WorldWideScience

Sample records for neural modules based

  1. Artificial neural network-based all-sky power estimation and fault detection in photovoltaic modules

    Science.gov (United States)

    Jazayeri, Kian; Jazayeri, Moein; Uysal, Sener

    2017-04-01

    The development of a system for output power estimation and fault detection in photovoltaic (PV) modules using an artificial neural network (ANN) is presented. Over 30,000 healthy and faulty data sets containing per-minute measurements of PV module output power (W) and irradiance (W/m2) along with real-time calculations of the Sun's position in the sky and the PV module surface temperature, collected during a three-month period, are fed to different ANNs as training paths. The first ANN being trained on healthy data is used for PV module output power estimation and the second ANN, which is trained on both healthy and faulty data, is utilized for PV module fault detection. The proposed PV module-level fault detection algorithm can expectedly be deployed in broader PV fleets by taking developmental considerations. The machine-learning-based automated system provides the possibility of all-sky real-time monitoring and fault detection of PV modules under any meteorological condition. Utilizing the proposed system, any power loss caused by damaged cells, shading conditions, accumulated dirt and dust on module surface, etc., is detected and reported immediately, potentially yielding increased reliability and efficiency of the PV systems and decreased support and maintenance costs.

  2. Neural Network-Based Receiver in Band-Limited Communication System with MPPSK Modulation

    Directory of Open Access Journals (Sweden)

    Wang Zixin

    2018-01-01

    Full Text Available As a type of the spectrally efficient modulation, the m-ary phase position shift keying (MPPSK has been considered to meet the increasing spectrum requirement in the future wireless system. To limit the signal bandwidth and cancel the out-band interference the band-pass filters are used, which introduce the waveform distortion and inter-symbol interference (ISI. Therefore, a single hidden-layer neural network (NN-based receiver is proposed to jointly equalize and demodulate the received signal. The impulse response of the system is static and the network parameters can be obtained after off-line training. The number of the hidden nodes is also determined through simulations. Simulation results show that the NN-based receiver works well in the communication system with different allocated bandwidths. By observing the modified confusion matrix, the false symbol decision is relevant to modulation index, waveform distortions and the ISI.

  3. Goal-Directed Modulation of Neural Memory Patterns: Implications for fMRI-Based Memory Detection.

    Science.gov (United States)

    Uncapher, Melina R; Boyd-Meredith, J Tyler; Chow, Tiffany E; Rissman, Jesse; Wagner, Anthony D

    2015-06-03

    Remembering a past event elicits distributed neural patterns that can be distinguished from patterns elicited when encountering novel information. These differing patterns can be decoded with relatively high diagnostic accuracy for individual memories using multivoxel pattern analysis (MVPA) of fMRI data. Brain-based memory detection--if valid and reliable--would have clear utility beyond the domain of cognitive neuroscience, in the realm of law, marketing, and beyond. However, a significant boundary condition on memory decoding validity may be the deployment of "countermeasures": strategies used to mask memory signals. Here we tested the vulnerability of fMRI-based memory detection to countermeasures, using a paradigm that bears resemblance to eyewitness identification. Participants were scanned while performing two tasks on previously studied and novel faces: (1) a standard recognition memory task; and (2) a task wherein they attempted to conceal their true memory state. Univariate analyses revealed that participants were able to strategically modulate neural responses, averaged across trials, in regions implicated in memory retrieval, including the hippocampus and angular gyrus. Moreover, regions associated with goal-directed shifts of attention and thought substitution supported memory concealment, and those associated with memory generation supported novelty concealment. Critically, whereas MVPA enabled reliable classification of memory states when participants reported memory truthfully, the ability to decode memory on individual trials was compromised, even reversing, during attempts to conceal memory. Together, these findings demonstrate that strategic goal states can be deployed to mask memory-related neural patterns and foil memory decoding technology, placing a significant boundary condition on their real-world utility. Copyright © 2015 the authors 0270-6474/15/358531-15$15.00/0.

  4. Neural stem cell sparing by linac based intensity modulated stereotactic radiotherapy in intracranial tumors.

    Science.gov (United States)

    Oehler, Julia; Brachwitz, Tim; Wendt, Thomas G; Banz, Nico; Walther, Mario; Wiezorek, Tilo

    2013-07-24

    Neurocognitive decline observed after radiotherapy (RT) for brain tumors in long time survivors is attributed to radiation exposure of the hippocampus and the subventricular zone (SVZ). The potential of sparing capabilities for both structures by optimized intensity modulated stereotactic radiotherapy (IMSRT) is investigated. Brain tumors were irradiated by stereotactic 3D conformal RT or IMSRT using m3 collimator optimized for PTV and for sparing of the conventional OARs (lens, retina, optic nerve, chiasm, cochlea, brain stem and the medulla oblongata). Retrospectively both hippocampi and SVZ were added to the list of OAR and their dose volume histograms were compared to those from two newly generated IMSRT plans using 7 or 14 beamlets (IMSRT-7, IMSRT-14) dedicated for optimized additional sparing of these structures. Conventional OAR constraints were kept constant. Impact of plan complexity and planning target volume (PTV) topography on sparing of both hippocampi and SVZ, conformity index (CI), the homogeneity index (HI) and quality of coverage (QoC) were analyzed. Limits of agreement were used to compare sparing of stem cell niches with either IMSRT-7 or IMSRT-14. The influence of treatment technique related to the topography ratio between PTV and OARs, realized in group A-D, was assessed by a mixed model. In 47 patients CI (p ≤  0.003) and HI (p  <  0.001) improved by IMSRT-7, IMSRT-14, QoC remained stable (p  ≥  0.50) indicating no compromise in radiotherapy. 90% of normal brain was exposed to a significantly higher dose using IMSRT. IMSRT-7 plans resulted in significantly lower biologically effective doses at all four neural stem cell structures, while contralateral neural stem cells are better spared compared to ipsilateral. A further increase of the number of beamlets (IMSRT-14) did not improve sparing significantly, so IMSRT-7 and IMSRT-14 can be used interchangeable. Patients with tumors contacting neither the subventricular zone nor the

  5. Modulation of grasping force in prosthetic hands using neural network-based predictive control.

    Science.gov (United States)

    Pasluosta, Cristian F; Chiu, Alan W L

    2015-01-01

    This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.

  6. When the choice is ours: context and agency modulate the neural bases of decision-making.

    Science.gov (United States)

    Forstmann, Birte U; Wolfensteller, Uta; Derrfuss, Jan; Neumann, Jane; Brass, Marcel; Ridderinkhof, K Richard; von Cramon, D Yves

    2008-04-02

    The option to choose between several courses of action is often associated with the feeling of being in control. Yet, in certain situations, one may prefer to decline such agency and instead leave the choice to others. In the present functional magnetic resonance imaging (fMRI) study, we provide evidence that the neural processes involved in decision-making are modulated not only by who controls our choice options (agency), but also by whether we have a say in who is in control (context). The fMRI results are noteworthy in that they reveal specific contributions of the anterior frontomedian cortex (viz. BA 10) and the rostral cingulate zone (RCZ) in decision-making processes. The RCZ is engaged when conditions clearly present us with the most choice options. BA 10 is engaged in particular when the choice is completely ours, as well as when it is completely up to others to choose for us which in turn gives rise to an attribution of control to oneself or someone else, respectively. After all, it does not only matter whether we have any options to choose from, but also who decides on that.

  7. Intrinsic gain modulation and adaptive neural coding.

    Directory of Open Access Journals (Sweden)

    Sungho Hong

    2008-07-01

    Full Text Available In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.

  8. Neural recording and modulation technologies

    Science.gov (United States)

    Chen, Ritchie; Canales, Andres; Anikeeva, Polina

    2017-01-01

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

  9. Optimal attentional modulation of neural population

    Directory of Open Access Journals (Sweden)

    Ali eBorji

    2014-03-01

    Full Text Available Top-down attention has often been separately studied in the contexts of eitheroptimal population coding or biasing of visual search. Yet, both are intimatelylinked, as they entail optimally modulating sensory variables in neural populationsaccording to top-down goals. Designing experiments to probe top-down attentionalmodulation is difficult because non-linear population dynamics are hardto predict in the absence of a concise theoretical framework. Here, we describea unified framework that encompasses both contexts. Our work sheds light ontothe ongoing debate on whether attention modulates neural response gain, tuningwidth, and/or preferred feature. We evaluate the framework by conducting simulationsfor two tasks: 1 classification (discrimination of two stimuli sa and sband 2 searching for a target T among distractors D. Results demonstrate that allof gain, tuning, and preferred feature modulation happen to different extents, dependingon stimulus conditions and task demands. The theoretical analysis showsthat task difficulty (linked to difference D between sa and sb, or T and D is acrucial factor in optimal modulation, with different effects in discrimination vs.search. Further, our framework allows us to quantify the relative utility of neuralparameters. In easy tasks (when D is large compared to the density of the neuralpopulation, modulating gains and preferred features is sufficient to yield nearlyoptimal performance; however, in difficult tasks (smaller D, modulating tuningwidth becomes necessary to improve performance. This suggests that the conflictingreports from different experimental studies may be due to differences intasks and in their difficulties. We further propose future electrophysiology experimentsto observe different types of attentional modulation in a same neuron.

  10. A neural network model of attention-modulated neurodynamics.

    Science.gov (United States)

    Gu, Yuqiao; Liljenström, Hans

    2007-12-01

    Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.

  11. Inferring oscillatory modulation in neural spike trains.

    Directory of Open Access Journals (Sweden)

    Kensuke Arai

    2017-10-01

    Full Text Available Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG and local field potential (LFP in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

  12. Inferring oscillatory modulation in neural spike trains.

    Science.gov (United States)

    Arai, Kensuke; Kass, Robert E

    2017-10-01

    Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

  13. Dynamic Object Identification with SOM-based neural networks

    Directory of Open Access Journals (Sweden)

    Aleksey Averkin

    2014-03-01

    Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.

  14. Aging Affects Neural Synchronization to Speech-Related Acoustic Modulations.

    Science.gov (United States)

    Goossens, Tine; Vercammen, Charlotte; Wouters, Jan; van Wieringen, Astrid

    2016-01-01

    As people age, speech perception problems become highly prevalent, especially in noisy situations. In addition to peripheral hearing and cognition, temporal processing plays a key role in speech perception. Temporal processing of speech features is mediated by synchronized activity of neural oscillations in the central auditory system. Previous studies indicate that both the degree and hemispheric lateralization of synchronized neural activity relate to speech perception performance. Based on these results, we hypothesize that impaired speech perception in older persons may, in part, originate from deviances in neural synchronization. In this study, auditory steady-state responses that reflect synchronized activity of theta, beta, low and high gamma oscillations (i.e., 4, 20, 40, and 80 Hz ASSR, respectively) were recorded in young, middle-aged, and older persons. As all participants had normal audiometric thresholds and were screened for (mild) cognitive impairment, differences in synchronized neural activity across the three age groups were likely to be attributed to age. Our data yield novel findings regarding theta and high gamma oscillations in the aging auditory system. At an older age, synchronized activity of theta oscillations is increased, whereas high gamma synchronization is decreased. In contrast to young persons who exhibit a right hemispheric dominance for processing of high gamma range modulations, older adults show a symmetrical processing pattern. These age-related changes in neural synchronization may very well underlie the speech perception problems in aging persons.

  15. Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

    Science.gov (United States)

    Naushad, Shaik Mohammad; Ramaiah, M Janaki; Pavithrakumari, Manickam; Jayapriya, Jaganathan; Hussain, Tajamul; Alrokayan, Salman A; Gottumukkala, Suryanarayana Raju; Digumarti, Raghunadharao; Kutala, Vijay Kumar

    2016-04-15

    In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how micronutrients modulate susceptibility to breast cancer. The developed ANN model explained 94.2% variability in breast cancer prediction. Fixed effect models of folate (400 μg/day) and B12 (6 μg/day) showed 33.3% and 11.3% risk reduction, respectively. Multifactor dimensionality reduction analysis showed the following interactions in responders to folate: RFC1 G80A × MTHFR C677T (primary), COMT H108L × CYP1A1 m2 (secondary), MTR A2756G (tertiary). The interactions among responders to B12 were RFC1G80A × cSHMT C1420T and CYP1A1 m2 × CYP1A1 m4. ANN simulations revealed that increased folate might restore ER and PR expression and reduce the promoter CpG island methylation of extra cellular superoxide dismutase and BRCA1. Dietary intake of folate appears to confer protection against breast cancer through its modulating effects on ER and PR expression and methylation of EC-SOD and BRCA1. Copyright © 2016 Elsevier B.V. All rights reserved.

  16. Modulating the neural bases of persuasion: why/how, gain/loss, and users/non-users.

    Science.gov (United States)

    Vezich, I Stephanie; Katzman, Perri L; Ames, Daniel L; Falk, Emily B; Lieberman, Matthew D

    2017-02-01

    Designing persuasive content is challenging, in part because people can be poor predictors of their actions. Medial prefrontal cortex (MPFC) activation during message exposure reliably predicts downstream behavior, but past work has been largely atheoretical. We replicated past results on this relationship and tested two additional framing effects known to alter message receptivity. First, we examined gain- vs. loss-framed reasons for a health behavior (sunscreen use). Consistent with predictions from prospect theory, we observed greater MPFC activity to gain- vs. loss-framed messages, and this activity was associated with behavior. This relationship was stronger for those who were not previously sunscreen users. Second, building on theories of action planning, we compared neural activity during messages regarding how vs. why to enact the behavior. We observed rostral inferior parietal lobule and posterior inferior frontal gyrus activity during action planning ("how" messages), and this activity was associated with behavior; this is in contrast to the relationship between MPFC activity during the "why" (i.e., gain and loss) messages and behavior. These results reinforce that persuasion occurs in part via self-value integration-seeing value and incorporating persuasive messages into one's self-concept-and extend this work to demonstrate how message framing and action planning may influence this process. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  17. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    Science.gov (United States)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  18. Neural entrainment to speech modulates speech intelligibility

    NARCIS (Netherlands)

    Riecke, Lars; Formisano, Elia; Sorger, Bettina; Başkent, Deniz; Gaudrain, Etienne

    2018-01-01

    Speech is crucial for communication in everyday life. Speech-brain entrainment, the alignment of neural activity to the slow temporal fluctuations (envelope) of acoustic speech input, is a ubiquitous element of current theories of speech processing. Associations between speech-brain entrainment and

  19. Neural modulation for hypertension and heart failure.

    Science.gov (United States)

    Smith, S; Rossignol, P; Willis, S; Zannad, F; Mentz, R; Pocock, S; Bisognano, J; Nadim, Y; Geller, N; Ruble, S; Linde, C

    2016-07-01

    Hypertension (HTN) and heart failure (HF) have a significant global impact on health, and lead to increased morbidity and mortality. Despite recent advances in pharmacologic and device therapy for these conditions, there is a need for additional treatment modalities. Patients with sub-optimally treated HTN have increased risk for stroke, renal failure and heart failure. The outcome of HF patients remains poor despite modern pharmacological therapy and with established device therapies such as CRT and ICDs. Therefore, the potential role of neuromodulation via renal denervation, baro-reflex modulation and vagal stimulation for the treatment of resistant HTN and HF is being explored. In this manuscript, we review current evidence for neuromodulation in relation to established drug and device therapies and how these therapies may be synergistic in achieving therapy goals in patients with treatment resistant HTN and heart failure. We describe lessons learned from recent neuromodulation trials and outline strategies to improve the potential for success in future trials. This review is based on discussions between scientists, clinical trialists, and regulatory representatives at the 11th annual CardioVascular Clinical Trialist Forum in Washington, DC on December 5-7, 2014. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  20. Priming Neural Circuits to Modulate Spinal Reflex Excitability

    OpenAIRE

    Estes, Stephen P.; Iddings, Jennifer A.; Field-Fote, Edelle C.

