affectively positive pictures was associated with activity in the inferior frontal, cingulate, inferior parietal, precuneus, and middle temporal regions. Lying about the valence of the affectively negative pictures, on the other hand, was associated with activity in the orbital and medial frontal regions. While a clear valence-related effect on deception was observed, common neural regions were also recruited for the process of deception about the valence of the affective pictures. These regions included the lateral prefrontal and inferior parietal regions. Activity in these regions has been widely reported in fMRI studies on deception using affectively-neutral stimuli. The findings of this study reveal the effect of valence on the neural activity associated with deception. Furthermore, the data also help to illustrate the complexity of the neural mechanisms underlying deception.

Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast

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Petr Maca

2014-01-01

Full Text Available The presented paper aims to analyze the influence of the selection of transfer function and training algorithms on neural network flood runoff forecast. Nine of the most significant flood events, caused by the extreme rainfall, were selected from 10 years of measurement on small headwater catchment in the Czech Republic, and flood runoff forecast was investigated using the extensive set of multilayer perceptrons with one hidden layer of neurons. The analyzed artificial neural network models with 11 different activation functions in hidden layer were trained using 7 local optimization algorithms. The results show that the Levenberg-Marquardt algorithm was superior compared to the remaining tested local optimization methods. When comparing the 11 nonlinear transfer functions, used in hidden layer neurons, the RootSig function was superior compared to the rest of analyzed activation functions.

Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

Energy Technology Data Exchange (ETDEWEB)

Wijayasekara, Dumidu, E-mail: wija2589@vandals.uidaho.edu [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Manic, Milos [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Sabharwall, Piyush [Idaho National Laboratory, Idaho Falls, ID (United States); Utgikar, Vivek [Department of Chemical Engineering, University of Idaho, Idaho Falls, ID 83402 (United States)

2011-07-15

Highlights: > Performance prediction of PCHE using artificial neural networks. > Evaluating artificial neural network performance for PCHE modeling. > Selection of over-training resilient artificial neural networks. > Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing

Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

International Nuclear Information System (INIS)

Wijayasekara, Dumidu; Manic, Milos; Sabharwall, Piyush; Utgikar, Vivek

2011-01-01

Highlights: → Performance prediction of PCHE using artificial neural networks. → Evaluating artificial neural network performance for PCHE modeling. → Selection of over-training resilient artificial neural networks. → Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the

Uncovering the neuroanatomical correlates of cognitive, affective and conative theory of mind in paediatric traumatic brain injury: a neural systems perspective.

Science.gov (United States)

Ryan, Nicholas P; Catroppa, Cathy; Beare, Richard; Silk, Timothy J; Hearps, Stephen J; Beauchamp, Miriam H; Yeates, Keith O; Anderson, Vicki A

2017-09-01

Deficits in theory of mind (ToM) are common after neurological insult acquired in the first and second decade of life, however the contribution of large-scale neural networks to ToM deficits in children with brain injury is unclear. Using paediatric traumatic brain injury (TBI) as a model, this study investigated the sub-acute effect of paediatric traumatic brain injury on grey-matter volume of three large-scale, domain-general brain networks (the Default Mode Network, DMN; the Central Executive Network, CEN; and the Salience Network, SN), as well as two domain-specific neural networks implicated in social-affective processes (the Cerebro-Cerebellar Mentalizing Network, CCMN and the Mirror Neuron/Empathy Network, MNEN). We also evaluated prospective structure-function relationships between these large-scale neural networks and cognitive, affective and conative ToM. 3D T1- weighted magnetic resonance imaging sequences were acquired sub-acutely in 137 children [TBI: n = 103; typically developing (TD) children: n = 34]. All children were assessed on measures of ToM at 24-months post-injury. Children with severe TBI showed sub-acute volumetric reductions in the CCMN, SN, MNEN, CEN and DMN, as well as reduced grey-matter volumes of several hub regions of these neural networks. Volumetric reductions in the CCMN and several of its hub regions, including the cerebellum, predicted poorer cognitive ToM. In contrast, poorer affective and conative ToM were predicted by volumetric reductions in the SN and MNEN, respectively. Overall, results suggest that cognitive, affective and conative ToM may be prospectively predicted by individual differences in structure of different neural systems-the CCMN, SN and MNEN, respectively. The prospective relationship between cerebellar volume and cognitive ToM outcomes is a novel finding in our paediatric brain injury sample and suggests that the cerebellum may play a role in the neural networks important for ToM. These findings are

Diminished Neural Processing of Aversive and Rewarding Stimuli During Selective Serotonin Reuptake Inhibitor Treatment

Science.gov (United States)

McCabe, Ciara; Mishor, Zevic; Cowen, Philip J.; Harmer, Catherine J.

2010-01-01

Background Selective serotonin reuptake inhibitors (SSRIs) are popular medications for anxiety and depression, but their effectiveness, particularly in patients with prominent symptoms of loss of motivation and pleasure, has been questioned. There are few studies of the effect of SSRIs on neural reward mechanisms in humans. Methods We studied 45 healthy participants who were randomly allocated to receive the SSRI citalopram, the noradrenaline reuptake inhibitor reboxetine, or placebo for 7 days in a double-blind, parallel group design. We used functional magnetic resonance imaging to measure the neural response to rewarding (sight and/or flavor of chocolate) and aversive stimuli (sight of moldy strawberries and/or an unpleasant strawberry taste) on the final day of drug treatment. Results Citalopram reduced activation to the chocolate stimuli in the ventral striatum and the ventral medial/orbitofrontal cortex. In contrast, reboxetine did not suppress ventral striatal activity and in fact increased neural responses within medial orbitofrontal cortex to reward. Citalopram also decreased neural responses to the aversive stimuli conditions in key “punishment” areas such as the lateral orbitofrontal cortex. Reboxetine produced a similar, although weaker effect. Conclusions Our findings are the first to show that treatment with SSRIs can diminish the neural processing of both rewarding and aversive stimuli. The ability of SSRIs to decrease neural responses to reward might underlie the questioned efficacy of SSRIs in depressive conditions characterized by decreased motivation and anhedonia and could also account for the experience of emotional blunting described by some patients during SSRI treatment. PMID:20034615

Spatial attention enhances the selective integration of activity from area MT.

Science.gov (United States)

Masse, Nicolas Y; Herrington, Todd M; Cook, Erik P

2012-09-01

Distinguishing which of the many proposed neural mechanisms of spatial attention actually underlies behavioral improvements in visually guided tasks has been difficult. One attractive hypothesis is that attention allows downstream neural circuits to selectively integrate responses from the most informative sensory neurons. This would allow behavioral performance to be based on the highest-quality signals available in visual cortex. We examined this hypothesis by asking how spatial attention affects both the stimulus sensitivity of middle temporal (MT) neurons and their corresponding correlation with behavior. Analyzing a data set pooled from two experiments involving four monkeys, we found that spatial attention did not appreciably affect either the stimulus sensitivity of the neurons or the correlation between their activity and behavior. However, for those sessions in which there was a robust behavioral effect of attention, focusing attention inside the neuron's receptive field significantly increased the correlation between these two metrics, an indication of selective integration. These results suggest that, similar to mechanisms proposed for the neural basis of perceptual learning, the behavioral benefits of focusing spatial attention are attributable to selective integration of neural activity from visual cortical areas by their downstream targets.

Specific and Nonspecific Neural Activity during Selective Processing of Visual Representations in Working Memory

Science.gov (United States)

Oh, Hwamee; Leung, Hoi-Chung

2010-01-01

In this fMRI study, we investigated prefrontal cortex (PFC) and visual association regions during selective information processing. We recorded behavioral responses and neural activity during a delayed recognition task with a cue presented during the delay period. A specific cue ("Face" or "Scene") was used to indicate which one of the two…

dNTP deficiency induced by HU via inhibiting ribonucleotide reductase affects neural tube development

International Nuclear Information System (INIS)

Guan, Zhen; Wang, Xiuwei; Dong, Yanting; Xu, Lin; Zhu, Zhiqiang; Wang, Jianhua; Zhang, Ting; Niu, Bo

2015-01-01

Highlights: • Murine NTDs were successfully induced by means of hydroxyurea (HU). • The impairment of dNTP was induced via inhibition of ribonucleotide reductase. • dNTP deficiency induced by HU caused defective DNA synthesis and repair. • Abnormal apoptosis and proliferation induced by HU affected neural tube development. - Abstract: Exposure to environmental toxic chemicals in utero during the neural tube development period can cause developmental disorders. To evaluate the disruption of neural tube development programming, the murine neural tube defects (NTDs) model was induced by interrupting folate metabolism using methotrexate in our previous study. The present study aimed to examine the effects of dNTP deficiency induced by hydroxyurea (HU), a specific ribonucleotide reductase (RNR) inhibitor, during murine neural tube development. Pregnant C57BL/6J mice were intraperitoneally injected with various doses of HU on gestation day (GD) 7.5, and the embryos were checked on GD 11.5. RNR activity and deoxynucleoside triphosphate (dNTP) levels were measured in the optimal dose. Additionally, DNA damage was examined by comet analysis and terminal deoxynucleotidyl transferase mediated dUTP nick end-labeling (TUNEL) assay. Cellular behaviors in NTDs embryos were evaluated with phosphorylation of histone H3 (PH-3) and caspase-3 using immunohistochemistry and western blot analysis. The results showed that NTDs were observed mostly with HU treatment at an optimal dose of 225 mg/kg b/w. RNR activity was inhibited and dNTP levels were decreased in HU-treated embryos with NTDs. Additionally, increased DNA damage, decreased proliferation, and increased caspase-3 were significant in NTDs embryos compared to the controls. Results indicated that HU induced murine NTDs model by disturbing dNTP metabolism and further led to the abnormal cell balance between proliferation and apoptosis

The Neural Mechanisms Underlying Internally and Externally Guided Task Selection

Science.gov (United States)

Orr, Joseph M.; Banich, Marie T.

2013-01-01

While some prior work suggests that medial prefrontal cortex (MFC) regions mediate freely chosen actions, other work suggests that the lateral frontal pole (LFP) is responsible for control of abstract, internal goals. The present study uses fMRI to determine whether the voluntary selection of a task in pursuit of an overall goal relies on MFC regions or the LFP. To do so, we used a modified voluntary task switching (VTS) paradigm, in which participants choose an individual task to perform on each trial (i.e., a subgoal), under instructions to perform the tasks equally often and in a random order (i.e. the overall goal). In conjunction, we examined patterns of activation in the face of irrelevant, but task-related external stimuli that might nonetheless influence task selection. While there was some evidence that the MFC was involved in voluntary task selection, we found that the LFP and anterior insula (AI) were crucial to task selection in the pursuit of an overall goal. In addition, activation of the LFP and AI increased in the face of environmental stimuli that might serve as an interfering or conflicting external bias on voluntary task choice. These findings suggest that the LFP supports task selection according to abstract, internal goals, and leaves open the possibility that MFC may guide action selection in situations lacking in such top-down biases. As such, the current study represents a critical step towards understanding the neural underpinnings of how tasks are selected voluntarily to enable an overarching goal. PMID:23994316

Power to punish norm violations affects the neural processes of fairness-related decision making

Directory of Open Access Journals (Sweden)

Xuemei eCheng

2015-12-01

Full Text Available Punishing norm violations is considered an important motive during rejection of unfair offers in the Ultimatum Game (UG. The present study investigates the impact of the power to punish norm violations on people’s responses to unfairness and associated neural correlates. In the UG condition participants had the power to punish norm violations, while an alternate condition, the Impunity Game (IG, was presented where participants had no power to punish norm violations since rejection only reduced the responder’s income to zero. Results showed that unfair offers were rejected more often in UG compared to IG. At the neural level, anterior insula and dorsal anterior cingulate cortex were more active when participants received and rejected unfair offers in both UG and IG. Moreover, greater dorsolateral prefrontal cortex activity was observed when participants rejected than accepted unfair offers in UG but not in IG. Ventromedial prefrontal cortex activation was higher in UG than IG when unfair offers were accepted as well as when rejecting unfair offers in IG as opposed to UG. Taken together, our results demonstrate that the power to punish norm violations affects not only people’s behavioral responses to unfairness but also the neural correlates of the fairness-related social decision-making process.

Prefrontal contributions to visual selective attention.

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Squire, Ryan F; Noudoost, Behrad; Schafer, Robert J; Moore, Tirin

2013-07-08

The faculty of attention endows us with the capacity to process important sensory information selectively while disregarding information that is potentially distracting. Much of our understanding of the neural circuitry underlying this fundamental cognitive function comes from neurophysiological studies within the visual modality. Past evidence suggests that a principal function of the prefrontal cortex (PFC) is selective attention and that this function involves the modulation of sensory signals within posterior cortices. In this review, we discuss recent progress in identifying the specific prefrontal circuits controlling visual attention and its neural correlates within the primate visual system. In addition, we examine the persisting challenge of precisely defining how behavior should be affected when attentional function is lost.

Forecasting Urban Air Quality via a Back-Propagation Neural Network and a Selection Sample Rule

Directory of Open Access Journals (Sweden)

Yonghong Liu

2015-07-01

Full Text Available In this paper, based on a sample selection rule and a Back Propagation (BP neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE values decreasing by 4% and R2 values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale.

Negative affect and neural response to palatable food intake in bulimia nervosa.

Science.gov (United States)

Bohon, Cara; Stice, Eric

2012-06-01

Binge eating is often preceded by reports of negative affect, but the mechanism by which affect may lead to binge eating is unclear. This study evaluated the effect of negative affect on neural response to anticipation and receipt of palatable food in women with bulimia nervosa (BN) versus healthy controls. We also evaluated connectivity between the amygdala and reward-related brain regions. Females with and without BN (n=26) underwent functional magnetic resonance imaging (fMRI) during receipt and anticipated receipt of chocolate milkshake and a tasteless solution. We measured negative affect just prior to the scan. Women with BN showed a positive correlation between negative affect and activity in the putamen, caudate, and pallidum during anticipated receipt of milkshake (versus tasteless solution). There were no significant relations between negative affect and receipt of milkshake. Connectivity analyses revealed a greater relation of amygdala activity to activation in the left putamen and insula during anticipated receipt of milkshake in the bulimia group relative to the control group. The opposite pattern was found for the taste of milkshake; the control group showed a greater relation of amygdala activity to activation in the left putamen and insula in response to milkshake receipt than the bulimia group. Results show that as negative affect increases, so does responsivity of reward regions to anticipated intake of palatable food, implying that negative affect may increase the reward value of food for individuals with bulimia nervosa or that negative affect has become a conditioned cue due to a history of binge eating in a negative mood. Copyright © 2012 Elsevier Ltd. All rights reserved.

Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

International Nuclear Information System (INIS)

Chitsaz, Hamed; Amjady, Nima; Zareipour, Hamidreza

2015-01-01

Highlights: • Presenting a Morlet wavelet neural network for wind power forecasting. • Proposing improved Clonal selection algorithm for training the model. • Applying Maximum Correntropy Criterion to evaluate the training performance. • Extensive testing of the proposed wind power forecast method on real-world data. - Abstract: With the integration of wind farms into electric power grids, an accurate wind power prediction is becoming increasingly important for the operation of these power plants. In this paper, a new forecasting engine for wind power prediction is proposed. The proposed engine has the structure of Wavelet Neural Network (WNN) with the activation functions of the hidden neurons constructed based on multi-dimensional Morlet wavelets. This forecast engine is trained by a new improved Clonal selection algorithm, which optimizes the free parameters of the WNN for wind power prediction. Furthermore, Maximum Correntropy Criterion (MCC) has been utilized instead of Mean Squared Error as the error measure in training phase of the forecasting model. The proposed wind power forecaster is tested with real-world hourly data of system level wind power generation in Alberta, Canada. In order to demonstrate the efficiency of the proposed method, it is compared with several other wind power forecast techniques. The obtained results confirm the validity of the developed approach

Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

International Nuclear Information System (INIS)

Zhou Gang; Ge Shengqi; Yang Li

2010-01-01

A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

Theta phase precession and phase selectivity: a cognitive device description of neural coding

Science.gov (United States)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to

Neural chips, neural computers and application in high and superhigh energy physics experiments

International Nuclear Information System (INIS)

Nikityuk, N.M.; )

2001-01-01

Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

The neural mechanisms of affect infusion in social economic decision-making: A mediating role of the anterior insula

NARCIS (Netherlands)

Harlé, K.M.; Chang, L.J.; Wout, M. van 't; Sanfey, A.G.

2012-01-01

Though emotions have been shown to have sometimes dramatic effects on decision-making, the neural mechanisms mediating these biases are relatively unexplored. Here, we investigated how incidental affect (i.e. emotional states unrelated to the decision at hand) may influence decisions, and how these

Analysis of Drug Design for a Selection of G Protein-Coupled Neuro-Receptors Using Neural Network Techniques

DEFF Research Database (Denmark)

Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.

2015-01-01

A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....... mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug-design.......925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical...

Neural effects of cognitive control load on auditory selective attention.

Science.gov (United States)

Sabri, Merav; Humphries, Colin; Verber, Matthew; Liebenthal, Einat; Binder, Jeffrey R; Mangalathu, Jain; Desai, Anjali

2014-08-01

Whether and how working memory disrupts or alters auditory selective attention is unclear. We compared simultaneous event-related potentials (ERP) and functional magnetic resonance imaging (fMRI) responses associated with task-irrelevant sounds across high and low working memory load in a dichotic-listening paradigm. Participants performed n-back tasks (1-back, 2-back) in one ear (Attend ear) while ignoring task-irrelevant speech sounds in the other ear (Ignore ear). The effects of working memory load on selective attention were observed at 130-210ms, with higher load resulting in greater irrelevant syllable-related activation in localizer-defined regions in auditory cortex. The interaction between memory load and presence of irrelevant information revealed stronger activations primarily in frontal and parietal areas due to presence of irrelevant information in the higher memory load. Joint independent component analysis of ERP and fMRI data revealed that the ERP component in the N1 time-range is associated with activity in superior temporal gyrus and medial prefrontal cortex. These results demonstrate a dynamic relationship between working memory load and auditory selective attention, in agreement with the load model of attention and the idea of common neural resources for memory and attention. Copyright © 2014 Elsevier Ltd. All rights reserved.

Using Artificial Neural Networks to Determine Significant Factors Affecting the Pricing of WPT Effluent for Industrial Uses in Isfahan

Directory of Open Access Journals (Sweden)

Masoud Mirmohamadsaseghi

2017-03-01

Full Text Available The evidence indicates increasing trend of use of municipal wastewater treatment effluent as an alternative source of water both in developed and developing countries. Proper pricing of this unconventional water is one of the most effective economic tools to encourage optimum use of fresh water resources. In this study, artificial neural network is employed to identify and assess the factors affecting effluent tariffs supplied to local industries in Isfahan region. Given the wide variety of factors involved in the ultimate value of wastewater traement plant effluent, an assortment of relevant factors  has been considered in this study; the factors include the population served by the treatment plant, volume of effluent produced, maintenance, repair and replacement. costs of operating plants, topography, different water uses in the region, industrial wastewater collection fees, unit cost of pipe and fittings, and the volumes of water supplied from springs and aqueducts  in the region. Neural network modeling is used as a tool to determine the significance of each factor for pricing effluent. Based on the available data and the neural network models, the effects of different model architectures with different intermediate layers and numbers of nodes in each layer on the price of wastewater were investigated to develop aand adopt a final neural network model. Results indicate that the proposed neural network model enjoys a high potential and has been well capable of determining the weights of the parameter affecting in pricing effluent. Based on the the results of this study, the factors with the greatest role in effluent pricing are unit cost of pipe and fittings, industrial use of water, and the costs of plant maintentance, repair and replacement.

Neural correlates of the affective properties of spontaneous and volitional laughter types.

Science.gov (United States)

Lavan, Nadine; Rankin, Georgia; Lorking, Nicole; Scott, Sophie; McGettigan, Carolyn

2017-01-27

Previous investigations of vocal expressions of emotion have identified acoustic and perceptual distinctions between expressions of different emotion categories, and between spontaneous and volitional (or acted) variants of a given category. Recent work on laughter has identified relationships between acoustic properties of laughs and their perceived affective properties (arousal and valence) that are similar across spontaneous and volitional types (Bryant & Aktipis, 2014; Lavan et al., 2016). In the current study, we explored the neural correlates of such relationships by measuring modulations of the BOLD response in the presence of itemwise variability in the subjective affective properties of spontaneous and volitional laughter. Across all laughs, and within spontaneous and volitional sets, we consistently observed linear increases in the response of bilateral auditory cortices (including Heschl's gyrus and superior temporal gyrus [STG]) associated with higher ratings of perceived arousal, valence and authenticity. Areas in the anterior medial prefrontal cortex (amPFC) showed negative linear correlations with valence and authenticity ratings across the full set of spontaneous and volitional laughs; in line with previous research (McGettigan et al., 2015; Szameitat et al., 2010), we suggest that this reflects increased engagement of these regions in response to laughter of greater social ambiguity. Strikingly, an investigation of higher-order relationships between the entire laughter set and the neural response revealed a positive quadratic profile of the BOLD response in right-dominant STG (extending onto the dorsal bank of the STS), where this region responded most strongly to laughs rated at the extremes of the authenticity scale. While previous studies claimed a role for right STG in bipolar representation of emotional valence, we instead argue that this may in fact exhibit a relatively categorical response to emotional signals, whether positive or negative

Neural Correlates of Affective Influence on Choice

Science.gov (United States)

Piech, Richard M.; Lewis, Jade; Parkinson, Caroline H.; Owen, Adrian M.; Roberts, Angela C.; Downing, Paul E.; Parkinson, John A.

2010-01-01

Making the right choice depends crucially on the accurate valuation of the available options in the light of current needs and goals of an individual. Thus, the valuation of identical options can vary considerably with motivational context. The present study investigated the neural structures underlying context dependent evaluation. We instructed…

Cognitive-affective neural plasticity following active-controlled mindfulness intervention

DEFF Research Database (Denmark)

Allen, Micah Galen

Mindfulness meditation is a set of attention-based, regulatory and self-inquiry training regimes. Although the impact of mindfulness meditation training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act through...... prefrontal cortex (mPFC), and right anterior insula during negative valence processing. Our findings highlight the importance of active control in MT research, indicate unique neural mechanisms for progressive stages of mindfulness training, and suggest that optimal application of MT may differ depending...

Cultures differ in the ability to enhance affective neural responses.

Science.gov (United States)

Varnum, Michael E W; Hampton, Ryan S

2017-10-01

The present study (N = 55) used an event-related potential paradigm to investigate whether cultures differ in the ability to upregulate affective responses. Using stimuli selected from the International Affective Picture System, we found that European-Americans (N = 29) enhanced central-parietal late positive potential (LPP) (400-800 ms post-stimulus) responses to affective stimuli when instructed to do so, whereas East Asians (N = 26) did not. We observed cultural differences in the ability to enhance central-parietal LPP responses for both positively and negativelyvalenced stimuli, and the ability to enhance these two types of responses was positively correlated for Americans but negatively for East Asians. These results are consistent with the notion that cultural variations in norms and values regarding affective expression and experiences shape how the brain regulates emotions.

Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2009-01-01

With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

Route Selection Problem Based on Hopfield Neural Network

Directory of Open Access Journals (Sweden)

N. Kojic

2013-12-01

Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.

Lymphotropic Virions Affect Chemokine Receptor-Mediated Neural Signaling and Apoptosis: Implications for Human Immunodeficiency Virus Type 1-Associated Dementia

Science.gov (United States)

Zheng, Jialin; Ghorpade, Anuja; Niemann, Douglas; Cotter, Robin L.; Thylin, Michael R.; Epstein, Leon; Swartz, Jennifer M.; Shepard, Robin B.; Liu, Xiaojuan; Nukuna, Adeline; Gendelman, Howard E.

1999-01-01

Chemokine receptors pivotal for human immunodeficiency virus type 1 (HIV-1) infection in lymphocytes and macrophages (CCR3, CCR5, and CXCR4) are expressed on neural cells (microglia, astrocytes, and/or neurons). It is these cells which are damaged during progressive HIV-1 infection of the central nervous system. We theorize that viral coreceptors could effect neural cell damage during HIV-1-associated dementia (HAD) without simultaneously affecting viral replication. To these ends, we studied the ability of diverse viral strains to affect intracellular signaling and apoptosis of neurons, astrocytes, and monocyte-derived macrophages. Inhibition of cyclic AMP, activation of inositol 1,4,5-trisphosphate, and apoptosis were induced by diverse HIV-1 strains, principally in neurons. Virions from T-cell-tropic (T-tropic) strains (MN, IIIB, and Lai) produced the most significant alterations in signaling of neurons and astrocytes. The HIV-1 envelope glycoprotein, gp120, induced markedly less neural damage than purified virions. Macrophage-tropic (M-tropic) strains (ADA, JR-FL, Bal, MS-CSF, and DJV) produced the least neural damage, while 89.6, a dual-tropic HIV-1 strain, elicited intermediate neural cell damage. All T-tropic strain-mediated neuronal impairments were blocked by the CXCR4 antibody, 12G5. In contrast, the M-tropic strains were only partially blocked by 12G5. CXCR4-mediated neuronal apoptosis was confirmed in pure populations of rat cerebellar granule neurons and was blocked by HA1004, an inhibitor of calcium/calmodulin-dependent protein kinase II, protein kinase A, and protein kinase C. Taken together, these results suggest that progeny HIV-1 virions can influence neuronal signal transduction and apoptosis. This process occurs, in part, through CXCR4 and is independent of CD4 binding. T-tropic viruses that traffic in and out of the brain during progressive HIV-1 disease may play an important role in HAD neuropathogenesis. PMID:10482576

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Temporal and Spatial Patterns of Neural Activity Associated with Information Selection in Open-ended Creativity.

Science.gov (United States)

Zhou, Siyuan; Chen, Shi; Wang, Shuang; Zhao, Qingbai; Zhou, Zhijin; Lu, Chunming

2018-02-10

Novel information selection is a crucial process in creativity and was found to be associated with frontal-temporal functional connectivity in the right brain in closed-ended creativity. Since it has distinct cognitive processing from closed-ended creativity, the information selection in open-ended creativity might be underlain by different neural activity. To address this issue, a creative generation task of Chinese two-part allegorical sayings was adopted, and the trials were classified into novel and normal solutions according to participants' self-ratings. The results showed that (1) novel solutions induced a higher lower alpha power in the temporal area, which might be associated with the automatic, unconscious mental process of retrieving extensive semantic information, and (2) upper alpha power in both frontal and temporal areas and frontal-temporal alpha coherence were higher in novel solutions than in normal solutions, which might reflect the selective inhibition of semantic information. Furthermore, lower alpha power in the temporal area showed a reduction with time, while the frontal-temporal and temporal-temporal coherence in the upper alpha band appeared to increase from the early to the middle phase. These dynamic changes in neural activity might reflect the transformation from divergent thinking to convergent thinking in the creative progress. The advantage of the right brain in frontal-temporal connectivity was not found in the present work, which might result from the diversity of solutions in open-ended creativity. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Altered Synchronizations among Neural Networks in Geriatric Depression.

Science.gov (United States)

Wang, Lihong; Chou, Ying-Hui; Potter, Guy G; Steffens, David C

2015-01-01

Although major depression has been considered as a manifestation of discoordinated activity between affective and cognitive neural networks, only a few studies have examined the relationships among neural networks directly. Because of the known disconnection theory, geriatric depression could be a useful model in studying the interactions among different networks. In the present study, using independent component analysis to identify intrinsically connected neural networks, we investigated the alterations in synchronizations among neural networks in geriatric depression to better understand the underlying neural mechanisms. Resting-state fMRI data was collected from thirty-two patients with geriatric depression and thirty-two age-matched never-depressed controls. We compared the resting-state activities between the two groups in the default-mode, central executive, attention, salience, and affective networks as well as correlations among these networks. The depression group showed stronger activity than the controls in an affective network, specifically within the orbitofrontal region. However, unlike the never-depressed controls, geriatric depression group lacked synchronized/antisynchronized activity between the affective network and the other networks. Those depressed patients with lower executive function has greater synchronization between the salience network with the executive and affective networks. Our results demonstrate the effectiveness of the between-network analyses in examining neural models for geriatric depression.

Neural and psychological underpinnings of gambling disorder: A review

OpenAIRE

Grant, Jon E; Odlaug, Brian L; Chamberlain, Sam

2015-01-01

Gambling disorder affects 0.4 to 1.6% of adults worldwide, and is highly comorbid with other mental health disorders. This article provides a concise primer on the neural and psychological underpinnings of gambling disorder based on a selective review of the literature. Gambling disorder is associated with dysfunction across multiple cognitive domains which can be considered in terms of impulsivity and compulsivity. Neuroimaging data suggest structural and functional abnormalities of networks...

Optical-Correlator Neural Network Based On Neocognitron

Science.gov (United States)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Consciousness and neural plasticity

DEFF Research Database (Denmark)

changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

Affective monitoring: A generic mechanism for affect elicitation

Directory of Open Access Journals (Sweden)

Hans ePhaf

2012-03-01

Full Text Available In this paper we sketch a new framework for affect elicitation, which is based on previous evolutionary and connectionist modeling and experimental work from our group. Affective monitoring is considered a local match-mismatch process within a module of the neural network. Negative affect is raised instantly by mismatches, incongruency, disfluency, novelty, incoherence, and dissonance, whereas positive affect follows from matches, congruency, fluency, familiarity, coherence, and resonance, at least when an initial mismatch can be solved quickly. Affective monitoring is considered an evolutionary-early conflict and change detection process operating at the same level as, for instance, attentional selection. It runs in parallel and imparts affective flavour to emotional behavior systems, which involve evolutionary-prepared stimuli and action tendencies related to for instance defensive, exploratory, attachment, or appetitive behavior. Positive affect is represented in the networks by high-frequency oscillations, presumably in the gamma band. Negative affect corresponds to more incoherent lower-frequency oscillations, presumably in the theta band. For affect to become conscious, large-scale synchronization of the oscillations over the network and the construction of emotional experiences are required. These constructions involve perceptions of bodily states and action tendencies, but also appraisals as well as efforts to regulate the emotion. Importantly, affective monitoring accompanies every kind of information processing, but conscious emotions, which result from the later integration of affect in a cognitive context, are much rarer events.

Distinct neural substrates of affective and cognitive theory of mind impairment in semantic dementia.

Science.gov (United States)

Bejanin, Alexandre; Chételat, Gaël; Laisney, Mickael; Pélerin, Alice; Landeau, Brigitte; Merck, Catherine; Belliard, Serge; de La Sayette, Vincent; Eustache, Francis; Desgranges, Béatrice

2017-06-01

Using structural MRI, we investigated the brain substrates of both affective and cognitive theory of mind (ToM) in 19 patients with semantic dementia. We also ran intrinsic connectivity analyses to identify the networks to which the substrates belong and whether they are functionally disturbed in semantic dementia. In line with previous studies, we observed a ToM impairment in patients with semantic dementia even when semantic memory was regressed out. Our results also highlighted different neural bases according to the nature (affective or cognitive) of the representations being inferred. The affective ToM deficit was associated with atrophy in the amygdala, suggesting the involvement of emotion-processing deficits in this impairment. By contrast, cognitive ToM performances were correlated with the volume of medial prefrontal and parietal regions, as well as the right frontal operculum. Intrinsic connectivity analyses revealed decreased functional connectivity, mainly between midline cortical regions and temporal regions. They also showed that left medial temporal regions were functionally isolated, a further possible hindrance to normal social cognitive functioning in semantic dementia. Overall, this study addressed for the first time the neuroanatomical substrates of both cognitive and affective ToM disruption in semantic dementia, highlighting disturbed connectivity within the networks that sustain these abilities.

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

Science.gov (United States)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect.

Directory of Open Access Journals (Sweden)

Luke J Chang

2015-06-01

Full Text Available Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121 and test (n = 61 samples (high-low emotion = 93.5% accuracy. It was unresponsive to physical pain (emotion-pain = 92% discriminative accuracy, demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional "emotion-related" regions (e.g., amygdala, insula or resting-state networks (e.g., "salience," "default mode". Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

Quantitative trait loci affecting phenotypic variation in the vacuolated lens mouse mutant, a multigenic mouse model of neural tube defects

NARCIS (Netherlands)

Korstanje, Ron; Desai, Jigar; Lazar, Gloria; King, Benjamin; Rollins, Jarod; Spurr, Melissa; Joseph, Jamie; Kadambi, Sindhuja; Li, Yang; Cherry, Allison; Matteson, Paul G.; Paigen, Beverly; Millonig, James H.