    2017-01-01

    While priming is most often thought of as a strategy for modulating neural excitability to facilitate voluntary motor control, priming stimulation can also be utilized to target spinal reflex excitability. In this application, priming can be used to modulate the involuntary motor output that often follows central nervous system injury. Individuals with spinal cord injury (SCI) often experience spasticity, for which antispasmodic medications are the most common treatment. Physical therapeutic/...

  1. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  2. 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.

  3. Task-dependent modulation of oscillatory neural activity during movements

    DEFF Research Database (Denmark)

    Herz, D. M.; Christensen, M. S.; Reck, C.

    2011-01-01

    -dependent modulation of frequency coupling within this network. To this end we recorded 122-multichannel EEG in 13 healthy subjects while they performed three simple motor tasks. EEG data source modeling using individual MR images was carried out with a multiple source beamformer approach. A bilateral motor network...... for inferring on architecture and coupling parameters of neural networks....

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

    Science.gov (United States)

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

    2017-01-01

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

  5. 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

  6. Neural Bases of Automaticity.

    Science.gov (United States)

    Servant, Mathieu; Cassey, Peter; Woodman, Geoffrey F; Logan, Gordon D

    2017-09-21

    Automaticity allows us to perform tasks in a fast, efficient, and effortless manner after sufficient practice. Theories of automaticity propose that across practice processing transitions from being controlled by working memory to being controlled by long-term memory retrieval. Recent event-related potential (ERP) studies have sought to test this prediction, however, these experiments did not use the canonical paradigms used to study automaticity. Specifically, automaticity is typically studied using practice regimes with consistent mapping between targets and distractors and spaced practice with individual targets, features that these previous studies lacked. The aim of the present work was to examine whether the practice-induced shift from working memory to long-term memory inferred from subjects' ERPs is observed under the conditions in which automaticity is traditionally studied. We found that to be the case in 3 experiments, firmly supporting the predictions of theories. In addition, we found that the temporal distribution of practice (massed vs. spaced) modulates the shape of learning curves. The ERP data revealed that the switch to long-term memory is slower for spaced than massed practice, suggesting that memory systems are used in a strategic manner. This finding provides new constraints for theories of learning and automaticity. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  7. Interpersonal liking modulates motor-related neural regions.

    Directory of Open Access Journals (Sweden)

    Mona Sobhani

    Full Text Available Observing someone perform an action engages brain regions involved in motor planning, such as the inferior frontal, premotor, and inferior parietal cortices. Recent research suggests that during action observation, activity in these neural regions can be modulated by membership in an ethnic group defined by physical differences. In this study we expanded upon previous research by matching physical similarity of two different social groups and investigating whether likability of an outgroup member modulates activity in neural regions involved in action observation. Seventeen Jewish subjects were familiarized with biographies of eight individuals, half of the individuals belonged to Neo-Nazi groups (dislikable and half of which did not (likable. All subjects and actors in the stimuli were Caucasian and physically similar. The subjects then viewed videos of actors portraying the characters performing simple motor actions (e.g. grasping a water bottle and raising it to the lips, while undergoing fMRI. Using multivariate pattern analysis (MVPA, we found that a classifier trained on brain activation patterns successfully discriminated between the likable and dislikable action observation conditions within the right ventral premotor cortex. These data indicate that the spatial pattern of activity in action observation related neural regions is modulated by likability even when watching a simple action such as reaching for a cup. These findings lend further support for the notion that social factors such as interpersonal liking modulate perceptual processing in motor-related cortices.

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

    Science.gov (United States)

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

    2008-01-22

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

  9. Sigma-delta cellular neural network for 2D modulation.

    Science.gov (United States)

    Aomori, Hisashi; Otake, Tsuyoshi; Takahashi, Nobuaki; Tanaka, Mamoru

    2008-01-01

    Although sigma-delta modulation is widely used for analog-to-digital (A/D) converters, sigma-delta concepts are only for 1D signals. Signal processing in the digital domain is extremely useful for 2D signals such as used in image processing, medical imaging, ultrasound imaging, and so on. The intricate task that provides true 2D sigma-delta modulation is feasible in the spatial domain sigma-delta modulation using the discrete-time cellular neural network (DT-CNN) with a C-template. In the proposed architecture, the A-template is used for a digital-to-analog converter (DAC), the C-template works as an integrator, and the nonlinear output function is used for the bilevel output. In addition, due to the cellular neural network (CNN) characteristics, each pixel of an image corresponds to a cell of a CNN, and each cell is connected spatially by the A-template. Therefore, the proposed system can be thought of as a very large-scale and super-parallel sigma-delta modulator. Moreover, the spatio-temporal dynamics is designed to obtain an optimal reconstruction signal. The experimental results show the excellent reconstruction performance and capabilities of the CNN as a sigma-delta modulator.

  10. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    OpenAIRE

    Marco Mainardi; Salvatore Fusco; Claudio Grassi

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural p...

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

    Directory of Open Access Journals (Sweden)

    Hao Tam Ho

    2011-10-01

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

  12. Aging Affects Neural Synchronization to Speech-Related Acoustic Modulations

    OpenAIRE

    Goossens, Tine; Vercammen, Charlotte; Wouters, Jan; van Wieringen, Astrid

    2016-01-01

    As people age, speech perception problems become highly prevalent, especially in noisy situations. In addition to peripheral hearing and cognition, temporal processing plays a key role in speech perception. Temporal processing of speech features is mediated by synchronized activity of neural oscillations in the central auditory system. Previous studies indicate that both the degree and hemispheric lateralization of synchronized neural activity relate to speech perception performance. Based on...

  13. Personality traits modulate neural responses to emotions expressed in music.

    Science.gov (United States)

    Park, Mona; Hennig-Fast, Kristina; Bao, Yan; Carl, Petra; Pöppel, Ernst; Welker, Lorenz; Reiser, Maximilian; Meindl, Thomas; Gutyrchik, Evgeny

    2013-07-26

    Music communicates and evokes emotions. The number of studies on the neural correlates of musical emotion processing is increasing but few have investigated the factors that modulate these neural activations. Previous research has shown that personality traits account for individual variability of neural responses. In this study, we used functional magnetic resonance imaging (fMRI) to investigate how the dimensions Extraversion and Neuroticism are related to differences in brain reactivity to musical stimuli expressing the emotions happiness, sadness and fear. 12 participants (7 female, M=20.33 years) completed the NEO-Five Factor Inventory (NEO-FFI) and were scanned while performing a passive listening task. Neurofunctional analyses revealed significant positive correlations between Neuroticism scores and activations in bilateral basal ganglia, insula and orbitofrontal cortex in response to music expressing happiness. Extraversion scores were marginally negatively correlated with activations in the right amygdala in response to music expressing fear. Our findings show that subjects' personality may have a predictive power in the neural correlates of musical emotion processing and should be considered in the context of experimental group homogeneity. Copyright © 2013 Elsevier B.V. All rights reserved.

  14. Role of neural modulation in the pathophysiology of atrial fibrillation

    Directory of Open Access Journals (Sweden)

    Shailesh Male

    2014-01-01

    Full Text Available Atrial-fibrillation (AF is the most common clinically encountered arrhythmia affecting over 1 per cent of population in the United States and its prevalence seems to be moving only in forward direction. A recent systemic review estimates global prevalence of AF to be 596.2 and 373.1 per 100,000 population in males and females respectively. Multiple mechanisms have been put forward in the pathogenesis of AF, however; multiple wavelet hypothesis is the most accepted theory so far. Similar to the conduction system of the heart, a neural network exists which surrounds the heart and plays an important role in formation of the substrate of AF and when a trigger is originated, usually from pulmonary vein sleeves, AF occurs. This neural network includes ganglionated plexi (GP located adjacent to pulmonary vein ostia which are under control of higher centers in normal people. When these GP become hyperactive owing to loss of inhibition from higher centers e.g. in elderly, AF can occur. We can control these hyperactive GP either by stimulating higher centers and their connections, e.g. vagus nerve stimulation or simply by ablating these GP. This review provides detailed information about the different proposed mechanisms underlying AF, the exact role of autonomic neural tone in the pathogenesis of AF and the possible role of neural modulation in the treatment of AF.

  15. Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods

    Energy Technology Data Exchange (ETDEWEB)

    Almonacid, F.; Rus, C.; Hontoria, L.; Munoz, F.J. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Ingenieria Electronica. E.P.S. Jaen., Universidad de Jaen. 23071- Jaen (Spain)

    2010-05-15

    The presence of PV modules made with new technologies and materials is increasing in PV market, in special Thin Film Solar Modules (TFSM). They are ready to make a substantial contribution to the world's electricity generation. Although Si wafer-based cells account for the most of increase, technologies of thin film have been those of the major growth in last three years. During 2007 they grew 133%. On the other hand, manufacturers provide ratings for PV modules for conditions referred to as Standard Test Conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors characterisation in standard test conditions of PV modules is a controversial issue. Therefore, to carry out a correct photovoltaic engineering, a suitable characterisation of PV module electrical behaviour is necessary. The IDEA Research Group from Jaen University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN was able to generate V-I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method is going to be applied for electrical characterisation of PV CIS modules. Finally, a comparative study with other methods, of electrical characterisation, is done. (author)

  16. Degraded attentional modulation of cortical neural populations in strabismic amblyopia

    Science.gov (United States)

    Hou, Chuan; Kim, Yee-Joon; Lai, Xin Jie; Verghese, Preeti

    2016-01-01

    Behavioral studies have reported reduced spatial attention in amblyopia, a developmental disorder of spatial vision. However, the neural populations in the visual cortex linked with these behavioral spatial attention deficits have not been identified. Here, we use functional MRI–informed electroencephalography source imaging to measure the effect of attention on neural population activity in the visual cortex of human adult strabismic amblyopes who were stereoblind. We show that compared with controls, the modulatory effects of selective visual attention on the input from the amblyopic eye are substantially reduced in the primary visual cortex (V1) as well as in extrastriate visual areas hV4 and hMT+. Degraded attentional modulation is also found in the normal-acuity fellow eye in areas hV4 and hMT+ but not in V1. These results provide electrophysiological evidence that abnormal binocular input during a developmental critical period may impact cortical connections between the visual cortex and higher level cortices beyond the known amblyopic losses in V1 and V2, suggesting that a deficit of attentional modulation in the visual cortex is an important component of the functional impairment in amblyopia. Furthermore, we find that degraded attentional modulation in V1 is correlated with the magnitude of interocular suppression and the depth of amblyopia. These results support the view that the visual suppression often seen in strabismic amblyopia might be a form of attentional neglect of the visual input to the amblyopic eye. PMID:26885628

  17. Using pulse width modulation for wireless transmission of neural signals in multichannel neural recording systems.

    Science.gov (United States)

    Yin, Ming; Ghovanloo, Maysam

    2009-08-01

    We have used a well-known technique in wireless communication, pulse width modulation (PWM) of time division multiplexed (TDM) signals, within the architecture of a novel wireless integrated neural recording (WINeR) system. We have evaluated the performance of the PWM-based architecture and indicated its accuracy and potential sources of error through detailed theoretical analysis, simulations, and measurements on a setup consisting of a 15-channel WINeR prototype as the transmitter and two types of receivers; an Agilent 89600 vector signal analyzer and a custom wideband receiver, with 36 and 75 MHz of maximum bandwidth, respectively. Furthermore, we present simulation results from a realistic MATLAB-Simulink model of the entire WINeR system to observe the system behavior in response to changes in various parameters. We have concluded that the 15-ch WINeR prototype, which is fabricated in a 0.5- mum standard CMOS process and consumes 4.5 mW from +/-1.5 V supplies, can acquire and wirelessly transmit up to 320 k-samples/s to a 75-MHz receiver with 8.4 bits of resolution, which is equivalent to a wireless data rate of approximately 2.56 Mb/s.

  18. Modulation of hippocampal neural plasticity by glucose-related signaling.

    Science.gov (United States)

    Mainardi, Marco; Fusco, Salvatore; Grassi, Claudio

    2015-01-01

    Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural plasticity (i.e., dynamics of dendritic spines), and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  19. Modulation of Hippocampal Neural Plasticity by Glucose-Related Signaling

    Directory of Open Access Journals (Sweden)

    Marco Mainardi

    2015-01-01

    Full Text Available Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression, structural plasticity (i.e., dynamics of dendritic spines, and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

  20. Threat modulates neural responses to looming visual stimuli.

    Science.gov (United States)

    Vagnoni, Eleonora; Lourenco, Stella F; Longo, Matthew R

    2015-09-01

    Objects on a collision course with an observer produce a specific pattern of optical expansion on the retina known as looming, which in theory exactly specifies the time-to-collision (TTC) of approaching objects. It was recently demonstrated that the affective content of looming stimuli influences perceived TTC, with threatening objects judged as approaching sooner than non-threatening objects. Here, the neural mechanisms by which perceived threat modulates spatiotemporal perception were investigated. Participants judged the TTC of threatening (snakes, spiders) or non-threatening (butterflies, rabbits) stimuli, which expanded in size at a rate indicating one of five TTCs. Visual-evoked potentials (VEPs) and oscillatory neural responses measured with electroencephalography were analysed. The arrival time of threatening stimuli was underestimated compared with non-threatening stimuli, though an interaction suggested that this underestimation was not constant across TTCs. Further, both speed of approach and threat modulated both VEPs and oscillatory responses. Speed of approach modulated the N1 parietal and oscillations in the beta band. Threat modulated several VEP components (P1, N1 frontal, N1 occipital, early posterior negativity and late positive potential) and oscillations in the alpha and high gamma band. The results for the high gamma band suggest an interaction between these two factors. Previous evidence suggests that looming stimuli activate sensorimotor areas, even in the absence of an intended action. The current results show that threat disrupts the synchronization over the sensorimotor areas that are likely activated by the presentation of a looming stimulus. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  1. Texture Based Image Analysis With Neural Nets

    Science.gov (United States)

    Ilovici, Irina S.; Ong, Hoo-Tee; Ostrander, Kim E.

    1990-03-01

    In this paper, we combine direct image statistics and spatial frequency domain techniques with a neural net model to analyze texture based images. The resultant optimal texture features obtained from the direct and transformed image form the exemplar pattern of the neural net. The proposed approach introduces an automated texture analysis applied to metallography for determining the cooling rate and mechanical working of the materials. The results suggest that the proposed method enhances the practical applications of neural nets and texture extraction features.