Korstanje R, Desai J, Lazar G, King B, Rollins J, Spurr M, Joseph J, Kadambi S, Li Y, Cherry A, Matteson PG, Paigen B, Millonig JH. Quantitative trait loci affecting phenotypic variation in the vacuolated lens mouse mutant, a multigenic mouse model of neural tube defects. Physiol Genomics 35:

Selective neuronal differentiation of neural stem cells induced by nanosecond microplasma agitation.

Science.gov (United States)

Xiong, Z; Zhao, S; Mao, X; Lu, X; He, G; Yang, G; Chen, M; Ishaq, M; Ostrikov, K

2014-03-01

An essential step for therapeutic and research applications of stem cells is their ability to differentiate into specific cell types. Neuronal cells are of great interest for medical treatment of neurodegenerative diseases and traumatic injuries of central nervous system (CNS), but efforts to produce these cells have been met with only modest success. In an attempt of finding new approaches, atmospheric-pressure room-temperature microplasma jets (MPJs) are shown to effectively direct in vitro differentiation of neural stem cells (NSCs) predominantly into neuronal lineage. Murine neural stem cells (C17.2-NSCs) treated with MPJs exhibit rapid proliferation and differentiation with longer neurites and cell bodies eventually forming neuronal networks. MPJs regulate ~75% of NSCs to differentiate into neurons, which is a higher efficiency compared to common protein- and growth factors-based differentiation. NSCs exposure to quantized and transient (~150 ns) micro-plasma bullets up-regulates expression of different cell lineage markers as β-Tubulin III (for neurons) and O4 (for oligodendrocytes), while the expression of GFAP (for astrocytes) remains unchanged, as evidenced by quantitative PCR, immunofluorescence microscopy and Western Blot assay. It is shown that the plasma-increased nitric oxide (NO) production is a factor in the fate choice and differentiation of NSCs followed by axonal growth. The differentiated NSC cells matured and produced mostly cholinergic and motor neuronal progeny. It is also demonstrated that exposure of primary rat NSCs to the microplasma leads to quite similar differentiation effects. This suggests that the observed effect may potentially be generic and applicable to other types of neural progenitor cells. The application of this new in vitro strategy to selectively differentiate NSCs into neurons represents a step towards reproducible and efficient production of the desired NSC derivatives. Published by Elsevier B.V.

Selective neuronal differentiation of neural stem cells induced by nanosecond microplasma agitation

Directory of Open Access Journals (Sweden)

Z. Xiong

2014-03-01

Full Text Available An essential step for therapeutic and research applications of stem cells is their ability to differentiate into specific cell types. Neuronal cells are of great interest for medical treatment of neurodegenerative diseases and traumatic injuries of central nervous system (CNS, but efforts to produce these cells have been met with only modest success. In an attempt of finding new approaches, atmospheric-pressure room-temperature microplasma jets (MPJs are shown to effectively direct in vitro differentiation of neural stem cells (NSCs predominantly into neuronal lineage. Murine neural stem cells (C17.2-NSCs treated with MPJs exhibit rapid proliferation and differentiation with longer neurites and cell bodies eventually forming neuronal networks. MPJs regulate ~75% of NSCs to differentiate into neurons, which is a higher efficiency compared to common protein- and growth factors-based differentiation. NSCs exposure to quantized and transient (~150 ns micro-plasma bullets up-regulates expression of different cell lineage markers as β-Tubulin III (for neurons and O4 (for oligodendrocytes, while the expression of GFAP (for astrocytes remains unchanged, as evidenced by quantitative PCR, immunofluorescence microscopy and Western Blot assay. It is shown that the plasma-increased nitric oxide (NO production is a factor in the fate choice and differentiation of NSCs followed by axonal growth. The differentiated NSC cells matured and produced mostly cholinergic and motor neuronal progeny. It is also demonstrated that exposure of primary rat NSCs to the microplasma leads to quite similar differentiation effects. This suggests that the observed effect may potentially be generic and applicable to other types of neural progenitor cells. The application of this new in vitro strategy to selectively differentiate NSCs into neurons represents a step towards reproducible and efficient production of the desired NSC derivatives.

Using Dual Process Models to Examine Impulsivity Throughout Neural Maturation.

Science.gov (United States)

Leshem, Rotem

2016-01-01

The multivariate construct of impulsivity is examined through neural systems and connections that comprise the executive functioning system. It is proposed that cognitive and behavioral components of impulsivity can be divided into two distinct groups, mediated by (1) the cognitive control system: deficits in top-down cognitive control processes referred to as action/cognitive impulsivity and (2) the socioemotional system: related to bottom-up affective/motivational processes referred to as affective impulsivity. Examination of impulsivity from a developmental viewpoint can guide future research, potentially enabling the selection of more effective interventions for impulsive individuals, based on the cognitive components requiring improvement.

Temporal Context in Speech Processing and Attentional Stream Selection: A Behavioral and Neural perspective

Science.gov (United States)

Zion Golumbic, Elana M.; Poeppel, David; Schroeder, Charles E.

2012-01-01

The human capacity for processing speech is remarkable, especially given that information in speech unfolds over multiple time scales concurrently. Similarly notable is our ability to filter out of extraneous sounds and focus our attention on one conversation, epitomized by the ‘Cocktail Party’ effect. Yet, the neural mechanisms underlying on-line speech decoding and attentional stream selection are not well understood. We review findings from behavioral and neurophysiological investigations that underscore the importance of the temporal structure of speech for achieving these perceptual feats. We discuss the hypothesis that entrainment of ambient neuronal oscillations to speech’s temporal structure, across multiple time-scales, serves to facilitate its decoding and underlies the selection of an attended speech stream over other competing input. In this regard, speech decoding and attentional stream selection are examples of ‘active sensing’, emphasizing an interaction between proactive and predictive top-down modulation of neuronal dynamics and bottom-up sensory input. PMID:22285024

Can modular psychological concepts like affect and emotion be assigned to a distinct subset of regional neural circuits?. Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al.

Science.gov (United States)

Fehr, Thorsten; Herrmann, Manfred

2015-06-01

The proposed Quartet Theory of Human Emotions by Koelsch and co-workers [11] adumbrates evidence from various scientific sources to integrate and assign the psychological concepts of 'affect' and 'emotion' to four brain circuits or to four neuronal core systems for affect-processing in the brain. The authors differentiate between affect and emotion and assign several facultative, or to say modular, psychological domains and principles of information processing, such as learning and memory, antecedents of affective activity, emotion satiation, cognitive complexity, subjective quality feelings, degree of conscious appraisal, to different affect systems. Furthermore, they relate orbito-frontal brain structures to moral affects as uniquely human, and the hippocampus to attachment-related affects. An additional feature of the theory describes 'emotional effector-systems' for motor-related processes (e.g., emotion-related actions), physiological arousal, attention and memory that are assumed to be cross-linked with the four proposed affect systems. Thus, higher principles of emotional information processing, but also modular affect-related issues, such as moral and attachment related affects, are thought to be handled by these four different physiological sub-systems that are on the other side assumed to be highly interwoven at both physiological and functional levels. The authors also state that the proposed sub-systems have many features in common, such as the selection and modulation of biological processes related to behaviour, perception, attention and memory. The latter aspect challenges an ongoing discussion about the mind-body problem: To which degree do the proposed sub-systems 'sufficiently' cover the processing of complex modular or facultative emotional/affective and/or cognitive phenomena? There are current models and scientific positions that almost completely reject the idea that modular psychological phenomena are handled by a distinct selection of

Music training relates to the development of neural mechanisms of selective auditory attention.

Science.gov (United States)

Strait, Dana L; Slater, Jessica; O'Connell, Samantha; Kraus, Nina

2015-04-01

Selective attention decreases trial-to-trial variability in cortical auditory-evoked activity. This effect increases over the course of maturation, potentially reflecting the gradual development of selective attention and inhibitory control. Work in adults indicates that music training may alter the development of this neural response characteristic, especially over brain regions associated with executive control: in adult musicians, attention decreases variability in auditory-evoked responses recorded over prefrontal cortex to a greater extent than in nonmusicians. We aimed to determine whether this musician-associated effect emerges during childhood, when selective attention and inhibitory control are under development. We compared cortical auditory-evoked variability to attended and ignored speech streams in musicians and nonmusicians across three age groups: preschoolers, school-aged children and young adults. Results reveal that childhood music training is associated with reduced auditory-evoked response variability recorded over prefrontal cortex during selective auditory attention in school-aged child and adult musicians. Preschoolers, on the other hand, demonstrate no impact of selective attention on cortical response variability and no musician distinctions. This finding is consistent with the gradual emergence of attention during this period and may suggest no pre-existing differences in this attention-related cortical metric between children who undergo music training and those who do not. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Integrating the behavioral and neural dynamics of response selection in a dual-task paradigm: a dynamic neural field model of Dux et al. (2009).

Science.gov (United States)

Buss, Aaron T; Wifall, Tim; Hazeltine, Eliot; Spencer, John P

2014-02-01

People are typically slower when executing two tasks than when only performing a single task. These dual-task costs are initially robust but are reduced with practice. Dux et al. (2009) explored the neural basis of dual-task costs and learning using fMRI. Inferior frontal junction (IFJ) showed a larger hemodynamic response on dual-task trials compared with single-task trial early in learning. As dual-task costs were eliminated, dual-task hemodynamics in IFJ reduced to single-task levels. Dux and colleagues concluded that the reduction of dual-task costs is accomplished through increased efficiency of information processing in IFJ. We present a dynamic field theory of response selection that addresses two questions regarding these results. First, what mechanism leads to the reduction of dual-task costs and associated changes in hemodynamics? We show that a simple Hebbian learning mechanism is able to capture the quantitative details of learning at both the behavioral and neural levels. Second, is efficiency isolated to cognitive control areas such as IFJ, or is it also evident in sensory motor areas? To investigate this, we restrict Hebbian learning to different parts of the neural model. None of the restricted learning models showed the same reductions in dual-task costs as the unrestricted learning model, suggesting that efficiency is distributed across cognitive control and sensory motor processing systems.

Neural Network approach to assess the thermal affected zone around the injection well in a groundwater heat pump system

Science.gov (United States)

Lo Russo, Stefano; Taddia, Glenda; Verda, Vittorio

2014-05-01

The common use of well doublets for groundwater-sourced heating or cooling results in a thermal plume of colder or warmer re-injected groundwater known as the Thermal Affected Zone(TAZ). The plumes may be regarded either as a potential anthropogenic geothermal resource or as pollution, depending on downstream aquifer usage. A fundamental aspect in groundwater heat pump (GWHP) plant design is the correct evaluation of the thermally affected zone that develops around the injection well. Temperature anomalies are detected through numerical methods. Crucial elements in the process of thermal impact assessment are the sizes of installations, their position, the heating/cooling load of the building, and the temperature drop/increase imposed on the re-injected water flow. For multiple-well schemes, heterogeneous aquifers, or variable heating and cooling loads, numerical models that simulate groundwater and heat transport are needed. These tools should consider numerous scenarios obtained considering different heating/cooling loads, positions, and operating modes. Computational fluid dynamic (CFD) models are widely used in this field because they offer the opportunity to calculate the time evolution of the thermal plume produced by a heat pump, depending on the characteristics of the subsurface and the heat pump. Nevertheless, these models require large computational efforts, and therefore their use may be limited to a reasonable number of scenarios. Neural networks could represent an alternative to CFD for assessing the TAZ under different scenarios referring to a specific site. The use of neural networks is proposed to determine the time evolution of the groundwater temperature downstream of an installation as a function of the possible utilization profiles of the heat pump. The main advantage of neural network modeling is the possibility of evaluating a large number of scenarios in a very short time, which is very useful for the preliminary analysis of future multiple

Neural bases of different cognitive strategies for facial affect processing in schizophrenia.

Science.gov (United States)

Fakra, Eric; Salgado-Pineda, Pilar; Delaveau, Pauline; Hariri, Ahmad R; Blin, Olivier

2008-03-01

To examine the neural basis and dynamics of facial affect processing in schizophrenic patients as compared to healthy controls. Fourteen schizophrenic patients and fourteen matched controls performed a facial affect identification task during fMRI acquisition. The emotional task included an intuitive emotional condition (matching emotional faces) and a more cognitively demanding condition (labeling emotional faces). Individual analysis for each emotional condition, and second-level t-tests examining both within-, and between-group differences, were carried out using a random effects approach. Psychophysiological interactions (PPI) were tested for variations in functional connectivity between amygdala and other brain regions as a function of changes in experimental conditions (labeling versus matching). During the labeling condition, both groups engaged similar networks. During the matching condition, schizophrenics failed to activate regions of the limbic system implicated in the automatic processing of emotions. PPI revealed an inverse functional connectivity between prefrontal regions and the left amygdala in healthy volunteers but there was no such change in patients. Furthermore, during the matching condition, and compared to controls, patients showed decreased activation of regions involved in holistic face processing (fusiform gyrus) and increased activation of regions associated with feature analysis (inferior parietal cortex, left middle temporal lobe, right precuneus). Our findings suggest that schizophrenic patients invariably adopt a cognitive approach when identifying facial affect. The distributed neocortical network observed during the intuitive condition indicates that patients may resort to feature-based, rather than configuration-based, processing and may constitute a compensatory strategy for limbic dysfunction.

The neural mechanisms of affect infusion in social economic decision-making: a mediating role of the anterior insula.

Science.gov (United States)

Harlé, Katia M; Chang, Luke J; van 't Wout, Mascha; Sanfey, Alan G

2012-05-15

Though emotions have been shown to have sometimes dramatic effects on decision-making, the neural mechanisms mediating these biases are relatively unexplored. Here, we investigated how incidental affect (i.e. emotional states unrelated to the decision at hand) may influence decisions, and how these biases are implemented in the brain. Nineteen adult participants made decisions which involved accepting or rejecting monetary offers from others in an Ultimatum Game while undergoing functional magnetic resonance imaging (fMRI). Prior to each set of decisions, participants watched a short video clip aimed at inducing either a sad or neutral emotional state. Results demonstrated that, as expected, sad participants rejected more unfair offers than those in the neutral condition. Neuroimaging analyses revealed that receiving unfair offers while in a sad mood elicited activity in brain areas related to aversive emotional states and somatosensory integration (anterior insula) and to cognitive conflict (anterior cingulate cortex). Sad participants also showed a diminished sensitivity in neural regions associated with reward processing (ventral striatum). Importantly, insular activation uniquely mediated the relationship between sadness and decision bias. This study is the first to reveal how subtle mood states can be integrated at the neural level to influence decision-making. Copyright © 2012 Elsevier Inc. All rights reserved.

Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Donghui Hu

2017-01-01

Full Text Available Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Directory of Open Access Journals (Sweden)

Kai Olav Ellefsen

2015-04-01

Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Imaging the neural circuitry and chemical control of aggressive motivation

Directory of Open Access Journals (Sweden)

Blanchard D Caroline

2008-11-01

Full Text Available Abstract Background With the advent of functional magnetic resonance imaging (fMRI in awake animals it is possible to resolve patterns of neuronal activity across the entire brain with high spatial and temporal resolution. Synchronized changes in neuronal activity across multiple brain areas can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviors. To this end, fMRI in conscious rats combined with 3D computational analysis was used to identifying the putative distributed neural circuit involved in aggressive motivation and how this circuit is affected by drugs that block aggressive behavior. Results To trigger aggressive motivation, male rats were presented with their female cage mate plus a novel male intruder in the bore of the magnet during image acquisition. As expected, brain areas previously identified as critical in the organization and expression of aggressive behavior were activated, e.g., lateral hypothalamus, medial basal amygdala. Unexpected was the intense activation of the forebrain cortex and anterior thalamic nuclei. Oral administration of a selective vasopressin V1a receptor antagonist SRX251 or the selective serotonin reuptake inhibitor fluoxetine, drugs that block aggressive behavior, both caused a general suppression of the distributed neural circuit involved in aggressive motivation. However, the effect of SRX251, but not fluoxetine, was specific to aggression as brain activation in response to a novel sexually receptive female was unaffected. Conclusion The putative neural circuit of aggressive motivation identified with fMRI includes neural substrates contributing to emotional expression (i.e. cortical and medial amygdala, BNST, lateral hypothalamus, emotional experience (i.e. hippocampus, forebrain cortex, anterior cingulate, retrosplenial cortex and the anterior thalamic nuclei that bridge the motor and cognitive components of aggressive responding

Practical neural network recipies in C++

CERN Document Server

Masters

2014-01-01

This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum

Neural systems supporting and affecting economically relevant behavior

Directory of Open Access Journals (Sweden)

Braeutigam S

2012-05-01

Full Text Available Sven BraeutigamOxford Centre for Human Brain Activity, University of Oxford, Oxford, United KingdomAbstract: For about a hundred years, theorists and traders alike have tried to unravel and understand the mechanisms and hidden rules underlying and perhaps determining economically relevant behavior. This review focuses on recent developments in neuroeconomics, where the emphasis is placed on two directions of research: first, research exploiting common experiences of urban inhabitants in industrialized societies to provide experimental paradigms with a broader real-life content; second, research based on behavioral genetics, which provides an additional dimension for experimental control and manipulation. In addition, possible limitations of state-of-the-art neuroeconomics research are addressed. It is argued that observations of neuronal systems involved in economic behavior converge to some extent across the technologies and paradigms used. Conceptually, the data available as of today raise the possibility that neuroeconomic research might provide evidence at the neuronal level for the existence of multiple systems of thought and for the importance of conflict. Methodologically, Bayesian approaches in particular may play an important role in identifying mechanisms and establishing causality between patterns of neural activity and economic behavior.Keywords: neuroeconomics, behavioral genetics, decision-making, consumer behavior, neural system

Neural bases of congenital amusia in tonal language speakers.

Science.gov (United States)

Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y

2017-03-01

Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the

Neural network signal understanding for instrumentation

DEFF Research Database (Denmark)

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

1990-01-01

understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given...

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

On the Problem of Attribute Selection for Software Cost Estimation: Input Backward Elimination Using Artificial Neural Networks

OpenAIRE

Papatheocharous , Efi; Andreou , Andreas S.

2010-01-01

International audience; Many parameters affect the cost evolution of software projects. In the area of software cost estimation and project management the main challenge is to understand and quantify the effect of these parameters, or 'cost drivers', on the effort expended to develop software systems. This paper aims at investigating the effect of cost attributes on software development effort using empirical databases of completed projects and building Artificial Neural Network (ANN) models ...

Investigating the Neural Correlates of Emotion–Cognition Interaction Using an Affective Stroop Task

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Nora M. Raschle

2017-09-01

Full Text Available The human brain has the capacity to integrate various sources of information and continuously adapts our behavior according to situational needs in order to allow a healthy functioning. Emotion–cognition interactions are a key example for such integrative processing. However, the neuronal correlates investigating the effects of emotion on cognition remain to be explored and replication studies are needed. Previous neuroimaging studies have indicated an involvement of emotion and cognition related brain structures including parietal and prefrontal cortices and limbic brain regions. Here, we employed whole brain event-related functional magnetic resonance imaging (fMRI during an affective number Stroop task and aimed at replicating previous findings using an adaptation of an existing task design in 30 healthy young adults. The Stroop task is an indicator of cognitive control and enables the quantification of interference in relation to variations in cognitive load. By the use of emotional primes (negative/neutral prior to Stroop task performance, an emotional variation is added as well. Behavioral in-scanner data showed that negative primes delayed and disrupted cognitive processing. Trials with high cognitive demand furthermore negatively influenced cognitive control mechanisms. Neuronally, the emotional primes consistently activated emotion-related brain regions (e.g., amygdala, insula, and prefrontal brain regions while Stroop task performance lead to activations in cognition networks of the brain (prefrontal cortices, superior temporal lobe, and insula. When assessing the effect of emotion on cognition, increased cognitive demand led to decreases in neural activation in response to emotional stimuli (negative > neutral within prefrontal cortex, amygdala, and insular cortex. Overall, these results suggest that emotional primes significantly impact cognitive performance and increasing cognitive demand leads to reduced neuronal activation in

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

Directory of Open Access Journals (Sweden)

S. Lee Hong

2012-09-01

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

Space-time adaptive decision feedback neural receivers with data selection for high-data-rate users in DS-CDMA systems.

Science.gov (United States)

de Lamare, Rodrigo C; Sampaio-Neto, Raimundo

2008-11-01

A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.

Vestibular hearing and neural synchronization.

Science.gov (United States)

Emami, Seyede Faranak; Daneshi, Ahmad

2012-01-01

Objectives. Vestibular hearing as an auditory sensitivity of the saccule in the human ear is revealed by cervical vestibular evoked myogenic potentials (cVEMPs). The range of the vestibular hearing lies in the low frequency. Also, the amplitude of an auditory brainstem response component depends on the amount of synchronized neural activity, and the auditory nerve fibers' responses have the best synchronization with the low frequency. Thus, the aim of this study was to investigate correlation between vestibular hearing using cVEMPs and neural synchronization via slow wave Auditory Brainstem Responses (sABR). Study Design. This case-control survey was consisted of twenty-two dizzy patients, compared to twenty healthy controls. Methods. Intervention comprised of Pure Tone Audiometry (PTA), Impedance acoustic metry (IA), Videonystagmography (VNG), fast wave ABR (fABR), sABR, and cVEMPs. Results. The affected ears of the dizzy patients had the abnormal findings of cVEMPs (insecure vestibular hearing) and the abnormal findings of sABR (decreased neural synchronization). Comparison of the cVEMPs at affected ears versus unaffected ears and the normal persons revealed significant differences (P < 0.05). Conclusion. Safe vestibular hearing was effective in the improvement of the neural synchronization.

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.

Fuzzy logic and neural networks basic concepts & application

CERN Document Server

Alavala, Chennakesava R

2008-01-01

About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

Emotional face processing and flat affect in schizophrenia: functional and structural neural correlates.

Science.gov (United States)

Lepage, M; Sergerie, K; Benoit, A; Czechowska, Y; Dickie, E; Armony, J L

2011-09-01

There is a general consensus in the literature that schizophrenia causes difficulties with facial emotion perception and discrimination. Functional brain imaging studies have observed reduced limbic activity during facial emotion perception but few studies have examined the relation to flat affect severity. A total of 26 people with schizophrenia and 26 healthy controls took part in this event-related functional magnetic resonance imaging study. Sad, happy and neutral faces were presented in a pseudo-random order and participants indicated the gender of the face presented. Manual segmentation of the amygdala was performed on a structural T1 image. Both the schizophrenia group and the healthy control group rated the emotional valence of facial expressions similarly. Both groups exhibited increased brain activity during the perception of emotional faces relative to neutral ones in multiple brain regions, including multiple prefrontal regions bilaterally, the right amygdala, right cingulate cortex and cuneus. Group comparisons, however, revealed increased activity in the healthy group in the anterior cingulate, right parahippocampal gyrus and multiple visual areas. In schizophrenia, the severity of flat affect correlated significantly with neural activity in several brain areas including the amygdala and parahippocampal region bilaterally. These results suggest that many of the brain regions involved in emotional face perception, including the amygdala, are equally recruited in both schizophrenia and controls, but flat affect can also moderate activity in some other brain regions, notably in the left amygdala and parahippocampal gyrus bilaterally. There were no significant group differences in the volume of the amygdala.

Neural correlates of executive functions in patients with obesity.

Science.gov (United States)

Ho, Ming-Chou; Chen, Vincent Chin-Hung; Chao, Seh-Huang; Fang, Ching-Tzu; Liu, Yi-Chun; Weng, Jun-Cheng

2018-01-01

Obesity is one of the most challenging problems in human health and is recognized as an important risk factor for many chronic diseases. It remains unclear how the neural systems (e.g., the mesolimbic "reward" and the prefrontal "control" neural systems) are correlated with patients' executive function (EF), conceptualized as the integration of "cool" EF and "hot" EF. "Cool" EF refers to relatively abstract, non-affective operations such as inhibitory control and mental flexibility. "Hot" EF refers to motivationally significant affective operations such as affective decision-making. We tried to find the correlation between structural and functional neuroimaging indices and EF in obese patients. The study population comprised seventeen patients with obesity (seven males and 10 females, BMI = 37.99 ± 5.40, age = 31.82 ± 8.75 year-old) preparing to undergo bariatric surgery. We used noninvasive diffusion tensor imaging, generalized q-sampling imaging, and resting-state functional magnetic resonance imaging to examine the neural correlations between structural and functional neuroimaging indices and EF performances in patients with obesity. We reported that many brain areas are correlated to the patients' EF performances. More interestingly, some correlations may implicate the possible associations of EF and the incentive motivational effects of food. The neural correlation between the left precuneus and middle occipital gyrus and inhibitory control may suggest that patients with a better ability to detect appetitive food may have worse inhibitory control. Also, the neural correlation between the superior frontal blade and affective decision-making may suggest that patients' affective decision-making may be associated with the incentive motivational effects of food. Our results provide evidence suggesting neural correlates of EF in patients with obesity.

Neural correlates of a single-session massage treatment.

Science.gov (United States)

Sliz, D; Smith, A; Wiebking, C; Northoff, G; Hayley, S

2012-03-01

The current study investigated the immediate neurophysiological effects of different types of massage in healthy adults using functional magnetic resonance imaging (fMRI). Much attention has been given to the default mode network, a set of brain regions showing greater activity in the resting state. These regions (i.e. insula, posterior and anterior cingulate, inferior parietal and medial prefrontal cortices) have been postulated to be involved in the neural correlates of consciousness, specifically in arousal and awareness. We posit that massage would modulate these same regions given the benefits and pleasant affective properties of touch. To this end, healthy participants were randomly assigned to one of four conditions: 1. Swedish massage, 2. reflexology, 3. massage with an object or 4. a resting control condition. The right foot was massaged while each participant performed a cognitive association task in the scanner. We found that the Swedish massage treatment activated the subgenual anterior and retrosplenial/posterior cingulate cortices. This increased blood oxygen level dependent (BOLD) signal was maintained only in the former brain region during performance of the cognitive task. Interestingly, the reflexology massage condition selectively affected the retrosplenial/posterior cingulate in the resting state, whereas massage with the object augmented the BOLD response in this region during the cognitive task performance. These findings should have implications for better understanding how alternative treatments might affect resting state neural activity and could ultimately be important for devising new targets in the management of mood disorders.

Neural system antigens are recognized by autoantibodies from patients affected by a new variant of endemic pemphigus foliaceus in Colombia.

Science.gov (United States)

Abreu-Velez, Ana Maria; Howard, Michael S; Yi, Hong; Gao, Weiqing; Hashimoto, Takashi; Grossniklaus, Hans E

2011-06-01

Endemic pemphigus foliaceus (EPF), is also known as "fogo selvagem" or "wild fire," reflecting the intense burning sensation of the skin reported by patients with this disease. Based on this finding, we tested for neural autoreactivity in patients affected by a new variant of EPF (El Bagre-EPF). We tested 20 El Bagre-EPF patients, 20 normal controls from the endemic area, and 20 age- and sex-matched normal controls from outside the endemic area. We tested for autoreactivity to several immunoglobulins and complement. Both human skin and bovine tail were used as antigens. We detected autoreactivity to neural structures, mechanoreceptors, nerves, perineural cell layers of the arachnoid envelope around the optic nerve, brain structures, and to neuromuscular spindles; these structures colocalized with several neural markers. The patient antibodies also colocalized with desmoplakins 1 and 2, with the armadillo repeat protein deleted in velo-cardio-facial syndrome and with p0071 antibodies. Autoreactivity was also found associated with neurovascular bundles innervating the skin, and immunoelectron microscopy using protein A gold against patient antibodies was positive against the nerve axons. Paucicellularity of the intraepidermal nerve endings and defragmentation of the neural plexus were seen in 70% of the cases and not in the controls from the endemic area (pEPF patients vis-a-vis the weakness of the extensor nerves, and furthermore, the autoreactivity to nerves in EPF could explain the "burning sensation" encountered in EPF disease.

Individual Differences in Neural Mechanisms of Selective Auditory Attention in Preschoolers from Lower Socioeconomic Status Backgrounds: An Event-Related Potentials Study

Science.gov (United States)

Isbell, Elif; Wray, Amanda Hampton; Neville, Helen J.

2016-01-01

Selective attention, the ability to enhance the processing of particular input while suppressing the information from other concurrent sources, has been postulated to be a foundational skill for learning and academic achievement. The neural mechanisms of this foundational ability are both vulnerable and enhanceable in children from lower…

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.

22nd Italian Workshop on Neural Nets

CERN Document Server

Bassis, Simone; Esposito, Anna; Morabito, Francesco

2013-01-01

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

Determining the confidence levels of sensor outputs using neural networks

Energy Technology Data Exchange (ETDEWEB)

Broten, G S; Wood, H C [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Electrical Engineering

1996-12-31

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network`s ability to determine the confidence level is influenced by the complexity of the sensor`s response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

Determining the confidence levels of sensor outputs using neural networks

International Nuclear Information System (INIS)

Broten, G.S.; Wood, H.C.

1995-01-01

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network's ability to determine the confidence level is influenced by the complexity of the sensor's response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

Classification of Urinary Calculi using Feed-Forward Neural Networks

African Journals Online (AJOL)

NJD

Genetic algorithms were used for optimization of neural networks and for selection of the ... Urinary calculi, infrared spectroscopy, classification, neural networks, variable ..... note that the best accuracy is obtained for whewellite, weddellite.

Neural growth into a microchannel network: towards a regenerative neural interface

NARCIS (Netherlands)

Wieringa, P.A.; Wiertz, Remy; le Feber, Jakob; Rutten, Wim

2009-01-01

We propose and validated a design for a highly selective 'endcap' regenerative neural interface towards a neuroprosthesis. In vitro studies using rat cortical neurons determine if a branching microchannel structure can counter fasciculated growth and cause neurites to separte from one another,

Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

International Nuclear Information System (INIS)

Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

2003-01-01

Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

Science.gov (United States)

Downer, Joshua D; Rapone, Brittany; Verhein, Jessica; O'Connor, Kevin N; Sutter, Mitchell L

2017-05-24

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations ( r noise ) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on r noise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in r noise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations ( r noise ) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on r noise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning

Adaptive competitive learning neural networks

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Ahmed R. Abas

2013-11-01

Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

Neural Network Classifiers for Local Wind Prediction.

Science.gov (United States)

Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

2004-05-01

This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

Science.gov (United States)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

Pulsed neural networks consisting of single-flux-quantum spiking neurons

International Nuclear Information System (INIS)

Hirose, T.; Asai, T.; Amemiya, Y.

2007-01-01

An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

Improved Extension Neural Network and Its Applications

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Yu Zhou

2014-01-01

Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.

Methodology of Neural Design: Applications in Microwave Engineering

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Z. Raida

2006-06-01

Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

Word selection affects perceptions of synthetic biology

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Tonidandel Scott

2011-07-01

Full Text Available Abstract Members of the synthetic biology community have discussed the significance of word selection when describing synthetic biology to the general public. In particular, many leaders proposed the word "create" was laden with negative connotations. We found that word choice and framing does affect public perception of synthetic biology. In a controlled experiment, participants perceived synthetic biology more negatively when "create" was used to describe the field compared to "construct" (p = 0.008. Contrary to popular opinion among synthetic biologists, however, low religiosity individuals were more influenced negatively by the framing manipulation than high religiosity people. Our results suggest that synthetic biologists directly influence public perception of their field through avoidance of the word "create".