  2. Context-dependent neural modulations in the perception of duration

    Directory of Open Access Journals (Sweden)

    Yuki eMurai

    2016-03-01

    Full Text Available Recent neuroimaging studies have revealed that distinct brain networks are recruited in the perception of sub- and supra-second timescales, whereas psychophysical studies have suggested that there are common or continuous mechanisms for perceiving these two durations. The present study aimed to elucidate the neural implementation of such continuity by examining the neural correlates of peri-second timing.We measured neural activity during a duration reproduction task using fMRI. Our results replicate the findings of previous studies in showing that separate neural networks are recruited for sub- versus supra-second time perception: motor systems including the motor cortex and the supplementary motor area for sub-second perception, and the frontal, parietal, and auditory cortical areas for supra-second perception. We further found that the peri-second perception activated both the sub- and supra-second networks, and that the timing system that processed duration perception in previous trials was more involved in subsequent peri-second processing. These results indicate that the sub- and supra-second timing systems overlap at around 1 second, and cooperate to optimally encode duration based on the hysteresis of previous trials.

  3. Memristor-based neural networks

    Science.gov (United States)

    Thomas, Andy

    2013-03-01

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

  4. Social influence modulates the neural computation of value.

    Science.gov (United States)

    Zaki, Jamil; Schirmer, Jessica; Mitchell, Jason P

    2011-07-01

    Social influence--individuals' tendency to conform to the beliefs and attitudes of others--has interested psychologists for decades. However, it has traditionally been difficult to distinguish true modification of attitudes from mere public compliance with social norms; this study addressed this challenge using functional neuroimaging. Participants rated the attractiveness of faces and subsequently learned how their peers ostensibly rated each face. Participants were then scanned using functional MRI while they rated each face a second time. The second ratings were influenced by social norms: Participants changed their ratings to conform to those of their peers. This social influence was accompanied by modulated engagement of two brain regions associated with coding subjective value--the nucleus accumbens and orbitofrontal cortex--a finding suggesting that exposure to social norms affected participants' neural representations of value assigned to stimuli. These findings document the utility of neuroimaging to demonstrate the private acceptance of social norms.

  5. Emotional sounds modulate early neural processing of emotional pictures

    Directory of Open Access Journals (Sweden)

    Antje B M Gerdes

    2013-10-01

    Full Text Available In our natural environment, emotional information is conveyed by converging visual and auditory information; multimodal integration is of utmost importance. In the laboratory, however, emotion researchers have mostly focused on the examination of unimodal stimuli. Few existing studies on multimodal emotion processing have focused on human communication such as the integration of facial and vocal expressions. Extending the concept of multimodality, the current study examines how the neural processing of emotional pictures is influenced by simultaneously presented sounds. Twenty pleasant, unpleasant, and neutral pictures of complex scenes were presented to 22 healthy participants. On the critical trials these pictures were paired with pleasant, unpleasant and neutral sounds. Sound presentation started 500 ms before picture onset and each stimulus presentation lasted for 2s. EEG was recorded from 64 channels and ERP analyses focused on the picture onset. In addition, valence, and arousal ratings were obtained. Previous findings for the neural processing of emotional pictures were replicated. Specifically, unpleasant compared to neutral pictures were associated with an increased parietal P200 and a more pronounced centroparietal late positive potential (LPP, independent of the accompanying sound valence. For audiovisual stimulation, increased parietal P100 and P200 were found in response to all pictures which were accompanied by unpleasant or pleasant sounds compared to pictures with neutral sounds. Most importantly, incongruent audiovisual pairs of unpleasant pictures and pleasant sounds enhanced parietal P100 and P200 compared to pairings with congruent sounds. Taken together, the present findings indicate that emotional sounds modulate early stages of visual processing and, therefore, provide an avenue by which multimodal experience may enhance perception.

  6. Task-modulated coactivation of vergence neural substrates.

    Science.gov (United States)

    Jaswal, Rajbir; Gohel, Suril; Biswal, Bharat B; Alvarez, Tara L

    2014-10-01

    While functional magnetic resonance imaging (fMRI) has identified which regions of interests (ROIs) are functionally active during a vergence movement (inward or outward eye rotation), task-modulated coactivation between ROIs is less understood. This study tested the following hypotheses: (1) significant task-modulated coactivation would be observed between the frontal eye fields (FEFs), the posterior parietal cortex (PPC), and the cerebellar vermis (CV); (2) significantly more functional activity and task-modulated coactivation would be observed in binocularly normal controls (BNCs) compared with convergence insufficiency (CI) subjects; and (3) after vergence training, the functional activity and task-modulated coactivation would increase in CIs compared with their baseline measurements. A block design of sustained fixation versus vergence eye movements stimulated activity in the FEFs, PPC, and CV. fMRI data from four CI subjects before and after vergence training were compared with seven BNCs. Functional activity was assessed using the blood oxygenation level dependent (BOLD) percent signal change. Task-modulated coactivation was assessed using an ROI-based task-modulated coactivation analysis that revealed significant correlation between the FEF, PPC, and CV ROIs. Prior to vergence training, the CIs had a reduced BOLD percent signal change compared with BNCs for the CV (p<0.05), FEFs, and PPC (p<0.01). The BOLD percent signal change increased within the CV, FEF, and PPC ROIs (p<0.001) as did the task-modulated coactivation between the FEFs and CV as well as the PPC and CV (p<0.05) when comparing the CI pre- and post-training datasets. Results from the Convergence Insufficiency Symptom Survey were correlated to the percent BOLD signal change from the FEFs and CV (p<0.05).

  7. Social discounting involves modulation of neural value signals by temporoparietal junction

    Science.gov (United States)

    Strombach, Tina; Weber, Bernd; Hangebrauk, Zsofia; Kenning, Peter; Karipidis, Iliana I.; Tobler, Philippe N.; Kalenscher, Tobias

    2015-01-01

    Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation. PMID:25605887

  8. Neural Mechanisms of Positive Mood Induced Modulation of Reality Monitoring

    Science.gov (United States)

    Subramaniam, Karuna; Gill, Jeevit; Slattery, Patrick; Shastri, Aditi; Mathalon, Daniel H.; Nagarajan, Srikantan; Vinogradov, Sophia

    2016-01-01

    This study investigates the neural mechanisms of mood induced modulation of cognition, specifically, on reality monitoring abilities. Reality monitoring is the ability to accurately distinguish the source of self-generated information from externally-presented contextual information. When participants were in a positive mood, compared to a neutral mood, they significantly improved their source memory identification abilities, particularly for self-generated information. However, being in a negative mood had no effect on reality monitoring abilities. Additionally, when participants were in a positive mood state, they showed activation in several regions that predisposed them to perform better at reality monitoring. Specifically, positive mood induced activity within the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) was associated with improvements in subsequent identification of self-generated information, and positive mood induced activation within the striatum (putamen) facilitated better identification of externally-presented information. These findings indicate that regions within mPFC, PCC and striatum are sensitive to positive mood-cognition enhancing effects that enable participants to be better prepared for subsequent reality monitoring decision-making. PMID:27895571

  9. Embodied emotion modulates neural signature of performance monitoring.

    Directory of Open Access Journals (Sweden)

    Daniel Wiswede

    Full Text Available BACKGROUND: Recent research on the "embodiment of emotion" implies that experiencing an emotion may involve perceptual, somatovisceral, and motor feedback aspects. For example, manipulations of facial expression and posture appear to induce emotional states and influence how affective information is processed. The present study investigates whether performance monitoring, a cognitive process known to be under heavy control of the dopaminergic system, is modulated by induced facial expressions. In particular, we focused on the error-related negativity, an electrophysiological correlate of performance monitoring. METHODS/PRINCIPAL FINDINGS: During a choice reaction task, participants held a Chinese chop stick either horizontally between the teeth ("smile" condition or, in different runs, vertically ("no smile" with the upper lip. In a third control condition, no chop stick was used ("no stick". It could be shown on a separate sample that the facial feedback procedure is feasible to induce mild changes in positive affect. In the ERP sample, the smile condition, hypothesized to lead to an increase in dopaminergic activity, was associated with a decrease of ERN amplitude relative to "no smile" and "no stick" conditions. CONCLUSION: Embodying emotions by induced facial expressions leads to a changes in the neural correlates of error detection. We suggest that this is due to the joint influence of the dopaminergic system on positive affect and performance monitoring.

  10. Neural bases of accented speech perception

    Directory of Open Access Journals (Sweden)

    Patti eAdank

    2015-10-01

    Full Text Available The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Adank, Evans, Stuart-Smith, & Scott, 2009; Floccia, Goslin, Girard, & Konopczynski, 2006. Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented speech, are beginning to be identified. This review will outline neural bases associated with perception of accented speech in the light of current models of speech perception, and compare these data to brain areas associated with processing other speech distortions. We will subsequently evaluate competing models of speech processing with regards to neural processing of accented speech. See Cristia et al. (2012 for an in-depth overview of behavioural aspects of accent processing.

  11. Neural bases of accented speech perception.

    Science.gov (United States)

    Adank, Patti; Nuttall, Helen E; Banks, Briony; Kennedy-Higgins, Daniel

    2015-01-01

    The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Floccia et al., 2006; Adank et al., 2009). Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented speech, are beginning to be identified. This review will outline neural bases associated with perception of accented speech in the light of current models of speech perception, and compare these data to brain areas associated with processing other speech distortions. We will subsequently evaluate competing models of speech processing with regards to neural processing of accented speech. See Cristia et al. (2012) for an in-depth overview of behavioral aspects of accent processing.

  12. Priming Neural Circuits to Modulate Spinal Reflex Excitability

    Science.gov (United States)

    Estes, Stephen P.; Iddings, Jennifer A.; Field-Fote, Edelle C.

    2017-01-01

    While priming is most often thought of as a strategy for modulating neural excitability to facilitate voluntary motor control, priming stimulation can also be utilized to target spinal reflex excitability. In this application, priming can be used to modulate the involuntary motor output that often follows central nervous system injury. Individuals with spinal cord injury (SCI) often experience spasticity, for which antispasmodic medications are the most common treatment. Physical therapeutic/electroceutic interventions offer an alternative treatment for spasticity, without the deleterious side effects that can accompany pharmacological interventions. While studies of physical therapeutic/electroceutic interventions have been published, a systematic comparison of these approaches has not been performed. The purpose of this study was to compare four non-pharmacological interventions to a sham-control intervention to assess their efficacy for spasticity reduction. Participants were individuals (n = 10) with chronic SCI (≥1 year) who exhibited stretch-induced quadriceps spasticity. Spasticity was quantified using the pendulum test before and at two time points after (immediate, 45 min delayed) each of four different physical therapeutic/electroceutic interventions, plus a sham-control intervention. Interventions included stretching, cyclic passive movement (CPM), transcutaneous spinal cord stimulation (tcSCS), and transcranial direct current stimulation (tDCS). The sham-control intervention consisted of a brief ramp-up and ramp-down of knee and ankle stimulation while reclined with legs extended. The order of interventions was randomized, and each was tested on a separate day with at least 48 h between sessions. Compared to the sham-control intervention, stretching, CPM, and tcSCS were associated with a significantly greater reduction in spasticity immediately after treatment. While the immediate effect was largest for stretching, the reduction persisted

  13. Crosslinking of extracellular matrix scaffolds derived from pluripotent stem cell aggregates modulates neural differentiation.

    Science.gov (United States)

    Sart, Sébastien; Yan, Yuanwei; Li, Yan; Lochner, Eric; Zeng, Changchun; Ma, Teng; Li, Yan

    2016-01-01

    cell-derived matrices have been assessed as tissue engineering scaffolds, the impact of crosslinking on the embryonic stem cell-derived matrices to modulate neural differentiation has not been studied. The results from this study provide novel knowledge on the interface of embryonic stem cell-derived extracellular matrix and neural aggregates. The findings reported in this manuscript are significant for stem cell differentiation toward the applications in stem cell-based drug screening, disease modeling, and cell therapies. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  14. DHODH modulates transcriptional elongation in the neural crest and melanoma.

    Science.gov (United States)

    White, Richard Mark; Cech, Jennifer; Ratanasirintrawoot, Sutheera; Lin, Charles Y; Rahl, Peter B; Burke, Christopher J; Langdon, Erin; Tomlinson, Matthew L; Mosher, Jack; Kaufman, Charles; Chen, Frank; Long, Hannah K; Kramer, Martin; Datta, Sumon; Neuberg, Donna; Granter, Scott; Young, Richard A; Morrison, Sean; Wheeler, Grant N; Zon, Leonard I

    2011-03-24

    Melanoma is a tumour of transformed melanocytes, which are originally derived from the embryonic neural crest. It is unknown to what extent the programs that regulate neural crest development interact with mutations in the BRAF oncogene, which is the most commonly mutated gene in human melanoma. We have used zebrafish embryos to identify the initiating transcriptional events that occur on activation of human BRAF(V600E) (which encodes an amino acid substitution mutant of BRAF) in the neural crest lineage. Zebrafish embryos that are transgenic for mitfa:BRAF(V600E) and lack p53 (also known as tp53) have a gene signature that is enriched for markers of multipotent neural crest cells, and neural crest progenitors from these embryos fail to terminally differentiate. To determine whether these early transcriptional events are important for melanoma pathogenesis, we performed a chemical genetic screen to identify small-molecule suppressors of the neural crest lineage, which were then tested for their effects on melanoma. One class of compound, inhibitors of dihydroorotate dehydrogenase (DHODH), for example leflunomide, led to an almost complete abrogation of neural crest development in zebrafish and to a reduction in the self-renewal of mammalian neural crest stem cells. Leflunomide exerts these effects by inhibiting the transcriptional elongation of genes that are required for neural crest development and melanoma growth. When used alone or in combination with a specific inhibitor of the BRAF(V600E) oncogene, DHODH inhibition led to a marked decrease in melanoma growth both in vitro and in mouse xenograft studies. Taken together, these studies highlight developmental pathways in neural crest cells that have a direct bearing on melanoma formation.

  15. Antipsychotic dose modulates behavioral and neural responses to feedback during reinforcement learning in schizophrenia.

    Science.gov (United States)

    Insel, Catherine; Reinen, Jenna; Weber, Jochen; Wager, Tor D; Jarskog, L Fredrik; Shohamy, Daphna; Smith, Edward E

    2014-03-01

    Schizophrenia is characterized by an abnormal dopamine system, and dopamine blockade is the primary mechanism of antipsychotic treatment. Consistent with the known role of dopamine in reward processing, prior research has demonstrated that patients with schizophrenia exhibit impairments in reward-based learning. However, it remains unknown how treatment with antipsychotic medication impacts the behavioral and neural signatures of reinforcement learning in schizophrenia. The goal of this study was to examine whether antipsychotic medication modulates behavioral and neural responses to prediction error coding during reinforcement learning. Patients with schizophrenia completed a reinforcement learning task while undergoing functional magnetic resonance imaging. The task consisted of two separate conditions in which participants accumulated monetary gain or avoided monetary loss. Behavioral results indicated that antipsychotic medication dose was associated with altered behavioral approaches to learning, such that patients taking higher doses of medication showed increased sensitivity to negative reinforcement. Higher doses of antipsychotic medication were also associated with higher learning rates (LRs), suggesting that medication enhanced sensitivity to trial-by-trial feedback. Neuroimaging data demonstrated that antipsychotic dose was related to differences in neural signatures of feedback prediction error during the loss condition. Specifically, patients taking higher doses of medication showed attenuated prediction error responses in the striatum and the medial prefrontal cortex. These findings indicate that antipsychotic medication treatment may influence motivational processes in patients with schizophrenia.