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Proposers’ Economic Status Affects Behavioral and Neural Responses to Unfairness

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Yijie Zheng

2017-05-01

Full Text Available Economic status played an important role in the modulation of economic decision making. The present fMRI study aimed at investigating how economic status modulated behavioral and neural responses to unfairness in a modified Ultimatum Game (UG. During scanning, participants played as responders in the UG, and they were informed of the economic status of proposers before receiving offers. At the behavioral level, higher rejection rates and lower fairness ratings were revealed when proposers were in high economic status than in low economic status. Besides, the most time-consuming decisions tended to occur at lower unfairness level when the proposers were in high (relative to low economic status. At the neural level, stronger activation of left thalamus was revealed when fair offers were proposed by proposers in high rather than in low economic status. Greater activation of right medial prefrontal cortex was revealed during acceptance to unfair offers in high economic status condition rather than in low economic status condition. Taken together, these findings shed light on the significance of proposers’ economic status in responders’ social decision making in UG.

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

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

2016-01-01

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

Dynamic Musical Communication of Core Affect

Directory of Open Access Journals (Sweden)

Nicole eFlaig

2014-03-01

Full Text Available Is there something special about the way music communicates feelings? Theorists since Meyer (1956 have attempted to explain how music could stimulate varied and subtle affective experiences by violating learned expectancies, or by mimicking other forms of social interaction. Our proposal is that music speaks to the brain in its own language; it need not imitate any other form of communication. We review recent theoretical and empirical literature, which suggests that all conscious processes consist of dynamic neural events, produced by spatially dispersed processes in the physical brain. Intentional thought and affective experience arise as dynamical aspects of neural events taking place in multiple brain areas simultaneously. At any given moment, this content comprises a unified scene that is integrated into a dynamic core through synchrony of neuronal oscillations. We propose that 1 neurodynamic synchrony with musical stimuli gives rise to musical qualia including tonal and temporal expectancies, and that 2 music-synchronous responses couple into core neurodynamics, enabling music to directly modulate core affect. Expressive music performance, for example, may recruit rhythm-synchronous neural responses to support affective communication. We suggest that the dynamic relationship between musical expression and the experience of affect presents a unique opportunity for the study of emotional experience. This may help elucidate the neural mechanisms underlying arousal and valence, and offer a new approach to exploring the complex dynamics of the how and why of emotional experience.

Efficient spiking neural network model of pattern motion selectivity in visual cortex.

Science.gov (United States)

Beyeler, Michael; Richert, Micah; Dutt, Nikil D; Krichmar, Jeffrey L

2014-07-01

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.

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.

Selecting decision strategies: the differential role of affect.

Science.gov (United States)

Scheibehenne, Benjamin; von Helversen, Bettina

2015-01-01

Many theories on cognition assume that people adapt their decision strategies depending on the situation they face. To test if and how affect guides the selection of decision strategies, we conducted an online study (N = 166), where different mood states were induced through video clips. Results indicate that mood influenced the use of decision strategies. Negative mood, in particular anger, facilitated the use of non-compensatory strategies, whereas positive mood promoted compensatory decision rules. These results are in line with the idea that positive mood broadens the focus of attention and thus increases the use of compensatory decision strategies that take many pieces of information into account, whereas negative mood narrows the focus of attention and thus fosters non-compensatory strategies that rely on a selective use of information. The results further indicate that gaining a deeper theoretical understanding of the cognitive mechanisms that govern decision processes requires taking emotions into account.

Ageing differentially affects neural processing of different conflict types-an fMRI study.

Science.gov (United States)

Korsch, Margarethe; Frühholz, Sascha; Herrmann, Manfred

2014-01-01

Interference control and conflict resolution is affected by ageing. There is increasing evidence that ageing does not compromise interference control in general but rather shows distinctive effects on different components of interference control. Different conflict types, [e.g., stimulus-stimulus (S-S) or stimulus-response (S-R) conflicts] trigger different cognitive processes and thus activate different neural networks. In the present functional magnetic resonance imaging (fMRI) study, we used a combined Flanker and Stimulus Response Conflict (SRC) task to investigate the effect of ageing on S-S and S-R conflicts. Behavioral data analysis revealed larger SRC effects in elderly. fMRI Results show that both age groups recruited similar regions [caudate nucleus, cingulate gyrus and middle occipital gyrus (MOG)] during Flanker conflict processing. Furthermore, elderly show an additional activation pattern in parietal and frontal areas. In contrast, no common activation of both age groups was found in response to the SRC. These data suggest that ageing has distinctive effects on S-S and S-R conflicts.

A role for adult TLX-positive neural stem cells in learning and behaviour.

Science.gov (United States)

Zhang, Chun-Li; Zou, Yuhua; He, Weimin; Gage, Fred H; Evans, Ronald M

2008-02-21

Neurogenesis persists in the adult brain and can be regulated by a plethora of external stimuli, such as learning, memory, exercise, environment and stress. Although newly generated neurons are able to migrate and preferentially incorporate into the neural network, how these cells are molecularly regulated and whether they are required for any normal brain function are unresolved questions. The adult neural stem cell pool is composed of orphan nuclear receptor TLX-positive cells. Here, using genetic approaches in mice, we demonstrate that TLX (also called NR2E1) regulates adult neural stem cell proliferation in a cell-autonomous manner by controlling a defined genetic network implicated in cell proliferation and growth. Consequently, specific removal of TLX from the adult mouse brain through inducible recombination results in a significant reduction of stem cell proliferation and a marked decrement in spatial learning. In contrast, the resulting suppression of adult neurogenesis does not affect contextual fear conditioning, locomotion or diurnal rhythmic activities, indicating a more selective contribution of newly generated neurons to specific cognitive functions.

Neural networks prediction and fault diagnosis applied to stationary and non stationary ARMA (Autoregressive moving average) modeled time series

International Nuclear Information System (INIS)

Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.

1992-01-01

The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)

Informative sensor selection and learning for prediction of lower limb kinematics using generative stochastic neural networks.

Science.gov (United States)

Eunsuk Chong; Taejin Choi; Hyungmin Kim; Seung-Jong Kim; Yoha Hwang; Jong Min Lee

2017-07-01

We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and real-valued. We addressed this problem by applying a generative stochastic neural network called the restricted Boltzmann machine, through which we could perform sampling based probability estimation. The mutual informations between inputs and outputs are evaluated in each backward sensor elimination step, and the least informative sensor is removed with its network connections. The entire network is fine-tuned by maximizing conditional likelihood in each step. Experimental results are shown for 4 healthy subjects walking with various speeds, recording 64 sensor measurements including electromyogram, acceleration, and foot-pressure sensors attached on both lower limbs for predicting hip and knee joint angles. For test set of walking with arbitrary speed, our results show that our suggested method can select informative sensors while maintaining a good prediction accuracy.

Selective comparison of gelling agents as neural cell culture matrices for long-term microelectrode array electrophysiology

Directory of Open Access Journals (Sweden)

Wilk Nicolai

2016-01-01

Full Text Available In classic monolayer cell culture, the world is flat. In contrast, tissue-embedded cells experience a three-dimensional context to interact with. We assessed a selection of natural gelling agents of non-animal origin (ι- and κ-carrageenan, gellan gum, guar gum, locust bean gum, sodium alginate, tragacanth and xanthan gum in serum-free medium at 1–4% (w/v concentration for their suitability as a more natural 3D culture environment for brain-derived cells. Their biophysical properties (viscosity, texture, transparency, gelling propensity resemble those of the extracellular matrix (ECM. Gels provide the neurons with a 3D scaffold to interact with and allow for an increase of the overall cell density compared to classical monolayer 2D culture. They not only protect neurons in cell culture from shear forces and medium evaporation, but stabilize the microenvironment around them for efficient glial proliferation, tissue-analog neural differentiation and neural communication. We report on their properties (viscosity, transparency, their ease of handling in a cell culture context and their possible use modalities (cell embedment, as a cell cover or as a cell culture substrate. Among the selected gels, guar gum and locust bean gum with intercalated laminin allowed for cortical cell embedment. Neurons plated on and migrating into gellan gum survived and differentiated even without the addition of laminin. Sodium alginate with laminin was a suitable cell cover. Finally, we exemplarily demonstrate how guar gum supported the functional survival of a cortical culture over a period of 79 days in a proof-of-concept long-term microelectrode array (MEA electrophysiology study.

Uncovering the Neural Bases of Cognitive and Affective Empathy Deficits in Alzheimer's Disease and the Behavioral-Variant of Frontotemporal Dementia.

Science.gov (United States)

Dermody, Nadene; Wong, Stephanie; Ahmed, Rebekah; Piguet, Olivier; Hodges, John R; Irish, Muireann

2016-05-30

Loss of empathy is a core presenting feature of the behavioral-variant of frontotemporal dementia (bvFTD), resulting in socioemotional difficulties and behavioral transgressions. In contrast, interpersonal functioning remains relatively intact in Alzheimer's disease (AD), despite marked cognitive decline. The neural substrates mediating these patterns of loss and sparing in social functioning remain unclear, yet are relevant for our understanding of the social brain. We investigated cognitive versus affective aspects of empathy using the Interpersonal Reactivity Index (IRI) in 25 AD and 24 bvFTD patients and contrasted their performance with 22 age- and education-matched controls. Cognitive empathy was comparably compromised in AD and bvFTD, whereas affective empathy was impaired exclusively in bvFTD. While controlling for overall cognitive dysfunction ameliorated perspective-taking deficits in AD, empathy loss persisted across cognitive and affective domains in bvFTD. Voxel-based morphometry analyses revealed divergent neural substrates of empathy loss in each patient group. Perspective-taking deficits correlated with predominantly left-sided temporoparietal atrophy in AD, whereas widespread bilateral frontoinsular, temporal, parietal, and occipital atrophy was implicated in bvFTD. Reduced empathic concern in bvFTD was associated with atrophy in the left orbitofrontal, inferior frontal, and insular cortices, and the bilateral mid-cingulate gyrus. Our findings suggest that social cognitive deficits in AD arise largely as a consequence of global cognitive dysfunction, rather than a loss of empathy per se. In contrast, loss of empathy in bvFTD reflects the deterioration of a distributed network of frontoinsular and temporal structures that appear crucial for monitoring and processing social information.

Nonlinear Time Series Prediction Using Chaotic Neural Networks

Science.gov (United States)

Li, Ke-Ping; Chen, Tian-Lun

2001-06-01

A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. The project supported by National Basic Research Project "Nonlinear Science" and National Natural Science Foundation of China under Grant No. 60074020

Stroke promotes survival of nearby transplanted neural stem cells by decreasing their activation of caspase 3 while not affecting their differentiation.

Science.gov (United States)

Kosi, Nina; Alić, Ivan; Salamon, Iva; Mitrečić, Dinko

2018-02-14

Although transplantation of stem cells improves recovery of the nervous tissue, little is known about the influence of different brain regions on transplanted cells. After we confirmed that cells with uniform differentiation potential can be generated in independent experiments, one million of neural stem cells isolated from B6.Cg-Tg(Thy1-YFP)16Jrs/J mouse embryos were transplanted into the brain 24 h after induction of stroke. The lateral ventricles, the corpus callosum and the striatum were tested. Two and four weeks after the transplantation, the cells transplanted in all three regions have been attracted to the ischemic core. The largest number of attracted cells has been observed after transplantation into the striatum. Their differentiation pattern and expression of neuroligin 1, SynCAM 1, postsynaptic density protein 95 and synapsin 1 followed the same pattern observed during in vitro cultivation and it did not differ among the tested regions. Differentiation pattern of the cells transplanted in the stroke-affected and healthy animals was the same. On the other hand, neural stem cells transplanted in the striatum of the animals affected by stroke exhibited significantly increased survival rates reaching 260 ± 19%, when compared to cells transplanted in their wild type controls. Surprisingly, improved survival two and four weeks after transplantation was not due to increased proliferation of the grafted cells and it was accompanied by decreased levels of activity of Casp3 (19.56 ± 3.1% in the stroke-affected vs. 30.14 ± 2.4% in healthy animals after four weeks). We assume that the decreased levels of Casp3 in cells transplanted near the ischemic region was linked to increased vasculogenesis, synaptogenesis, astrocytosis and axonogenesis detected in the host tissue affected by ischemia. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural responses to affective and cognitive theory of mind in children and adolescents with autism spectrum disorder.

Science.gov (United States)

Kim, Eunjoo; Kyeong, Sunghyon; Cheon, Keun-Ah; Park, Bumhee; Oh, Maeng-Keun; Chun, Ji Won; Park, Hae-Jeong; Kim, Jae-Jin; Song, Dong-Ho

2016-05-16

Children and adolescents with Autism Spectrum Disorder (ASD) are characterized by an impaired Theory of Mind (ToM). Recent evidence suggested that two aspects of ToM (cognitive ToM versus affective ToM) are differentially impaired in individuals with ASD. In this study, we examined the neural correlates of cognitive and affective ToM in children and adolescents with ASD compared to typically developing children (TDCs). Twelve children and adolescents with ASD and 12 age, IQ matched TDCs participated in this functional MRI study. The ToM task involved the attribution of cognitive and affective mental states to a cartoon character based on verbal and eye-gaze cues. In cognitive ToM tasks, ASD participants recruited the medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and superior temporal gyrus (STG) to a greater extent than did TDCs. In affective ToM tasks, both ASD and TDC participants showed more activation in the insula and other subcortical regions than in cognitive ToM tasks. Correlational analysis revealed that greater activation of the mPFC/ACC regions was associated with less symptom severity in ASD patients. In sum, our study suggests that the recruitment of additional prefrontal resources can compensate for the successful behavioral performance in the ToM task in ASD participants. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Perceived threat predicts the neural sequelae of combat stress

NARCIS (Netherlands)

van Wingen, G. A.; Geuze, E.; Vermetten, E.; Fernández, G.

2011-01-01

Exposure to severe stressors increases the risk for psychiatric disorders in vulnerable individuals, but can lead to positive outcomes for others. However, it remains unknown how severe stress affects neural functioning in humans and what factors mediate individual differences in the neural sequelae

Perceived threat predicts the neural sequelae of combat stress.

NARCIS (Netherlands)

Wingen, G.A. van; Geuze, E.; Vermetten, E.; Fernandez, G.S.E.

2011-01-01

Exposure to severe stressors increases the risk for psychiatric disorders in vulnerable individuals, but can lead to positive outcomes for others. However, it remains unknown how severe stress affects neural functioning in humans and what factors mediate individual differences in the neural sequelae

Optimization of the selection process of the co-substrates for chicken manure fermentation using neural modeling

Directory of Open Access Journals (Sweden)

Lewicki Andrzej

2016-01-01

Full Text Available Intense development of research equipment leads directly to increasing cognitive abilities. However, along with the raising amount of data generated, the development of the techniques allowing the analysis is also essential. Currently, one of the most dynamically developing branch of computer science and mathematics are the Artificial Neural Networks (ANN. Their main advantage is very high ability to solve the regression and approximation issues. This paper presents the possibility of application of artificial intelligence methods to optimize the selection of co-substrates intended for methane fermentation of chicken manure. 4-layer MLP network has proven to be the optimal structure modeling the obtained empirical data.

Neural Correlates of Processing Negative and Sexually Arousing Pictures

Science.gov (United States)

Bailey, Kira; West, Robert; Mullaney, Kellie M.

2012-01-01

Recent work has questioned whether the negativity bias is a distinct component of affective picture processing. The current study was designed to determine whether there are different neural correlates of processing positive and negative pictures using event-related brain potentials. The early posterior negativity and late positive potential were greatest in amplitude for erotic pictures. Partial Least Squares analysis revealed one latent variable that distinguished erotic pictures from neutral and positive pictures and another that differentiated negative pictures from neutral and positive pictures. The effects of orienting task on the neural correlates of processing negative and erotic pictures indicate that affective picture processing is sensitive to both stimulus-driven, and attentional or decision processes. The current data, together with other recent findings from our laboratory, lead to the suggestion that there are distinct neural correlates of processing negative and positive stimuli during affective picture processing. PMID:23029071

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Neural representations of emotion are organized around abstract event features.

Science.gov (United States)

Skerry, Amy E; Saxe, Rebecca

2015-08-03

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Distribution network fault section identification and fault location using artificial neural network

DEFF Research Database (Denmark)

Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

2018-01-01

In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

Runoff Modelling in Urban Storm Drainage by Neural Networks

DEFF Research Database (Denmark)

Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

1995-01-01

A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

Preventing neural tube defects in Europe : A missed opportunity

NARCIS (Netherlands)

Busby, A; Armstrong, B; Dolk, H; Armstrong, N; Haeusler, M; Berghold, A; Gillerot, Y; Baguette, A; Gjerga, R; Barisic, [No Value; Christiansen, M; Goujard, J; Steinbicker, [No Value; Rosch, C; McDonnell, R; Scarano, G; Calzolari, E; Neville, A; Cocchi, G; Bianca, S; Gatt, M; De Walle, H; Braz, P; Latos-Bielenska, A; Gener, B; Portillor, [No Value; Addor, MC; Abramsky, L; Ritvanen, A; Robert-Gnansia, E; Daltveit, AK; Aneren, G; Olars, B; Edwards, G

2005-01-01

Each year, more than 4500 pregnancies in the European Union are affected by neural tube defects (NTD). Unambiguous evidence of the effectiveness of peri conceptional folic acid in preventing the majority of neural tube defects has been available since 1991. We report on trends in the total

Emotion and decision making: multiple modulatory neural circuits.

Science.gov (United States)

Phelps, Elizabeth A; Lempert, Karolina M; Sokol-Hessner, Peter

2014-01-01

Although the prevalent view of emotion and decision making is derived from the notion that there are dual systems of emotion and reason, a modulatory relationship more accurately reflects the current research in affective neuroscience and neuroeconomics. Studies show two potential mechanisms for affect's modulation of the computation of subjective value and decisions. Incidental affective states may carry over to the assessment of subjective value and the decision, and emotional reactions to the choice may be incorporated into the value calculation. In addition, this modulatory relationship is reciprocal: Changing emotion can change choices. This research suggests that the neural mechanisms mediating the relation between affect and choice vary depending on which affective component is engaged and which decision variables are assessed. We suggest that a detailed and nuanced understanding of emotion and decision making requires characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.

Selected aspects of modelling of foreign exchange rates with neural networks

Directory of Open Access Journals (Sweden)

Václav Mastný

2005-01-01

Full Text Available This paper deals with forecasting of the high-frequency foreign exchange market with neural networks. The objective is to investigate some aspects of modelling with neural networks (impact of topology, size of training set and time horizon of the forecast on the performance of the network. The data used for the purpose of this paper contain 15-minute time series of US dollar against other major currencies, Japanese Yen, British Pound and Euro. The results show, that performance of the network in terms of correct directorial change is negatively influenced by increasing number of hidden neurons and decreasing size of training set. The performance of the network is influenced by sampling frequency.

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

Science.gov (United States)

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

2016-04-01

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

Selection of Inhibitor-Resistant Viral Potassium Channels Identifies a Selectivity Filter Site that Affects Barium and Amantadine Block

Science.gov (United States)

Fujiwara, Yuichiro; Arrigoni, Cristina; Domigan, Courtney; Ferrara, Giuseppina; Pantoja, Carlos; Thiel, Gerhard; Moroni, Anna; Minor, Daniel L.

2009-01-01

Background Understanding the interactions between ion channels and blockers remains an important goal that has implications for delineating the basic mechanisms of ion channel function and for the discovery and development of ion channel directed drugs. Methodology/Principal Findings We used genetic selection methods to probe the interaction of two ion channel blockers, barium and amantadine, with the miniature viral potassium channel Kcv. Selection for Kcv mutants that were resistant to either blocker identified a mutant bearing multiple changes that was resistant to both. Implementation of a PCR shuffling and backcrossing procedure uncovered that the blocker resistance could be attributed to a single change, T63S, at a position that is likely to form the binding site for the inner ion in the selectivity filter (site 4). A combination of electrophysiological and biochemical assays revealed a distinct difference in the ability of the mutant channel to interact with the blockers. Studies of the analogous mutation in the mammalian inward rectifier Kir2.1 show that the T→S mutation affects barium block as well as the stability of the conductive state. Comparison of the effects of similar barium resistant mutations in Kcv and Kir2.1 shows that neighboring amino acids in the Kcv selectivity filter affect blocker binding. Conclusions/Significance The data support the idea that permeant ions have an integral role in stabilizing potassium channel structure, suggest that both barium and amantadine act at a similar site, and demonstrate how genetic selections can be used to map blocker binding sites and reveal mechanistic features. PMID:19834614

Psychological and neural mechanisms of experimental extinction: a selective review.

Science.gov (United States)

Delamater, Andrew R; Westbrook, R Frederick

2014-02-01

The present review examines key psychological concepts in the study of experimental extinction and implications these have for an understanding of the underlying neurobiology of extinction learning. We suggest that many of the signature characteristics of extinction learning (spontaneous recovery, renewal, reinstatement, rapid reacquisition) can be accommodated by the standard associative learning theory assumption that extinction results in partial erasure of the original learning together with new inhibitory learning. Moreover, we consider recent behavioral and neural evidence that supports the partial erasure view of extinction, but also note shortcomings in our understanding of extinction circuits as these relate to the negative prediction error concept. Recent work suggests that common prediction error and stimulus-specific prediction error terms both may be required to explain neural plasticity both in acquisition and extinction learning. In addition, we suggest that many issues in the content of extinction learning have not been fully addressed in current research, but that neurobiological approaches should be especially helpful in addressing such issues. These include questions about the nature of extinction learning (excitatory CS-No US, inhibitory CS-US learning, occasion setting processes), especially as this relates to studies of the micro-circuitry of extinction, as well as its representational content (sensory, motivational, response). An additional understudied problem in extinction research is the role played by attention processes and their underlying neural networks, although some research and theory converge on the idea that extinction is accompanied by attention decrements (i.e., habituation-like processes). Copyright © 2013 Elsevier Inc. All rights reserved.

International Conference on Artificial Neural Networks (ICANN)

CERN Document Server

Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

2015-01-01

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

Introduction to neural networks

International Nuclear Information System (INIS)

Pavlopoulos, P.

1996-01-01

This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

Focal Suppression of Distractor Sounds by Selective Attention in Auditory Cortex.

Science.gov (United States)

Schwartz, Zachary P; David, Stephen V

2018-01-01

Auditory selective attention is required for parsing crowded acoustic environments, but cortical systems mediating the influence of behavioral state on auditory perception are not well characterized. Previous neurophysiological studies suggest that attention produces a general enhancement of neural responses to important target sounds versus irrelevant distractors. However, behavioral studies suggest that in the presence of masking noise, attention provides a focal suppression of distractors that compete with targets. Here, we compared effects of attention on cortical responses to masking versus non-masking distractors, controlling for effects of listening effort and general task engagement. We recorded single-unit activity from primary auditory cortex (A1) of ferrets during behavior and found that selective attention decreased responses to distractors masking targets in the same spectral band, compared with spectrally distinct distractors. This suppression enhanced neural target detection thresholds, suggesting that limited attention resources serve to focally suppress responses to distractors that interfere with target detection. Changing effort by manipulating target salience consistently modulated spontaneous but not evoked activity. Task engagement and changing effort tended to affect the same neurons, while attention affected an independent population, suggesting that distinct feedback circuits mediate effects of attention and effort in A1. © The Author 2017. Published by Oxford University Press.

Selection of Photon Gluon Fusion Events in DIS

International Nuclear Information System (INIS)

Kowalik, K.; Rondio, E.; Sulej, R.; Zaremba, K.

2001-01-01

A selection of the Photon Gluon Fusion (PGF) process with light quarks for deep inelastic scattering events is presented. This process is directly sensitive to gluon polarization and our goal is to find out the most effective selection on a sample of events simulated for the SMC experiment. We compare two general multi-class classification methods - Bayes method and neural network with a conventional selection procedure. The neural network algorithm presented here is a modification of method belonging to the family of directional minimization algorithms. This method is convenient and effective for photon gluon fusion selection and determination of gluon polarization. Finally we present the estimation for precision of gluon polarization for neural network method. (author)

Tuning Neural Phase Entrainment to Speech.

Science.gov (United States)

Falk, Simone; Lanzilotti, Cosima; Schön, Daniele

2017-08-01

Musical rhythm positively impacts on subsequent speech processing. However, the neural mechanisms underlying this phenomenon are so far unclear. We investigated whether carryover effects from a preceding musical cue to a speech stimulus result from a continuation of neural phase entrainment to periodicities that are present in both music and speech. Participants listened and memorized French metrical sentences that contained (quasi-)periodic recurrences of accents and syllables. Speech stimuli were preceded by a rhythmically regular or irregular musical cue. Our results show that the presence of a regular cue modulates neural response as estimated by EEG power spectral density, intertrial coherence, and source analyses at critical frequencies during speech processing compared with the irregular condition. Importantly, intertrial coherences for regular cues were indicative of the participants' success in memorizing the subsequent speech stimuli. These findings underscore the highly adaptive nature of neural phase entrainment across fundamentally different auditory stimuli. They also support current models of neural phase entrainment as a tool of predictive timing and attentional selection across cognitive domains.

The neural correlates of perceptual load induced attentional selection: an fMRI study.

Science.gov (United States)

Wei, P; Szameitat, A J; Müller, H J; Schubert, T; Zhou, X

2013-10-10

The neural correlates of perceptual load induced attentional selection were investigated in an functional magnetic resonance imaging (fMRI) experiment in which attentional selection was manipulated through the variation of perceptual load in target search. Participants searched for a vertically or horizontally oriented bar among heterogeneously (the high load condition) or homogeneously (the low load condition) oriented distractor bars in the central display, which was flanked by a vertical or horizontal bar presented at the left or the right periphery. The search reaction times were longer when the central display was of high load than of low load, and were longer when the flanker was incongruent than congruent with the target. Importantly, the flanker congruency effect was manifested only in the low load condition, not in the high load condition, indicating that the perceptual load in target search determined whether the task-irrelevant flanker was processed. Imaging analyses revealed a set of fronto-parietal regions having higher activations in the high than in the low load condition. Anterior cingulate cortex (ACC) was more activated for the incongruent than for the congruent trials. Moreover, ACC and bilateral anterior insula were sensitive to the interaction between perceptual load and flanker congruency such that the activation differences between the incongruent and congruent conditions were significant in the low, but not in the high load condition. These results are consistent with the claim that ACC and bilateral anterior insula may exert executive control by selectively biasing processing in favor of task-relevant information and this biasing depends on the resources currently available to the control system. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Lulu Lu

2017-01-01

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

On the identification of instabilities with neural networks on JET

International Nuclear Information System (INIS)

Murari, A.; Arena, P.; Buscarino, A.; Fortuna, L.; Iachello, M.

2013-01-01

JET plasmas are affected by various instabilities, which can be particularly dangerous in high performance discharges. An identification method, based on the use of advanced neural networks, called Recurrent Neural Networks (RNNs), has been applied to ELMs. The potential of the recurrent networks to identify the dynamics of the instabilities has been first tested using synthetic data. The networks have then been applied to JET experimental signals. An appropriate selection of the networks topology allows identifying quite well the time evolution of the edge temperature and of the magnetic fields, considered the best indicators of the ELMs. A quite limited number of periodic oscillations are used to train the networks, which then manage to follow quite well the dynamics of the instabilities, in a recurrent configuration on one of the inputs. The time evolution of the aforementioned signals, also during intervals not used in the training and never seen by the networks, are properly reproduced. A careful analysis of the various terms in the RNNs has the potential to give clear indications about the nature of these instabilities and their dynamical behaviour

Ageing differentially affects neural processing of different conflict types – an fMRI study

Directory of Open Access Journals (Sweden)

Margarethe eKorsch

2014-04-01

Full Text Available Interference control and conflict resolution is affected by ageing. There is increasing evidence that ageing does not compromise interference control in general but rather shows distinctive effects on different components of interference control. Different conflict types, (e.g. stimulus-stimulus (S-S or stimulus-response (S-R conflicts trigger different cognitive processes and thus activate different neural networks. In the present functional magnetic resonance imaging (fMRI study, we used a combined Flanker and Stimulus Response Conflict (SRC task to investigate the effect of ageing on S-S and S-R conflicts. Behavioral data analysis revealed larger SRC effects in elderly. fMRI Results show that both age groups recruited similar regions (caudate nucleus, cingulate gyrus and middle occipital gyrus during Flanker conflict processing. Furthermore, elderly show an additional activation pattern in parietal and frontal areas. In contrast, no common activation of both age groups was found in response to the SRC. These data suggest that ageing has distinctive effects on S-S and S-R conflicts.

Neural networks for event filtering at D/O/

International Nuclear Information System (INIS)

Cutts, D.; Hoftun, J.S.; Sornborger, A.; Johnson, C.R.; Zeller, R.T.

1989-01-01

Neural networks may provide important tools for pattern recognition in high energy physics. We discuss an initial exploration of these techniques, presenting the result of network simulations of several filter algorithms. The D0 data acquisition system, a MicroVAX farm, will perform critical event selection; we describe a possible implementation of neural network algorithms in this system. 7 refs., 4 figs

Selection of radio pulsar candidates using artificial neural networks

OpenAIRE

Eatough, R. P.; Molkenthin, N.; Kramer, M.; Noutsos, A.; Keith, M. J.; Stappers, B. W.; Lyne, A. G.

2010-01-01

Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.

Trust as commodity: social value orientation affects the neural substrates of learning to cooperate.

Science.gov (United States)

Lambert, Bruno; Declerck, Carolyn H; Emonds, Griet; Boone, Christophe

2017-04-01

Individuals differ in their motives and strategies to cooperate in social dilemmas. These differences are reflected by an individual's social value orientation: proselfs are strategic and motivated to maximize self-interest, while prosocials are more trusting and value fairness. We hypothesize that when deciding whether or not to cooperate with a random member of a defined group, proselfs, more than prosocials, adapt their decisions based on past experiences: they 'learn' instrumentally to form a base-line expectation of reciprocity. We conducted an fMRI experiment where participants (19 proselfs and 19 prosocials) played 120 sequential prisoner's dilemmas against randomly selected, anonymous and returning partners who cooperated 60% of the time. Results indicate that cooperation levels increased over time, but that the rate of learning was steeper for proselfs than for prosocials. At the neural level, caudate and precuneus activation were more pronounced for proselfs relative to prosocials, indicating a stronger reliance on instrumental learning and self-referencing to update their trust in the cooperative strategy. © The Author (2017). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A fuzzy neural network for sensor signal estimation

International Nuclear Information System (INIS)

Na, Man Gyun

2000-01-01

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique. Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors

γ-Secretase modulators reduce endogenous amyloid β42 levels in human neural progenitor cells without altering neuronal differentiation

Science.gov (United States)

D’Avanzo, Carla; Sliwinski, Christopher; Wagner, Steven L.; Tanzi, Rudolph E.; Kim, Doo Yeon; Kovacs, Dora M.

2015-01-01

Soluble γ-secretase modulators (SGSMs) selectively decrease toxic amyloid β (Aβ) peptides (Aβ42). However, their effect on the physiologic functions of γ-secretase has not been tested in human model systems. γ-Secretase regulates fate determination of neural progenitor cells. Thus, we studied the impact of SGSMs on the neuronal differentiation of ReNcell VM (ReN) human neural progenitor cells (hNPCs). Quantitative PCR analysis showed that treatment of neurosphere-like ReN cell aggregate cultures with γ-secretase inhibitors (GSIs), but not SGSMs, induced a 2- to 4-fold increase in the expression of the neuronal markers Tuj1 and doublecortin. GSI treatment also induced neuronal marker protein expression, as shown by Western blot analysis. In the same conditions, SGSM treatment selectively reduced endogenous Aβ42 levels by ∼80%. Mechanistically, we found that Notch target gene expressions were selectively inhibited by a GSI, not by SGSM treatment. We can assert, for the first time, that SGSMs do not affect the neuronal differentiation of hNPCs while selectively decreasing endogenous Aβ42 levels in the same conditions. Our results suggest that our hNPC differentiation system can serve as a useful model to test the impact of GSIs and SGSMs on both endogenous Aβ levels and γ-secretase physiologic functions including endogenous Notch signaling.—D’Avanzo, C., Sliwinski, C., Wagner, S. L., Tanzi, R. E., Kim, D. Y., Kovacs, D. M. γ-Secretase modulators reduce endogenous amyloid β42 levels in human neural progenitor cells without altering neuronal differentiation. PMID:25903103

The role of selective attention on academic foundations: A cognitive neuroscience perspective

Science.gov (United States)

Stevens, Courtney; Bavelier, Daphne

2011-01-01

To the extent that selective attention skills are relevant for academic foundations and amenable to training, they represent an important focus for the field of education. Here, drawing on research on the neurobiology of attention, we review hypothesized links between selective attention and processing across three domains important to early academic skills. First, we provide a brief review of the neural bases of selective attention, emphasizing the effects of selective attention on neural processing, as well as the neural systems important to deploying selective attention and managing response conflict. Second, we examine the developmental time course of selective attention. It is argued that developmental differences in selective attention are related to the neural systems important for deploying selective attention and managing response conflict. In contrast, once effectively deployed, selective attention acts through very similar neural mechanisms across ages. In the third section, we relate the processes of selective attention to three domains important to academic foundations: language, literacy, and mathematics. Fourth, drawing on recent literatures on the effects of video-game play and mind-brain training on selective attention, we discuss the possibility of training selective attention. The final section examines the application of these principles to educationally-focused attention-training programs for children. PMID:22682909

The role of selective attention on academic foundations: a cognitive neuroscience perspective.

Science.gov (United States)

Stevens, Courtney; Bavelier, Daphne

2012-02-15

To the extent that selective attention skills are relevant for academic foundations and amenable to training, they represent an important focus for the field of education. Here, drawing on research on the neurobiology of attention, we review hypothesized links between selective attention and processing across three domains important to early academic skills. First, we provide a brief review of the neural bases of selective attention, emphasizing the effects of selective attention on neural processing, as well as the neural systems important to deploying selective attention and managing response conflict. Second, we examine the developmental time course of selective attention. It is argued that developmental differences in selective attention are related to the neural systems important for deploying selective attention and managing response conflict. In contrast, once effectively deployed, selective attention acts through very similar neural mechanisms across ages. In the third section, we relate the processes of selective attention to three domains important to academic foundations: language, literacy, and mathematics. Fourth, drawing on recent literatures on the effects of video-game play and mind-brain training on selective attention, we discuss the possibility of training selective attention. The final section examines the application of these principles to educationally-focused attention-training programs for children. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neural networks for combined control of capacitor banks and voltage regulators in distribution systems

Energy Technology Data Exchange (ETDEWEB)

Gu, Z.; Rizy, D.T.

1996-02-01

A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.

Introduction to neural networks with electric power applications

International Nuclear Information System (INIS)

Wildberger, A.M.; Hickok, K.A.

1990-01-01

This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

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Tayfun Gokmen

2017-10-01

Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Effects of musical tempo on physiological, affective, and perceptual variables and performance of self-selected walking pace.

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Almeida, Flávia Angélica Martins; Nunes, Renan Felipe Hartmann; Ferreira, Sandro Dos Santos; Krinski, Kleverton; Elsangedy, Hassan Mohamed; Buzzachera, Cosme Franklin; Alves, Ragami Chaves; Gregorio da Silva, Sergio

2015-06-01

[Purpose] This study investigated the effects of musical tempo on physiological, affective, and perceptual responses as well as the performance of self-selected walking pace. [Subjects] The study included 28 adult women between 29 and 51 years old. [Methods] The subjects were divided into three groups: no musical stimulation group (control), and 90 and 140 beats per minute musical tempo groups. Each subject underwent three experimental sessions: involved familiarization with the equipment, an incremental test to exhaustion, and a 30-min walk on a treadmill at a self-selected pace, respectively. During the self-selected walking session, physiological, perceptual, and affective variables were evaluated, and walking performance was evaluated at the end. [Results] There were no significant differences in physiological variables or affective response among groups. However, there were significant differences in perceptual response and walking performance among groups. [Conclusion] Fast music (140 beats per minute) promotes a higher rating of perceived exertion and greater performance in self-selected walking pace without significantly altering physiological variables or affective response.

Control of beam halo-chaos using neural network self-adaptation method

International Nuclear Information System (INIS)

Fang Jinqing; Huang Guoxian; Luo Xiaoshu

2004-11-01

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

How emotion leads to selective memory: neuroimaging evidence.

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Waring, Jill D; Kensinger, Elizabeth A

2011-06-01

Often memory for emotionally arousing items is enhanced relative to neutral items within complex visual scenes, but this enhancement can come at the expense of memory for peripheral background information. This 'trade-off' effect has been elicited by a range of stimulus valence and arousal levels, yet the magnitude of the effect has been shown to vary with these factors. Using fMRI, this study investigated the neural mechanisms underlying this selective memory for emotional scenes. Further, we examined how these processes are affected by stimulus dimensions of arousal and valence. The trade-off effect in memory occurred for low to high arousal positive and negative scenes. There was a core emotional memory network associated with the trade-off among all the emotional scene types, however, there were additional regions that were uniquely associated with the trade-off for each individual scene type. These results suggest that there is a common network of regions associated with the emotional memory trade-off effect, but that valence and arousal also independently affect the neural activity underlying the effect. Copyright © 2011 Elsevier Ltd. All rights reserved.

Neural correlates of HIV risk feelings.

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Häcker, Frank E K; Schmälzle, Ralf; Renner, Britta; Schupp, Harald T

2015-04-01

Field studies on HIV risk perception suggest that people rely on impressions they have about the safety of their partner. The present fMRI study investigated the neural correlates of the intuitive perception of risk. First, during an implicit condition, participants viewed a series of unacquainted persons and performed a task unrelated to HIV risk. In the following explicit condition, participants evaluated the HIV risk for each presented person. Contrasting responses for high and low HIV risk revealed that risky stimuli evoked enhanced activity in the anterior insula and medial prefrontal regions, which are involved in salience processing and frequently activated by threatening and negative affect-related stimuli. Importantly, neural regions responding to explicit HIV risk judgments were also enhanced in the implicit condition, suggesting a neural mechanism for intuitive impressions of riskiness. Overall, these findings suggest the saliency network as neural correlate for the intuitive sensing of risk. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

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

International Nuclear Information System (INIS)

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

2001-01-01

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

Neural stem cell sex dimorphism in aromatase (CYP19 expression: a basis for differential neural fate

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Jay Waldron

2010-11-01

Full Text Available Jay Waldron1, Althea McCourty1, Laurent Lecanu1,21The Research Institute of the McGill University Health Centre, Montreal, Canada; 2Department of Medicine, McGill University, Quebec, CanadaPurpose: Neural stem cell (NSC transplantation and pharmacologic activation of endogenous neurogenesis are two approaches that trigger a great deal of interest as brain repair strategies. However, the success rate of clinical attempts using stem cells to restore neurologic functions altered either after traumatic brain injury or as a consequence of neurodegenerative disease remains rather disappointing. This suggests that factors affecting the fate of grafted NSCs are largely understudied and remain to be characterized. We recently reported that aging differentially affects the neurogenic properties of male and female NSCs. Although the sex steroids androgens and estrogens participate in the regulation of neurogenesis, to our knowledge, research on how gender-based differences affect the capacity of NSCs to differentiate and condition their neural fate is lacking. In the present study, we explored further the role of cell sex as a determining factor of the neural fate followed by differentiating NSCs and its relationship with a potential differential expression of aromatase (CYP19, the testosterone-metabolizing enzyme.Results: Using NSCs isolated from the subventricular zone of three-month-old male and female Long-Evans rats and maintained as neurospheres, we showed that differentiation triggered by retinoic acid resulted in a neural phenotype that depends on cell sex. Differentiated male NSCs mainly expressed markers of neuronal fate, including ßIII-tubulin, microtubule associated protein 2, growth-associated protein 43, and doublecortin. In contrast, female NSCs essentially expressed the astrocyte marker glial fibrillary acidic protein. Quantification of the expression of aromatase showed a very low level of expression in undifferentiated female NSCs

Neural network modelling of planform geometry of headland-bay beaches

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Iglesias, G.; López, I.; Castro, A.; Carballo, R.

2009-02-01

The shoreline of beaches in the lee of coastal salients or man-made structures, usually known as headland-bay beaches, has a distinctive curvature; wave fronts curve as a result of wave diffraction at the headland and in turn cause the shoreline to bend. The ensuing curved planform is of great interest both as a peculiar landform and in the context of engineering projects in which it is necessary to predict how a coastal structure will affect the sandy shoreline in its lee. A number of empirical models have been put forward, each based on a specific equation. A novel approach, based on the application of artificial neural networks, is presented in this work. Unlike the conventional method, no particular equation of the planform is embedded in the model. Instead, it is the model itself that learns about the problem from a series of examples of headland-bay beaches (the training set) and thereafter applies this self-acquired knowledge to other cases (the test set) for validation. Twenty-three headland-bay beaches from around the world were selected, of which sixteen and seven make up the training and test sets, respectively. As there is no well-developed theory for deciding upon the most convenient neural network architecture to deal with a particular data set, an experimental study was conducted in which ten different architectures with one and two hidden neuron layers and five training algorithms - 50 different options combining network architecture and training algorithm - were compared. Each of these options was implemented, trained and tested in order to find the best-performing approach for modelling the planform of headland-bay beaches. Finally, the selected neural network model was compared with a state-of-the-art planform model and was shown to outperform it.

Racial bias in neural empathic responses to pain.

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Luis Sebastian Contreras-Huerta

Full Text Available Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to

Racial Bias in Neural Empathic Responses to Pain

Science.gov (United States)

Contreras-Huerta, Luis Sebastian; Baker, Katharine S.; Reynolds, Katherine J.; Batalha, Luisa; Cunnington, Ross

2013-01-01

Recent studies have shown that perceiving the pain of others activates brain regions in the observer associated with both somatosensory and affective-motivational aspects of pain, principally involving regions of the anterior cingulate and anterior insula cortex. The degree of these empathic neural responses is modulated by racial bias, such that stronger neural activation is elicited by observing pain in people of the same racial group compared with people of another racial group. The aim of the present study was to examine whether a more general social group category, other than race, could similarly modulate neural empathic responses and perhaps account for the apparent racial bias reported in previous studies. Using a minimal group paradigm, we assigned participants to one of two mixed-race teams. We use the term race to refer to the Chinese or Caucasian appearance of faces and whether the ethnic group represented was the same or different from the appearance of the participant' own face. Using fMRI, we measured neural empathic responses as participants observed members of their own group or other group, and members of their own race or other race, receiving either painful or non-painful touch. Participants showed clear group biases, with no significant effect of race, on behavioral measures of implicit (affective priming) and explicit group identification. Neural responses to observed pain in the anterior cingulate cortex, insula cortex, and somatosensory areas showed significantly greater activation when observing pain in own-race compared with other-race individuals, with no significant effect of minimal groups. These results suggest that racial bias in neural empathic responses is not influenced by minimal forms of group categorization, despite the clear association participants showed with in-group more than out-group members. We suggest that race may be an automatic and unconscious mechanism that drives the initial neural responses to observed pain in

Within- and trans-generational plasticity affects the opportunity for selection in barbed goatgrass (Aegilops triuncialis).

Science.gov (United States)

Espeland, Erin K; Rice, Kevin J

2012-12-01

Environments are composed of selective agents, and environments may also modify the efficacy of these agents. Environments affect the rate of maximum evolutionary change by influencing variation in relative fitness (i.e., the opportunity for selection, or I). Within- and transgenerational plastic environmental responses may affect I, speeding or slowing processes of local adaptation. • We determined whether environmental factors affected the opportunity for selection (I) in Aegilops triuncialis (barbed goatgrass) by measuring I as a within- and transgenerational plastic response to two maternal glasshouse environments (serpentine/dry and loam/moist). We also determined whether this species' two most common genetic lineages (determined by DNA microsatellite length polymorphism) varied in response to glasshouse treatments. • Opportunity for selection was less for plants grown in the dry serpentine environment than for plants grown in the moist loam environment. This response varied between genetic lineages. The east lineage exhibited a within-generation response to the dry serpentine environment. For both seed mass and average seed weight in this lineage, the opportunity for selection was lower in dry serpentine than in moist loam. The west lineage had a transgenerational response to the dry serpentine such that the opportunity for selection for seed number and seed mass was lower for plants produced by mothers grown in dry serpentine than for plants produced by mothers in moist loam. • Phenotypic variation in relative fitness is constrained by the dry serpentine environment, which leads to lower evolvability in this environment. Within- and transgenerational effects of the environment may slow local adaptation to serpentine soils.

Effects of selective attention on the electrophysiological representation of concurrent sounds in the human auditory cortex.

Science.gov (United States)

Bidet-Caulet, Aurélie; Fischer, Catherine; Besle, Julien; Aguera, Pierre-Emmanuel; Giard, Marie-Helene; Bertrand, Olivier

2007-08-29

In noisy environments, we use auditory selective attention to actively ignore distracting sounds and select relevant information, as during a cocktail party to follow one particular conversation. The present electrophysiological study aims at deciphering the spatiotemporal organization of the effect of selective attention on the representation of concurrent sounds in the human auditory cortex. Sound onset asynchrony was manipulated to induce the segregation of two concurrent auditory streams. Each stream consisted of amplitude modulated tones at different carrier and modulation frequencies. Electrophysiological recordings were performed in epileptic patients with pharmacologically resistant partial epilepsy, implanted with depth electrodes in the temporal cortex. Patients were presented with the stimuli while they either performed an auditory distracting task or actively selected one of the two concurrent streams. Selective attention was found to affect steady-state responses in the primary auditory cortex, and transient and sustained evoked responses in secondary auditory areas. The results provide new insights on the neural mechanisms of auditory selective attention: stream selection during sound rivalry would be facilitated not only by enhancing the neural representation of relevant sounds, but also by reducing the representation of irrelevant information in the auditory cortex. Finally, they suggest a specialization of the left hemisphere in the attentional selection of fine-grained acoustic information.

Can responses to basic non-numerical visual features explain neural numerosity responses?

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Harvey, Ben M; Dumoulin, Serge O

2017-04-01

Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs. Copyright © 2017 Elsevier Inc. All rights reserved.

Mapping face categorization in the human ventral occipitotemporal cortex with direct neural intracranial recordings.

Science.gov (United States)

Rossion, Bruno; Jacques, Corentin; Jonas, Jacques

2018-02-26

The neural basis of face categorization has been widely investigated with functional magnetic resonance imaging (fMRI), identifying a set of face-selective local regions in the ventral occipitotemporal cortex (VOTC). However, indirect recording of neural activity with fMRI is associated with large fluctuations of signal across regions, often underestimating face-selective responses in the anterior VOTC. While direct recording of neural activity with subdural grids of electrodes (electrocorticography, ECoG) or depth electrodes (stereotactic electroencephalography, SEEG) offers a unique opportunity to fill this gap in knowledge, these studies rather reveal widely distributed face-selective responses. Moreover, intracranial recordings are complicated by interindividual variability in neuroanatomy, ambiguity in definition, and quantification of responses of interest, as well as limited access to sulci with ECoG. Here, we propose to combine SEEG in large samples of individuals with fast periodic visual stimulation to objectively define, quantify, and characterize face categorization across the whole VOTC. This approach reconciles the wide distribution of neural face categorization responses with their (right) hemispheric and regional specialization, and reveals several face-selective regions in anterior VOTC sulci. We outline the challenges of this research program to understand the neural basis of face categorization and high-level visual recognition in general. © 2018 New York Academy of Sciences.

Sexual selection affects local extinction and turnover in bird communities

Science.gov (United States)

Doherty, P.F.; Sorci, G.; Royle, J. Andrew; Hines, J.E.; Nichols, J.D.; Boulinier, T.

2003-01-01

Predicting extinction risks has become a central goal for conservation and evolutionary biologists interested in population and community dynamics. Several factors have been put forward to explain risks of extinction, including ecological and life history characteristics of individuals. For instance, factors that affect the balance between natality and mortality can have profound effects on population persistence. Sexual selection has been identified as one such factor. Populations under strong sexual selection experience a number of costs ranging from increased predation and parasitism to enhanced sensitivity to environmental and demographic stochasticity. These findings have led to the prediction that local extinction rates should be higher for species/populations with intense sexual selection. We tested this prediction by analyzing the dynamics of natural bird communities at a continental scale over a period of 21 years (1975-1996), using relevant statistical tools. In agreement with the theoretical prediction, we found that sexual selection increased risks of local extinction (dichromatic birds had on average a 23% higher local extinction rate than monochromatic species). However, despite higher local extinction probabilities, the number of dichromatic species did not decrease over the period considered in this study. This pattern was caused by higher local turnover rates of dichromatic species, resulting in relatively stable communities for both groups of species. Our results suggest that these communities function as metacommunities, with frequent local extinctions followed by colonization. Anthropogenic factors impeding dispersal might therefore have a significant impact on the global persistence of sexually selected species.

Selective population rate coding: a possible computational role of gamma oscillations in selective attention.

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Masuda, Naoki

2009-12-01

Selective attention is often accompanied by gamma oscillations in local field potentials and spike field coherence in brain areas related to visual, motor, and cognitive information processing. Gamma oscillations are implicated to play an important role in, for example, visual tasks including object search, shape perception, and speed detection. However, the mechanism by which gamma oscillations enhance cognitive and behavioral performance of attentive subjects is still elusive. Using feedforward fan-in networks composed of spiking neurons, we examine a possible role for gamma oscillations in selective attention and population rate coding of external stimuli. We implement the concept proposed by Fries ( 2005 ) that under dynamic stimuli, neural populations effectively communicate with each other only when there is a good phase relationship among associated gamma oscillations. We show that the downstream neural population selects a specific dynamic stimulus received by an upstream population and represents it by population rate coding. The encoded stimulus is the one for which gamma rhythm in the corresponding upstream population is resonant with the downstream gamma rhythm. The proposed role for gamma oscillations in stimulus selection is to enable top-down control, a neural version of time division multiple access used in communication engineering.

Neural network for solving convex quadratic bilevel programming problems.

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He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie

2014-03-01

In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

Local Dynamics in Trained Recurrent Neural Networks.

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Rivkind, Alexander; Barak, Omri

2017-06-23

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Local Dynamics in Trained Recurrent Neural Networks

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-01

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Adjunctive selective estrogen receptor modulator increases neural activity in the hippocampus and inferior frontal gyrus during emotional face recognition in schizophrenia.

Science.gov (United States)

Ji, E; Weickert, C S; Lenroot, R; Kindler, J; Skilleter, A J; Vercammen, A; White, C; Gur, R E; Weickert, T W

2016-05-03

Estrogen has been implicated in the development and course of schizophrenia with most evidence suggesting a neuroprotective effect. Treatment with raloxifene, a selective estrogen receptor modulator, can reduce symptom severity, improve cognition and normalize brain activity during learning in schizophrenia. People with schizophrenia are especially impaired in the identification of negative facial emotions. The present study was designed to determine the extent to which adjunctive raloxifene treatment would alter abnormal neural activity during angry facial emotion recognition in schizophrenia. Twenty people with schizophrenia (12 men, 8 women) participated in a 13-week, randomized, double-blind, placebo-controlled, crossover trial of adjunctive raloxifene treatment (120 mg per day orally) and performed a facial emotion recognition task during functional magnetic resonance imaging after each treatment phase. Two-sample t-tests in regions of interest selected a priori were performed to assess activation differences between raloxifene and placebo conditions during the recognition of angry faces. Adjunctive raloxifene significantly increased activation in the right hippocampus and left inferior frontal gyrus compared with the placebo condition (family-wise error, Precognition in schizophrenia. These findings support the hypothesis that estrogen plays a modifying role in schizophrenia and shows that adjunctive raloxifene treatment may reverse abnormal neural activity during facial emotion recognition, which is relevant to impaired social functioning in men and women with schizophrenia.

Integration trumps selection in object recognition

Science.gov (United States)

Saarela, Toni P.; Landy, Michael S.

2015-01-01

Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Integration trumps selection in object recognition.

Science.gov (United States)

Saarela, Toni P; Landy, Michael S

2015-03-30

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modality-Specific Axonal Regeneration: Towards selective regenerative neural interfaces

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Parisa eLotfi

2011-10-01

Full Text Available Regenerative peripheral nerve interfaces have been proposed as viable alternatives for the natural control of robotic prosthetic devices. However, sensory and motor axons at the neural interface are of mixed submodality types, which difficult the specific recording from motor axons and the eliciting of precise sensory modalities through selective stimulation. Here we evaluated the possibility of using type-specific neurotrophins to preferentially entice the regeneration of defined axonal populations from transected peripheral nerves into separate compartments. Segregation of mixed sensory fibers from dorsal root ganglion neurons was evaluated in vitro by compartmentalized diffusion delivery of nerve growth factor (NGF and neurotrophin-3 (NT-3, to preferentially entice the growth of TrkA+ nociceptive and TrkC+ proprioceptive subsets of sensory neurons, respectively. The average axon length in the NGF channel increased 2.5 fold compared to that in saline or NT-3, whereas the number of branches increased 3 fold in the NT-3 channels. These results were confirmed using a 3-D Y-shaped in vitro assay showing that the arm containing NGF was able to entice a 5-fold increase in axonal length of unbranched fibers. To address if such segregation can be enticed in vivo, a Y-shaped tubing was used to allow regeneration of the transected adult rat sciatic nerve into separate compartments filled with either NFG or NT-3. A significant increase in the number of CGRP+ pain fibers were attracted towards the sural nerve, while N-52+ large diameter axons were observed in the tibial and NT-3 compartments. This study demonstrates the guided enrichment of sensory axons in specific regenerative chambers, and supports the notion that neurotrophic factors can be used to segregate sensory and perhaps motor axons in separate peripheral interfaces.

Neural responses to macronutrients: hedonic and homeostatic mechanisms.

Science.gov (United States)

Tulloch, Alastair J; Murray, Susan; Vaicekonyte, Regina; Avena, Nicole M

2015-05-01

The brain responds to macronutrients via intricate mechanisms. We review how the brain's neural systems implicated in homeostatic control of feeding and hedonic responses are influenced by the ingestion of specific types of food. We discuss how these neural systems are dysregulated in preclinical models of obesity. Findings from these studies can increase our understanding of overeating and, perhaps in some cases, the development of obesity. In addition, a greater understanding of the neural circuits affected by the consumption of specific macronutrients, and by obesity, might lead to new treatments and strategies for preventing unhealthy weight gain. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Optimized Neural Network for Fault Diagnosis and Classification

International Nuclear Information System (INIS)

Elaraby, S.M.

2005-01-01

This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

Results from an on-line non-leptonic neural trigger implemented in an experiment looking for beauty

International Nuclear Information System (INIS)

Baldanza, C.; Musico, P.; Novelli, P.; Passaseo, M.

1995-01-01

Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV negative pions on a fixed Cu target, are presented. The neural trigger has been used to send events selected by means of a non-leptonic signature based on microvertex detector information to a special data stream, meant for early analysis. The non-leptonic signature, defined in a neural-network fashion, was devised so as to enrich the selected sample in the number of events containing C3 secondary vertices (i.e, vertices having three tracks with sum of electric charges equal to +1 or -1), which are sought for further analysis to identify charm and beauty non-leptonic decays. The neural trigger module consists of a VME crate hosting two MA16 digital neural chips from Siemens and two ETANN analog neural chips from Intel. During the experimental run, only the ETANN chips were operational. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6-7.1 (depending on the event sample considered), which multiplied by that already provided by the standard trigger leads to a global C3 enrichment factor of similar 150. In the event sample selected by the neural trigger, one every similar 7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 μs. (orig.)

Results from an on-line non-leptonic neural trigger implemented in an experiment looking for beauty

Energy Technology Data Exchange (ETDEWEB)

Baldanza, C. [INFN, Bologna (Italy). ANNETTHE; Bisi, F. [INFN, Bologna (Italy). ANNETTHE; Cotta-Ramusino, A. [INFN, Bologna (Italy). ANNETTHE; D`Antone, I. [INFN, Bologna (Italy). ANNETTHE; Malferrari, L. [INFN, Bologna (Italy). ANNETTHE; Mazzanti, P. [INFN, Bologna (Italy). ANNETTHE; Odorici, F. [INFN, Bologna (Italy). ANNETTHE; Odorico, R. [INFN, Bologna (Italy). ANNETTHE; Zuffa, M. [INFN, Bologna (Italy). ANNETTHE; Bruschini, C. [Istituto Nazionale di Fisica Nucleare, Genoa (Italy); Musico, P. [Istituto Nazionale di Fisica Nucleare, Genoa (Italy); Novelli, P. [Istituto Nazionale di Fisica Nucleare, Genoa (Italy); Passaseo, M. [European Organization for Nuclear Research, Geneva (Switzerland)

1995-07-15

Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV negative pions on a fixed Cu target, are presented. The neural trigger has been used to send events selected by means of a non-leptonic signature based on microvertex detector information to a special data stream, meant for early analysis. The non-leptonic signature, defined in a neural-network fashion, was devised so as to enrich the selected sample in the number of events containing C3 secondary vertices (i.e, vertices having three tracks with sum of electric charges equal to +1 or -1), which are sought for further analysis to identify charm and beauty non-leptonic decays. The neural trigger module consists of a VME crate hosting two MA16 digital neural chips from Siemens and two ETANN analog neural chips from Intel. During the experimental run, only the ETANN chips were operational. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6-7.1 (depending on the event sample considered), which multiplied by that already provided by the standard trigger leads to a global C3 enrichment factor of similar 150. In the event sample selected by the neural trigger, one every similar 7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 {mu}s. (orig.).

Neural Adaptation Effects in Conceptual Processing

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Barbara F. M. Marino

2015-07-01

Full Text Available We investigated the conceptual processing of nouns referring to objects characterized by a highly typical color and orientation. We used a go/no-go task in which we asked participants to categorize each noun as referring or not to natural entities (e.g., animals after a selective adaptation of color-edge neurons in the posterior LV4 region of the visual cortex was induced by means of a McCollough effect procedure. This manipulation affected categorization: the green-vertical adaptation led to slower responses than the green-horizontal adaptation, regardless of the specific color and orientation of the to-be-categorized noun. This result suggests that the conceptual processing of natural entities may entail the activation of modality-specific neural channels with weights proportional to the reliability of the signals produced by these channels during actual perception. This finding is discussed with reference to the debate about the grounded cognition view.

Comparative sensitivity of human and rat neural cultures to chemical-induced inhibition of neurite outgrowth

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Harrill, Joshua A.; Freudenrich, Theresa M.; Robinette, Brian L.; Mundy, William R., E-mail: mundy.william@epa.gov

2011-11-15

There is a need for rapid, efficient and cost-effective alternatives to traditional in vivo developmental neurotoxicity testing. In vitro cell culture models can recapitulate many of the key cellular processes of nervous system development, including neurite outgrowth, and may be used as screening tools to identify potential developmental neurotoxicants. The present study compared primary rat cortical cultures and human embryonic stem cell-derived neural cultures in terms of: 1) reproducibility of high content image analysis based neurite outgrowth measurements, 2) dynamic range of neurite outgrowth measurements and 3) sensitivity to chemicals which have been shown to inhibit neurite outgrowth. There was a large increase in neurite outgrowth between 2 and 24 h in both rat and human cultures. Image analysis data collected across multiple cultures demonstrated that neurite outgrowth measurements in rat cortical cultures were more reproducible and had higher dynamic range as compared to human neural cultures. Human neural cultures were more sensitive than rat cortical cultures to chemicals previously shown to inhibit neurite outgrowth. Parallel analysis of morphological (neurite count, neurite length) and cytotoxicity (neurons per field) measurements were used to detect selective effects on neurite outgrowth. All chemicals which inhibited neurite outgrowth in rat cortical cultures did so at concentrations which did not concurrently affect the number of neurons per field, indicating selective effects on neurite outgrowth. In contrast, more than half the chemicals which inhibited neurite outgrowth in human neural cultures did so at concentrations which concurrently decreased the number of neurons per field, indicating that effects on neurite outgrowth were secondary to cytotoxicity. Overall, these data demonstrate that the culture models performed differently in terms of reproducibility, dynamic range and sensitivity to neurite outgrowth inhibitors. While human neural

Comparative sensitivity of human and rat neural cultures to chemical-induced inhibition of neurite outgrowth

International Nuclear Information System (INIS)

Harrill, Joshua A.; Freudenrich, Theresa M.; Robinette, Brian L.; Mundy, William R.

2011-01-01

There is a need for rapid, efficient and cost-effective alternatives to traditional in vivo developmental neurotoxicity testing. In vitro cell culture models can recapitulate many of the key cellular processes of nervous system development, including neurite outgrowth, and may be used as screening tools to identify potential developmental neurotoxicants. The present study compared primary rat cortical cultures and human embryonic stem cell-derived neural cultures in terms of: 1) reproducibility of high content image analysis based neurite outgrowth measurements, 2) dynamic range of neurite outgrowth measurements and 3) sensitivity to chemicals which have been shown to inhibit neurite outgrowth. There was a large increase in neurite outgrowth between 2 and 24 h in both rat and human cultures. Image analysis data collected across multiple cultures demonstrated that neurite outgrowth measurements in rat cortical cultures were more reproducible and had higher dynamic range as compared to human neural cultures. Human neural cultures were more sensitive than rat cortical cultures to chemicals previously shown to inhibit neurite outgrowth. Parallel analysis of morphological (neurite count, neurite length) and cytotoxicity (neurons per field) measurements were used to detect selective effects on neurite outgrowth. All chemicals which inhibited neurite outgrowth in rat cortical cultures did so at concentrations which did not concurrently affect the number of neurons per field, indicating selective effects on neurite outgrowth. In contrast, more than half the chemicals which inhibited neurite outgrowth in human neural cultures did so at concentrations which concurrently decreased the number of neurons per field, indicating that effects on neurite outgrowth were secondary to cytotoxicity. Overall, these data demonstrate that the culture models performed differently in terms of reproducibility, dynamic range and sensitivity to neurite outgrowth inhibitors. While human neural

Neural codes of seeing architectural styles.

Science.gov (United States)

Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

2017-01-10

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

δ-Protocadherins: Organizers of neural circuit assembly.

Science.gov (United States)

Light, Sarah E W; Jontes, James D

2017-09-01

The δ-protocadherins comprise a small family of homophilic cell adhesion molecules within the larger cadherin superfamily. They are essential for neural development as mutations in these molecules give rise to human neurodevelopmental disorders, such as schizophrenia and epilepsy, and result in behavioral defects in animal models. Despite their importance to neural development, a detailed understanding of their mechanisms and the ways in which their loss leads to changes in neural function is lacking. However, recent results have begun to reveal roles for the δ-protocadherins in both regulation of neurogenesis and lineage-dependent circuit assembly, as well as in contact-dependent motility and selective axon fasciculation. These evolutionarily conserved mechanisms could have a profound impact on the robust assembly of the vertebrate nervous system. Future work should be focused on unraveling the molecular mechanisms of the δ-protocadherins and understanding how this family functions broadly to regulate neural development. Copyright © 2017 Elsevier Ltd. All rights reserved.

23rd Workshop of the Italian Neural Networks Society (SIREN)

CERN Document Server

Esposito, Anna; Morabito, Francesco

2014-01-01

This volume collects a selection of contributions which has been presented at the 23rd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Vietri sul Mare, Salerno, Italy during May 23-24, 2013. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in two main components, a special session and a group of regular sessions featuring different aspects and point of views of artificial neural networks, artificial and natural intelligence, as well as psychological and cognitive theories for modeling human behaviors and human machine interactions, including Information Communication applications of compelling interest.  .

The Neural Basis of Risky Choice with Affective Outcomes

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Suter, Renata S.; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor; Biele, Guido

2015-01-01

Both normative and many descriptive theories of decision making under risk are based on the notion that outcomes are weighted by their probability, with subsequent maximization of the (subjective) expected outcome. Numerous investigations from psychology, economics, and neuroscience have produced evidence consistent with this notion. However, this research has typically investigated choices involving relatively affect-poor, monetary outcomes. We compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems. Computational modeling of behavioral data and model-based neuroimaging analyses provide converging evidence for substantial differences in the respective decision mechanisms. Relative to affect-poor choices, affect-rich choices yielded a more strongly curved probability weighting function of cumulative prospect theory, thus signaling that the psychological impact of probabilities is strongly diminished for affect-rich outcomes. Examining task-dependent brain activation, we identified a region-by-condition interaction indicating qualitative differences of activation between affect-rich and affect-poor choices. Moreover, brain activation in regions that were more active during affect-poor choices (e.g., the supramarginal gyrus) correlated with individual trial-by-trial decision weights, indicating that these regions reflect processing of probabilities. Formal reverse inference Neurosynth meta-analyses suggested that whereas affect-poor choices seem to be based on brain mechanisms for calculative processes, affect-rich choices are driven by the representation of outcomes’ emotional value and autobiographical memories associated with them. These results provide evidence that the traditional notion of expectation maximization may not apply in the context of outcomes laden with affective responses, and that understanding the brain mechanisms of decision making requires the domain of the decision

The neural basis of risky choice with affective outcomes.