  16. SAR ATR Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tian Zhuangzhuang

    2016-06-01

    Full Text Available This study presents a new method of Synthetic Aperture Radar (SAR image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.

  17. ESTIMATION OF PV MODULE SURFACE TEMPERATURE USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Can Coskun

    2016-12-01

    Full Text Available This study aimed to use the artificial neural network (ANN method to estimate the surface temperature of a photovoltaic (PV panel. Using the experimentally obtained PV data, the accuracy of the ANN model was evaluated. To train the artificial neural network (ANN, outer temperature solar radiation and wind speed values were inputs and surface temperature was an output. The ANN was used to estimate PV panel surface temperature. Using the Levenberg-Marquardt (LM algorithm the feed forward artificial neural network was trained. Two back propagation type ANN algorithms were used and their performance was compared with the estimate from the LM algorithm. To train the artificial neural network, experimental data were used for two thirds with the remaining third used for testing. Additionally scaled conjugate gradient (SCG back propagation and resilient back propagation (RB type ANN algorithms were used for comparison with the LM algorithm. The performances of these three types of artificial neural network were compared and mean error rates of between 0.005962 and 0.012177% were obtained. The best estimate was produced by the LM algorithm. Estimation of PV surface temperature with artificial neural networks provides better results than conventional correlation methods. This study showed that artificial neural networks may be effectively used to estimate PV surface temperature.

  18. Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

    Science.gov (United States)

    Warlaumont, Anne S; Finnegan, Megan K

    2016-01-01

    At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one's own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop in

  19. Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity

    Science.gov (United States)

    Warlaumont, Anne S.; Finnegan, Megan K.

    2016-01-01

    At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant’s nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model’s frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one’s own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop

  20. Bidirectional reachability-based modules

    CSIR Research Space (South Africa)

    Nortje, R

    2011-07-01

    Full Text Available The authors introduce an algorithm for MinA extraction in EL based on bidirectional reachability. They obtain a significant reduction in the size of modules extracted at almost no additional cost to that of extracting standard reachability...

  1. Cognitive emotion regulation in children: Reappraisal of emotional faces modulates neural source activity in a frontoparietal network.

    Science.gov (United States)

    Wessing, Ida; Rehbein, Maimu A; Romer, Georg; Achtergarde, Sandra; Dobel, Christian; Zwitserlood, Pienie; Fürniss, Tilman; Junghöfer, Markus

    2015-06-01

    Emotion regulation has an important role in child development and psychopathology. Reappraisal as cognitive regulation technique can be used effectively by children. Moreover, an ERP component known to reflect emotional processing called late positive potential (LPP) can be modulated by children using reappraisal and this modulation is also related to children's emotional adjustment. The present study seeks to elucidate the neural generators of such LPP effects. To this end, children aged 8-14 years reappraised emotional faces, while neural activity in an LPP time window was estimated using magnetoencephalography-based source localization. Additionally, neural activity was correlated with two indexes of emotional adjustment and age. Reappraisal reduced activity in the left dorsolateral prefrontal cortex during down-regulation and enhanced activity in the right parietal cortex during up-regulation. Activity in the visual cortex decreased with increasing age, more adaptive emotion regulation and less anxiety. Results demonstrate that reappraisal changed activity within a frontoparietal network in children. Decreasing activity in the visual cortex with increasing age is suggested to reflect neural maturation. A similar decrease with adaptive emotion regulation and less anxiety implies that better emotional adjustment may be associated with an advance in neural maturation. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. Cognitive emotion regulation in children: Reappraisal of emotional faces modulates neural source activity in a frontoparietal network

    Directory of Open Access Journals (Sweden)

    Ida Wessing

    2015-06-01

    Full Text Available Emotion regulation has an important role in child development and psychopathology. Reappraisal as cognitive regulation technique can be used effectively by children. Moreover, an ERP component known to reflect emotional processing called late positive potential (LPP can be modulated by children using reappraisal and this modulation is also related to children's emotional adjustment. The present study seeks to elucidate the neural generators of such LPP effects. To this end, children aged 8–14 years reappraised emotional faces, while neural activity in an LPP time window was estimated using magnetoencephalography-based source localization. Additionally, neural activity was correlated with two indexes of emotional adjustment and age. Reappraisal reduced activity in the left dorsolateral prefrontal cortex during down-regulation and enhanced activity in the right parietal cortex during up-regulation. Activity in the visual cortex decreased with increasing age, more adaptive emotion regulation and less anxiety. Results demonstrate that reappraisal changed activity within a frontoparietal network in children. Decreasing activity in the visual cortex with increasing age is suggested to reflect neural maturation. A similar decrease with adaptive emotion regulation and less anxiety implies that better emotional adjustment may be associated with an advance in neural maturation.

  3. Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

    CERN Document Server

    Hu, H P

    2004-01-01

    In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

  4. Arterial pulse modulated activity is expressed in respiratory neural output

    National Research Council Canada - National Science Library

    Thomas E. Dick; Roger Shannon; Bruce G. Lindsey; Sarah C. Nuding; Lauren S. Segers; David M. Baekey; Kendall F. Morris

    2005-01-01

    .... Even though previous studies have suggested the existence of pulse modulation in respiratory neurons, they could not exclude the possibility that such cells were involved in cardiovascular rather...

  5. Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Miriam eZacksenhouse

    2015-05-01

    Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

  6. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)

    2006-10-15

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  7. Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks

    Science.gov (United States)

    Wang, L.; Zhang, Y. Y.; Ding, L.

    2006-10-01

    The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.

  8. Transient Modulations of Neural Responses to Heartbeats Covary with Bodily Self-Consciousness.

    Science.gov (United States)

    Park, Hyeong-Dong; Bernasconi, Fosco; Bello-Ruiz, Javier; Pfeiffer, Christian; Salomon, Roy; Blanke, Olaf

    2016-08-10

    Recent research has investigated self-consciousness associated with the multisensory processing of bodily signals (e.g., somatosensory, visual, vestibular signals), a notion referred to as bodily self-consciousness, and these studies have shown that the manipulation of bodily inputs induces changes in bodily self-consciousness such as self-identification. Another line of research has highlighted the importance of signals from the inside of the body (e.g., visceral signals) and proposed that neural representations of internal bodily signals underlie self-consciousness, which to date has been based on philosophical inquiry, clinical case studies, and behavioral studies. Here, we investigated the relationship of bodily self-consciousness with the neural processing of internal bodily signals. By combining electrical neuroimaging, analysis of peripheral physiological signals, and virtual reality technology in humans, we show that transient modulations of neural responses to heartbeats in the posterior cingulate cortex covary with changes in bodily self-consciousness induced by the full-body illusion. Additional analyses excluded that measured basic cardiorespiratory parameters or interoceptive sensitivity traits could account for this finding. These neurophysiological data link experimentally the cortical mapping of the internal body to self-consciousness. What are the brain mechanisms of self-consciousness? Prominent views propose that the neural processing associated with signals from the internal organs (such as the heart and the lung) plays a critical role in self-consciousness. Although this hypothesis dates back to influential views in philosophy and psychology (e.g., William James), definitive experimental evidence supporting this idea is lacking despite its recent impact in neuroscience. In the present study, we show that posterior cingulate activities responding to heartbeat signals covary with changes in participants' conscious self-identification with a body

  9. Seeding neural progenitor cells on silicon-based neural probes.

    Science.gov (United States)

    Azemi, Erdrin; Gobbel, Glenn T; Cui, Xinyan Tracy

    2010-09-01

    Chronically implanted neural electrode arrays have the potential to be used as neural prostheses in patients with various neurological disorders. While these electrodes perform well in acute recordings, they often fail to function reliably in clinically relevant chronic settings because of glial encapsulation and the loss of neurons. Surface modification of these implants may provide a means of improving their biocompatibility and integration within host brain tissue. The authors proposed a method of improving the brain-implant interface by seeding the implant's surface with a layer of neural progenitor cells (NPCs) derived from adult murine subependyma. Neural progenitor cells may reduce the foreign body reaction by presenting a tissue-friendly surface and repair implant-induced injury and inflammation by releasing neurotrophic factors. In this study, the authors evaluated the growth and differentiation of NPCs on laminin-immobilized probe surfaces and explored the potential impact on transplant survival of these cells. Laminin protein was successfully immobilized on the silicon surface via covalent binding using silane chemistry. The growth, adhesion, and differentiation of NPCs expressing green fluorescent protein (GFP) on laminin-modified silicon surfaces were characterized in vitro by using immunocytochemical techniques. Shear forces were applied to NPC cultures in growth medium to evaluate their shearing properties. In addition, neural probes seeded with GFP-labeled NPCs cultured in growth medium for 14 days were implanted in murine cortex. The authors assessed the adhesion properties of these cells during implantation conditions. Moreover, the tissue response around NPC-seeded implants was observed after 1 and 7 days postimplantation. Significantly improved NPC attachment and growth was found on the laminin-immobilized surface compared with an unmodified control before and after shear force application. The NPCs grown on the laminin-immobilized surface

  10. Radar based autonomous sensor module

    Science.gov (United States)

    Styles, Tim

    2016-10-01

    Most surveillance systems combine camera sensors with other detection sensors that trigger an alert to a human operator when an object is detected. The detection sensors typically require careful installation and configuration for each application and there is a significant burden on the operator to react to each alert by viewing camera video feeds. A demonstration system known as Sensing for Asset Protection with Integrated Electronic Networked Technology (SAPIENT) has been developed to address these issues using Autonomous Sensor Modules (ASM) and a central High Level Decision Making Module (HLDMM) that can fuse the detections from multiple sensors. This paper describes the 24 GHz radar based ASM, which provides an all-weather, low power and license exempt solution to the problem of wide area surveillance. The radar module autonomously configures itself in response to tasks provided by the HLDMM, steering the transmit beam and setting range resolution and power levels for optimum performance. The results show the detection and classification performance for pedestrians and vehicles in an area of interest, which can be modified by the HLDMM without physical adjustment. The module uses range-Doppler processing for reliable detection of moving objects and combines Radar Cross Section and micro-Doppler characteristics for object classification. Objects are classified as pedestrian or vehicle, with vehicle sub classes based on size. Detections are reported only if the object is detected in a task coverage area and it is classified as an object of interest. The system was shown in a perimeter protection scenario using multiple radar ASMs, laser scanners, thermal cameras and visible band cameras. This combination of sensors enabled the HLDMM to generate reliable alerts with improved discrimination of objects and behaviours of interest.

  11. The neural bases of feeling understood and not understood.

    Science.gov (United States)

    Morelli, Sylvia A; Torre, Jared B; Eisenberger, Naomi I

    2014-12-01

    Past research suggests that feeling understood enhances both personal and social well-being. However, little research has examined the neurobiological bases of feeling understood and not understood. We addressed these gaps by experimentally inducing felt understanding and not understanding as participants underwent functional magnetic resonance imaging. The results demonstrated that feeling understood activated neural regions previously associated with reward and social connection (i.e. ventral striatum and middle insula), while not feeling understood activated neural regions previously associated with negative affect (i.e. anterior insula). Both feeling understood and not feeling understood activated different components of the mentalizing system (feeling understood: precuneus and temporoparietal junction; not feeling understood: dorsomedial prefrontal cortex). Neural responses were associated with subsequent feelings of social connection and disconnection and were modulated by individual differences in rejection sensitivity. Thus, this study provides insight into the psychological processes underlying feeling understood (or not) and may suggest new avenues for targeted interventions that amplify the benefits of feeling understood or buffer individuals from the harmful consequences of not feeling understood. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  12. CNS immunological modulation of neural graft rejection and survival.

    Science.gov (United States)

    Borlongan, C V; Stahl, C E; Cameron, D F; Saporta, S; Freeman, T B; Cahill, D W; Sanberg, P R

    1996-08-01

    Neural transplantation therapy as a possible alternative treatment for neurological movement disorders, such as in Parkinson's disease (PD), has accentuated research interest on the immune status of the central nervous system (CNS). Most animal studies concerned with neural transplantation for the treatment of PD have utilized dopamine (DA) neurons from tissues of the embryonic ventral mesencephalon. Rat embryonic DA neurons, grafted either as solid blocks or dissociated into a cell suspension and stereotaxically injected intraparenchymally into a rat lesion model of PD, have been shown to survive and form connections with the host brain, and ameliorate the behavioral deficits of PD. Similarly, studies on nonhuman primate models of PD provide considerable support for neural transplantation of DA neurons as an experimental clinical procedure for the treatment of PD. To this end, experimental clinical trials have been centered upon transplantation of the embryonic ventral mesencephalic cells for PD patients. Although not conclusive, the findings from clinical studies have provided some evidence that most patients with marked increases in fluorodopa uptake (indicating graft survival) have been immunosuppressed. Furthermore, immune reactions have been observed in rats xenografted with human embryonic tissue. Of note, embryonic ventral mesencephalic tissues compared to adult tissues produce better morphological and long-lasting behavioral amelioration of the neurobehavioral deficits of PD, thus advocating the use of grafts from young donors (embryo) to circumvent the CNS immune rejection. The possible graft rejection due to CNS immune reactions, coupled with the social and ethical problems surrounding the use of embryonic neural tissue, and the logistical problems concerning tissue availability have prompted the development of alternative sources of DA-secreting cells. To circumvent these obstacles, several methods have been suggested including the use of

  13. General anesthesia: a gateway to modulate synapse formation and neural plasticity?

    Science.gov (United States)

    Vutskits, Laszlo

    2012-11-01

    Appropriate balance between excitatory and inhibitory neural activity patterns is of utmost importance in the maintenance of neuronal homeostasis. General anesthetic-induced pharmacological interference with this equilibrium results not only in a temporary loss of consciousness but can also initiate long-term changes in brain function. Although these alterations were initially considered deleterious, recent observations suggest that at least under some specific conditions, they may eventually improve neural function. The goal of this review is to provide insights into the mechanisms underlying these dual effects. Basic science issues on the important role of critical periods during neural circuitry assembly will be discussed to better understand how even brief exposures to general anesthetics could initiate context-dependent lasting changes in neuronal structure and function. Recent series of observations suggesting a developmental stage-dependent impact of these drugs on synaptogenesis will then be summarized together with currently known molecular mechanisms underlying these effects. Particular emphasis will be placed on how anesthetic drugs modulate neural plasticity in the adult brain and how this may improve neural function under some pathological states. The ensemble of these new observations strongly suggests that general anesthetics should not merely be considered toxic drugs but rather acknowledged as robust, context-dependent modulators of neural plasticity.

  14. Abacus Training Modulates the Neural Correlates of Exact and Approximate Calculations in Chinese Children: An fMRI Study

    Directory of Open Access Journals (Sweden)

    Fenglei Du

    2013-01-01

    Full Text Available Exact (EX and approximate (AP calculations rely on distinct neural circuits. However, the training effect on the neural correlates of EX and AP calculations is largely unknown, especially for the AP calculation. Abacus-based mental calculation (AMC is a particular arithmetic skill that can be acquired by long-term abacus training. The present study investigated whether and how the abacus training modulates the neural correlates of EX and AP calculations by functional magnetic resonance imaging (fMRI. Neural activations were measured in 20 abacus-trained and 19 nontrained Chinese children during AP and EX calculation tasks. Our results demonstrated that: (1 in nontrained children, similar neural regions were activated in both tasks, while the size of activated regions was larger in AP than those in the EX; (2 in abacus-trained children, no significant difference was found between these two tasks; (3 more visuospatial areas were activated in abacus-trained children under the EX task compared to the nontrained. These results suggested that more visuospatial strategies were used by the nontrained children in the AP task compared to the EX; abacus-trained children adopted a similar strategy in both tasks; after long-term abacus training, children were more inclined to apply a visuospatial strategy during processing EX calculations.

  15. Abacus training modulates the neural correlates of exact and approximate calculations in Chinese children: an fMRI study.

    Science.gov (United States)

    Du, Fenglei; Chen, Feiyan; Li, Yongxin; Hu, Yuzheng; Tian, Mei; Zhang, Hong

    2013-01-01

    Exact (EX) and approximate (AP) calculations rely on distinct neural circuits. However, the training effect on the neural correlates of EX and AP calculations is largely unknown, especially for the AP calculation. Abacus-based mental calculation (AMC) is a particular arithmetic skill that can be acquired by long-term abacus training. The present study investigated whether and how the abacus training modulates the neural correlates of EX and AP calculations by functional magnetic resonance imaging (fMRI). Neural activations were measured in 20 abacus-trained and 19 nontrained Chinese children during AP and EX calculation tasks. Our results demonstrated that: (1) in nontrained children, similar neural regions were activated in both tasks, while the size of activated regions was larger in AP than those in the EX; (2) in abacus-trained children, no significant difference was found between these two tasks; (3) more visuospatial areas were activated in abacus-trained children under the EX task compared to the nontrained. These results suggested that more visuospatial strategies were used by the nontrained children in the AP task compared to the EX; abacus-trained children adopted a similar strategy in both tasks; after long-term abacus training, children were more inclined to apply a visuospatial strategy during processing EX calculations.

  16. Modulation of the homophilic interaction between the first and second Ig modules of neural cell adhesion molecule by heparin

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Rudenko, Olga; Kiselyov, V.

    2005-01-01

    The second Ig module (IgII) of the neural cell adhesion molecule (NCAM) is known to bind to the first Ig module (IgI) of NCAM (so-called homophilic binding) and to interact with heparan sulfate and chondroitin sulfate glycoconjugates. We here show by NMR that the heparin and chondroitin sulfate......II. Accordingly, we show that treatment of cerebellar granule neurons (CGNs) with heparin inhibits NCAM-mediated outgrowth. In contrast, treatment with heparinase III or chondroitinase ABC abrogates NCAM-mediated neurite outgrowth in CGNs emphasizing the importance of the presence of heparan/chondroitin sulfates...

  17. Diminished behavioral and neural sensitivity to sound modulation is associated with moderate developmental hearing loss.

    Directory of Open Access Journals (Sweden)

    Merri J Rosen

    Full Text Available The acoustic rearing environment can alter central auditory coding properties, yet altered neural coding is seldom linked with specific deficits to adult perceptual skills. To test whether developmental hearing loss resulted in comparable changes to perception and sensory coding, we examined behavioral and neural detection thresholds for sinusoidally amplitude modulated (sAM stimuli. Behavioral sAM detection thresholds for slow (5 Hz modulations were significantly worse for animals reared with bilateral conductive hearing loss (CHL, as compared to controls. This difference could not be attributed to hearing thresholds, proficiency at the task, or proxies for attention. Detection thresholds across the groups did not differ for fast (100 Hz modulations, a result paralleling that seen in humans. Neural responses to sAM stimuli were recorded in single auditory cortex neurons from separate groups of awake animals. Neurometric analyses indicated equivalent thresholds for the most sensitive neurons, but a significantly poorer detection threshold for slow modulations across the population of CHL neurons as compared to controls. The magnitude of the neural deficit matched that of the behavioral differences, suggesting that a reduction of sensory information can account for limitations to perceptual skills.

  18. Attenuation of β-Amyloid Deposition and Neurotoxicity by Chemogenetic Modulation of Neural Activity.

    Science.gov (United States)

    Yuan, Peng; Grutzendler, Jaime

    2016-01-13

    Aberrant neural hyperactivity has been observed in early stages of Alzheimer's disease (AD) and may be a driving force in the progression of amyloid pathology. Evidence for this includes the findings that neural activity may modulate β-amyloid (Aβ) peptide secretion and experimental stimulation of neural activity can increase amyloid deposition. However, whether long-term attenuation of neural activity prevents the buildup of amyloid plaques and associated neural pathologies remains unknown. Using viral-mediated delivery of designer receptors exclusively activated by designer drugs (DREADDs), we show in two AD-like mouse models that chronic intermittent increases or reductions of activity have opposite effects on Aβ deposition. Neural activity reduction markedly decreases Aβ aggregation in regions containing axons or dendrites of DREADD-expressing neurons, suggesting the involvement of synaptic and nonsynaptic Aβ release mechanisms. Importantly, activity attenuation is associated with a reduction in axonal dystrophy and synaptic loss around amyloid plaques. Thus, modulation of neural activity could constitute a potential therapeutic strategy for ameliorating amyloid-induced pathology in AD. A novel chemogenetic approach to upregulate and downregulate neuronal activity in Alzheimer's disease (AD) mice was implemented. This led to the first demonstration that chronic intermittent attenuation of neuronal activity in vivo significantly reduces amyloid deposition. The study also demonstrates that modulation of β-amyloid (Aβ) release can occur at both axonal and dendritic fields, suggesting the involvement of synaptic and nonsynaptic Aβ release mechanisms. Activity reductions also led to attenuation of the synaptic pathology associated with amyloid plaques. Therefore, chronic attenuation of neuronal activity could constitute a novel therapeutic approach for AD. Copyright © 2016 the authors 0270-6474/16/360632-10$15.00/0.

  19. Task relevance modulates the behavioural and neural effects of sensory predictions.

    Science.gov (United States)

    Auksztulewicz, Ryszard; Friston, Karl J; Nobre, Anna C

    2017-12-01

    The brain is thought to generate internal predictions to optimize behaviour. However, it is unclear whether predictions signalling is an automatic brain function or depends on task demands. Here, we manipulated the spatial/temporal predictability of visual targets, and the relevance of spatial/temporal information provided by auditory cues. We used magnetoencephalography (MEG) to measure participants' brain activity during task performance. Task relevance modulated the influence of predictions on behaviour: spatial/temporal predictability improved spatial/temporal discrimination accuracy, but not vice versa. To explain these effects, we used behavioural responses to estimate subjective predictions under an ideal-observer model. Model-based time-series of predictions and prediction errors (PEs) were associated with dissociable neural responses: predictions correlated with cue-induced beta-band activity in auditory regions and alpha-band activity in visual regions, while stimulus-bound PEs correlated with gamma-band activity in posterior regions. Crucially, task relevance modulated these spectral correlates, suggesting that current goals influence PE and prediction signalling.

  20. Modulation of Neural Activity during Guided Viewing of Visual Art

    Directory of Open Access Journals (Sweden)

    Guillermo Herrera-Arcos

    2017-11-01

    Full Text Available Mobile Brain-Body Imaging (MoBI technology was deployed to record multi-modal data from 209 participants to examine the brain’s response to artistic stimuli at the Museo de Arte Contemporáneo (MARCO in Monterrey, México. EEG signals were recorded as the subjects walked through the exhibit in guided groups of 6–8 people. Moreover, guided groups were either provided with an explanation of each art piece (Guided-E, or given no explanation (Guided-NE. The study was performed using portable Muse (InteraXon, Inc, Toronto, ON, Canada headbands with four dry electrodes located at AF7, AF8, TP9, and TP10. Each participant performed a baseline (BL control condition devoid of artistic stimuli and selected his/her favorite piece of art (FP during the guided tour. In this study, we report data related to participants’ demographic information and aesthetic preference as well as effects of art viewing on neural activity (EEG in a select subgroup of 18–30 year-old subjects (Nc = 25 that generated high-quality EEG signals, on both BL and FP conditions. Dependencies on gender, sensor placement, and presence or absence of art explanation were also analyzed. After denoising, clustering of spectral EEG models was used to identify neural patterns associated with BL and FP conditions. Results indicate statistically significant suppression of beta band frequencies (15–25 Hz in the prefrontal electrodes (AF7 and AF8 during appreciation of subjects’ favorite painting, compared to the BL condition, which was significantly different from EEG responses to non-favorite paintings (NFP. No significant differences in brain activity in relation to the presence or absence of explanation during exhibit tours were found. Moreover, a frontal to posterior asymmetry in neural activity was observed, for both BL and FP conditions. These findings provide new information about frequency-related effects of preferred art viewing in brain activity, and support the view that art

  1. Modulation of Neural Activity during Guided Viewing of Visual Art

    Science.gov (United States)

    Herrera-Arcos, Guillermo; Tamez-Duque, Jesús; Acosta-De-Anda, Elsa Y.; Kwan-Loo, Kevin; de-Alba, Mayra; Tamez-Duque, Ulises; Contreras-Vidal, Jose L.; Soto, Rogelio

    2017-01-01

    Mobile Brain-Body Imaging (MoBI) technology was deployed to record multi-modal data from 209 participants to examine the brain’s response to artistic stimuli at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México. EEG signals were recorded as the subjects walked through the exhibit in guided groups of 6–8 people. Moreover, guided groups were either provided with an explanation of each art piece (Guided-E), or given no explanation (Guided-NE). The study was performed using portable Muse (InteraXon, Inc, Toronto, ON, Canada) headbands with four dry electrodes located at AF7, AF8, TP9, and TP10. Each participant performed a baseline (BL) control condition devoid of artistic stimuli and selected his/her favorite piece of art (FP) during the guided tour. In this study, we report data related to participants’ demographic information and aesthetic preference as well as effects of art viewing on neural activity (EEG) in a select subgroup of 18–30 year-old subjects (Nc = 25) that generated high-quality EEG signals, on both BL and FP conditions. Dependencies on gender, sensor placement, and presence or absence of art explanation were also analyzed. After denoising, clustering of spectral EEG models was used to identify neural patterns associated with BL and FP conditions. Results indicate statistically significant suppression of beta band frequencies (15–25 Hz) in the prefrontal electrodes (AF7 and AF8) during appreciation of subjects’ favorite painting, compared to the BL condition, which was significantly different from EEG responses to non-favorite paintings (NFP). No significant differences in brain activity in relation to the presence or absence of explanation during exhibit tours were found. Moreover, a frontal to posterior asymmetry in neural activity was observed, for both BL and FP conditions. These findings provide new information about frequency-related effects of preferred art viewing in brain activity, and support the view that art

  2. Modulation of Neural Activity during Guided Viewing of Visual Art.

    Science.gov (United States)

    Herrera-Arcos, Guillermo; Tamez-Duque, Jesús; Acosta-De-Anda, Elsa Y; Kwan-Loo, Kevin; de-Alba, Mayra; Tamez-Duque, Ulises; Contreras-Vidal, Jose L; Soto, Rogelio

    2017-01-01

    Mobile Brain-Body Imaging (MoBI) technology was deployed to record multi-modal data from 209 participants to examine the brain's response to artistic stimuli at the Museo de Arte Contemporáneo (MARCO) in Monterrey, México. EEG signals were recorded as the subjects walked through the exhibit in guided groups of 6-8 people. Moreover, guided groups were either provided with an explanation of each art piece (Guided-E), or given no explanation (Guided-NE). The study was performed using portable Muse (InteraXon, Inc, Toronto, ON, Canada) headbands with four dry electrodes located at AF7, AF8, TP9, and TP10. Each participant performed a baseline (BL) control condition devoid of artistic stimuli and selected his/her favorite piece of art (FP) during the guided tour. In this study, we report data related to participants' demographic information and aesthetic preference as well as effects of art viewing on neural activity (EEG) in a select subgroup of 18-30 year-old subjects (Nc = 25) that generated high-quality EEG signals, on both BL and FP conditions. Dependencies on gender, sensor placement, and presence or absence of art explanation were also analyzed. After denoising, clustering of spectral EEG models was used to identify neural patterns associated with BL and FP conditions. Results indicate statistically significant suppression of beta band frequencies (15-25 Hz) in the prefrontal electrodes (AF7 and AF8) during appreciation of subjects' favorite painting, compared to the BL condition, which was significantly different from EEG responses to non-favorite paintings (NFP). No significant differences in brain activity in relation to the presence or absence of explanation during exhibit tours were found. Moreover, a frontal to posterior asymmetry in neural activity was observed, for both BL and FP conditions. These findings provide new information about frequency-related effects of preferred art viewing in brain activity, and support the view that art appreciation is

  3. Oxytocin modulation of neural circuits for social behavior.

    Science.gov (United States)

    Marlin, Bianca J; Froemke, Robert C

    2017-02-01

    Oxytocin is a hypothalamic neuropeptide that has gained attention for the effects on social behavior. Recent findings shed new light on the mechanisms of oxytocin in synaptic plasticity and adaptively modifying neural circuits for social interactions such as conspecific recognition, pair bonding, and maternal care. Here, we review several of these newer studies on oxytocin in the context of previous findings, with an emphasis on social behavior and circuit plasticity in various brain regions shown to be enriched for oxytocin receptors. We provide a framework that highlights current circuit-level mechanisms underlying the widespread action of oxytocin. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 169-189, 2017. © 2016 Wiley Periodicals, Inc.

  4. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  5. Abnormal Task Modulation of Oscillatory Neural Activity in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Elisa C Dias

    2013-08-01

    Full Text Available Schizophrenia patients have deficits in cognitive function that are a core feature of the disorder. AX-CPT is commonly used to study cognition in schizophrenia, and patients have characteristic pattern of behavioral and ERP response. In AX-CPT subjects respond when a flashed cue A is followed by a target X, ignoring other letter combinations. Patients show reduced hit rate to go trials, and increased false alarms to sequences that require inhibition of a prepotent response. EEG recordings show reduced sensory (P1/N1, as well as later cognitive components (N2, P3, CNV. Behavioral deficits correlate most strongly with sensory dysfunction. Oscillatory analyses provide critical information regarding sensory/cognitive processing over and above standard ERP analyses. Recent analyses of induced oscillatory activity in single trials during AX-CPT in healthy volunteers showed characteristic response patterns in theta, alpha and beta frequencies tied to specific sensory and cognitive processes. Alpha and beta modulated during the trials and beta modulation over the frontal cortex correlated with reaction time. In this study, EEG data was obtained from 18 schizophrenia patients and 13 controls during AX-CPT performance, and single trial decomposition of the signal yielded power in the target wavelengths.Significant task-related event-related desynchronization (ERD was observed in both alpha and beta frequency bands over parieto-occipital cortex related to sensory encoding of the cue. This modulation was reduced in patients for beta, but not for alpha. In addition, significant beta ERD was observed over motor cortex, related to motor preparation for the response, and was also reduced in patients. These findings demonstrate impaired dynamic modulation of beta frequency rhythms in schizophrenia, and suggest that failures of oscillatory activity may underlie impaired sensory information processing in schizophrenia that in turn contributes to cognitive deficits.