Directory of Open Access Journals (Sweden)

Renata S Suter

Full Text Available Both normative and many descriptive theories of decision making under risk are based on the notion that outcomes are weighted by their probability, with subsequent maximization of the (subjective expected outcome. Numerous investigations from psychology, economics, and neuroscience have produced evidence consistent with this notion. However, this research has typically investigated choices involving relatively affect-poor, monetary outcomes. We compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems. Computational modeling of behavioral data and model-based neuroimaging analyses provide converging evidence for substantial differences in the respective decision mechanisms. Relative to affect-poor choices, affect-rich choices yielded a more strongly curved probability weighting function of cumulative prospect theory, thus signaling that the psychological impact of probabilities is strongly diminished for affect-rich outcomes. Examining task-dependent brain activation, we identified a region-by-condition interaction indicating qualitative differences of activation between affect-rich and affect-poor choices. Moreover, brain activation in regions that were more active during affect-poor choices (e.g., the supramarginal gyrus correlated with individual trial-by-trial decision weights, indicating that these regions reflect processing of probabilities. Formal reverse inference Neurosynth meta-analyses suggested that whereas affect-poor choices seem to be based on brain mechanisms for calculative processes, affect-rich choices are driven by the representation of outcomes' emotional value and autobiographical memories associated with them. These results provide evidence that the traditional notion of expectation maximization may not apply in the context of outcomes laden with affective responses, and that understanding the brain mechanisms of decision making requires the domain

The neural basis of risky choice with affective outcomes.

Science.gov (United States)

Suter, Renata S; Pachur, Thorsten; Hertwig, Ralph; Endestad, Tor; Biele, Guido

2015-01-01

Both normative and many descriptive theories of decision making under risk are based on the notion that outcomes are weighted by their probability, with subsequent maximization of the (subjective) expected outcome. Numerous investigations from psychology, economics, and neuroscience have produced evidence consistent with this notion. However, this research has typically investigated choices involving relatively affect-poor, monetary outcomes. We compared choice in relatively affect-poor, monetary lottery problems with choice in relatively affect-rich medical decision problems. Computational modeling of behavioral data and model-based neuroimaging analyses provide converging evidence for substantial differences in the respective decision mechanisms. Relative to affect-poor choices, affect-rich choices yielded a more strongly curved probability weighting function of cumulative prospect theory, thus signaling that the psychological impact of probabilities is strongly diminished for affect-rich outcomes. Examining task-dependent brain activation, we identified a region-by-condition interaction indicating qualitative differences of activation between affect-rich and affect-poor choices. Moreover, brain activation in regions that were more active during affect-poor choices (e.g., the supramarginal gyrus) correlated with individual trial-by-trial decision weights, indicating that these regions reflect processing of probabilities. Formal reverse inference Neurosynth meta-analyses suggested that whereas affect-poor choices seem to be based on brain mechanisms for calculative processes, affect-rich choices are driven by the representation of outcomes' emotional value and autobiographical memories associated with them. These results provide evidence that the traditional notion of expectation maximization may not apply in the context of outcomes laden with affective responses, and that understanding the brain mechanisms of decision making requires the domain of the decision to

Distinct effects of protracted withdrawal on affect, craving, selective attention and executive functions among alcohol-dependent patients.

Science.gov (United States)

Cordovil De Sousa Uva, Mariana; Luminet, Olivier; Cortesi, Marie; Constant, Eric; Derely, Marc; De Timary, Philippe

2010-01-01

The present study examined the effects of protracted alcohol withdrawal on affectivity, craving, selective attention and executive functions (EFs) in alcohol-dependent patients. Selective attention (The D2 Cancellation Test), flexibility (Trail Making Test), inhibition (Stroop Task), decision making (Iowa Gambling Task), craving (Obsessive-Compulsive Drinking Scale) and state affectivity (Positive and Negative Affectivity Schedule) were assessed in alcohol-dependent patients (DSM-IV, n = 35) matched to non-alcohol-dependent participants (n = 22) at the onset (T1: day 1 or 2) and at the end (T2: days 14-18) of protracted withdrawal during rehab. Alcohol-dependent patients' abilities to focus their attention on relevant information, to switch from one pattern to another, to inhibit irrelevant information and to make advantageous choices were lower than those of control participants during both times of a withdrawal cure. No effect of time emerged from analyses for selective attention and EF deficits. Conversely, significant differences between T1 and T2 were observed for craving and affect scores indicating a weakening of alcohol craving and negative affect as well as an improvement of positive affect among patients from onset to the end of cure. Control functions of the Supervisory Attentional System (Norman and Shallice, 1986) were impaired and did not improve during a 3-week withdrawal cure, whereas alcohol craving and negative state affectivity significantly improved in parallel during this period. Implications for understanding the clinical processes of withdrawal are discussed.

Selective attention modulates neural substrates of repetition priming and "implicit" visual memory: suppressions and enhancements revealed by FMRI.

Science.gov (United States)

Vuilleumier, Patrik; Schwartz, Sophie; Duhoux, Stéphanie; Dolan, Raymond J; Driver, Jon

2005-08-01

Attention can enhance processing for relevant information and suppress this for ignored stimuli. However, some residual processing may still arise without attention. Here we presented overlapping outline objects at study, with subjects attending to those in one color but not the other. Attended objects were subsequently recognized on a surprise memory test, whereas there was complete amnesia for ignored items on such direct explicit testing; yet reliable behavioral priming effects were found on indirect testing. Event-related fMRI examined neural responses to previously attended or ignored objects, now shown alone in the same or mirror-reversed orientation as before, intermixed with new items. Repetition-related decreases in fMRI responses for objects previously attended and repeated in the same orientation were found in the right posterior fusiform, lateral occipital, and left inferior frontal cortex. More anterior fusiform regions also showed some repetition decreases for ignored objects, irrespective of orientation. View-specific repetition decreases were found in the striate cortex, particularly for previously attended items. In addition, previously ignored objects produced some fMRI response increases in the bilateral lingual gyri, relative to new objects. Selective attention at exposure can thus produce several distinct long-term effects on processing of stimuli repeated later, with neural response suppression stronger for previously attended objects, and some response enhancement for previously ignored objects, with these effects arising in different brain areas. Although repetition decreases may relate to positive priming phenomena, the repetition increases for ignored objects shown here for the first time might relate to processes that can produce "negative priming" in some behavioral studies. These results reveal quantitative and qualitative differences between neural substrates of long-term repetition effects for attended versus unattended objects.

Signs of noise-induced neural degeneration in humans

DEFF Research Database (Denmark)

Holtegaard, Pernille; Olsen, Steen Østergaard

2015-01-01

of background noise, while leaving the processing of low-level stimuli unaffected. The purpose of this study was to investigate if signs of such primary neural damage from noise-exposure could also be found in noiseexposed human individuals. It was investigated: (1) if noise-exposed listeners with hearing......Animal studies demonstrated that noise exposure causes a primary and selective loss of auditory-nerve fibres with low spontaneous firing rate. This neuronal impairment, if also present in humans, can be assumed to affect the processing of supra-threshold stimuli, especially in the presence...... thresholds within the “normal” range perform poorer, in terms of their speech recognition threshold in noise (SRTN), and (2) if auditory brainstem responses (ABR) reveal lower amplitude of wave I in the noise-exposed listeners. A test group of noise/music-exposed individuals and a control group were...

Research of convolutional neural networks for traffic sign recognition

OpenAIRE

Stadalnikas, Kasparas

2017-01-01

In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...

Epithelial–Mesenchymal Transitions during Neural Crest and Somite Development

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Chaya Kalcheim

2015-12-01

Full Text Available Epithelial-to-mesenchymal transition (EMT is a central process during embryonic development that affects selected progenitor cells of all three germ layers. In addition to driving the onset of cellular migrations and subsequent tissue morphogenesis, the dynamic conversions of epithelium into mesenchyme and vice-versa are intimately associated with the segregation of homogeneous precursors into distinct fates. The neural crest and somites, progenitors of the peripheral nervous system and of skeletal tissues, respectively, beautifully illustrate the significance of EMT to the above processes. Ongoing studies progressively elucidate the gene networks underlying EMT in each system, highlighting the similarities and differences between them. Knowledge of the mechanistic logic of this normal ontogenetic process should provide important insights to the understanding of pathological conditions such as cancer metastasis, which shares some common molecular themes.

Alpha spectral analysis via artificial neural networks

International Nuclear Information System (INIS)

Kangas, L.J.; Hashem, S.; Keller, P.E.; Kouzes, R.T.; Troyer, G.L.

1994-10-01

An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. The investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system

Equivalent neural responses in children and adolescents with and without autism during judgments of affect

Directory of Open Access Journals (Sweden)

Brent C. Vander Wyk

2014-04-01

Full Text Available Previous research has noted disrupted patterns of neural activation during emotion, processing in individuals with autism spectrum disorders (ASD. However, prior research relied on, designs that may place greater cognitive load on individuals with ASD. In order to address this issue, we adapted the fMRI task of Ochsner et al. (2004a for children by, presenting fewer stimuli, with fewer valence levels, and longer stimuli duration. A localizer sample of, typically developing children (n = 26 was used to construct regions of interest involved in emotional, processing. Activations in these regions during self- and other-referential emotion processing was, compared in age, IQ, gender matched groups (n = 17 ASD, n = 16 TD. Matched samples replicate, condition contrasts of the localizer, but no group differences were found in behavior measures or, neural activation. An exploratory functional connectivity analysis in a subset of the matched groups, also did not detect striking differences between the groups. These findings suggest that disruptions in activation in emotion processing neural networks in ASD is partially a function of task related cognitive load.

Periodicity and stability for variable-time impulsive neural networks.

Science.gov (United States)

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Visual attention mitigates information loss in small- and large-scale neural codes

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Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-01-01

Summary The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires processing sensory signals in a manner that protects information about relevant stimuli from degradation. Such selective processing – or selective attention – is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. PMID:25769502

The power of emotional valence – From cognitive to affective processes in reading

Directory of Open Access Journals (Sweden)

Ulrike eAltmann

2012-06-01

Full Text Available The comprehension of stories requires the reader to imagine the cognitive and affective states of the characters. The content of many stories is unpleasant, as they often deal with conflict, disturbance or crisis. Nevertheless, unpleasant stories can be liked and enjoyed. In this fMRI study, we used a parametric approach to examine (1 the capacity of increasing negative valence of story contents to activate the mentalizing network (cognitive and affective theory of mind, ToM, and (2 the neural substrate of liking negatively valenced narratives. A set of 80 short narratives was compiled, ranging from neutral to negative emotional valence. For each story mean rating values on valence and liking were obtained from a group of 32 participants in a prestudy, and later included as parametric regressors in the fMRI analysis. Another group of 24 participants passively read the narratives in a 3 Tesla MRI scanner. Results revealed a stronger engagement of affective ToM-related brain areas with increasingly negative story valence. Stories that were unpleasant, but simulatiously liked, selectively engaged the medial prefrontal cortex (mPFC, which might reflect the moral exploration of the story content. Further analysis showed that the more the mPFC becomes engaged during the reading of negatively valenced stories, the more coactivation can be observed in other brain areas related to the neural processing of affective ToM and empathy.

Distinct Neural Activity Associated with Focused-Attention Meditation and Loving-Kindness Meditation

Science.gov (United States)

Lee, Tatia M. C.; Leung, Mei-Kei; Hou, Wai-Kai; Tang, Joey C. Y.; Yin, Jing; So, Kwok-Fai; Lee, Chack-Fan; Chan, Chetwyn C. H.

2012-01-01

This study examined the dissociable neural effects of ānāpānasati (focused-attention meditation, FAM) and mettā (loving-kindness meditation, LKM) on BOLD signals during cognitive (continuous performance test, CPT) and affective (emotion-processing task, EPT, in which participants viewed affective pictures) processing. Twenty-two male Chinese expert meditators (11 FAM experts, 11 LKM experts) and 22 male Chinese novice meditators (11 FAM novices, 11 LKM novices) had their brain activity monitored by a 3T MRI scanner while performing the cognitive and affective tasks in both meditation and baseline states. We examined the interaction between state (meditation vs. baseline) and expertise (expert vs. novice) separately during LKM and FAM, using a conjunction approach to reveal common regions sensitive to the expert meditative state. Additionally, exclusive masking techniques revealed distinct interactions between state and group during LKM and FAM. Specifically, we demonstrated that the practice of FAM was associated with expertise-related behavioral improvements and neural activation differences in attention task performance. However, the effect of state LKM meditation did not carry over to attention task performance. On the other hand, both FAM and LKM practice appeared to affect the neural responses to affective pictures. For viewing sad faces, the regions activated for FAM practitioners were consistent with attention-related processing; whereas responses of LKM experts to sad pictures were more in line with differentiating emotional contagion from compassion/emotional regulation processes. Our findings provide the first report of distinct neural activity associated with forms of meditation during sustained attention and emotion processing. PMID:22905090

Burst firing enhances neural output correlation

Directory of Open Access Journals (Sweden)

Ho Ka eChan

2016-05-01

Full Text Available Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.

Attributes affecting campsite selection at two types of campgrounds in the Adirondack Park

Science.gov (United States)

Kye-Young Choi; Chad P. Dawson

2003-01-01

This study compared the important attributes affecting campers' decisions in selecting their preferred campsites at two different types of New York State Department of Environmental Conservation (NYSDEC) campgrounds in the Adirondack Park. Mail surveys were sent to campers using six NYSDEC campgrounds (three less-developed campgrounds and three more-developed...

Neural basis of stereotype-induced shifts in women's mental rotation performance.

Science.gov (United States)

Wraga, Maryjane; Helt, Molly; Jacobs, Emily; Sullivan, Kerry

2007-03-01

Recent negative focus on women's academic abilities has fueled disputes over gender disparities in the sciences. The controversy derives, in part, from women's relatively poorer performance in aptitude tests, many of which require skills of spatial reasoning. We used functional magnetic imaging to examine the neural structure underlying shifts in women's performance of a spatial reasoning task induced by positive and negative stereotypes. Three groups of participants performed a task involving imagined rotations of the self. Prior to scanning, the positive stereotype group was exposed to a false but plausible stereotype of women's superior perspective-taking abilities; the negative stereotype group was exposed to the pervasive stereotype that men outperform women on spatial tasks; and the control group received neutral information. The significantly poorer performance we found in the negative stereotype group corresponded to increased activation in brain regions associated with increased emotional load. In contrast, the significantly improved performance we found in the positive stereotype group was associated with increased activation in visual processing areas and, to a lesser degree, complex working memory processes. These findings suggest that stereotype messages affect the brain selectively, with positive messages producing relatively more efficient neural strategies than negative messages.

Neural responses to nostalgia-evoking music modeled by elements of dynamic musical structure and individual differences in affective traits.

Science.gov (United States)

Barrett, Frederick S; Janata, Petr

2016-10-01

Nostalgia is an emotion that is most commonly associated with personally and socially relevant memories. It is primarily positive in valence and is readily evoked by music. It is also an idiosyncratic experience that varies between individuals based on affective traits. We identified frontal, limbic, paralimbic, and midbrain brain regions in which the strength of the relationship between ratings of nostalgia evoked by music and blood-oxygen-level-dependent (BOLD) signal was predicted by affective personality measures (nostalgia proneness and the sadness scale of the Affective Neuroscience Personality Scales) that are known to modulate the strength of nostalgic experiences. We also identified brain areas including the inferior frontal gyrus, substantia nigra, cerebellum, and insula in which time-varying BOLD activity correlated more strongly with the time-varying tonal structure of nostalgia-evoking music than with music that evoked no or little nostalgia. These findings illustrate one way in which the reward and emotion regulation networks of the brain are recruited during the experiencing of complex emotional experiences triggered by music. These findings also highlight the importance of considering individual differences when examining the neural responses to strong and idiosyncratic emotional experiences. Finally, these findings provide a further demonstration of the use of time-varying stimulus-specific information in the investigation of music-evoked experiences. Copyright © 2016 Elsevier Ltd. All rights reserved.

Interactive extraction of neural structures with user-guided morphological diffusion

KAUST Repository

Yong Wan,; Otsuna, H.; Chi-Bin Chien,; Hansen, C.

2012-01-01

Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

Interactive extraction of neural structures with user-guided morphological diffusion

KAUST Repository

Yong Wan,

2012-10-01

Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

Benign meningiomas: primary treatment selection affects survival

International Nuclear Information System (INIS)

Condra, Kellie S.; Buatti, John M.; Mendenhall, William M.; Friedman, William A.; Marcus, Robert B.; Rhoton, Albert L.

1997-01-01

Purpose: To examine the effect of primary treatment selection on outcomes for benign intracranial meningiomas at the University of Florida. Methods and Materials: For 262 patients, the impact of age, Karnofsky performance status, pathologic features, tumor size, tumor location, and treatment modality on local control and cause-specific survival was analyzed (minimum potential follow-up, 2 years; median follow-up, 8.2 years). Extent of surgery was classified by Simpson grade. Treatment groups: surgery alone (n = 229), surgery and postoperative radiotherapy (RT) (n = 21), RT alone (n = 7), radiosurgery alone (n = 5). Survival analysis: Kaplan-Meier method with univariate and multivariate analysis. Results: At 15 years, local control was 76% after total excision (TE) and 87% after subtotal excision plus RT (SE+RT), both significantly better (p = 0.0001) than after SE alone (30%). Cause-specific survival at 15 years was reduced after treatment with SE alone (51%), compared with TE (88%) or SE+RT (86%) (p = 0.0003). Recurrence after primary treatment portended decreased survival, independent of initial treatment group or salvage treatment selection (p = 0.001). Atypical pathologic features predicted reduced 15-year local control (54 vs. 71%) and cause-specific survival rates (57 vs. 86%). Multivariate analysis for cause-specific survival revealed treatment group (SE vs. others; p = 0.0001), pathologic features (atypical vs. typical; p = 0.0056), and Karnofsky performance status (≥80 vs. <80; p = 0.0153) as significant variables. Conclusion: Benign meningiomas are well managed by TE or SE+RT. SE alone is inadequate therapy and adversely affects cause-specific survival. Atypical pathologic features predict a poorer outcome, suggesting possible benefit from more aggressive treatment. Because local recurrence portends lower survival rates, primary treatment choice is important

Oxytocin increases attention to the eyes and selectively enhances self-reported affective empathy for fear.

Science.gov (United States)

Hubble, Kelly; Daughters, Katie; Manstead, Antony S R; Rees, Aled; Thapar, Anita; van Goozen, Stephanie H M

2017-11-01

Oxytocin (OXT) has previously been implicated in a range of prosocial behaviors such as trust and emotion recognition. Nevertheless, recent studies have questioned the evidence for this link. In addition, there has been relatively little conclusive research on the effect of OXT on empathic ability and such studies as there are have not examined the mechanisms through which OXT might affect empathy, or whether OXT selectively facilitates empathy for specific emotions. In the current study, we used eye-tracking to assess attention to socially relevant information while participants viewed dynamic, empathy-inducing video clips, in which protagonists expressed sadness, happiness, pain or fear. In a double-blind, within-subjects, randomized control trial, 40 healthy male participants received 24 IU intranasal OXT or placebo in two identical experimental sessions, separated by a 2-week interval. OXT led to an increase in time spent fixating upon the eye-region of the protagonist's face across emotions. OXT also selectively enhanced self-reported affective empathy for fear, but did not affect cognitive or affective empathy for other emotions. Nevertheless, there was no positive relationship between eye-gaze patterns and affective empathy, suggesting that although OXT influences eye-gaze and may enhance affective empathy for fear, these two systems are independent. Future studies need to further examine the effect of OXT on eye-gaze to fully ascertain whether this can explain the improvements in emotional behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

Energy Technology Data Exchange (ETDEWEB)

Ahmed, M; Gu, F; Ball, A, E-mail: M.Ahmed@hud.ac.uk [Diagnostic Engineering Research Group, University of Huddersfield, HD1 3DH (United Kingdom)

2011-07-19

Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.

Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network

International Nuclear Information System (INIS)

Ahmed, M; Gu, F; Ball, A

2011-01-01

Reciprocating compressors are widely used in industry for various purposes and faults occurring in them can degrade their performance, consume additional energy and even cause severe damage to the machine. Vibration monitoring techniques are often used for early fault detection and diagnosis, but it is difficult to prescribe a given set of effective diagnostic features because of the wide variety of operating conditions and the complexity of the vibration signals which originate from the many different vibrating and impact sources. This paper studies the use of genetic algorithms (GAs) and neural networks (NNs) to select effective diagnostic features for the fault diagnosis of a reciprocating compressor. A large number of common features are calculated from the time and frequency domains and envelope analysis. Applying GAs and NNs to these features found that envelope analysis has the most potential for differentiating three common faults: valve leakage, inter-cooler leakage and a loose drive belt. Simultaneously, the spread parameter of the probabilistic NN was also optimised. The selected subsets of features were examined based on vibration source characteristics. The approach developed and the trained NN are confirmed as possessing general characteristics for fault detection and diagnosis.

Modelling the perceptual similarity of facial expressions from image statistics and neural responses.

Science.gov (United States)

Sormaz, Mladen; Watson, David M; Smith, William A P; Young, Andrew W; Andrews, Timothy J

2016-04-01

The ability to perceive facial expressions of emotion is essential for effective social communication. We investigated how the perception of facial expression emerges from the image properties that convey this important social signal, and how neural responses in face-selective brain regions might track these properties. To do this, we measured the perceptual similarity between expressions of basic emotions, and investigated how this is reflected in image measures and in the neural response of different face-selective regions. We show that the perceptual similarity of different facial expressions (fear, anger, disgust, sadness, happiness) can be predicted by both surface and feature shape information in the image. Using block design fMRI, we found that the perceptual similarity of expressions could also be predicted from the patterns of neural response in the face-selective posterior superior temporal sulcus (STS), but not in the fusiform face area (FFA). These results show that the perception of facial expression is dependent on the shape and surface properties of the image and on the activity of specific face-selective regions. Copyright © 2016 Elsevier Inc. All rights reserved.

Distinct Neural-Functional Effects of Treatments With Selective Serotonin Reuptake Inhibitors, Electroconvulsive Therapy, and Transcranial Magnetic Stimulation and Their Relations to Regional Brain Function in Major Depression: A Meta-analysis.

Science.gov (United States)

Chau, David T; Fogelman, Phoebe; Nordanskog, Pia; Drevets, Wayne C; Hamilton, J Paul

2017-05-01

Functional neuroimaging studies have examined the neural substrates of treatments for major depressive disorder (MDD). Low sample size and methodological heterogeneity, however, undermine the generalizability of findings from individual studies. We conducted a meta-analysis to identify reliable neural changes resulting from different modes of treatment for MDD and compared them with each other and with reliable neural functional abnormalities observed in depressed versus control samples. We conducted a meta-analysis of studies reporting changes in brain activity (e.g., as indexed by positron emission tomography) following treatments with selective serotonin reuptake inhibitors (SSRIs), electroconvulsive therapy (ECT), or transcranial magnetic stimulation. Additionally, we examined the statistical reliability of overlap among thresholded meta-analytic SSRI, ECT, and transcranial magnetic stimulation maps as well as a map of abnormal neural function in MDD. Our meta-analysis revealed that 1) SSRIs decrease activity in the anterior insula, 2) ECT decreases activity in central nodes of the default mode network, 3) transcranial magnetic stimulation does not result in reliable neural changes, and 4) regional effects of these modes of treatment do not significantly overlap with each other or with regions showing reliable functional abnormality in MDD. SSRIs and ECT produce neurally distinct effects relative to each other and to the functional abnormalities implicated in depression. These treatments therefore may exert antidepressant effects by diminishing neural functions not implicated in depression but that nonetheless impact mood. We discuss how the distinct neural changes resulting from SSRIs and ECT can account for both treatment effects and side effects from these therapies as well as how to individualize these treatments. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Foreign currency rate forecasting using neural networks

Science.gov (United States)

Pandya, Abhijit S.; Kondo, Tadashi; Talati, Amit; Jayadevappa, Suryaprasad

2000-03-01

Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

Nano-topography Enhances Communication in Neural Cells Networks

KAUST Repository

Onesto, V.

2017-08-23

Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can direct nerve cells assembly into computational efficient networks may provide new tools and criteria for tissue engineering and regenerative medicine. In this work, we used information theory approaches and functional multi calcium imaging (fMCI) techniques to examine how information flows in neural networks cultured on surfaces with controlled topography. We found that substrate roughness Sa affects networks topology. In the low nano-meter range, S-a = 0-30 nm, information increases with Sa. Moreover, we found that energy density of a network of cells correlates to the topology of that network. This reinforces the view that information, energy and surface nano-topography are tightly inter-connected and should not be neglected when studying cell-cell interaction in neural tissue repair and regeneration.

A test of the critical assumption of the sensory bias model for the evolution of female mating preference using neural networks.

Science.gov (United States)

Fuller, Rebecca C

2009-07-01

The sensory bias model for the evolution of mating preferences states that mating preferences evolve as correlated responses to selection on nonmating behaviors sharing a common sensory system. The critical assumption is that pleiotropy creates genetic correlations that affect the response to selection. I simulated selection on populations of neural networks to test this. First, I selected for various combinations of foraging and mating preferences. Sensory bias predicts that populations with preferences for like-colored objects (red food and red mates) should evolve more readily than preferences for differently colored objects (red food and blue mates). Here, I found no evidence for sensory bias. The responses to selection on foraging and mating preferences were independent of one another. Second, I selected on foraging preferences alone and asked whether there were correlated responses for increased mating preferences for like-colored mates. Here, I found modest evidence for sensory bias. Selection for a particular foraging preference resulted in increased mating preference for similarly colored mates. However, the correlated responses were small and inconsistent. Selection on foraging preferences alone may affect initial levels of mating preferences, but these correlations did not constrain the joint evolution of foraging and mating preferences in these simulations.

Web Page Classification Method Using Neural Networks

Science.gov (United States)

Selamat, Ali; Omatu, Sigeru; Yanagimoto, Hidekazu; Fujinaka, Toru; Yoshioka, Michifumi

Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is combined with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.

The neural basis of financial risk taking.

Science.gov (United States)

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

Changes in neural network homeostasis trigger neuropsychiatric symptoms.

Science.gov (United States)

Winkelmann, Aline; Maggio, Nicola; Eller, Joanna; Caliskan, Gürsel; Semtner, Marcus; Häussler, Ute; Jüttner, René; Dugladze, Tamar; Smolinsky, Birthe; Kowalczyk, Sarah; Chronowska, Ewa; Schwarz, Günter; Rathjen, Fritz G; Rechavi, Gideon; Haas, Carola A; Kulik, Akos; Gloveli, Tengis; Heinemann, Uwe; Meier, Jochen C

2014-02-01

The mechanisms that regulate the strength of synaptic transmission and intrinsic neuronal excitability are well characterized; however, the mechanisms that promote disease-causing neural network dysfunction are poorly defined. We generated mice with targeted neuron type-specific expression of a gain-of-function variant of the neurotransmitter receptor for glycine (GlyR) that is found in hippocampectomies from patients with temporal lobe epilepsy. In this mouse model, targeted expression of gain-of-function GlyR in terminals of glutamatergic cells or in parvalbumin-positive interneurons persistently altered neural network excitability. The increased network excitability associated with gain-of-function GlyR expression in glutamatergic neurons resulted in recurrent epileptiform discharge, which provoked cognitive dysfunction and memory deficits without affecting bidirectional synaptic plasticity. In contrast, decreased network excitability due to gain-of-function GlyR expression in parvalbumin-positive interneurons resulted in an anxiety phenotype, but did not affect cognitive performance or discriminative associative memory. Our animal model unveils neuron type-specific effects on cognition, formation of discriminative associative memory, and emotional behavior in vivo. Furthermore, our data identify a presynaptic disease-causing molecular mechanism that impairs homeostatic regulation of neural network excitability and triggers neuropsychiatric symptoms.

Tinnitus and neural plasticity of the brain

NARCIS (Netherlands)

Bartels, Hilke; Staal, Michiel J.; Albers, Frans W. J.

Objective: To describe the current ideas about the manifestations of neural plasticity in generating tinnitus. Data Sources: Recently published source articles were identified using MEDLINE, PubMed, and Cochrane Library according to the key words mentioned below. Study Selection: Review articles and

Visual attention mitigates information loss in small- and large-scale neural codes.

Science.gov (United States)

Sprague, Thomas C; Saproo, Sameer; Serences, John T

2015-04-01

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing--or selective attention--is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fundamental study of interpretation technique for 3-D magnetotelluric data using neural networks; Neural network wo mochiita sanjigen MT ho data kaishaku gijutsu no kisoteki kenkyu

Energy Technology Data Exchange (ETDEWEB)

Kobayashi, T; Fukuoka, K; Shima, H [Oyo Corp., Tokyo (Japan); Mogi, T [Kyushu University, Fukuoka (Japan). Faculty of Engineering; Spichak, V

1997-05-27

The research and development have been conducted to apply neural networks to interpretation technique for 3-D MT data. In this study, a data base of various data was made from the numerical modeling of 3-D fault model, and the data base management system was constructed. In addition, an unsupervised neural network for treating noise and a supervised neural network for estimating fault parameters such as dip, strike and specific resistance were made, and a basic neural network system was constructed. As a result of the application to the various data, basically sufficient performance for estimating the fault parameters was confirmed. Thus, the optimum MT data for this system were selected. In future, it is necessary to investigate the optimum model and the number of models for learning these neural networks. 3 refs., 5 figs., 2 tabs.

Neural-fuzzy control of adept one SCARA

International Nuclear Information System (INIS)

Er, M.J.; Toh, B.H.; Toh, B.Y.

1998-01-01

This paper presents an Intelligent Control Strategy for the Adept One SCARA (Selective Compliance Assembly Robot Arm). It covers the design and simulation study of a Neural-Fuzzy Controller (NFC) for the SCARA with a view of tracking a predetermined trajectory of motion in the joint space. The SCARA was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. The overall performance of the control system under different conditions, namely variation in playload, variations in coefficients of static, dynamic and viscous friction and different trajectories were studied and comparison made with an existing Neural Network Controller and two Computed Torque Controllers. The NFC was shown to be robust and is able to overcome the drawback of the existing Neural Network Controller

Eddy Current Flaw Characterization Using Neural Networks

International Nuclear Information System (INIS)

Song, S. J.; Park, H. J.; Shin, Y. K.

1998-01-01

Determination of location, shape and size of a flaw from its eddy current testing signal is one of the fundamental issues in eddy current nondestructive evaluation of steam generator tubes. Here, we propose an approach to this problem; an inversion of eddy current flaw signal using neural networks trained by finite element model-based synthetic signatures. Total 216 eddy current signals from four different types of axisymmetric flaws in tubes are generated by finite element models of which the accuracy is experimentally validated. From each simulated signature, total 24 eddy current features are extracted and among them 13 features are finally selected for flaw characterization. Based on these features, probabilistic neural networks discriminate flaws into four different types according to the location and the shape, and successively back propagation neural networks determine the size parameters of the discriminated flaw

Analysis of the DWPF glass pouring system using neural networks

International Nuclear Information System (INIS)

Calloway, T.B. Jr.; Jantzen, C.M.