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

    Science.gov (United States)

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

    2017-08-01

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

  7. Neural bases for addictive properties of benzodiazepines.

    Science.gov (United States)

    Tan, Kelly R; Brown, Matthew; Labouèbe, Gwenaël; Yvon, Cédric; Creton, Cyril; Fritschy, Jean-Marc; Rudolph, Uwe; Lüscher, Christian

    2010-02-11

    Benzodiazepines are widely used in clinics and for recreational purposes, but will lead to addiction in vulnerable individuals. Addictive drugs increase the levels of dopamine and also trigger long-lasting synaptic adaptations in the mesolimbic reward system that ultimately may induce the pathological behaviour. The neural basis for the addictive nature of benzodiazepines, however, remains elusive. Here we show that benzodiazepines increase firing of dopamine neurons of the ventral tegmental area through the positive modulation of GABA(A) (gamma-aminobutyric acid type A) receptors in nearby interneurons. Such disinhibition, which relies on alpha1-containing GABA(A) receptors expressed in these cells, triggers drug-evoked synaptic plasticity in excitatory afferents onto dopamine neurons and underlies drug reinforcement. Taken together, our data provide evidence that benzodiazepines share defining pharmacological features of addictive drugs through cell-type-specific expression of alpha1-containing GABA(A) receptors in the ventral tegmental area. The data also indicate that subunit-selective benzodiazepines sparing alpha1 may be devoid of addiction liability.

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

    Directory of Open Access Journals (Sweden)

    Katja Wiech

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

  9. Broadband optical modulators based on graphene supercapacitors.

    Science.gov (United States)

    Polat, Emre O; Kocabas, Coskun

    2013-01-01

    Optical modulators are commonly used in communication and information technology to control intensity, phase, or polarization of light. Electro-optic, electroabsorption, and acousto-optic modulators based on semiconductors and compound semiconductors have been used to control the intensity of light. Because of gate tunable optical properties, graphene introduces new potentials for optical modulators. The operation wavelength of graphene-based modulators, however, is limited to infrared wavelengths due to inefficient gating schemes. Here, we report a broadband optical modulator based on graphene supercapacitors formed by graphene electrodes and electrolyte medium. The transparent supercapacitor structure allows us to modulate optical transmission over a broad range of wavelengths from 450 nm to 2 μm under ambient conditions. We also provide various device geometries including multilayer graphene electrodes and reflection type device geometries that provide modulation of 35%. The graphene supercapacitor structure together with the high-modulation efficiency can enable various active devices ranging from plasmonics to optoelectronics.

  10. Object Classification Using Substance Based Neural Network

    Directory of Open Access Journals (Sweden)

    P. Sengottuvelan

    2014-01-01

    Full Text Available Object recognition has shown tremendous increase in the field of image analysis. The required set of image objects is identified and retrieved on the basis of object recognition. In this paper, we propose a novel classification technique called substance based image classification (SIC using a wavelet neural network. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect the shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions, the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10–15%.

  11. How instructed knowledge modulates the neural systems of reward learning.

    Science.gov (United States)

    Li, Jian; Delgado, Mauricio R; Phelps, Elizabeth A

    2011-01-04

    Recent research in neuroeconomics has demonstrated that the reinforcement learning model of reward learning captures the patterns of both behavioral performance and neural responses during a range of economic decision-making tasks. However, this powerful theoretical model has its limits. Trial-and-error is only one of the means by which individuals can learn the value associated with different decision options. Humans have also developed efficient, symbolic means of communication for learning without the necessity for committing multiple errors across trials. In the present study, we observed that instructed knowledge of cue-reward probabilities improves behavioral performance and diminishes reinforcement learning-related blood-oxygen level-dependent (BOLD) responses to feedback in the nucleus accumbens, ventromedial prefrontal cortex, and hippocampal complex. The decrease in BOLD responses in these brain regions to reward-feedback signals was functionally correlated with activation of the dorsolateral prefrontal cortex (DLPFC). These results suggest that when learning action values, participants use the DLPFC to dynamically adjust outcome responses in valuation regions depending on the usefulness of action-outcome information.

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

    Science.gov (United States)

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

    2012-03-01

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

  13. Modulated neural processing of Western harmony in folk musicians.

    Science.gov (United States)

    Brattico, Elvira; Tupala, Tiina; Glerean, Enrico; Tervaniemi, Mari

    2013-07-01

    A chord deviating from the conventions of Western tonal music elicits an early right anterior negativity (ERAN) in inferofrontal brain regions. Here, we tested whether the ERAN is modulated by expertise in more than one music culture, as typical of folk musicians. Finnish folk musicians and nonmusicians participated in electroencephalography recordings. The cadences consisted of seven chords. In incongruous cadences, the third, fifth, or seventh chord was a Neapolitan. The ERAN to the Neapolitans was enhanced in folk musicians compared to nonmusicians. Folk musicians showed an enhanced P3a for the ending Neapolitan. The Neapolitan at the fifth position was perceived differently and elicited a late enhanced ERAN in folk musicians. Hence, expertise in more than one music culture seems to modify chord processing by enhancing the ERAN to ambivalent chords and the P3a to incongruous chords, and by altering their perceptual attributes. Copyright © 2013 Society for Psychophysiological Research.

  14. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

    Full Text Available In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1 early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2 subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circuits are “hard-wired” in a purely activity-independent manner. This conclusion has been challenged recently through the use of new transgenic tools employed in the powerful Drosophila system, which have allowed unprecedented temporal control and single neuron imaging resolution. These recent studies reveal that activity-dependent mechanisms are indeed required to refine circuit maps in Drosophila during precise, restricted windows of late-phase development. Such mechanisms of circuit refinement may be key to understanding a number of human neurological diseases, including developmental disorders such as Fragile X syndrome (FXS and autism, which are hypothesized to result from defects in synaptic connectivity and activity-dependent circuit function. This review focuses on our current understanding of activity-dependent synaptic connectivity in Drosophila, primarily through analyzing the role of the fragile X mental retardation protein (FMRP in the Drosophila FXS disease model. The particular emphasis of this review is on the expanding array of new genetically-encoded tools that are allowing cellular events and molecular players to be dissected with ever greater precision and detail.

  15. Single dose antidepressant administration modulates the neural processing of self-referent personality trait words

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla; Papadatou-Pastou, Marietta; Cowen, Philip J

    2007-01-01

    Drugs which inhibit the re-uptake of monoamines in the brain are effective in the treatment of depression; however, the neuropsychological mechanisms which lead to the resolution of depressive symptomatology are unclear. Behavioral studies in healthy volunteers suggest that acute administration...... of the selective norepinephrine reuptake inhibitor reboxetine modulates emotional processing. The current study therefore explored the neural basis of this effect. A single dose of reboxetine (4 mg) or placebo was administered to 24 healthy volunteers in a double-blind between-group design. Neural responses during...... for positive self-referent material. These results support the hypothesis that antidepressants have early effects on the neural processing of emotional material which may be important in their therapeutic actions....

  16. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  17. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  18. Money talks: neural substrate of modulation of fairness by monetary incentives.

    Science.gov (United States)

    Zhou, Yuan; Wang, Yun; Rao, Li-Lin; Yang, Liu-Qing; Li, Shu

    2014-01-01

    A unique feature of the human species is compliance with social norms, e.g., fairness, even though this normative decision means curbing self-interest. However, sometimes people prefer to pursue wealth at the expense of moral goodness. Specifically, deviations from a fairness-related normative choice have been observed in the presence of a high monetary incentive. The neural mechanism underlying this deviation from the fairness-related normative choice has yet to be determined. In order to address this issue, using functional magnetic resonance imaging we employed an ultimatum game (UG) paradigm in which fairness and a proposed monetary amount were orthogonally varied. We found evidence for a significant modulation by the proposed amount on fairness in the right lateral prefrontal cortex (PFC) and the bilateral insular cortices. Additionally, the insular subregions showed dissociable modulation patterns. Inter-individual differences in the modulation effects in the left inferior frontal gyrus (IFG) accounted for inter-individual differences in the behavioral modulation effect as measured by the rejection rate, supporting the concept that the PFC plays a critical role in making fairness-related normative decisions in a social interaction condition. Our findings provide neural evidence for the modulation of fairness by monetary incentives as well as accounting for inter-individual differences.

  19. Money talks: Neural substrate of modulation of fairness by monetary incentives

    Directory of Open Access Journals (Sweden)

    Yuan eZhou

    2014-05-01

    Full Text Available A unique feature of the human species is compliance with social norms, e.g., fairness, even though this normative decision means curbing self-interest. However, sometimes people prefer to pursue wealth at the expense of moral goodness. Specifically, deviations from a fairness-related normative choice have been observed in the presence of a high monetary incentive. The neural mechanism underlying this deviation from the fairness-related normative choice has yet to be determined. In order to address this issue, using functional magnetic resonance imaging we employed an ultimatum game paradigm in which fairness and a proposed monetary amount were orthogonally varied. We found evidence for a significant modulation by the proposed amount on fairness in the right lateral prefrontal cortex and the bilateral insular cortices. Additionally, the insular subregions showed dissociable modulation patterns. Inter-individual differences in the modulation effects in the left inferior frontal gyrus accounted for inter-individual differences in the behavioral modulation effect as measured by the rejection rate, supporting the concept that the prefrontal cortex plays a critical role in making fairness-related normative decisions in a social interaction condition. Our findings provide neural evidence for the modulation of fairness by monetary incentives as well as accounting for inter-individual differences.

  20. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  1. Neural bases of accented speech perception

    OpenAIRE

    Patti eAdank; Nuttall, Helen E.; Briony eBanks; Dan eKennedy-Higgins

    2015-01-01

    The recognition of unfamiliar regional and foreign accents represents a challenging task for the speech perception system (Adank, Evans, Stuart-Smith, & Scott, 2009; Floccia, Goslin, Girard, & Konopczynski, 2006). Despite the frequency with which we encounter such accents, the neural mechanisms supporting successful perception of accented speech are poorly understood. Nonetheless, candidate neural substrates involved in processing speech in challenging listening conditions, including accented...

  2. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  3. Modeling anterior development in mice: diet as modulator of risk for neural tube defects.

    Science.gov (United States)

    Kappen, Claudia

    2013-11-01

    Head morphogenesis is a complex process that is controlled by multiple signaling centers. The most common defects of cranial development are craniofacial defects, such as cleft lip and cleft palate, and neural tube defects, such as anencephaly and encephalocoele in humans. More than 400 genes that contribute to proper neural tube closure have been identified in experimental animals, but only very few causative gene mutations have been identified in humans, supporting the notion that environmental influences are critical. The intrauterine environment is influenced by maternal nutrition, and hence, maternal diet can modulate the risk for cranial and neural tube defects. This article reviews recent progress toward a better understanding of nutrients during pregnancy, with particular focus on mouse models for defective neural tube closure. At least four major patterns of nutrient responses are apparent, suggesting that multiple pathways are involved in the response, and likely in the underlying pathogenesis of the defects. Folic acid has been the most widely studied nutrient, and the diverse responses of the mouse models to folic acid supplementation indicate that folic acid is not universally beneficial, but that the effect is dependent on genetic configuration. If this is the case for other nutrients as well, efforts to prevent neural tube defects with nutritional supplementation may need to become more specifically targeted than previously appreciated. Mouse models are indispensable for a better understanding of nutrient-gene interactions in normal pregnancies, as well as in those affected by metabolic diseases, such as diabetes and obesity. © 2013 Wiley Periodicals, Inc.

  4. Complex Wavelet Based Modulation Analysis

    DEFF Research Database (Denmark)

    Luneau, Jean-Marc; Lebrun, Jérôme; Jensen, Søren Holdt

    2008-01-01

    Low-frequency modulation of sound carry important information for speech and music. The modulation spectrum i commonly obtained by spectral analysis of the sole temporal envelopes of the sub-bands out of a time-frequency analysis. Processing in this domain usually creates undesirable distortions...... polynomial trends. Moreover an analytic Hilbert-like transform is possible with complex wavelets implemented as an orthogonal filter bank. By working in an alternative transform domain coined as “Modulation Subbands”, this transform shows very promising denoising capabilities and suggests new approaches for joint...

  5. H-1 and N-15 resonance assignment of the second fibronectin type III module of the neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Kiselyov, Vladislav V; Berezin, Vladimir; Bock, Elisabeth

    2008-01-01

    We report here the NMR assignment of the second fibronectin type III module of the neural cell adhesion molecule (NCAM). This module has previously been shown to interact with the fibroblast growth factor receptor (FGFR), and the FGFR-binding site was mapped by NMR to the FG-loop region of the mo......We report here the NMR assignment of the second fibronectin type III module of the neural cell adhesion molecule (NCAM). This module has previously been shown to interact with the fibroblast growth factor receptor (FGFR), and the FGFR-binding site was mapped by NMR to the FG-loop region...

  6. Randomised prior feedback modulates neural signals of outcome monitoring.

    Science.gov (United States)

    Mushtaq, Faisal; Wilkie, Richard M; Mon-Williams, Mark A; Schaefer, Alexandre

    2016-01-15

    Substantial evidence indicates that decision outcomes are typically evaluated relative to expectations learned from relatively long sequences of previous outcomes. This mechanism is thought to play a key role in general learning and adaptation processes but relatively little is known about the determinants of outcome evaluation when the capacity to learn from series of prior events is difficult or impossible. To investigate this issue, we examined how the feedback-related negativity (FRN) is modulated by information briefly presented before outcome evaluation. The FRN is a brain potential time-locked to the delivery of decision feedback and it is widely thought to be sensitive to prior expectations. We conducted a multi-trial gambling task in which outcomes at each trial were fully randomised to minimise the capacity to learn from long sequences of prior outcomes. Event-related potentials for outcomes (Win/Loss) in the current trial (Outcomet) were separated according to the type of outcomes that occurred in the preceding two trials (Outcomet-1 and Outcomet-2). We found that FRN voltage was more positive during the processing of win feedback when it was preceded by wins at Outcomet-1 compared to win feedback preceded by losses at Outcomet-1. However, no influence of preceding outcomes was found on FRN activity relative to the processing of loss feedback. We also found no effects of Outcomet-2 on FRN amplitude relative to current feedback. Additional analyses indicated that this effect was largest for trials in which participants selected a decision different to the gamble chosen in the previous trial. These findings are inconsistent with models that solely relate the FRN to prediction error computation. Instead, our results suggest that if stable predictions about future events are weak or non-existent, then outcome processing can be determined by affective systems. More specifically, our results indicate that the FRN is likely to reflect the activity of positive

  7. Gaze Direction Modulates the Relation between Neural Responses to Faces and Visual Awareness.

    Science.gov (United States)

    Madipakkam, Apoorva Rajiv; Rothkirch, Marcus; Guggenmos, Matthias; Heinz, Andreas; Sterzer, Philipp

    2015-09-30

    Gaze direction and especially direct gaze is a powerful nonverbal cue that plays an important role in social interactions. Here we studied the neural mechanisms underlying the privileged access of direct gaze to visual awareness. We performed functional magnetic resonance imaging in healthy human volunteers who were exposed to faces with direct or averted gaze under continuous flash suppression, thereby manipulating their awareness of the faces. A gaze processing network comprising fusiform face area (FFA), superior temporal sulcus, amygdala, and intraparietal sulcus showed overall reduced neural responses when participants reported to be unaware of the faces. Interestingly, direct gaze elicited greater responses than averted gaze when participants were aware of the faces, but smaller responses when they were unaware. Additional between-subject correlation and single-trial analyses indicated that this pattern of results was due to a modulation of the relationship between neural responses and awareness by gaze direction: with increasing neural activation in the FFA, direct-gaze faces entered awareness more readily than averted-gaze faces. These findings suggest that for direct gaze, lower levels of neural activity are sufficient to give rise to awareness than for averted gaze, thus providing a neural basis for privileged access of direct gaze to awareness. Significance statement: Another person's eye gaze directed at oneself is a powerful social signal acting as a catalyst for further communication. Here, we studied the neural mechanisms underlying the prioritized access of direct gaze to visual awareness in healthy human volunteers and show that with increasing neural activation, direct-gaze faces enter awareness more readily than averted-gaze faces. This suggests that for a socially highly relevant cue like direct gaze, lower levels of neural activity are sufficient to give rise to awareness compared with averted gaze, possibly because the human brain is attuned

  8. Tactile, gustatory, and visual biofeedback stimuli modulate neural substrates of deglutition.

    Science.gov (United States)

    Humbert, Ianessa A; Joel, Suresh

    2012-01-16

    It has been well established that swallowing kinematics are modified with different forms of exogenous and endogenous input, however the underlying neural substrates associated with these effects are largely unknown. Our objective was to determine whether the swallowing BOLD response is modulated with heightened sensory modalities (taste, cutaneous electrical stimulation, and visual biofeedback) compared to water ingestion (control) in healthy adults across the age span. Habituation and sensitization were also examined for each sensory condition. Our principal findings are that each sensory swallowing condition activated components of the swallowing cortical network, plus regions associated with the particular sensory modality (i.e. primarily frontal motor planning and integration areas with visual condition). Overall, the insula was most commonly active among the sensory modalities. We also discuss gradual increases and decreases in BOLD signal with repeated exposures for each condition. We conclude that both stimulus- and intention-based inputs have unique cortical swallowing networks relative to their modality. This scientific contribution advances our understanding of the mechanisms of normal swallowing cortical control and has the potential to impact clinical uses of these modalities in treatments for neurogenic dysphagia. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Material procedure quality forecast based on genetic BP neural network

    Science.gov (United States)

    Zheng, Bao-Hua

    2017-07-01

    Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

  10. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Consequently, many structures based on artificial neural network (ANN) have been developed in the literature, The most significant ... Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ..... and pure shunt active fitters, IEEE 38th Conf on Industry Applications, Vol. 2, pp.

  11. CDMA and TDMA based neural nets.

    Science.gov (United States)

    Herrero, J C

    2001-06-01

    CDMA and TDMA telecommunication techniques were established long time ago, but they have acquired a renewed presence due to the rapidly increasing mobile phones demand. In this paper, we are going to see they are suitable for neural nets, if we leave the concept "connection" between processing units and we adopt the concept "messages" exchanged between them. This may open the door to neural nets with a higher number of processing units and flexible configuration.

  12. Human Neural Cell-Based Biosensor

    Science.gov (United States)

    2013-05-28

    Orlando R, Stice SL. Membrane proteomic signatures of karyotypically normal and abnormal human embryonic stem cell lines and derivatives. Proteomics. 2011...format (96-,384-well) assays, 2) grow as adherent monolayers, and 3) possess a stable karyotype for multiple (>10) passages with a doubling time of ~36...derived neural progenitor cell line working stock has been amplified, characterized for karyotype and evaluated for the expression of neural progenitor

  13. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  14. Evidence of Rapid Modulation by Social Information of Subjective, Physiological, and Neural Responses to Emotional Expressions

    Directory of Open Access Journals (Sweden)

    Martial Mermillod

    2018-01-01

    Full Text Available Recent research suggests that conceptual or emotional factors could influence the perceptual processing of stimuli. In this article, we aimed to evaluate the effect of social information (positive, negative, or no information related to the character of the target on subjective (perceived and felt valence and arousal, physiological (facial mimicry as well as on neural (P100 and N170 responses to dynamic emotional facial expressions (EFE that varied from neutral to one of the six basic emotions. Across three studies, the results showed reduced ratings of valence and arousal of EFE associated with incongruent social information (Study 1, increased electromyographical responses (Study 2, and significant modulation of P100 and N170 components (Study 3 when EFE were associated with social (positive and negative information (vs. no information. These studies revealed that positive or negative social information reduces subjective responses to incongruent EFE and produces a similar neural and physiological boost of the early perceptual processing of EFE irrespective of their congruency. In conclusion, the article suggests that the presence of positive or negative social context modulates early physiological and neural activity preceding subsequent behavior.

  15. Neural Network-Based Segmentation of Textures Using Gabor Features

    OpenAIRE

    Ramakrishnan, AG; Raja, Kumar S; Ram, Ragu HV

    2002-01-01

    The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled environment. The neural network used for texture identification is based on the multilayer perceptr...

  16. D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

    Directory of Open Access Journals (Sweden)

    Shu-zhi Gao

    2014-01-01

    Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

  17. Analog neural network-based helicopter gearbox health monitoring system.

    Science.gov (United States)

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  18. Weather forecasting based on hybrid neural model

    Science.gov (United States)

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

    2017-02-01

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

  19. Representations in neural network based empirical potentials

    Science.gov (United States)

    Cubuk, Ekin D.; Malone, Brad D.; Onat, Berk; Waterland, Amos; Kaxiras, Efthimios

    2017-07-01

    Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a "black box" tool. This lack of understanding is not desirable especially for applications of neural networks in scientific inquiry. We argue that machine learning models trained on physical systems can be used as more than just approximations since they had to "learn" physical concepts in order to reproduce the labels they were trained on. We use dimensionality reduction techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model atomic interactions.

  20. 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.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

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

  3. 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

  4. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

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

  5. The neural bases for valuing social equality.

    Science.gov (United States)

    Aoki, Ryuta; Yomogida, Yukihito; Matsumoto, Kenji

    2015-01-01

    The neural basis of how humans value and pursue social equality has become a major topic in social neuroscience research. Although recent studies have identified a set of brain regions and possible mechanisms that are involved in the neural processing of equality of outcome between individuals, how the human brain processes equality of opportunity remains unknown. In this review article, first we describe the importance of the distinction between equality of outcome and equality of opportunity, which has been emphasized in philosophy and economics. Next, we discuss possible approaches for empirical characterization of human valuation of equality of opportunity vs. equality of outcome. Understanding how these two concepts are distinct and interact with each other may provide a better explanation of complex human behaviors concerning fairness and social equality. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  6. Corticofugal modulation of initial neural processing of sound information from the ipsilateral ear in the mouse.

    Directory of Open Access Journals (Sweden)

    Xiuping Liu

    2010-11-01

    Full Text Available Cortical neurons implement a high frequency-specific modulation of subcortical nuclei that includes the cochlear nucleus. Anatomical studies show that corticofugal fibers terminating in the auditory thalamus and midbrain are mostly ipsilateral. Differently, corticofugal fibers terminating in the cochlear nucleus are bilateral, which fits to the needs of binaural hearing that improves hearing quality. This leads to our hypothesis that corticofugal modulation of initial neural processing of sound information from the contralateral and ipsilateral ears could be equivalent or coordinated at the first sound processing level.With the focal electrical stimulation of the auditory cortex and single unit recording, this study examined corticofugal modulation of the ipsilateral cochlear nucleus. The same methods and procedures as described in our previous study of corticofugal modulation of contralateral cochlear nucleus were employed simply for comparison. We found that focal electrical stimulation of cortical neurons induced substantial changes in the response magnitude, response latency and receptive field of ipsilateral cochlear nucleus neurons. Cortical stimulation facilitated auditory response and shortened the response latency of physiologically matched neurons whereas it inhibited auditory response and lengthened the response latency of unmatched neurons. Finally, cortical stimulation shifted the best frequencies of cochlear neurons towards those of stimulated cortical neurons.Our data suggest that cortical neurons enable a high frequency-specific remodelling of sound information processing in the ipsilateral cochlear nucleus in the same manner as that in the contralateral cochlear nucleus.

  7. Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

    Science.gov (United States)

    Smith, Aaron; Evans, Michael; Downey, Joseph

    2017-01-01

    National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

  8. Corticofugal modulation of initial neural processing of sound information from the ipsilateral ear in the mouse.

    Science.gov (United States)

    Liu, Xiuping; Yan, Yuchu; Wang, Yalong; Yan, Jun

    2010-11-16

    Cortical neurons implement a high frequency-specific modulation of subcortical nuclei that includes the cochlear nucleus. Anatomical studies show that corticofugal fibers terminating in the auditory thalamus and midbrain are mostly ipsilateral. Differently, corticofugal fibers terminating in the cochlear nucleus are bilateral, which fits to the needs of binaural hearing that improves hearing quality. This leads to our hypothesis that corticofugal modulation of initial neural processing of sound information from the contralateral and ipsilateral ears could be equivalent or coordinated at the first sound processing level. With the focal electrical stimulation of the auditory cortex and single unit recording, this study examined corticofugal modulation of the ipsilateral cochlear nucleus. The same methods and procedures as described in our previous study of corticofugal modulation of contralateral cochlear nucleus were employed simply for comparison. We found that focal electrical stimulation of cortical neurons induced substantial changes in the response magnitude, response latency and receptive field of ipsilateral cochlear nucleus neurons. Cortical stimulation facilitated auditory response and shortened the response latency of physiologically matched neurons whereas it inhibited auditory response and lengthened the response latency of unmatched neurons. Finally, cortical stimulation shifted the best frequencies of cochlear neurons towards those of stimulated cortical neurons. Our data suggest that cortical neurons enable a high frequency-specific remodelling of sound information processing in the ipsilateral cochlear nucleus in the same manner as that in the contralateral cochlear nucleus.

  9. Neural activity in human primary motor cortex areas 4a and 4p is modulated differentially by attention to action

    OpenAIRE

    Binkofski, F.; Fink, Gereon R.; Geyer, Stefan; Buccino, G.; Gruber, Oliver; Shah, N. Jon; Taylor, John G.; Seitz, Rüdiger J.; Zilles, Karl; Freund, Hans-Joachim

    2002-01-01

    The mechanisms underlying attention to action are poorly understood. Although distracted by something else, we often maintain the accuracy of a movement, which suggests that differential neural mechanisms for the control of attended and nonattended action exist. Using functional magnetic resonance imaging (fMRI) in normal volunteers and probabilistic cytoarchitectonic maps, we observed that neural activity in subarea 4p (posterior) within the primary motor cortex was modulated by attention to...

  10. Touching moments: desire modulates the neural anticipation of active romantic caress.

    Science.gov (United States)

    Ebisch, Sjoerd J; Ferri, Francesca; Gallese, Vittorio

    2014-01-01

    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.

  11. 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.

  12. Touching moments: desire modulates the neural anticipation of active romantic caress

    Science.gov (United States)

    Ebisch, Sjoerd J.; Ferri, Francesca; Gallese, Vittorio

    2014-01-01

    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. PMID:24616676

  13. Splanchnic neural activity modulates ultradian and circadian rhythms in adrenocortical secretion in awake rats.

    Science.gov (United States)

    Jasper, M S; Engeland, W C

    1994-02-01

    An ultradian rhythm in adrenal secretion of corticosterone has been described in awake rats using intra-adrenal microdialysis. To determine the role of the autonomic innervation of the adrenal on the expression of the corticosterone rhythm, adrenal extracellular fluid was sampled by intra-adrenal microdialysis in intact (CTRL) and splanchnicectomized (SPLNX) rats 5-7 h before (light period) and after dark onset (dark period). Experiments conducted 1, 2, or 5 days after surgical insertion of the microdialysis probe consisted of continuous collection of dialysate at intervals of 10 min. Time domain pulse detection using PC-PULSAR showed that 5 days after surgery, SPLNX decreased interpulse interval (IPI) during the light period, but had no effect during the dark period, resulting in the loss of the diurnal rhythm in corticosterone secretion. Although diurnal modulation of both pulse amplitude and pulse frequency was observed, only the frequency was altered by SPLNX. In CTRL animals IPI increased at 5 days postsurgery, relative to 1 and 2 days, but the amplitude of normalized secretory pulses did not change. The decrease in IPI caused by SPLNX was observed 5 days, but not 1 or 2 days after surgery, suggesting that surgical stress obscures the inhibitory effect of splanchnic neural activity. Power spectral analysis showed significant periodicities in corticosterone secretion rate in individual CTRL and SPLNX animals at 1, 2, and 5 days. One day after surgery, SPLNX reduced the frequency of the ultradian rhythm detected by power spectral analysis. This finding suggests that splanchnic neural activity may increase pulse frequency in stressed rats, in opposition to the effect seen after extended recovery from surgery. In conclusion, our data suggest that the nadir of the diurnal rhythm in corticosterone secretion results in part from neural inhibitory control. Splanchnic neural innervation may also have an excitatory role in the adrenocortical stress response.

  14. Microscopic neural image registration based on the structure of mitochondria

    Science.gov (United States)

    Cao, Huiwen; Han, Hua; Rao, Qiang; Xiao, Chi; Chen, Xi

    2017-02-01

    Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of specimen. During image registration, various distortions need to be corrected, including image rotation, translation, tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and the thickness of the sections. These factors make the microscopic neural image registration a challenging problem. To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed widely in the image. The proposed image registration method contains three parts: landmarks extraction between adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We demonstrate the performance of our method with SEM images of drosophila brain.

  15. Robust spike classification based on frequency domain neural waveform features.

    Science.gov (United States)

    Yang, Chenhui; Yuan, Yuan; Si, Jennie

    2013-12-01

    We introduce a new spike classification algorithm based on frequency domain features of the spike snippets. The goal for the algorithm is to provide high classification accuracy, low false misclassification, ease of implementation, robustness to signal degradation, and objectivity in classification outcomes. In this paper, we propose a spike classification algorithm based on frequency domain features (CFDF). It makes use of frequency domain contents of the recorded neural waveforms for spike classification. The self-organizing map (SOM) is used as a tool to determine the cluster number intuitively and directly by viewing the SOM output map. After that, spike classification can be easily performed using clustering algorithms such as the k-Means. In conjunction with our previously developed multiscale correlation of wavelet coefficient (MCWC) spike detection algorithm, we show that the MCWC and CFDF detection and classification system is robust when tested on several sets of artificial and real neural waveforms. The CFDF is comparable to or outperforms some popular automatic spike classification algorithms with artificial and real neural data. The detection and classification of neural action potentials or neural spikes is an important step in single-unit-based neuroscientific studies and applications. After the detection of neural snippets potentially containing neural spikes, a robust classification algorithm is applied for the analysis of the snippets to (1) extract similar waveforms into one class for them to be considered coming from one unit, and to (2) remove noise snippets if they do not contain any features of an action potential. Usually, a snippet is a small 2 or 3 ms segment of the recorded waveform, and differences in neural action potentials can be subtle from one unit to another. Therefore, a robust, high performance classification system like the CFDF is necessary. In addition, the proposed algorithm does not require any assumptions on statistical

  16. Genetic learning in rule-based and neural systems

    Science.gov (United States)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

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

    Science.gov (United States)

    Bhanji, Jamil P; Beer, Jennifer S

    2013-05-29

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

  18. Orthonormal bases for α-modulation spaces

    DEFF Research Database (Denmark)

    Nielsen, Morten

    We construct an orthonormal basis for the family of bi-variate a-modulation spaces. The construction is based on local trigonometric bases, and the basis elements are closely related to so-called brushlets. As an application, we show that m-term nonlinear approximation with the system in an a...