1997-01-01

Neural networks were used to determine the sensitivity of 39 selected Melter/Melter Off Gas and Melter Feed System process parameters as related to the Defense Waste Processing Facility (DWPF) Melter Pour Spout Pressure during the overall analysis and resolution of the DWPF glass production and pouring issues. Two different commercial neural network software packages were used for this analysis. Models were developed and used to determine the critical parameters which accurately describe the DWPF Pour Spout Pressure. The model created using a low-end software package has a root mean square error of ± 0.35 inwc ( 2 = 0.77) with respect to the plant data used to validate and test the model. The model created using a high-end software package has a R 2 = 0.97 with respect to the plant data used to validate and test the model. The models developed for this application identified the key process parameters which contribute to the control of the DWPF Melter Pour Spout pressure during glass pouring operations. The relative contribution and ranking of the selected parameters was determined using the modeling software. Neural network computing software was determined to be a cost-effective software tool for process engineers performing troubleshooting and system performance monitoring activities. In remote high-level waste processing environments, neural network software is especially useful as a replacement for sensors which have failed and are costly to replace. The software can be used to accurately model critical remotely installed plant instrumentation. When the instrumentation fails, the software can be used to provide a soft sensor to replace the actual sensor, thereby decreasing the overall operating cost. Additionally, neural network software tools require very little training and are especially useful in mining or selecting critical variables from the vast amounts of data collected from process computers

Topology influences performance in the associative memory neural networks

International Nuclear Information System (INIS)

Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang

2006-01-01

To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures. In this Letter, we found that the memory performance of neural networks obtained through asynchronous updating from 'larger' nodes to 'smaller' nodes are better than asynchronous updating in random order, especially for the scale-free topology. The computational performance of associative memory neural networks linked by the above-mentioned network topologies with the same amounts of nodes (neurons) and edges (synapses) were studied respectively. Along with topologies becoming more random and less locally disordered, we will see that the performance of associative memory neural network is quite improved. By comparing, we show that the regular lattice and random network form two extremes in terms of patterns stability and retrievability. For a network, its patterns stability and retrievability can be largely enhanced by adding a random component or some shortcuts to its structured component. According to the conclusions of this Letter, we can design the associative memory neural networks with high performance and minimal interconnect requirements

Losing Control in Social Situations: How the Presence of Others Affects Neural Processes Related to Sense of Agency.

Science.gov (United States)

Beyer, Frederike; Sidarus, Nura; Fleming, Stephen; Haggard, Patrick

2018-01-01

Social contexts substantially influence individual behavior, but little is known about how they affect cognitive processes related to voluntary action. Previously, it has been shown that social context reduces participants' sense of agency over the outcomes of their actions and outcome monitoring. In this fMRI study on human volunteers, we investigated the neural mechanisms by which social context alters sense of agency. Participants made costly actions to stop inflating a balloon before it burst. On "social" trials, another player could act in their stead, but we analyzed only trials in which the other player remained passive. We hypothesized that mentalizing processes during social trials would affect decision-making fluency and lead to a decreased sense of agency. In line with this hypothesis, we found increased activity in the bilateral temporo-parietal junction (TPJ), precuneus, and middle frontal gyrus during social trials compared with nonsocial trials. Activity in the precuneus was, in turn, negatively related to sense of agency at a single-trial level. We further found a double dissociation between TPJ and angular gyrus (AG): activity in the left AG was not sensitive to social context but was negatively related to sense of agency. In contrast, activity in the TPJ was modulated by social context but was not sensitive to sense of agency.

Application of neural network in τ→ρυτ polarization analysis

International Nuclear Information System (INIS)

Zhang Ziping; Wang Yifang; Innocente, V.

1994-01-01

An artificial neutral network was built to select events in the τ→ρυ τ polarization analysis at LEP/L3, much better selection efficiency has been achieved. Detailed studies show that no systematic errors or bias have been introduced by the application of neural network. A polarization of P τ = -0.129 +- 0.050 +- 0.050 for this channel was obtained by using a sample of 8977 τ + τ - pairs collected near the peak of Z 0 resonance. The neural network training method and some details are described

Artificial neural network decision support systems for new product development project selection

NARCIS (Netherlands)

Thieme, R.J.; Song, Michael; Calantone, R.J.

2000-01-01

The authors extend and develop an artificial neural network decision support system and demonstrate how it can guide managers when they make complex new product development decisions. The authors use data from 612 projects to compare this new method with traditional methods for predicting various

The neural components of empathy: Predicting daily prosocial behavior

Science.gov (United States)

Rameson, Lian T.; Lieberman, Matthew D.

2014-01-01

Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation. PMID:22887480

The neural components of empathy: predicting daily prosocial behavior.

Science.gov (United States)

Morelli, Sylvia A; Rameson, Lian T; Lieberman, Matthew D

2014-01-01

Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation.

Young adult smokers' neural response to graphic cigarette warning labels

Directory of Open Access Journals (Sweden)

Adam E. Green

2016-06-01

Conclusions: In this sample of young adult smokers, GWLs promoted neural activation in brain regions involved in cognitive and affective decision-making and memory formation and the effects of GWLs did not differ on branded or plain cigarette packaging. These findings complement other recent neuroimaging GWL studies conducted with older adult smokers and with adolescents by demonstrating similar patterns of neural activation in response to GWLs among young adult smokers.

Large motor units are selectively affected following a stroke.

Science.gov (United States)

Lukács, M; Vécsei, L; Beniczky, S

2008-11-01

Previous studies have revealed a loss of functioning motor units in stroke patients. However, it remained unclear whether the motor units are affected randomly or in some specific pattern. We assessed whether there is a selective loss of the large (high recruitment threshold) or the small (low recruitment threshold) motor units following a stroke. Forty-five stroke patients and 40 healthy controls participated in the study. Macro-EMG was recorded from the abductor digiti minimi muscle at two levels of force output (low and high). The median macro motor unit potential (macro-MUP) amplitude on the paretic side was compared with those on the unaffected side and in the controls. In the control group and on the unaffected side, the macro-MUPs were significantly larger at the high force output than at the low one. However, on the paretic side the macro-MUPs at the high force output had the same amplitude as those recorded at the low force output. These changes correlated with the severity of the paresis. Following a stroke, there is a selective functional loss of the large, high-threshold motor units. These changes are related to the severity of the symptoms. Our findings furnish further insight into the pathophysiology of the motor deficit following a stroke.

Conflicts during response selection affect response programming: reactions toward the source of stimulation.

Science.gov (United States)

Buetti, Simona; Kerzel, Dirk

2009-06-01

In the Simon effect, participants make a left or right keypress in response to a nonspatial attribute (e.g., color) that is presented on the left or right. Reaction times (RTs) increase when the response activated by the irrelevant stimulus location and the response retrieved by instruction are in conflict. The authors measured RTs and movement parameters (MPs) of pointing responses in a typical Simon task. Their results show that the trajectories veer toward the imperative stimulus. This bias decreased as RTs increased. The authors suggest that the time course of trajectory deviations reflects the resolution of the response conflict over time. Further, time pressure did not affect the size of the Simon effect in MPs or its time course, but strongly reduced the Simon effect in RTs. In contrast, response selection before the onset of a go signal on the left or right did not affect the Simon effect in RTs, but reduced the Simon effect in MPs and reversed the time course. The authors speculate about independent Simon effects associated with response selection and programming. (c) 2009 APA, all rights reserved.

AN ASSESSMENT OF FACTORS AFFECTING MATERIAL STOCK CONTROL PRACTICE ON SELECTED CONSTRUCTION SITES IN NIGERIA

OpenAIRE

Adafin, Johnson Kayode; Ayodele, Elijah Olusegun; Daramola, Olufemi

2011-01-01

This research examines the stock control methods utilized by construction firms on construction sites with a view to assessing the factors affecting material stock control practice by construction firms as well as determining the impact of factors affecting material stock control on building project performance. Data were collected with the aid of well-structured questionnaire administered on a number of construction professionals and technicians in some randomly selected building constructio...

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

Directory of Open Access Journals (Sweden)

Andrea Maesani

2015-11-01

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

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

Science.gov (United States)

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

2014-12-01

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

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

Science.gov (United States)

Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

2014-12-01

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

Neutron spectrometry and dosimetry by means of evolutive neural networks

International Nuclear Information System (INIS)

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

2008-01-01

The artificial neural networks and the genetic algorithms are two relatively new areas of research, which have been subject to a growing interest during the last years. Both models are inspired by the nature, however, the neural networks are interested in the learning of a single individual, which is defined as fenotypic learning, while the evolutionary algorithms are interested in the adaptation of a population to a changing environment, that which is defined as genotypic learning. Recently, the use of the technology of neural networks has been applied with success in the area of the nuclear sciences, mainly in the areas of neutron spectrometry and dosimetry. The structure (network topology), as well as the learning parameters of a neural network, are factors that contribute in a significant way with the acting of the same one, however, it has been observed that the investigators in this area, carry out the selection of the network parameters through the essay and error technique, that which produces neural networks of poor performance and low generalization capacity. From the revised sources, it has been observed that the use of the evolutionary algorithms, seen as search techniques, it has allowed him to be possible to evolve and to optimize different properties of the neural networks, just as the initialization of the synaptic weights, the network architecture or the training algorithms without the human intervention. The objective of the present work is focused in analyzing the intersection of the neural networks and the evolutionary algorithms, analyzing like it is that the same ones can be used to help in the design processes and training of a neural network, this is, in the good selection of the structural parameters and of network learning, improving its generalization capacity, in such way that the same one is able to reconstruct in an efficient way neutron spectra and to calculate equivalent doses starting from the counting rates of a Bonner sphere

Using function approximation to determine neural network accuracy

International Nuclear Information System (INIS)

Wichman, R.F.; Alexander, J.

2013-01-01

Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data.

Science.gov (United States)

Ye, Fei

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks.

Improved Local Weather Forecasts Using Artificial Neural Networks

DEFF Research Database (Denmark)

Wollsen, Morten Gill; Jørgensen, Bo Nørregaard

2015-01-01

Solar irradiance and temperature forecasts are used in many different control systems. Such as intelligent climate control systems in commercial greenhouses, where the solar irradiance affects the use of supplemental lighting. This paper proposes a novel method to predict the forthcoming weather...... using an artificial neural network. The neural network used is a NARX network, which is known to model non-linear systems well. The predictions are compared to both a design reference year as well as commercial weather forecasts based upon numerical modelling. The results presented in this paper show...

The neural organization of perception in chess experts.

Science.gov (United States)

Krawczyk, Daniel C; Boggan, Amy L; McClelland, M Michelle; Bartlett, James C

2011-07-20

The human visual system responds to expertise, and it has been suggested that regions that process faces also process other objects of expertise including chess boards by experts. We tested whether chess and face processing overlap in brain activity using fMRI. Chess experts and novices exhibited face selective areas, but these regions showed no selectivity to chess configurations relative to other stimuli. We next compared neural responses to chess and to scrambled chess displays to isolate areas relevant to expertise. Areas within the posterior cingulate, orbitofrontal cortex, and right temporal cortex were active in this comparison in experts over novices. We also compared chess and face responses within the posterior cingulate and found this area responsive to chess only in experts. These findings indicate that the configurations in chess are not strongly processed by face-selective regions that are selective for faces in individuals who have expertise in both domains. Further, the area most consistently involved in chess did not show overlap with faces. Overall, these results suggest that expert visual processing may be similar at the level of recognition, but need not show the same neural correlates. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Neural plasticity and its initiating conditions in tinnitus.

Science.gov (United States)

Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

Mitochondrial metabolism in early neural fate and its relevance for neuronal disease modeling.

Science.gov (United States)

Lorenz, Carmen; Prigione, Alessandro

2017-12-01

Modulation of energy metabolism is emerging as a key aspect associated with cell fate transition. The establishment of a correct metabolic program is particularly relevant for neural cells given their high bioenergetic requirements. Accordingly, diseases of the nervous system commonly involve mitochondrial impairment. Recent studies in animals and in neural derivatives of human pluripotent stem cells (PSCs) highlighted the importance of mitochondrial metabolism for neural fate decisions in health and disease. The mitochondria-based metabolic program of early neurogenesis suggests that PSC-derived neural stem cells (NSCs) may be used for modeling neurological disorders. Understanding how metabolic programming is orchestrated during neural commitment may provide important information for the development of therapies against conditions affecting neural functions, including aging and mitochondrial disorders. Copyright © 2017. Published by Elsevier Ltd.

The role of selective attention on academic foundations: A cognitive neuroscience perspective

OpenAIRE

Stevens, Courtney; Bavelier, Daphné

2012-01-01

To the extent that selective attention skills are relevant for academic foundations and amenable to training, they represent an important focus for the field of education. Here, drawing on research on the neurobiology of attention, we review hypothesized links between selective attention and processing across three domains important to early academic skills. First, we provide a brief review of the neural bases of selective attention, emphasizing the effects of selective attention on neural pr...

Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.

Science.gov (United States)

Hutt, Axel; Buhry, Laure

2014-12-01

Anaesthetic agents are known to affect extra-synaptic GABAergic receptors, which induce tonic inhibitory currents. Since these receptors are very sensitive to small concentrations of agents, they are supposed to play an important role in the underlying neural mechanism of general anaesthesia. Moreover anaesthetic agents modulate the encephalographic activity (EEG) of subjects and hence show an effect on neural populations. To understand better the tonic inhibition effect in single neurons on neural populations and hence how it affects the EEG, the work considers single neurons and neural populations in a steady-state and studies numerically and analytically the modulation of their firing rate and nonlinear gain with respect to different levels of tonic inhibition. We consider populations of both type-I (Leaky Integrate-and-Fire model) and type-II (Morris-Lecar model) neurons. To bridge the single neuron description to the population description analytically, a recently proposed statistical approach is employed which allows to derive new analytical expressions for the population firing rate for type-I neurons. In addition, the work shows the derivation of a novel transfer function for type-I neurons as considered in neural mass models and studies briefly the interaction of synaptic and extra-synaptic inhibition. We reveal a strong subtractive and divisive effect of tonic inhibition in type-I neurons, i.e. a shift of the firing rate to higher excitation levels accompanied by a change of the nonlinear gain. Tonic inhibition shortens the excitation window of type-II neurons and their populations while maintaining the nonlinear gain. The gained results are interpreted in the context of recent experimental findings under propofol-induced anaesthesia.

Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data

International Nuclear Information System (INIS)

Balabin, Roman M.; Smirnov, Sergey V.

2011-01-01

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm -1 ) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic

Aging affects hemispheric asymmetry in the neural representation of speech sounds.

Science.gov (United States)

Bellis, T J; Nicol, T; Kraus, N

2000-01-15

Hemispheric asymmetries in the processing of elemental speech sounds appear to be critical for normal speech perception. This study investigated the effects of age on hemispheric asymmetry observed in the neurophysiological responses to speech stimuli in three groups of normal hearing, right-handed subjects: children (ages, 8-11 years), young adults (ages, 20-25 years), and older adults (ages > 55 years). Peak-to-peak response amplitudes of the auditory cortical P1-N1 complex obtained over right and left temporal lobes were examined to determine the degree of left/right asymmetry in the neurophysiological responses elicited by synthetic speech syllables in each of the three subject groups. In addition, mismatch negativity (MMN) responses, which are elicited by acoustic change, were obtained. Whereas children and young adults demonstrated larger P1-N1-evoked response amplitudes over the left temporal lobe than over the right, responses from elderly subjects were symmetrical. In contrast, MMN responses, which reflect an echoic memory process, were symmetrical in all subject groups. The differences observed in the neurophysiological responses were accompanied by a finding of significantly poorer ability to discriminate speech syllables involving rapid spectrotemporal changes in the older adult group. This study demonstrates a biological, age-related change in the neural representation of basic speech sounds and suggests one possible underlying mechanism for the speech perception difficulties exhibited by aging adults. Furthermore, results of this study support previous findings suggesting a dissociation between neural mechanisms underlying those processes that reflect the basic representation of sound structure and those that represent auditory echoic memory and stimulus change.

Fibrous dysplasia of the cranial vault: quantitative analysis based on neural networks

International Nuclear Information System (INIS)

Arana, E.; Marti-Bonmati, L.; Paredes, R.; Molla, E.

1998-01-01

To assess the utility of statistical analysis and neural networks in the quantitative analysis of fibrous dysplasia of the cranial vault. Ten patients with fibrous dysplasia (six women and four men with a mean age of 23.60±17.85 years) were selected from a series of 167 patients with lesions of the cranial vault evaluated by plain radiography and computed tomography (CT). Nineteen variables were taken from their medical records and radiological study. Their characterization was based on statistical analysis and neural network, and was validated by means of the leave-one-out method. The performance of the neural network was estimated by means of receiver operating characteristics (ROC) curves, using as a parameter the area under the curve A z . Bivariate analysis identified age, duration of symptoms, lytic and sclerotic patterns, sclerotic margin, ovoid shape, soft-tissue mas and periosteal reaction as significant variables. The area under the neural network curve was 0.9601±0.0435. The network selected the matrix and soft-tissue mass a variables that were indispensable for diagnosis. The neural network presents a high performance in the characterization of fibrous dysplasia of the cranial vault, disclosing occult interactions among the variables. (Author) 24 refs

Selected issues affecting Indian tribes in the implementation of the NWPA

International Nuclear Information System (INIS)

Sampson, M.

1987-01-01

Enactment of the NWPA requires that a candidate site is selected for the first high-level waste (HLW) repository based on a formal comparative evaluation of the three sites under characterization as approved by the U.S. President. However, the nominated sites can only be compared with one another if there is such a common basis for scientific judgment. The development of such a scientific basis prior to the start-up of site characterization activities entails several important issues which potentially affect the rights of the Indian Tribes. This paper describes the issues

Impact parameter determination in experimental analysis using neural network

International Nuclear Information System (INIS)

Haddad, F.; David, C.; Freslier, M.; Aichelin, J.; Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.

1997-01-01

A neural network is used to determine the impact parameter in 40 Ca + 40 Ca reactions. The effect of the detection efficiency as well as the model dependence of the training procedure have been studied carefully. An overall improvement of the impact parameter determination of 25 % is obtained using this technique. The analysis of Amphora 40 Ca+ 40 Ca data at 35 MeV per nucleon using a neural network shows two well separated classes of events among the selected 'complete' events. (authors)

Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton.

Science.gov (United States)

Yu, Wen; Rosen, Jacob

2013-04-01

In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed.

Controlling the dynamics of multi-state neural networks

International Nuclear Information System (INIS)

Jin, Tao; Zhao, Hong

2008-01-01

In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks

NNSYSID and NNCTRL Tools for system identification and control with neural networks

DEFF Research Database (Denmark)

Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

2001-01-01

choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

NNSYSID and NNCTRL Tools for system identification and control with neural networks

DEFF Research Database (Denmark)

Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

2001-01-01

a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Zainal Ahmad

2017-05-01

Full Text Available Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN is shown to be able to predict the Air Pollution Index (API with a Mean Squared Error (MSE and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

Individual Identification Using Functional Brain Fingerprint Detected by Recurrent Neural Network.

Science.gov (United States)

Chen, Shiyang; Hu, Xiaoping P

2018-03-20

Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network based model for identifying individuals based on only a short segment of resting state functional MRI data. In addition, we demonstrate how the global signal and differences in atlases affect the individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.

Kontrol Kecepatan Motor Induksi menggunakan Algoritma Backpropagation Neural Network

Directory of Open Access Journals (Sweden)

MUHAMMAD RUSWANDI DJALAL

2017-07-01

rad / s. The speed change was set at every 0.3 second interval and the maximum simulation time was 0.9 seconds. Case 1, without control, shows the torque response and velocity of three-phase induction motor without control. Although the speed of three phase induction motor was set to change at every 0.3 seconds, it would not affect the torque. The uncontrolled three-phase induction motors had poor performance due to induction motor speeds could not be adjusted as needed. Case 2 with backpropagation neural network control, although the speed of three phase induction motor changing at every 0.3 seconds would not affect the torque. In addition, the backpropagation neural network control had a good performance because the speed of induction motor could be adjusted as needed.Keywords: Backpropagation Neural Network (BPNN, NN Training, NN Testing, Motor

Effects of category-specific costs on neural systems for perceptual decision-making

DEFF Research Database (Denmark)

Fleming, Stephen M; Whiteley, Louise Emma; Hulme, Oliver James

2010-01-01

Perceptual judgments are often biased by prospective losses, leading to changes in decision criteria. Little is known about how and where sensory evidence and cost information interact in the brain to influence perceptual categorization. Here we show that prospective losses systematically bias...... functions enact a particular task set that is communicated to visual regions. Across subjects, greater shifts in decision criteria were associated with greater activation of the anterior cingulate cortex (ACC). Our results support a hypothesis that costs bias an intermediate representation between...... perception and action, expressed via general effects on frontal cortex, and selective effects on extrastriate cortex. These findings indicate that asymmetric costs may affect a neural implementation of perceptual decision making in a similar manner to changes in category expectation, constituting a step...

Detection of heat abduction on the walls by artificial neural network and selection of materials with decision support system

Directory of Open Access Journals (Sweden)

Egemen Tekkanat

2017-08-01

Full Text Available Today energy conservation is a very important issue in the world and Turkey. The aim of this study is to minimize the heat abduction, thus to save energy by utilizing the factors to prevent the heat abduction on the walls of buildings. First of all, a back-propagation network model with artificial neural network model was used for the factors that can cause heat loss on the walls. Whether the walls have insulation were considered. After that, Decision Support Systems were used for heat insulation to select the appropriate materials. A Decision Support Model with Analytic Hierarchy Process (AHP was recommended to meet the needs of a customer best and to make better decisions for the selection of the materials. The method was used by construction firms for their decision processes for the best materials and the results were evaluated. After the evaluations were done, the factors that cause heat loss were considered and it became clear which factors were more important for the prevention of heat loss.

Automatic determination of pathological voice transformation coefficients for TDPDOLA using neural network

International Nuclear Information System (INIS)

Belgacem, H.; Cherif, A.

2011-01-01

One of the biggest challenges in vocal transformation with TD-PSOLA technique is the selection of modified parameters that will make a successful speech resynthesis. The best selection methods are by using human ratters. This study focuses on automatic determination of the pathological voice transformation coefficients using an Artificial Neural Network this way by comparing the results to the previous manual work. Four characterizied parameters (RATA-PLP, Jitter, Shimmer and RAP) were chosen. The system is developed with supervised training, consists of recognition (neural network) for synthesis (TD-PSOLA). The experimental results show that the parameter sets selected by the proposed system can be successfully used to resynthesize and demonstrating that our system can assist in vocal of pathological voice's transformation.

The role of selective attention in perceptual and affective priming

Science.gov (United States)

Stone, M.; Ladd, S. L.; Gabrieli, J. D.

2000-01-01

Two kinds of perceptual priming (word identification and word fragment completion), as well as preference priming (that may rely on special affective mechanisms) were examined after participants either read or named the colors of words and nonwords at study. Participants named the colors of words more slowly than the colors of nonwords, indicating that lexical processing of the words occurred at study. Nonetheless, priming on all three tests was lower after color naming than after reading, despite evidence of lexical processing during color naming shown by slower responses to words than to nonwords. These results indicate that selective attention to (rather than the mere processing of) letter string identity at study is important for subsequent repetition priming.

PWR system simulation and parameter estimation with neural networks

Energy Technology Data Exchange (ETDEWEB)

Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

2002-11-01

A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

PWR system simulation and parameter estimation with neural networks

International Nuclear Information System (INIS)

Akkurt, Hatice; Colak, Uener

2002-01-01

A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

Robo signaling regulates the production of cranial neural crest cells.

Science.gov (United States)

Li, Yan; Zhang, Xiao-Tan; Wang, Xiao-Yu; Wang, Guang; Chuai, Manli; Münsterberg, Andrea; Yang, Xuesong

2017-12-01

Slit/Robo signaling plays an important role in the guidance of developing neurons in developing embryos. However, it remains obscure whether and how Slit/Robo signaling is involved in the production of cranial neural crest cells. In this study, we examined Robo1 deficient mice to reveal developmental defects of mouse cranial frontal and parietal bones, which are derivatives of cranial neural crest cells. Therefore, we determined the production of HNK1 + cranial neural crest cells in early chick embryo development after knock-down (KD) of Robo1 expression. Detection of markers for pre-migratory and migratory neural crest cells, PAX7 and AP-2α, showed that production of both was affected by Robo1 KD. In addition, we found that the transcription factor slug is responsible for the aberrant delamination/EMT of cranial neural crest cells induced by Robo1 KD, which also led to elevated expression of E- and N-Cadherin. N-Cadherin expression was enhanced when blocking FGF signaling with dominant-negative FGFR1 in half of the neural tube. Taken together, we show that Slit/Robo signaling influences the delamination/EMT of cranial neural crest cells, which is required for cranial bone development. Copyright © 2017. Published by Elsevier Inc.

Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

International Nuclear Information System (INIS)

Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R.; Seyedkashi, S. M. H.

2011-01-01

Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

Evaluation of the legal consequences of action affects neural activity and emotional experience during the resolution of moral dilemmas.

Science.gov (United States)

Pletti, Carolina; Sarlo, Michela; Palomba, Daniela; Rumiati, Rino; Lotto, Lorella

2015-03-01

In any modern society killing is regarded as a severe violation of the legal codes that is subjected to penal judgment. Therefore, it is likely that people take legal consequences into account when deciding about the hypothetical killing of one person in classic moral dilemmas, with legal concerns contributing to decision-making. In particular, by differing for the degree of intentionality and emotional salience, Footbridge- and Trolley-type dilemmas might promote differential assignment of blame and punishment while implicating the same severity of harm. The present study was aimed at comparing the neural activity, subjective emotional reactions, and behavioral choices in two groups of participants who either took (Legal group) or did not take (No Legal group) legal consequences into account when deciding on Footbridge-type and Trolley-type moral dilemmas. Stimulus- and response-locked ERPs were measured to investigate the neural activity underlying two separate phases of the decision process. No difference in behavioral choices was found between groups. However, the No Legal group reported greater overall emotional impact, associated with lower preparation for action, suggesting greater conflict between alternative motor responses representing the different decision choices. In contrast, the Legal group showed an overall dampened affective experience during decision-making associated with greater overall action readiness and intention to act, reflecting lower conflict in responding. On these bases, we suggest that in moral dilemmas legal consequences of actions provide a sort of reference point on which people can rely to support a decision, independent of dilemma type. Copyright © 2015 Elsevier Inc. All rights reserved.

Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation.

Science.gov (United States)

Salavatian, Siamak; Beaumont, Eric; Longpré, Jean-Philippe; Armour, J Andrew; Vinet, Alain; Jacquemet, Vincent; Shivkumar, Kalyanam; Ardell, Jeffrey L

2016-11-01

Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines (n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal (n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory. Copyright © 2016 the American Physiological Society.

Pre-Exposure to Context Affects Learning Strategy Selection in Mice

Science.gov (United States)

Tunur, Tumay; Dohanich, Gary P.; Schrader, Laura A.

2010-01-01

The multiple memory systems hypothesis proposes that different types of learning strategies are mediated by distinct neural systems in the brain. Male and female mice were tested on a water plus-maze task that could be solved by either a place or response strategy. One group of mice was pre-exposed to the same context as training and testing (PTC)…

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

Science.gov (United States)

Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

Directory of Open Access Journals (Sweden)

Dongrui Wu

Full Text Available Brain-computer interaction (BCI and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL, active class selection (ACS, and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Task-dependent neural bases of perceiving emotionally expressive targets

Directory of Open Access Journals (Sweden)

Jamil eZaki

2012-08-01

Full Text Available Social cognition is fundamentally interpersonal: individuals’ behavior and dispositions critically affect their interaction partners’ information processing. However, cognitive neuroscience studies, partially because of methodological constraints, have remained largely perceiver-centric: focusing on the abilities, motivations, and goals of social perceivers while largely ignoring interpersonal effects. Here, we address this knowledge gap by examining the neural bases of perceiving emotionally expressive and inexpressive social targets. Sixteen perceivers were scanned using fMRI while they watched targets discussing emotional autobiographical events. Perceivers continuously rated each target’s emotional state or eye-gaze direction. The effects of targets’ emotional expressivity on perceiver’s brain activity depended on task set: when perceivers explicitly attended to targets’ emotions, expressivity predicted activity in neural structures—including medial prefrontal and posterior cingulate cortex—associated with drawing inferences about mental states. When perceivers instead attended to targets’ eye-gaze, target expressivity predicted activity in regions—including somatosensory cortex, fusiform gyrus, and motor cortex—associated with monitoring sensorimotor states and biological motion. These findings suggest that expressive targets affect information processing in manner that depends on perceivers’ goals. More broadly, these data provide an early step towards understanding the neural bases of interpersonal social cognition.

Improving the Robustness of Deep Neural Networks via Stability Training

OpenAIRE

Zheng, Stephan; Song, Yang; Leung, Thomas; Goodfellow, Ian

2016-01-01

In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such...

Hearing loss impacts neural alpha oscillations under adverse listening conditions

OpenAIRE

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

2015-01-01

Degradations in external, acoustic stimulation have long been suspected to increase the load on working memory (WM). One neural signature of WM load is enhanced power of alpha oscillations (6–12 Hz). However, it is unknown to what extent common internal, auditory degradation, that is, hearing impairment, affects the neural mechanisms of WM when audibility has been ensured via amplification. Using an adapted auditory Sternberg paradigm, we varied the orthogonal factors memory load and backgrou...

Anomaly detection in an automated safeguards system using neural networks

International Nuclear Information System (INIS)

Whiteson, R.; Howell, J.A.

1992-01-01

An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

Diminished neural responses predict enhanced intrinsic motivation and sensitivity to external incentive.

Science.gov (United States)

Marsden, Karen E; Ma, Wei Ji; Deci, Edward L; Ryan, Richard M; Chiu, Pearl H

2015-06-01

The duration and quality of human performance depend on both intrinsic motivation and external incentives. However, little is known about the neuroscientific basis of this interplay between internal and external motivators. Here, we used functional magnetic resonance imaging to examine the neural substrates of intrinsic motivation, operationalized as the free-choice time spent on a task when this was not required, and tested the neural and behavioral effects of external reward on intrinsic motivation. We found that increased duration of free-choice time was predicted by generally diminished neural responses in regions associated with cognitive and affective regulation. By comparison, the possibility of additional reward improved task accuracy, and specifically increased neural and behavioral responses following errors. Those individuals with the smallest neural responses associated with intrinsic motivation exhibited the greatest error-related neural enhancement under the external contingency of possible reward. Together, these data suggest that human performance is guided by a "tonic" and "phasic" relationship between the neural substrates of intrinsic motivation (tonic) and the impact of external incentives (phasic).

Transient analysis for PWR reactor core using neural networks predictors

International Nuclear Information System (INIS)

Gueray, B.S.

2001-01-01

In this study, transient analysis for a Pressurized Water Reactor core has been performed. A lumped parameter approximation is preferred for that purpose, to describe the reactor core together with mechanism which play an important role in dynamic analysis. The dynamic behavior of the reactor core during transients is analyzed considering the transient initiating events, wich are an essential part of Safety Analysis Reports. several transients are simulated based on the employed core model. Simulation results are in accord the physical expectations. A neural network is developed to predict the future response of the reactor core, in advance. The neural network is trained using the simulation results of a number of representative transients. Structure of the neural network is optimized by proper selection of transfer functions for the neurons. Trained neural network is used to predict the future responses following an early observation of the changes in system variables. Estimated behaviour using the neural network is in good agreement with the simulation results for various for types of transients. Results of this study indicate that the designed neural network can be used as an estimator of the time dependent behavior of the reactor core under transient conditions

Enhanced online convolutional neural networks for object tracking

Science.gov (United States)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

Losing Control in Social Situations: How the Presence of Others Affects Neural Processes Related to Sense of Agency

Science.gov (United States)

Fleming, Stephen

2018-01-01

Social contexts substantially influence individual behavior, but little is known about how they affect cognitive processes related to voluntary action. Previously, it has been shown that social context reduces participants’ sense of agency over the outcomes of their actions and outcome monitoring. In this fMRI study on human volunteers, we investigated the neural mechanisms by which social context alters sense of agency. Participants made costly actions to stop inflating a balloon before it burst. On “social” trials, another player could act in their stead, but we analyzed only trials in which the other player remained passive. We hypothesized that mentalizing processes during social trials would affect decision-making fluency and lead to a decreased sense of agency. In line with this hypothesis, we found increased activity in the bilateral temporo-parietal junction (TPJ), precuneus, and middle frontal gyrus during social trials compared with nonsocial trials. Activity in the precuneus was, in turn, negatively related to sense of agency at a single-trial level. We further found a double dissociation between TPJ and angular gyrus (AG): activity in the left AG was not sensitive to social context but was negatively related to sense of agency. In contrast, activity in the TPJ was modulated by social context but was not sensitive to sense of agency. PMID:29527568

Genetic optimization of neural network architecture

International Nuclear Information System (INIS)

Harp, S.A.; Samad, T.

1994-03-01

Neural networks are now a popular technology for a broad variety of application domains, including the electric utility industry. Yet, as the technology continues to gain increasing acceptance, it is also increasingly apparent that the power that neural networks provide is not an unconditional blessing. Considerable care must be exercised during application development if the full benefit of the technology is to be realized. At present, no fully general theory or methodology for neural network design is available, and application development is a trial-and-error process that is time-consuming and expertise-intensive. Each application demands appropriate selections of the network input space, the network structure, and values of learning algorithm parameters-design choices that are closely coupled in ways that largely remain a mystery. This EPRI-funded exploratory research project was initiated to take the key next step in this research program: the validation of the approach on a realistic problem. We focused on the problem of modeling the thermal performance of the TVA Sequoyah nuclear power plant (units 1 and 2)

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

Science.gov (United States)

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

Internal representation of task rules by recurrent dynamics: the importance of the diversity of neural responses

Directory of Open Access Journals (Sweden)

Mattia Rigotti

2010-10-01

Full Text Available Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics, the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding. A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation.

Dysfunction of Rapid Neural Adaptation in Dyslexia.

Science.gov (United States)

Perrachione, Tyler K; Del Tufo, Stephanie N; Winter, Rebecca; Murtagh, Jack; Cyr, Abigail; Chang, Patricia; Halverson, Kelly; Ghosh, Satrajit S; Christodoulou, Joanna A; Gabrieli, John D E

2016-12-21

Identification of specific neurophysiological dysfunctions resulting in selective reading difficulty (dyslexia) has remained elusive. In addition to impaired reading development, individuals with dyslexia frequently exhibit behavioral deficits in perceptual adaptation. Here, we assessed neurophysiological adaptation to stimulus repetition in adults and children with dyslexia for a wide variety of stimuli, spoken words, written words, visual objects, and faces. For every stimulus type, individuals with dyslexia exhibited significantly diminished neural adaptation compared to controls in stimulus-specific cortical areas. Better reading skills in adults and children with dyslexia were associated with greater repetition-induced neural adaptation. These results highlight a dysfunction of rapid neural adaptation as a core neurophysiological difference in dyslexia that may underlie impaired reading development. Reduced neurophysiological adaptation may relate to prior reports of reduced behavioral adaptation in dyslexia and may reveal a difference in brain functions that ultimately results in a specific reading impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

Reconstruction of neutron spectra through neural networks

International Nuclear Information System (INIS)

Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.

2003-01-01

A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

A Neural Model of Visually Guided Steering, Obstacle Avoidance, and Route Selection

Science.gov (United States)

Elder, David M.; Grossberg, Stephen; Mingolla, Ennio

2009-01-01

A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3-dimensional virtual reality environment to determine the position of objects on the basis of motion discontinuities and computes heading direction,…

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.

The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder

Directory of Open Access Journals (Sweden)

Belinda J. Liddell

2016-06-01

Full Text Available A significant body of literature documents the neural mechanisms involved in the development and maintenance of posttraumatic stress disorder (PTSD. However, there is very little empirical work considering the influence of culture on these underlying mechanisms. Accumulating cultural neuroscience research clearly indicates that cultural differences in self-representation modulate many of the same neural processes proposed to be aberrant in PTSD. The objective of this review paper is to consider how culture may impact on the neural mechanisms underlying PTSD. We first outline five key affective and cognitive functions and their underlying neural correlates that have been identified as being disrupted in PTSD: (1 fear dysregulation; (2 attentional biases to threat; (3 emotion and autobiographical memory; (4 self-referential processing; and (5 attachment and interpersonal processing. Second, we consider prominent cultural theories and review the empirical research that has demonstrated the influence of cultural variations in self-representation on the neural substrates of these same five affective and cognitive functions. Finally, we propose a conceptual model that suggests that these five processes have major relevance to considering how culture may influence the neural processes underpinning PTSD. Highlights of the article:

The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder.

Science.gov (United States)

Liddell, Belinda J; Jobson, Laura

2016-01-01

A significant body of literature documents the neural mechanisms involved in the development and maintenance of posttraumatic stress disorder (PTSD). However, there is very little empirical work considering the influence of culture on these underlying mechanisms. Accumulating cultural neuroscience research clearly indicates that cultural differences in self-representation modulate many of the same neural processes proposed to be aberrant in PTSD. The objective of this review paper is to consider how culture may impact on the neural mechanisms underlying PTSD. We first outline five key affective and cognitive functions and their underlying neural correlates that have been identified as being disrupted in PTSD: (1) fear dysregulation; (2) attentional biases to threat; (3) emotion and autobiographical memory; (4) self-referential processing; and (5) attachment and interpersonal processing. Second, we consider prominent cultural theories and review the empirical research that has demonstrated the influence of cultural variations in self-representation on the neural substrates of these same five affective and cognitive functions. Finally, we propose a conceptual model that suggests that these five processes have major relevance to considering how culture may influence the neural processes underpinning PTSD.

Research on the Direct Carbon Emission Forecast of CHINA'S Provincial Residents Based on Neural Network

Science.gov (United States)

Zhang, T.; Zhou, B.; Zhou, S.; Yan, W.

2018-04-01

Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It's particularly important to make accurate predictions of carbon emissions based on current carbon emissions. This paper accounted out the direct consumption of carbon emissions data from 1995 to 2014 about 30 provinces (the data of Tibet, Hong Kong, Macao and Taiwan is missing) and the whole of China. And it selected the optimal models from BP, RBF and Elman neural network for direct carbon emission prediction, what aim was to select the optimal prediction method and explore the possibility of reaching the peak of residents direct carbon emissions of China in 2030. Research shows that: 1) Residents' direct carbon emissions per capita of all provinces showed an upward trend in 20 years. 2) The accuracy of the prediction results by Elman neural network model is higher than others and more suitable for carbon emission data projections. 3) With the situation of residents' direct carbon emissions free development, the direct carbon emissions will show a fast to slow upward trend in the next few years and began to flatten after 2020, and the direct carbon emissions of per capita will reach the peak in 2032. This is also confirmed that China is expected to reach its peak in carbon emissions by 2030 in theory.

Temporal dynamics and neural architecture of action selection

OpenAIRE

Buc Calderon, Cristian

2016-01-01

In this thesis we pitted two views of action selection. On the one hand, a traditional view suggesting that action selection emerges from a sequential process whereby perception, cognition and action proceed serially and are subtended by distinct brain areas. On the other hand, an ecological view (formalized in the affordance competition hypothesis) advocating that action selection stems from the parallel implementation of potential action plans. In parallel, the competition between these act...

Neural ECM in addiction, schizophrenia, and mood disorder

NARCIS (Netherlands)

Lubbers, B.R.; Smit, A.B.; Spijker, S.; van den Oever, M.C.

2014-01-01

The extracellular matrix (ECM) has a prominent role in brain development, maturation of neural circuits, and adult neuroplasticity. This multifactorial role of the ECM suggests that processes that affect composition or turnover of ECM in the brain could lead to altered brain function, possibly

Applying neural networks as software sensors for enzyme engineering.

Science.gov (United States)

Linko, S; Zhu, Y H; Linko, P

1999-04-01

The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.

Neural modeling of prefrontal executive function

Energy Technology Data Exchange (ETDEWEB)

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

1996-12-31

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

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

International Nuclear Information System (INIS)

Ortiz R, J. M.; Martinez B, M. R.; Vega C, H. R.; Gallego D, E.; Lorente F, A.; Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E.

2011-01-01

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

Acoustic stimulation can induce a selective neural network response mediated by piezoelectric nanoparticles

Science.gov (United States)

Rojas, Camilo; Tedesco, Mariateresa; Massobrio, Paolo; Marino, Attilio; Ciofani, Gianni; Martinoia, Sergio; Raiteri, Roberto

2018-06-01

Objective. We aim to develop a novel non-invasive or minimally invasive method for neural stimulation to be applied in the study and treatment of brain (dys)functions and neurological disorders. Approach. We investigate the electrophysiological response of in vitro neuronal networks when subjected to low-intensity pulsed acoustic stimulation, mediated by piezoelectric nanoparticles adsorbed on the neuronal membrane. Main results. We show that the presence of piezoelectric barium titanate nanoparticles induces, in a reproducible way, an increase in network activity when excited by stationary ultrasound waves in the MHz regime. Such a response can be fully recovered when switching the ultrasound pulse off, depending on the generated pressure field amplitude, whilst it is insensitive to the duration of the ultrasound pulse in the range 0.5 s–1.5 s. We demonstrate that the presence of piezoelectric nanoparticles is necessary, and when applying the same acoustic stimulation to neuronal cultures without nanoparticles or with non-piezoelectric nanoparticles with the same size distribution, no network response is observed. Significance. We believe that our results open up an extremely interesting approach when coupled with suitable functionalization strategies of the nanoparticles in order to address specific neurons and/or brain areas and applied in vivo, thus enabling remote, non-invasive, and highly selective modulation of the activity of neuronal subpopulations of the central nervous system of mammalians.

Social brains in context: lesions targeted to the song control system in female cowbirds affect their social network.

Science.gov (United States)

Maguire, Sarah E; Schmidt, Marc F; White, David J

2013-01-01

Social experiences can organize physiological, neural, and reproductive function, but there are few experimental preparations that allow one to study the effect individuals have in structuring their social environment. We examined the connections between mechanisms underlying individual behavior and social dynamics in flocks of brown-headed cowbirds (Molothrus ater). We conducted targeted inactivations of the neural song control system in female subjects. Playback tests revealed that the lesions affected females' song preferences: lesioned females were no longer selective for high quality conspecific song. Instead, they reacted to all cowbird songs vigorously. When lesioned females were introduced into mixed-sex captive flocks, they were less likely to form strong pair-bonds, and they no longer showed preferences for dominant males. This in turn created a cascade of effects through the groups. Social network analyses showed that the introduction of the lesioned females created instabilities in the social structure: males in the groups changed their dominance status and their courtship patterns, and even the competitive behavior of other female group-mates was affected. These results reveal that inactivation of the song control system in female cowbirds not only affects individual behavior, but also exerts widespread effects on the stability of the entire social system.

Neural network based electron identification in the ZEUS calorimeter

International Nuclear Information System (INIS)

Abramowicz, H.; Caldwell, A.; Sinkus, R.

1995-01-01

We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

Directory of Open Access Journals (Sweden)

Christopher L Buckley

2018-01-01

Full Text Available During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

Science.gov (United States)

Buckley, Christopher L; Toyoizumi, Taro

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence

Effects of Chronic Low-Dose Radiation on Human Neural Progenitor Cells

Science.gov (United States)

Katsura, Mari; Cyou-Nakamine, Hiromasa; Zen, Qin; Zen, Yang; Nansai, Hiroko; Amagasa, Shota; Kanki, Yasuharu; Inoue, Tsuyoshi; Kaneki, Kiyomi; Taguchi, Akashi; Kobayashi, Mika; Kaji, Toshiyuki; Kodama, Tatsuhiko; Miyagawa, Kiyoshi; Wada, Youichiro; Akimitsu, Nobuyoshi; Sone, Hideko

2016-01-01

The effects of chronic low-dose radiation on human health have not been well established. Recent studies have revealed that neural progenitor cells are present not only in the fetal brain but also in the adult brain. Since immature cells are generally more radiosensitive, here we investigated the effects of chronic low-dose radiation on cultured human neural progenitor cells (hNPCs) derived from embryonic stem cells. Radiation at low doses of 31, 124 and 496 mGy per 72 h was administered to hNPCs. The effects were estimated by gene expression profiling with microarray analysis as well as morphological analysis. Gene expression was dose-dependently changed by radiation. By thirty-one mGy of radiation, inflammatory pathways involving interferon signaling and cell junctions were altered. DNA repair and cell adhesion molecules were affected by 124 mGy of radiation while DNA synthesis, apoptosis, metabolism, and neural differentiation were all affected by 496 mGy of radiation. These in vitro results suggest that 496 mGy radiation affects the development of neuronal progenitor cells while altered gene expression was observed at a radiation dose lower than 100 mGy. This study would contribute to the elucidation of the clinical and subclinical phenotypes of impaired neuronal development induced by chronic low-dose radiation.

Variation in the serotonin transporter gene modulates selective attention to threat.

Science.gov (United States)

Osinsky, Roman; Reuter, Martin; Küpper, Yvonne; Schmitz, Anja; Kozyra, Eva; Alexander, Nina; Hennig, Jürgen

2008-08-01

The 5-HTTLPR is an insertion/deletion polymorphism in the promoter region of the serotonin transporter gene. Prior research has revealed associations between the short-allele variant of this polymorphism, enhanced self-reported negative emotionality, and hypersensitivity of fear relevant neural circuits. In a sample of 50 healthy women we examined the role of 5-HTTLPR for cognitive-affective processing of phylogenetical fear-relevant stimuli (spiders) in a dot probe task. In contrast to homozygote long-allele carriers (ll), participants carrying at least 1 short allele (ss and sl) selectively shifted attention toward pictures of spiders, when these were presented for a duration of 2,000 ms. These results argue for an involvement of 5-HTTLPR in cognitive processing of threatening stimuli and thus, underpin its general role for individual differences in negative affect.

Separating true V0's from combinatoric background with a neural network

International Nuclear Information System (INIS)

Justice, M.

1997-01-01

A feedforward multilayered neural network has been trained to ''recognize'' true V0's in the presence of a large combinatoric background using simulated data for 2 GeV/nucleon Ni + Cu interactions. The resulting neural network filter has been applied to actual data from the EOS TPC experiment. An enhancement of signal to background over more traditional selection mechanisms has been observed. (orig.)

Conflicting selective forces affect T cell receptor contacts in an immunodominant human immunodeficiency virus epitope

DEFF Research Database (Denmark)

Iversen, Astrid K N; Stewart-Jones, Guillaume; Learn, Gerald H

2006-01-01

two principal, diametrically opposed evolutionary pathways that exclusively affect T cell-receptor contact residues. One pathway was characterized by acquisition of CTL escape mutations and the other by selection for wild-type amino acids. The pattern of CTL responses to epitope variants shaped which...

Consecutive Acupuncture Stimulations Lead to Significantly Decreased Neural Responses

NARCIS (Netherlands)

Yeo, S.; Choe, I.H.; Noort, M.W.M.L. van den; Bosch, M.P.C.; Lim, S.

2010-01-01

Objective: Functional magnetic resonance imaging (fMRI), in combination with block design paradigms with consecutive acupuncture stimulations, has often been used to investigate the neural responses to acupuncture. In this study, we investigated whether previous acupuncture stimulations can affect

The Neural Basis of Aversive Pavlovian Guidance during Planning.

Science.gov (United States)

Lally, Níall; Huys, Quentin J M; Eshel, Neir; Faulkner, Paul; Dayan, Peter; Roiser, Jonathan P

2017-10-18

Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences

The Neural Basis of Aversive Pavlovian Guidance during Planning

Science.gov (United States)

Faulkner, Paul

2017-01-01

Important real-world decisions are often arduous as they frequently involve sequences of choices, with initial selections affecting future options. Evaluating every possible combination of choices is computationally intractable, particularly for longer multistep decisions. Therefore, humans frequently use heuristics to reduce the complexity of decisions. We recently used a goal-directed planning task to demonstrate the profound behavioral influence and ubiquity of one such shortcut, namely aversive pruning, a reflexive Pavlovian process that involves neglecting parts of the decision space residing beyond salient negative outcomes. However, how the brain implements this important decision heuristic and what underlies individual differences have hitherto remained unanswered. Therefore, we administered an adapted version of the same planning task to healthy male and female volunteers undergoing functional magnetic resonance imaging (fMRI) to determine the neural basis of aversive pruning. Through both computational and standard categorical fMRI analyses, we show that when planning was influenced by aversive pruning, the subgenual cingulate cortex was robustly recruited. This neural signature was distinct from those associated with general planning and valuation, two fundamental cognitive components elicited by our task but which are complementary to aversive pruning. Furthermore, we found that individual variation in levels of aversive pruning was associated with the responses of insula and dorsolateral prefrontal cortices to the receipt of large monetary losses, and also with subclinical levels of anxiety. In summary, our data reveal the neural signatures of an important reflexive Pavlovian process that shapes goal-directed evaluations and thereby determines the outcome of high-level sequential cognitive processes. SIGNIFICANCE STATEMENT Multistep decisions are complex because initial choices constrain future options. Evaluating every path for long decision sequences

The consequences of neural degeneration regarding optimal cochlear implant position in scala tympani: a model approach.

Science.gov (United States)

Briaire, Jeroen J; Frijns, Johan H M

2006-04-01

Cochlear implant research endeavors to optimize the spatial selectivity, threshold and dynamic range with the objective of improving the speech perception performance of the implant user. One of the ways to achieve some of these goals is by electrode design. New cochlear implant electrode designs strive to bring the electrode contacts into close proximity to the nerve fibers in the modiolus: this is done by placing the contacts on the medial side of the array and positioning the implant against the medial wall of scala tympani. The question remains whether this is the optimal position for a cochlea with intact neural fibers and, if so, whether it is also true for a cochlea with degenerated neural fibers. In this study a computational model of the implanted human cochlea is used to investigate the optimal position of the array with respect to threshold, dynamic range and spatial selectivity for a cochlea with intact nerve fibers and for degenerated nerve fibers. In addition, the model is used to evaluate the predictive value of eCAP measurements for obtaining peri-operative information on the neural status. The model predicts improved threshold, dynamic range and spatial selectivity for the peri-modiolar position at the basal end of the cochlea, with minimal influence of neural degeneration. At the apical end of the array (1.5 cochlear turns), the dynamic range and the spatial selectivity are limited due to the occurrence of cross-turn stimulation, with the exception of the condition without neural degeneration and with the electrode array along the lateral wall of scala tympani. The eCAP simulations indicate that a large P(0) peak occurs before the N(1)P(1) complex when the fibers are not degenerated. The absence of this peak might be used as an indicator for neural degeneration.

Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

DEFF Research Database (Denmark)

Martínez, Héctor Pérez

strategies (error functions and training algorithms) for artificial neural networks are examined across synthetic and psycho-physiological datasets, and compared against support vector machines and Cohen’s method. Results reveal the best training strategies for neural networks and suggest their superiority...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...

Motivational Factors Affecting Athletes in Selecting the Sport Branches of Athletics, Ski and Tennis

Science.gov (United States)

Akyüz, Murat; Agar, Muharrem; Akyüz, Öznur; Dogru, Yeliz

2016-01-01

The aim of this study is to research the motivational factors affecting athletes to select the branches of athletics, ski and tennis. Within the scope of the research, the survey developed by H. Sunay in 1996 was implemented and solution for the problem of the research was searched through the findings that were obtained from the survey. SPSS…

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

Science.gov (United States)

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

2017-01-01

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

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

Directory of Open Access Journals (Sweden)

Xinyu Guo

2017-08-01

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

A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

Directory of Open Access Journals (Sweden)

Ashfaq Ahmad

2015-12-01

Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .

Impact parameter determination for 40Ca + 40Ca reactions using a neural network

International Nuclear Information System (INIS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1995-01-01

A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs

Back propagation artificial neural network for community Alzheimer's disease screening in China.

Science.gov (United States)

Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

2013-01-25

Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.

Back propagation artificial neural network for community Alzheimer's disease screening in China★

Science.gov (United States)

Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

2013-01-01

Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598

The sleep and circadian modulation of neural reward pathways: a protocol for a pair of systematic reviews.

Science.gov (United States)

Byrne, Jamie E M; Murray, Greg

2017-12-02

Animal research suggests that neural reward activation may be systematically modulated by sleep and circadian function. Whether humans also exhibit sleep and circadian modulation of neural reward pathways is unclear. This area is in need of further research, as it has implications for the involvement of sleep and circadian function in reward-related disorders. The aim of this paper is to describe the protocol for a pair of systematic literature reviews to synthesise existing literature related to (1) sleep and (2) circadian modulation of neural reward pathways in healthy human populations. A systematic review of relevant online databases (Scopus, PubMed, Web of Science, ProQuest, PsycINFO and EBSCOhost) will be conducted. Reference lists, relevant reviews and supplementary data will be searched for additional articles. Articles will be included if (a) they contain a sleep- or circadian-related predictor variable with a neural reward outcome variable, (b) use a functional magnetic resonance imaging protocol and (c) use human samples. Articles will be excluded if study participants had disorders known to affect the reward system. The articles will be screened by two independent authors. Two authors will complete the data extraction form, with two authors independently completing the quality assessment tool for the selected articles, with a consensus reached with a third author if needed. Narrative synthesis methods will be used to analyse the data. The findings from this pair of systematic literature reviews will assist in the identification of the pathways involved in the sleep and circadian function modulation of neural reward in healthy individuals, with implications for disorders characterised by dysregulation in sleep, circadian rhythms and reward function. PROSPERO CRD42017064994.

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Efficient Cancer Detection Using Multiple Neural Networks.

Science.gov (United States)

Shell, John; Gregory, William D

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

Densely ionizing radiation affects DNA methylation of selective LINE-1 elements

Energy Technology Data Exchange (ETDEWEB)

Prior, Sara; Miousse, Isabelle R. [Department of Environmental and Occupational Health, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Nzabarushimana, Etienne [Department of Environmental and Occupational Health, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Department of Bioinformatics, School of Informatics and Computing, Indiana University, Bloomington, IN 47405 (United States); Pathak, Rupak [Division of Radiation Health, Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Skinner, Charles; Kutanzi, Kristy R. [Department of Environmental and Occupational Health, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Allen, Antiño R. [Division of Radiation Health, Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Raber, Jacob [Departments of Behavioral Neuroscience, Neurology, and Radiation Medicine, Division of Neuroscience, ONPRC, Oregon Health & Science University, Portland, OR 97239 (United States); Tackett, Alan J. [Department of Biochemistry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Hauer-Jensen, Martin [Division of Radiation Health, Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Nelson, Gregory A. [Department of Basic Sciences, Division of Radiation Research, Loma Linda University, Loma Linda, CA 92350 (United States); and others

2016-10-15

Long Interspersed Nucleotide Element 1 (LINE-1) retrotransposons are heavily methylated and are the most abundant transposable elements in mammalian genomes. Here, we investigated the differential DNA methylation within the LINE-1 under normal conditions and in response to environmentally relevant doses of sparsely and densely ionizing radiation. We demonstrate that DNA methylation of LINE-1 elements in the lungs of C57BL6 mice is dependent on their evolutionary age, where the elder age of the element is associated with the lower extent of DNA methylation. Exposure to 5-aza-2′-deoxycytidine and methionine-deficient diet affected DNA methylation of selective LINE-1 elements in an age- and promoter type-dependent manner. Exposure to densely IR, but not sparsely IR, resulted in DNA hypermethylation of older LINE-1 elements, while the DNA methylation of evolutionary younger elements remained mostly unchanged. We also demonstrate that exposure to densely IR increased mRNA and protein levels of LINE-1 via the loss of the histone H3K9 dimethylation and an increase in the H3K4 trimethylation at the LINE-1 5′-untranslated region, independently of DNA methylation. Our findings suggest that DNA methylation is important for regulation of LINE-1 expression under normal conditions, but histone modifications may dictate the transcriptional activity of LINE-1 in response to exposure to densely IR. - Highlights: • DNA methylation of LINE-1 elements is dependent on their evolutionary age. • Densely ionizing radiation affects DNA methylation of selective LINE-1 elements. • Radiation-induced reactivation of LINE-1 is DNA methylation-independent. • Histone modifications dictate the transcriptional activity of LINE-1.

Densely ionizing radiation affects DNA methylation of selective LINE-1 elements

International Nuclear Information System (INIS)

Prior, Sara; Miousse, Isabelle R.; Nzabarushimana, Etienne; Pathak, Rupak; Skinner, Charles; Kutanzi, Kristy R.; Allen, Antiño R.; Raber, Jacob; Tackett, Alan J.; Hauer-Jensen, Martin; Nelson, Gregory A.

2016-01-01

Long Interspersed Nucleotide Element 1 (LINE-1) retrotransposons are heavily methylated and are the most abundant transposable elements in mammalian genomes. Here, we investigated the differential DNA methylation within the LINE-1 under normal conditions and in response to environmentally relevant doses of sparsely and densely ionizing radiation. We demonstrate that DNA methylation of LINE-1 elements in the lungs of C57BL6 mice is dependent on their evolutionary age, where the elder age of the element is associated with the lower extent of DNA methylation. Exposure to 5-aza-2′-deoxycytidine and methionine-deficient diet affected DNA methylation of selective LINE-1 elements in an age- and promoter type-dependent manner. Exposure to densely IR, but not sparsely IR, resulted in DNA hypermethylation of older LINE-1 elements, while the DNA methylation of evolutionary younger elements remained mostly unchanged. We also demonstrate that exposure to densely IR increased mRNA and protein levels of LINE-1 via the loss of the histone H3K9 dimethylation and an increase in the H3K4 trimethylation at the LINE-1 5′-untranslated region, independently of DNA methylation. Our findings suggest that DNA methylation is important for regulation of LINE-1 expression under normal conditions, but histone modifications may dictate the transcriptional activity of LINE-1 in response to exposure to densely IR. - Highlights: • DNA methylation of LINE-1 elements is dependent on their evolutionary age. • Densely ionizing radiation affects DNA methylation of selective LINE-1 elements. • Radiation-induced reactivation of LINE-1 is DNA methylation-independent. • Histone modifications dictate the transcriptional activity of LINE-1.

Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

Directory of Open Access Journals (Sweden)

Ryszard Hejmanowski

2015-01-01

Full Text Available Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP, which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased. Similar conclusions were drawn for the network with a small number of hidden neurons. The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value.

Data acquisition in modeling using neural networks and decision trees

Directory of Open Access Journals (Sweden)

R. Sika

2011-04-01

Full Text Available The paper presents a comparison of selected models from area of artificial neural networks and decision trees in relation with actualconditions of foundry processes. The work contains short descriptions of used algorithms, their destination and method of data preparation,which is a domain of work of Data Mining systems. First part concerns data acquisition realized in selected iron foundry, indicating problems to solve in aspect of casting process modeling. Second part is a comparison of selected algorithms: a decision tree and artificial neural network, that is CART (Classification And Regression Trees and BP (Backpropagation in MLP (Multilayer Perceptron networks algorithms.Aim of the paper is to show an aspect of selecting data for modeling, cleaning it and reducing, for example due to too strong correlationbetween some of recorded process parameters. Also, it has been shown what results can be obtained using two different approaches:first when modeling using available commercial software, for example Statistica, second when modeling step by step using Excel spreadsheetbasing on the same algorithm, like BP-MLP. Discrepancy of results obtained from these two approaches originates from a priorimade assumptions. Mentioned earlier Statistica universal software package, when used without awareness of relations of technologicalparameters, i.e. without user having experience in foundry and without scheduling ranks of particular parameters basing on acquisition, can not give credible basis to predict the quality of the castings. Also, a decisive influence of data acquisition method has been clearly indicated, the acquisition should be conducted according to repetitive measurement and control procedures. This paper is based on about 250 records of actual data, for one assortment for 6 month period, where only 12 data sets were complete (including two that were used for validation of neural network and useful for creating a model. It is definitely too

Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

Science.gov (United States)

Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

2013-10-02

Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

Science.gov (United States)

Harrington, Peter DEB.; Zheng, Peng

1995-01-01

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Experience affects the outcome of agonistic contests without affecting the selective advantage of size.

Science.gov (United States)

Kasumovic, Michael M; Elias, Damian O; Punzalan, David; Mason, Andrew C; Andrade, Maydianne C B

2009-06-01

In the field, phenotypic determinants of competitive success are not always absolute. For example, contest experience may alter future competitive performance. As future contests are not determined solely on phenotypic attributes, prior experience could also potentially alter phenotype-fitness associations. In this study, we examined the influence of single and multiple experiences on contest outcomes in the jumping spider Phidippus clarus. We also examined whether phenotype-fitness associations altered as individuals gained more experience. Using both size-matched contests and a tournament design, we found that both winning and losing experience affected future contest success; males with prior winning experience were more likely to win subsequent contests. Although experience was a significant determinant of success in future contests, male weight was approximately 1.3 times more important than experience in predicting contest outcomes. Despite the importance of experience in determining contest outcomes, patterns of selection did not change between rounds. Overall, our results show that experience can be an important determinant in contest outcomes, even in short-lived invertebrates, and that experience alone is unlikely to alter phenotype-fitness associations.

Analysis of the heat affected zone in CO2 laser cutting of stainless steel

Directory of Open Access Journals (Sweden)

Madić Miloš J.

2012-01-01

Full Text Available This paper presents an investigation into the effect of the laser cutting parameters on the heat affected zone in CO2 laser cutting of AISI 304 stainless steel. The mathematical model for the heat affected zone was expressed as a function of the laser cutting parameters such as the laser power, cutting speed, assist gas pressure and focus position using the artificial neural network. To obtain experimental database for the artificial neural network training, laser cutting experiment was planned as per Taguchi’s L27 orthogonal array with three levels for each of the cutting parameter. Using the 27 experimental data sets, the artificial neural network was trained with gradient descent with momentum algorithm and the average absolute percentage error was 2.33%. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 6.46%. Statistically assessed as adequate, the artificial neural network model was then used to investigate the effect of the laser cutting parameters on the heat affected zone. To analyze the main and interaction effect of the laser cutting parameters on the heat affected zone, 2-D and 3-D plots were generated. The analysis revealed that the cutting speed had maximum influence on the heat affected zone followed by the laser power, focus position and assist gas pressure. Finally, using the Monte Carlo method the optimal laser cutting parameter values that minimize the heat affected zone were identified.

Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

Directory of Open Access Journals (Sweden)

Ding Fang

2013-06-01

Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

Hepatocyte growth factor/scatter factor-MET signaling in neural crest-derived melanocyte development.

Science.gov (United States)

Kos, L; Aronzon, A; Takayama, H; Maina, F; Ponzetto, C; Merlino, G; Pavan, W

1999-02-01

The mechanisms governing development of neural crest-derived melanocytes, and how alterations in these pathways lead to hypopigmentation disorders, are not completely understood. Hepatocyte growth factor/scatter factor (HGF/SF) signaling through the tyrosine-kinase receptor, MET, is capable of promoting the proliferation, increasing the motility, and maintaining high tyrosinase activity and melanin synthesis of melanocytes in vitro. In addition, transgenic mice that ubiquitously overexpress HGF/SF demonstrate hyperpigmentation in the skin and leptomenigenes and develop melanomas. To investigate whether HGF/ SF-MET signaling is involved in the development of neural crest-derived melanocytes, transgenic embryos, ubiquitously overexpressing HGF/SF, were analyzed. In HGF/SF transgenic embryos, the distribution of melanoblasts along the characteristic migratory pathway was not affected. However, additional ectopically localized melanoblasts were also observed in the dorsal root ganglia and neural tube, as early as 11.5 days post coitus (p.c.). We utilized an in vitro neural crest culture assay to further explore the role of HGF/SF-MET signaling in neural crest development. HGF/SF added to neural crest cultures increased melanoblast number, permitted differentiation into pigmented melanocytes, promoted melanoblast survival, and could replace mast-cell growth factor/Steel factor (MGF) in explant cultures. To examine whether HGF/SF-MET signaling is required for the proper development of melanocytes, embryos with a targeted Met null mutation (Met-/-) were analysed. In Met-/- embryos, melanoblast number and location were not overtly affected up to 14 days p.c. These results demonstrate that HGF/SF-MET signaling influences, but is not required for, the initial development of neural crest-derived melanocytes in vivo and in vitro.

Neural bases of orthographic long-term memory and working memory in dysgraphia.

Science.gov (United States)

Rapp, Brenda; Purcell, Jeremy; Hillis, Argye E; Capasso, Rita; Miceli, Gabriele

2016-02-01

Spelling a word involves the retrieval of information about the word's letters and their order from long-term memory as well as the maintenance and processing of this information by working memory in preparation for serial production by the motor system. While it is known that brain lesions may selectively affect orthographic long-term memory and working memory processes, relatively little is known about the neurotopographic distribution of the substrates that support these cognitive processes, or the lesions that give rise to the distinct forms of dysgraphia that affect these cognitive processes. To examine these issues, this study uses a voxel-based mapping approach to analyse the lesion distribution of 27 individuals with dysgraphia subsequent to stroke, who were identified on the basis of their behavioural profiles alone, as suffering from deficits only affecting either orthographic long-term or working memory, as well as six other individuals with deficits affecting both sets of processes. The findings provide, for the first time, clear evidence of substrates that selectively support orthographic long-term and working memory processes, with orthographic long-term memory deficits centred in either the left posterior inferior frontal region or left ventral temporal cortex, and orthographic working memory deficits primarily arising from lesions of the left parietal cortex centred on the intraparietal sulcus. These findings also contribute to our understanding of the relationship between the neural instantiation of written language processes and spoken language, working memory and other cognitive skills. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Access to health care for children with neural tube defects: Experiences of mothers in Zambia

Directory of Open Access Journals (Sweden)

Micah M. Simpamba

2016-12-01

Full Text Available Introduction: In Zambia, all children born with neural tube defects requiring surgery need to be referred to a tertiary level hospital in Lusaka, the capital city, where the specialists are based. The aim of this study was to explore the experiences of mothers accessing health care who had recently given birth to a child with a neural tube defect. Methods and analysis: In-depth interviews were conducted with a purposively selected sample of 20 mothers at the tertiary level hospital. The interviews were audiotaped, transcribed verbatim and translated. Content analysis was used to identify codes, which were later collapsed into categories and themes. Findings: Five themes emerged: access to health care, access to transport, access to information, concerns about family and support needs. Discussion: Barriers to access to health care included geographical barriers and barriers linked to availability. Geographical barriers were related to distance between home and the health centre, and referral between health facilities. Barriers to availability included the lack of specialist health workers at various levels, and insufficient hospital vehicles to transport mothers and children to the tertiary level hospital. The main barrier to affordability was the cost of transport, which was alleviated by either family or government support. Acceptability of the health services was affected by a lack of information, incorrect advice, the attitude of health workers and the beliefs of the family. Conclusion: Access to health care by mothers of children with neural tube defects in Zambia is affected by geographical accessibility, availability, affordability and acceptability. The supply-side barriers and demand-side barriers require different interventions to address them. This suggests that health policy is needed which ensures access to surgery and follow-up care.

Age-related difference in the effective neural connectivity associated with probabilistic category learning

Energy Technology Data Exchange (ETDEWEB)

Yoon, Eun Jin; Cho, Sang Soo; Kim, Hee Jung; Bang, Seong Ae; Park, Hyun Soo; Kim, Yu Kyeong; Kim, Sang Eun [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of)

2007-07-01

Although it is well known that explicit memory is affected by the deleterious changes in brain with aging, but effect of aging in implicit memory such as probabilistic category learning (PCL) is not clear. To identify the effect of aging on the neural interaction for successful PCL, we investigated the neural substrates of PCL and the age-related changes of the neural network between these brain regions. 23 young (age, 252 y; 11 males) and 14 elderly (673 y; 7 males) healthy subjects underwent FDG PET during a resting state and 150-trial weather prediction (WP) task. Correlations between the WP hit rates and regional glucose metabolism were assessed using SPM2 (P<0.05 uncorrected). For path analysis, seven brain regions (bilateral middle frontal gyri and putamen, left fusiform gyrus, anterior cingulate and right parahippocampal gyri) were selected based on the results of the correlation analysis. Model construction and path analysis processing were done by AMOS 5.0. The elderly had significantly lower total hit rates than the young (P<0.005). In the correlation analysis, both groups showed similar metabolic correlation in frontal and striatal area. But correlation in the medial temporal lobe (MTL) was found differently by group. In path analysis, the functional networks for the constructed model was accepted (X(2) =0.80, P=0.67) and it proved to be significantly different between groups (X{sub diff}(37) = 142.47, P<0.005), Systematic comparisons of each path revealed that frontal crosscallosal and the frontal to parahippocampal connection were most responsible for the model differences (P<0.05). For the successful PCL, the elderly recruits the basal ganglia implicit memory system but MTL recruitment differs from the young. The inadequate MTL correlation pattern in the elderly is may be caused by the changes of the neural pathway related with explicit memory. These neural changes can explain the decreased performance of PCL in elderly subjects.

Selection on alleles affecting human longevity and late-life disease: the example of apolipoprotein E.

Directory of Open Access Journals (Sweden)

Fotios Drenos

2010-04-01

Full Text Available It is often claimed that genes affecting health in old age, such as cardiovascular and Alzheimer diseases, are beyond the reach of natural selection. We show in a simulation study based on known genetic (apolipoprotein E and non-genetic risk factors (gender, diet, smoking, alcohol, exercise that, because there is a statistical distribution of ages at which these genes exert their influence on morbidity and mortality, the effects of selection are in fact non-negligible. A gradual increase with each generation of the epsilon2 and epsilon3 alleles of the gene at the expense of the epsilon4 allele was predicted from the model. The epsilon2 allele frequency was found to increase slightly more rapidly than that for epsilon3, although there was no statistically significant difference between the two. Our result may explain the recent evolutionary history of the epsilon 2, 3 and 4 alleles of the apolipoprotein E gene and has wider relevance for genes affecting human longevity.

Losses Disguised as Wins Affect Game Selection on Multiline Slots.

Science.gov (United States)

Graydon, Candice; Stange, Madison; Dixon, Mike J

2018-05-05

Multiline slots are exciting games that contain features which make them alluring. One such feature is a loss disguised as a win (LDW); wherein, players win less than they wager (e.g., bet 2 dollars, win back 50 cents), but this net loss is disguised by flashing graphics and winning sounds. Research to date concludes that LDWs are both rewarding and reinforcing. Here, we investigated whether LDWs affect players' game selection. Thirty-two undergraduate students with experience playing slot machines played 100 spins on four games-two had positive payback percentages (115%) and two had negative payback percentages (85%) after 100 spins. For each payback percentage condition, there was a game with no LDWs and a game with a moderate number of LDWs. For the 100 spins, players could choose to play whichever game they wished. They then rated their preference for each game following the 100-spins and chose a game to continue playing. The majority of players preferred playing the positive payback percentage game with LDWs and chose to continue playing this game over the three other games. We conclude that in addition to LDWs being reinforcing and rewarding, LDWs do in fact influence game selection. We conclude that responsible gambling initiatives should educate players about LDWs.

Effects of selective serotonin reuptake inhibition on neural activity related to risky decisions and monetary rewards in healthy males

DEFF Research Database (Denmark)

Macoveanu, Julian; Fisher, Patrick M; Haahr, Mette E

2014-01-01

the involvement of the normally functioning 5HT-system in decision-making under risk and processing of monetary rewards. The data suggest that prolonged SSRI treatment might reduce emotional engagement by reducing the impact of risk during decision-making or the impact of reward during outcome evaluation.......Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are commonly prescribed antidepressant drugs targeting the dysfunctional serotonin (5-HT) system, yet little is known about the functional effects of prolonged serotonin reuptake inhibition in healthy individuals. Here we used...... functional MRI (fMRI) to investigate how a three-week fluoxetine intervention influences neural activity related to risk taking and reward processing. Employing a double-blinded parallel-group design, 29 healthy young males were randomly assigned to receive 3 weeks of a daily dose of 40 mg fluoxetine...

Selecting for memory? The influence of selective attention on the mnemonic binding of contextual information.

Science.gov (United States)

Uncapher, Melina R; Rugg, Michael D

2009-06-24

Not all of what is experienced is remembered later. Behavioral evidence suggests that the manner in which an event is processed influences which aspects of the event will later be remembered. The present experiment investigated the neural correlates of "selective encoding," or the mechanisms that support the encoding of some elements of an event in preference to others. Event-related MRI data were acquired while volunteers selectively attended to one of two different contextual features of study items (color or location). A surprise memory test for the items and both contextual features was subsequently administered to determine the influence of selective attention on the neural correlates of contextual encoding. Activity in several cortical regions indexed later memory success selectively for color or location information, and this encoding-related activity was enhanced by selective attention to the relevant feature. Critically, a region in the hippocampus responded selectively to attended source information (whether color or location), demonstrating encoding-related activity for attended but not for nonattended source features. Together, the findings suggest that selective attention modulates the magnitude of activity in cortical regions engaged by different aspects of an event, and hippocampal encoding mechanisms seem to be sensitive to this modulation. Thus, the information that is encoded into a memory representation is biased by selective attention, and this bias is mediated by cortical-hippocampal interactions.

Selecting for memory? The influence of selective attention on the mnemonic binding of contextual information

Science.gov (United States)

Uncapher, Melina R.; Rugg, Michael D.

2009-01-01

Not all of what is experienced is remembered later. Behavioral evidence suggests that the manner in which an event is processed influences which aspects of the event will later be remembered. The present experiment investigated the neural correlates of ‘selective encoding’, or the mechanisms that support the encoding of some elements of an event in preference to others. Event-related functional magnetic resonance imaging (fMRI) data were acquired while volunteers selectively attended to one of two different contextual features of study items (color or location). A surprise memory test for the items and both contextual features was subsequently administered to determine the influence of selective attention on the neural correlates of contextual encoding. Activity in several cortical regions indexed later memory success selectively for color or location information, and this encoding-related activity was enhanced by selective attention to the relevant feature. Critically, a region in the hippocampus responded selectively to attended source information (whether color or location), demonstrating encoding-related activity for attended but not for nonattended source features. Together, the findings suggest that selective attention modulates the magnitude of activity in cortical regions engaged by different aspects of an event, and hippocampal encoding mechanisms seem to be sensitive to this modulation. Thus, the information that is encoded into a memory representation is biased by selective attention, and this bias is mediated by cortico-hippocampal interactions. PMID:19553466

Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation

Science.gov (United States)

Salavatian, Siamak; Beaumont, Eric; Longpré, Jean-Philippe; Armour, J. Andrew; Vinet, Alain; Jacquemet, Vincent; Shivkumar, Kalyanam

2016-01-01

Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines (n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal (n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P < 0.001). Convergent LCNs were preferentially activated by MNS. Preemptive RCV reduced MNS-induced changes in LCN activity (by 70%) while mitigating MNS-induced AF (by 75%). Preemptive LCV reduced LCN activity by 60% while mitigating AF potential by 40%. IC neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory. PMID:27591222

Neural changes in extinction recall following prolonged exposure treatment for PTSD: A longitudinal fMRI study

Directory of Open Access Journals (Sweden)

Liat Helpman, PhD

2016-01-01

Conclusions: Prolonged exposure treatment appears to alter neural activation in PTSD patients during recall of fear extinction, and change in extinction recall (measured by SCR is associated with symptom reduction. We discuss results in the context of neural systems involved in response to affective stimuli.

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.

Canonical Wnt signaling transiently stimulates proliferation and enhances neurogenesis in neonatal neural progenitor cultures

International Nuclear Information System (INIS)

Hirsch, Cordula; Campano, Louise M.; Woehrle, Simon; Hecht, Andreas

2007-01-01

Canonical Wnt signaling triggers the formation of heterodimeric transcription factor complexes consisting of β-catenin and T cell factors, and thereby controls the execution of specific genetic programs. During the expansion and neurogenic phases of embryonic neural development canonical Wnt signaling initially controls proliferation of neural progenitor cells, and later neuronal differentiation. Whether Wnt growth factors affect neural progenitor cells postnatally is not known. Therefore, we have analyzed the impact of Wnt signaling on neural progenitors isolated from cerebral cortices of newborn mice. Expression profiling of pathway components revealed that these cells are fully equipped to respond to Wnt signals. However, Wnt pathway activation affected only a subset of neonatal progenitors and elicited a limited increase in proliferation and neuronal differentiation in distinct subsets of cells. Moreover, Wnt pathway activation only transiently stimulated S-phase entry but did not support long-term proliferation of progenitor cultures. The dampened nature of the Wnt response correlates with the predominant expression of inhibitory pathway components and the rapid actuation of negative feedback mechanisms. Interestingly, in differentiating cell cultures activation of canonical Wnt signaling reduced Hes1 and Hes5 expression suggesting that during postnatal neural development, Wnt/β-catenin signaling enhances neurogenesis from progenitor cells by interfering with Notch pathway activity

Binocular Rivalry in a Competitive Neural Network with Synaptic Depression

KAUST Repository

Kilpatrick, Zachary P.; Bressloff, Paul C.

2010-01-01

We study binocular rivalry in a competitive neural network with synaptic depression. In particular, we consider two coupled hypercolums within primary visual cortex (V1), representing orientation selective cells responding to either left or right

Neural network for prediction of superheater fireside corrosion

Energy Technology Data Exchange (ETDEWEB)

Makkonen, P [Foster Wheeler Energia Oy, Karhula R and D Center, Karhula (Finland)

1999-12-31

Superheater corrosion causes vast annual losses to the power companies. If the corrosion could be reliably predicted, new power plants could be designed accordingly, and knowledge of fuel selection and determination of process conditions could be utilized to minimize superheater corrosion. If relations between inputs and the output are poorly known, conventional models depending on corrosion theories will fail. A prediction model based on a neural network is capable of learning from errors and improving its performance as the amount of data increases. The neural network developed during this study predicts superheater corrosion with 80 % accuracy at early stage of the project. (orig.) 10 refs.

Neural network for prediction of superheater fireside corrosion

Energy Technology Data Exchange (ETDEWEB)

Makkonen, P. [Foster Wheeler Energia Oy, Karhula R and D Center, Karhula (Finland)

1998-12-31

Superheater corrosion causes vast annual losses to the power companies. If the corrosion could be reliably predicted, new power plants could be designed accordingly, and knowledge of fuel selection and determination of process conditions could be utilized to minimize superheater corrosion. If relations between inputs and the output are poorly known, conventional models depending on corrosion theories will fail. A prediction model based on a neural network is capable of learning from errors and improving its performance as the amount of data increases. The neural network developed during this study predicts superheater corrosion with 80 % accuracy at early stage of the project. (orig.) 10 refs.

A continuous-time neural model for sequential action.

Science.gov (United States)

Kachergis, George; Wyatte, Dean; O'Reilly, Randall C; de Kleijn, Roy; Hommel, Bernhard

2014-11-05

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task.

Science.gov (United States)

Goto, Nobuhiko; Mushtaq, Faisal; Shee, Dexter; Lim, Xue Li; Mortazavi, Matin; Watabe, Motoki; Schaefer, Alexandre

2017-09-01

We investigated whether well-known neural markers of selective attention to motivationally-relevant stimuli were modulated by variations in subjective preference towards consumer goods in a virtual shopping task. Specifically, participants viewed and rated pictures of various goods on the extent to which they wanted each item, which they could potentially purchase afterwards. Using the event-related potentials (ERP) method, we found that variations in subjective preferences for consumer goods strongly modulated positive slow waves (PSW) from 800 to 3000 milliseconds after stimulus onset. We also found that subjective preferences modulated the N200 and the late positive potential (LPP). In addition, we found that both PSW and LPP were modulated by subsequent buying decisions. Overall, these findings show that well-known brain event-related potentials reflecting selective attention processes can reliably index preferences to consumer goods in a shopping environment. Based on a large body of previous research, we suggest that early ERPs (e.g. the N200) to consumer goods could be indicative of preferences driven by unconditional and automatic processes, whereas later ERPs such as the LPP and the PSW could reflect preferences built upon more elaborative and conscious cognitive processes. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural systems for guilt from actions affecting self versus others

Science.gov (United States)

Morey, Rajendra A.; McCarthy, Gregory; Selgrade, Elizabeth S.; Seth, Srishti; Nasser, Jessica D.; LaBar, Kevin S.

2012-01-01

Guilt is a core emotion governing social behavior by promoting compliance with social norms or self-imposed standards. The goal of this study was to contrast guilty responses to actions that affect self versus others, since actions with social consequences are hypothesized to yield greater guilty feelings due to adopting the perspective and subjective emotional experience of others. Sixteen participants were presented with brief hypothetical scenarios in which the participant’s actions resulted in harmful consequences to self (guilt-self) or to others (guilt-other) during functional MRI. Participants felt more intense guilt for guilt-other than guilt-self and guilt-neutral scenarios. Guilt scenarios revealed distinct regions of activity correlated with intensity of guilt, social consequences of actions, and the interaction of guilt by social consequence. Guilt intensity was associated with activation of the dorsomedial PFC, superior frontal gyrus, supramarginal gyrus, and anterior inferior frontal gyrus. Guilt accompanied by social consequences was associated with greater activation than without social consequences in the ventromedial and dorsomedial PFC, precuneus, posterior cingulate, and posterior superior temporal sulcus. Finally, the interaction analysis highlighted select regions that were more strongly correlated with guilt intensity as a function of social consequence, including the left anterior inferior frontal gyrus, left ventromedial PFC, and left anterior inferior parietal cortex. Our results suggest these regions intensify guilt where harm to others may incur a greater social cost. PMID:22230947

The fidelity of Kepler eclipsing binary parameters inferred by the neural network

Science.gov (United States)

Holanda, N.; da Silva, J. R. P.

2018-04-01

This work aims to test the fidelity and efficiency of obtaining automatic orbital elements of eclipsing binary systems, from light curves using neural network models. We selected a random sample with 78 systems, from over 1400 eclipsing binary detached obtained from the Kepler Eclipsing Binaries Catalog, processed using the neural network approach. The orbital parameters of the sample systems were measured applying the traditional method of light curve adjustment with uncertainties calculated by the bootstrap method, employing the JKTEBOP code. These estimated parameters were compared with those obtained by the neural network approach for the same systems. The results reveal a good agreement between techniques for the sum of the fractional radii and moderate agreement for e cos ω and e sin ω, but orbital inclination is clearly underestimated in neural network tests.

Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

such as the neural network model is not appropriate if the data is generated by a linear mechanism. Hence, it might be appropriate to test the null of linearity prior to building a nonlinear model. We investigate whether this kind of pretesting improves the forecast accuracy compared to the case where...

What are the odds? The neural correlates of active choice during gambling

Directory of Open Access Journals (Sweden)

Bettina eStuder

2012-04-01

Full Text Available Gambling is a widespread recreational activity and requires pitting the values of potential wins and losses against their probability of occurrence. Neuropsychological research showed that betting behavior on laboratory gambling tasks is highly sensitive to focal lesions to the ventromedial prefrontal cortex (vmPFC and insula. In the current study, we assessed the neural basis of betting choices in healthy participants, using functional magnetic resonance imaging of the Roulette Betting Task. In half of the trials participants actively chose their bets; in the other half the computer dictated the bet size. Our results highlight the impact of volitional choice upon the neural substrates of gambling: Neural activity in a distributed network - including key structures of the reward circuitry (midbrain, striatum - was higher during active compared to computer-dictated bet selection. In line with neuropsychological data, the anterior insula and vmPFC were more activated during self-directed bet selection, and responses in these areas were differentially modulated by the odds of winning in the two choice conditions. In addition, responses in the vmPFC and ventral striatum were modulated by the bet size. Convergent with electrophysiological research in macaques, our results further implicate the inferior parietal cortex (IPC in the processing of the likelihood of potential outcomes: Neural responses in the IPC bilaterally reflected the probability of winning during bet selection. Moreover, the IPC was particularly sensitive to the odds of winning in the active choice condition, where this information was used to guide bet selection. Our results indicate a neglected role of the IPC in human decision-making under risk and help to integrate neuropsychological data of risk-taking following vmPFC and insula damage with models of choice derived from human neuroimaging and monkey electrophysiology.

Neural Blockade for Persistent Pain After Breast Cancer Surgery

DEFF Research Database (Denmark)

Wijayasinghe, Nelun; Andersen, Kenneth Geving; Kehlet, Henrik

2014-01-01

involved in neuropathic pain syndromes or to be used as a treatment in its own right. The purpose of this review was to examine the evidence for neural blockade as a potential diagnostic tool or treatment for persistent pain after breast cancer surgery. In this systematic review, we found only 7 studies (n......Persistent pain after breast cancer surgery is predominantly a neuropathic pain syndrome affecting 25% to 60% of patients and related to injury of the intercostobrachial nerve, intercostal nerves, and other nerves in the region. Neural blockade can be useful for the identification of nerves...

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

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

Toward an affective neuroscience account of financial risk taking

Directory of Open Access Journals (Sweden)

Charlene C. Wu

2012-11-01

Full Text Available To explain human financial risk taking, economic and finance theories typically refer to the mathematical properties of financial options, whereas psychological theories have emphasized the influence of emotion and cognition on choice. From a neuroscience perspective, choice emanates from a dynamic multicomponential process. Recent technological advances in neuroimaging have made it possible for researchers to separately visualize perceptual input, intermediate processing, and motor output. An affective neuroscience account of financial risk taking thus might illuminate affective mediators that bridge the gap between statistical input and choice output. To test this hypothesis, we conducted a quantitative meta-analysis (via activation likelihood estimate or ALE of functional magnetic resonance imaging experiments that focused on neural responses to financial options with varying statistical moments (i.e., mean, variance, skewness. Results suggested that different statistical moments elicit both common and distinct patterns of neural activity. Across studies, high versus low mean had the highest probability of increasing ventral striatal activity, but high versus low variance had the highest probability of increasing anterior insula activity. Further, high versus low skewness had the highest probability of increasing ventral striatal activity. Since ventral striatal activity has been associated with positive aroused affect (e.g. excitement, whereas anterior insular activity has been associated with negative aroused affect (e.g. anxiety or general arousal, these findings are consistent with the notion that statistical input influences choice output by eliciting anticipatory affect. The findings also imply that neural activity can be used to predict financial risk taking – both when it conforms to and violates traditional models of choice.

Using c-Jun to identify fear extinction learning-specific patterns of neural activity that are affected by single prolonged stress.

Science.gov (United States)

Knox, Dayan; Stanfield, Briana R; Staib, Jennifer M; David, Nina P; DePietro, Thomas; Chamness, Marisa; Schneider, Elizabeth K; Keller, Samantha M; Lawless, Caroline

2018-04-02

Neural circuits via which stress leads to disruptions in fear extinction is often explored in animal stress models. Using the single prolonged stress (SPS) model of post traumatic stress disorder and the immediate early gene (IEG) c-Fos as a measure of neural activity, we previously identified patterns of neural activity through which SPS disrupts extinction retention. However, none of these stress effects were specific to fear or extinction learning and memory. C-Jun is another IEG that is sometimes regulated in a different manner to c-Fos and could be used to identify emotional learning/memory specific patterns of neural activity that are sensitive to SPS. Animals were either fear conditioned (CS-fear) or presented with CSs only (CS-only) then subjected to extinction training and testing. C-Jun was then assayed within neural substrates critical for extinction memory. Inhibited c-Jun levels in the hippocampus (Hipp) and enhanced functional connectivity between the ventromedial prefrontal cortex (vmPFC) and basolateral amygdala (BLA) during extinction training was disrupted by SPS in the CS-fear group only. As a result, these effects were specific to emotional learning/memory. SPS also disrupted inhibited Hipp c-Jun levels, enhanced BLA c-Jun levels, and altered functional connectivity among the vmPFC, BLA, and Hipp during extinction testing in SPS rats in the CS-fear and CS-only groups. As a result, these effects were not specific to emotional learning/memory. Our findings suggest that SPS disrupts neural activity specific to extinction memory, but may also disrupt the retention of fear extinction by mechanisms that do not involve emotional learning/memory. Copyright © 2017 Elsevier B.V. All rights reserved.

Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification.

Science.gov (United States)

Kang, Jin Kyu; Hong, Hyung Gil; Park, Kang Ryoung

2017-07-08

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.

Selective attention to affective value alters how the brain processes olfactory stimuli.

Science.gov (United States)

Rolls, Edmund T; Grabenhorst, Fabian; Margot, Christian; da Silva, Maria A A P; Velazco, Maria Ines

2008-10-01

How does selective attention to affect influence sensory processing? In a functional magnetic resonance imaging investigation, when subjects were instructed to remember and rate the pleasantness of a jasmine odor, activations were greater in the medial orbito-frontal and pregenual cingulate cortex than when subjects were instructed to remember and rate the intensity of the odor. When the subjects were instructed to remember and rate the intensity, activations were greater in the inferior frontal gyrus. These top-down effects occurred not only during odor delivery but started in a preparation period after the instruction before odor delivery, and continued after termination of the odor in a short-term memory period. Thus, depending on the context in which odors are presented and whether affect is relevant, the brain prepares itself, responds to, and remembers an odor differently. These findings show that when attention is paid to affective value, the brain systems engaged to prepare for, represent, and remember a sensory stimulus are different from those engaged when attention is directed to the physical properties of a stimulus such as its intensity. This differential biasing of brain regions engaged in processing a sensory stimulus depending on whether the cognitive demand is for affect-related versus more sensory-related processing may be an important aspect of cognition and attention. This has many implications for understanding the effects not only of olfactory but also of other sensory stimuli.

Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

Science.gov (United States)

Manning, Katherine E.; Holland, Anthony J.

2015-01-01

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study. PMID:28943631

Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

Directory of Open Access Journals (Sweden)

Katherine E. Manning

2015-12-01

Full Text Available Prader-Willi syndrome (PWS is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

CERN Document Server

Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

Common neural substrates for visual working memory and attention.

Science.gov (United States)

Mayer, Jutta S; Bittner, Robert A; Nikolić, Danko; Bledowski, Christoph; Goebel, Rainer; Linden, David E J

2007-06-01

Humans are severely limited in their ability to memorize visual information over short periods of time. Selective attention has been implicated as a limiting factor. Here we used functional magnetic resonance imaging to test the hypothesis that this limitation is due to common neural resources shared by visual working memory (WM) and selective attention. We combined visual search and delayed discrimination of complex objects and independently modulated the demands on selective attention and WM encoding. Participants were presented with a search array and performed easy or difficult visual search in order to encode one or three complex objects into visual WM. Overlapping activation for attention-demanding visual search and WM encoding was observed in distributed posterior and frontal regions. In the right prefrontal cortex and bilateral insula blood oxygen-level-dependent activation additively increased with increased WM load and attentional demand. Conversely, several visual, parietal and premotor areas showed overlapping activation for the two task components and were severely reduced in their WM load response under the condition with high attentional demand. Regions in the left prefrontal cortex were selectively responsive to WM load. Areas selectively responsive to high attentional demand were found within the right prefrontal and bilateral occipital cortex. These results indicate that encoding into visual WM and visual selective attention require to a high degree access to common neural resources. We propose that competition for resources shared by visual attention and WM encoding can limit processing capabilities in distributed posterior brain regions.

Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances

Science.gov (United States)

Soltoggio, Andrea; Lemme, Andre; Reinhart, Felix; Steil, Jochen J.

2013-01-01

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms. PMID:23565092

Neural substrates of sublexical processing for spelling.

Science.gov (United States)

DeMarco, Andrew T; Wilson, Stephen M; Rising, Kindle; Rapcsak, Steven Z; Beeson, Pélagie M

2017-01-01

We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.

(GAGs) in normal and ethanol-induced chick embryo during neural

African Journals Online (AJOL)

Administrator

2011-09-14

Sep 14, 2011 ... Alcohol as a teratogenic agent inhibits cell growth, function, proliferation and migration by affecting .... formed along the right and left side of the neural tube .... of neurons by harming the developing brain and also can.

Particle swarm optimization of a neural network model in a ...

Indian Academy of Sciences (India)

. Since tool life is critically affected by the tool wear, accurate prediction of this wear ... In their work, they established an improvement in the quality ... objective optimization of hard turning using neural network modelling and swarm intelligence ...

Neuroplasticity beyond Sounds: Neural Adaptations Following Long-Term Musical Aesthetic Experiences

Directory of Open Access Journals (Sweden)

Mark Reybrouck

2015-03-01

Full Text Available Capitalizing from neuroscience knowledge on how individuals are affected by the sound environment, we propose to adopt a cybernetic and ecological point of view on the musical aesthetic experience, which includes subprocesses, such as feature extraction and integration, early affective reactions and motor actions, style mastering and conceptualization, emotion and proprioception, evaluation and preference. In this perspective, the role of the listener/composer/performer is seen as that of an active “agent” coping in highly individual ways with the sounds. The findings concerning the neural adaptations in musicians, following long-term exposure to music, are then reviewed by keeping in mind the distinct subprocesses of a musical aesthetic experience. We conclude that these neural adaptations can be conceived of as the immediate and lifelong interactions with multisensorial stimuli (having a predominant auditory component, which result in lasting changes of the internal state of the “agent”. In a continuous loop, these changes affect, in turn, the subprocesses involved in a musical aesthetic experience, towards the final goal of achieving better perceptual, motor and proprioceptive responses to the immediate demands of the sounding environment. The resulting neural adaptations in musicians closely depend on the duration of the interactions, the starting age, the involvement of attention, the amount of motor practice and the musical genre played.

Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

Directory of Open Access Journals (Sweden)

W. L. C. Rutten

2006-01-01

Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

Identification of the neural component of torque during manually-applied spasticity assessments in children with cerebral palsy

NARCIS (Netherlands)

Bar-On, L.; Desloovere, K.; Molenaers, G.; Harlaar, J.; Kindt, T.; Aertbelien, E.

2014-01-01

Clinical assessment of spasticity is compromised by the difficulty to distinguish neural from non-neural components of increased joint torque. Quantifying the contributions of each of these components is crucial to optimize the selection of anti-spasticity treatments such as botulinum toxin (BTX).

Time-lapse imaging of neural development: zebrafish lead the way into the fourth dimension.

Science.gov (United States)

Rieger, Sandra; Wang, Fang; Sagasti, Alvaro

2011-07-01

Time-lapse imaging is often the only way to appreciate fully the many dynamic cell movements critical to neural development. Zebrafish possess many advantages that make them the best vertebrate model organism for live imaging of dynamic development events. This review will discuss technical considerations of time-lapse imaging experiments in zebrafish, describe selected examples of imaging studies in zebrafish that revealed new features or principles of neural development, and consider the promise and challenges of future time-lapse studies of neural development in zebrafish embryos and adults. Copyright © 2011 Wiley-Liss, Inc.

A purple giraffe is faster than a purple elephant: Inconsistent phonology affects determiner selection in English

NARCIS (Netherlands)

Spalek, K.; Bock, K.; Schriefers, H.J.

2010-01-01

The form of a determiner is dependent on different contextual factors: in some languages grammatical number and grammatical gender determine the choice of a determiner variant. In other languages, the phonological onset of the element immediately following the determiner affects selection, too.

TOUCHING MOMENTS: DESIRE MODULATES THE NEURAL ANTICIPATION OF ACTIVE ROMANTIC CARESS

Directory of Open Access Journals (Sweden)

Sjoerd J.H. Ebisch

2014-02-01

Full Text Available A romantic caress is a basic expression of affiliative behavior and a primary reinforcer. Given its inherent affective valence, its performance also would imply the prediction of reward values. For example, touching a person for whom one has strong passionate feelings likely is motivated by a strong desire for physical contact and associated with the anticipation of hedonic experiences. The present study aims at investigating how the anticipatory neural processes of active romantic caress are modulated by the intensity of the desire for affective contact as reflected by passionate feelings for the other. Functional magnetic resonance imaging scanning was performed in romantically involved partners using a paradigm that allowed to isolate the specific anticipatory representations of active romantic caress, compared with control caress, while testing for the relationship between neural activity and measures of feelings of passionate love for the other. The results demonstrated that right posterior insula activity in anticipation of romantic caress significantly co-varied with the intensity of desire for union with the other. This effect was independent of the sensory-affective properties of the performed touch, like its pleasantness. Furthermore, functional connectivity analysis showed that the same posterior insula cluster interacted with brain regions related to sensory-motor functions as well as to the processing and anticipation of reward. The findings provide insight on the neural substrate mediating between the desire for and the performance of romantic caress. In particular, we propose that anticipatory activity patterns in posterior insula may modulate subsequent sensory-affective processing of skin-to-skin contact.

Genetic attack on neural cryptography.

Science.gov (United States)

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

2006-03-01

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