  19. Expectation violation and attention to pain jointly modulate neural gain in somatosensory cortex.

    Science.gov (United States)

    Fardo, Francesca; Auksztulewicz, Ryszard; Allen, Micah; Dietz, Martin J; Roepstorff, Andreas; Friston, Karl J

    2017-06-01

    The neural processing and experience of pain are influenced by both expectations and attention. For example, the amplitude of event-related pain responses is enhanced by both novel and unexpected pain, and by moving the focus of attention towards a painful stimulus. Under predictive coding, this congruence can be explained by appeal to a precision-weighting mechanism, which mediates bottom-up and top-down attentional processes by modulating the influence of feedforward and feedback signals throughout the cortical hierarchy. The influence of expectation and attention on pain processing can be mapped onto changes in effective connectivity between or within specific neuronal populations, using a canonical microcircuit (CMC) model of hierarchical processing. We thus implemented a CMC within dynamic causal modelling for magnetoencephalography in human subjects, to investigate how expectation violation and attention to pain modulate intrinsic (within-source) and extrinsic (between-source) connectivity in the somatosensory hierarchy. This enabled us to establish whether both expectancy and attentional processes are mediated by a similar precision-encoding mechanism within a network of somatosensory, frontal and parietal sources. We found that both unexpected and attended pain modulated the gain of superficial pyramidal cells in primary and secondary somatosensory cortex. This modulation occurred in the context of increased lateralized recurrent connectivity between somatosensory and fronto-parietal sources, driven by unexpected painful occurrences. Finally, the strength of effective connectivity parameters in S1, S2 and IFG predicted individual differences in subjective pain modulation ratings. Our findings suggest that neuromodulatory gain control in the somatosensory hierarchy underlies the influence of both expectation violation and attention on cortical processing and pain perception. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non......-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi......-Newton optimization algorithm. The performance is demonstrated on a pneumatic servo system....

  1. Subgradient-based neural networks for nonsmooth nonconvex optimization problems.

    Science.gov (United States)

    Bian, Wei; Xue, Xiaoping

    2009-06-01

    This paper presents a subgradient-based neural network to solve a nonsmooth nonconvex optimization problem with a nonsmooth nonconvex objective function, a class of affine equality constraints, and a class of nonsmooth convex inequality constraints. The proposed neural network is modeled with a differential inclusion. Under a suitable assumption on the constraint set and a proper assumption on the objective function, it is proved that for a sufficiently large penalty parameter, there exists a unique global solution to the neural network and the trajectory of the network can reach the feasible region in finite time and stay there thereafter. It is proved that the trajectory of the neural network converges to the set which consists of the equilibrium points of the neural network, and coincides with the set which consists of the critical points of the objective function in the feasible region. A condition is given to ensure the convergence to the equilibrium point set in finite time. Moreover, under suitable assumptions, the coincidence between the solution to the differential inclusion and the "slow solution" of it is also proved. Furthermore, three typical examples are given to present the effectiveness of the theoretic results obtained in this paper and the good performance of the proposed neural network.

  2. Image Restoration Technology Based on Discrete Neural network

    Directory of Open Access Journals (Sweden)

    Zhou Duoying

    2015-01-01

    Full Text Available With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, this paper verifies that the discrete neural network has a good convergence and identification capability in the image restoration technology with a better effect than that of the feedforward network. The restoration technology based on the discrete neural network can provide a reliable mathematical model for this field.

  3. A novel word spotting method based on recurrent neural networks.

    Science.gov (United States)

    Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

    2012-02-01

    Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

  4. Neural bases of syntax-semantics interface processing.

    Science.gov (United States)

    Malaia, Evguenia; Newman, Sharlene

    2015-06-01

    The binding problem-question of how information between the modules of the linguistic system is integrated during language processing-is as yet unresolved. The remarkable speed of language processing and comprehension (Pulvermüller et al. 2009) suggests that at least coarse semantic information (e.g. noun animacy) and syntactically-relevant information (e.g. verbal template) are integrated rapidly to allow for coarse comprehension. This EEG study investigated syntax-semantics interface processing during word-by-word sentence reading. As alpha-band neural activity serves as an inhibition mechanism for local networks, we used topographical distribution of alpha power to help identify the timecourse of the binding process. We manipulated the syntactic parameter of verbal event structure, and semantic parameter of noun animacy in reduced relative clauses (RRCs, e.g. "The witness/mansion seized/protected by the agent was in danger"), to investigate the neural bases of interaction between syntactic and semantic networks during sentence processing. The word-by-word stimulus presentation method in the present experiment required manipulation of both syntactic structure and semantic features in the working memory. The results demonstrated a gradient distribution of early components (biphasic posterior P1-N2 and anterior N1-P2) over function words "by" and "the", and the verb, corresponding to facilitation or conflict resulting from the syntactic (telicity) and semantic (animacy) cues in the preceding portion of the sentence. This was followed by assimilation of power distribution in the α band at the second noun. The flattened distribution of α power during the mental manipulation with high demand on working memory-thematic role re-assignment-demonstrates a state of α equilibrium with strong functional coupling between posterior and anterior regions. These results demonstrate that the processing of semantic and syntactic features during sentence comprehension proceeds

  5. The neural bases of orthographic working memory

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2014-04-01

    First, these results reveal a neurotopography of OWM lesion sites that is well-aligned with results from neuroimaging of orthographic working memory in neurally intact participants (Rapp & Dufor, 2011. Second, the dorsal neurotopography of the OWM lesion overlap is clearly distinct from what has been reported for lesions associated with either lexical or sublexical deficits (e.g., Henry, Beeson, Stark, & Rapcsak, 2007; Rapcsak & Beeson, 2004; these have, respectively, been identified with the inferior occipital/temporal and superior temporal/inferior parietal regions. These neurotopographic distinctions support the claims of the computational distinctiveness of long-term vs. working memory operations. The specific lesion loci raise a number of questions to be discussed regarding: (a the selectivity of these regions and associated deficits to orthographic working memory vs. working memory more generally (b the possibility that different lesion sub-regions may correspond to different components of the OWM system.

  6. Space photovoltaic modules based on reflective optics

    Science.gov (United States)

    Andreev, V. M.; Larionov, V. R.; Rumyantsev, V. D.; Shvarts, M. Z.

    1995-01-01

    The conceptual design and experimental results for two types of space application concentrator photovoltaic modules, employing reflective optical elements, are presented. The first type is based on the use of compound parabolic concentrators, the second type is based on the use of line-focus parabolic troughs. Lightweight concentrators are formed with nickel foil coated silver with a diamond-like carbon layer protection. Secondary optical elements, including lenses and cones, are introduced for a better matching of concentrators and solar cells. Both types of modules are characterized by concentration ratios in the range 20x to 30x, depending on the chosen range of misorientation angles. The estimated specific parameters of these modules operating with single junction AlGaAs/GaAs solar cells are 240 W/sq m and 3 kg/sq m.

  7. Unfolding code for neutron spectrometry based on neural nets technology

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)

    2012-10-15

    The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)

  8. STAT3 modulation to enhance motor neuron differentiation in human neural stem cells.

    Directory of Open Access Journals (Sweden)

    Rajalaxmi Natarajan

    Full Text Available Spinal cord injury or amyotrophic lateral sclerosis damages spinal motor neurons and forms a glial scar, which prevents neural regeneration. Signal transducer and activator of transcription 3 (STAT3 plays a critical role in astrogliogenesis and scar formation, and thus a fine modulation of STAT3 signaling may help to control the excessive gliogenic environment and enhance neural repair. The objective of this study was to determine the effect of STAT3 inhibition on human neural stem cells (hNSCs. In vitro hNSCs primed with fibroblast growth factor 2 (FGF2 exhibited a lower level of phosphorylated STAT3 than cells primed by epidermal growth factor (EGF, which correlated with a higher number of motor neurons differentiated from FGF2-primed hNSCs. Treatment with STAT3 inhibitors, Stattic and Niclosamide, enhanced motor neuron differentiation only in FGF2-primed hNSCs, as shown by increased homeobox gene Hb9 mRNA levels as well as HB9+ and microtubule-associated protein 2 (MAP2+ co-labeled cells. The increased motor neuron differentiation was accompanied by a decrease in the number of glial fibrillary acidic protein (GFAP-positive astrocytes. Interestingly, Stattic and Niclosamide did not affect the level of STAT3 phosphorylation; rather, they perturbed the nuclear translocation of phosphorylated STAT3. In summary, we demonstrate that FGF2 is required for motor neuron differentiation from hNSCs and that inhibition of STAT3 further increases motor neuron differentiation at the expense of astrogliogenesis. Our study thus suggests a potential benefit of targeting the STAT3 pathway for neurotrauma or neurodegenerative diseases.

  9. Sleep modulates the neural substrates of both spatial and contextual memory consolidation.

    Directory of Open Access Journals (Sweden)

    Géraldine Rauchs

    -training sleep modulates the neural substrates of the consolidation of both the spatial and contextual memories acquired during virtual navigation.

  10. Software based controls module development

    Energy Technology Data Exchange (ETDEWEB)

    Graves, v.b.; kelley, g; welch, j.c.

    1999-12-10

    A project was initiated at the Oak Ridge Y-12 Plant to implement software geometric error compensation within a PC-based machine tool controller from Manufacturing Data Systems, Inc. This project may be the first in which this type of compensation system was implemented in a commercially available machine tool controller totally in software. Previous implementations typically required using an external computer and hardware to interface through the position feedback loop of the controller because direct access to the controller software was not available. The test-bed machine for this project was a 2-axis Excello 921 T-base lathe. A mathematical error model of the lathe was created using homogeneous transformation matrices to relate the positions of the machine's slides to each other and to a world reference system. Equations describing the effects of the geometric errors were derived from the model. A software architecture was developed to support geometric error compensation for machine tools with up to 3 linear axes. Rotary axes were not supported in this implementation, but the developed architecture would not preclude their support in the future. Specific implementations will be dependent upon the configuration of the machine tool. A laser measuring system from Automated Precision, Inc. was used to characterize the lathe's geometric errors as functions of axis position and direction of motion. Multiple data files generated by the laser system were combined into a single Error File that was read at system startup and used by the compensation system to provide real-time position adjustments to the axis servos. A Renishaw Ballbar was used to evaluate the compensation system. Static positioning tests were conducted in an attempt to observe improved positioning accuracy with the compensation system enabled. These tests gave inconsistent results due to the lathe's inability to position the tool repeatably. The development of the architecture and

  11. Modulating neural plasticity with non-invasive brain stimulation in schizophrenia.

    Science.gov (United States)

    Hasan, Alkomiet; Wobrock, Thomas; Rajji, Tarek; Malchow, Berend; Daskalakis, Zafiris J

    2013-12-01

    Schizophrenia is a severe mental disorder characterised by a complex phenotype including positive, negative, affective and cognitive symptoms. Various theories have been developed to integrate the clinical phenotype into a strong neurobiological framework. One theory describes schizophrenia as a disorder of impaired neural plasticity. Recently, non-invasive brain stimulation techniques have garnered much attention to their ability to modulate plasticity and treat schizophrenia. The aim of this review is to introduce the basic physiological principles of conventional non-invasive brain stimulation techniques and to review the available evidence for schizophrenia. Despite promising evidence for efficacy in a large number of clinical trials, we continue to have a rudimentary understanding of the underlying neurobiology. Additional investigation is required to improve the response rates to non-invasive brain stimulation, to reduce the interindividual variability and to improve the understanding of non-invasive brain stimulation in schizophrenia.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    Wang, Yang; Qu, Chen; Luo, Qiuling; Qu, Lulu; Li, Xuebing

    2014-01-01

    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.

  14. Speaker's hand gestures modulate speech perception through phase resetting of ongoing neural oscillations.

    Science.gov (United States)

    Biau, Emmanuel; Torralba, Mireia; Fuentemilla, Lluis; de Diego Balaguer, Ruth; Soto-Faraco, Salvador

    2015-07-01

    Speakers often accompany speech with spontaneous beat gestures in natural spoken communication. These gestures are usually aligned with lexical stress and can modulate the saliency of their affiliate words. Here we addressed the consequences of beat gestures on the neural correlates of speech perception. Previous studies have highlighted the role played by theta oscillations in temporal prediction of speech. We hypothesized that the sight of beat gestures may influence ongoing low-frequency neural oscillations around the onset of the corresponding words. Electroencephalographic (EEG) recordings were acquired while participants watched a continuous, naturally recorded discourse. The phase-locking value (PLV) at word onset was calculated from the EEG from pairs of identical words that had been pronounced with and without a concurrent beat gesture in the discourse. We observed an increase in PLV in the 5-6 Hz theta range as well as a desynchronization in the 8-10 Hz alpha band around the onset of words preceded by a beat gesture. These findings suggest that beats help tune low-frequency oscillatory activity at relevant moments during natural speech perception, providing a new insight of how speech and paralinguistic information are integrated. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. Age of acquisition modulates neural activity for both regular and irregular syntactic functions.

    Science.gov (United States)

    Hernandez, Arturo E; Hofmann, Juliane; Kotz, Sonja A

    2007-07-01

    Studies have found that neural activity is greater for irregular grammatical items than regular items. Findings with monolingual Spanish speakers have revealed a similar effect when making gender decisions for visually presented nouns. The current study extended previous studies by looking at the role of regularity in modulating differences in groups that differ in the age of acquisition of a language. Early and late learners of Spanish matched on measures of language proficiency were asked to make gender decisions to regular (-o for masculine and -a for feminine) and irregular items (which can end in e, l, n, r, s, t and z). Results revealed increased activity in left BA 44 for irregular compared to regular items in separate comparisons for both early and late learners. In addition, within-group comparisons revealed that neural activity for irregulars extended into left BA 47 for late learners and into left BA 6 for early learners. Direct comparisons between groups revealed increased activity in left BA 44/45 for irregular items indicating the need for more extensive syntactic processing in late learners. The results revealed that processing of irregular grammatical gender leads to increased activity in left BA 44 and adjacent areas in the left IFG regardless of when a language is learned. Furthermore, these findings suggest differential recruitment of brain areas associated with grammatical processing in late learners. The results are discussed with regard to a model which considers L2 learning as emerging from the competitive interplay between two languages.

  16. Context and hand posture modulate the neural dynamics of tool-object perception.

    Science.gov (United States)

    Natraj, Nikhilesh; Poole, Victoria; Mizelle, J C; Flumini, Andrea; Borghi, Anna M; Wheaton, Lewis A

    2013-02-01

    Prior research has linked visual perception of tools with plausible motor strategies. Thus, observing a tool activates the putative action-stream, including the left posterior parietal cortex. Observing a hand functionally grasping a tool involves the inferior frontal cortex. However, tool-use movements are performed in a contextual and grasp specific manner, rather than relative isolation. Our prior behavioral data has demonstrated that the context of tool-use (by pairing the tool with different objects) and varying hand grasp postures of the tool can interact to modulate subjects' reaction times while evaluating tool-object content. Specifically, perceptual judgment was delayed in the evaluation of functional tool-object pairings (Correct context) when the tool was non-functionally (Manipulative) grasped. Here, we hypothesized that this behavioral interference seen with the Manipulative posture would be due to increased and extended left parietofrontal activity possibly underlying motor simulations when resolving action conflict due to this particular grasp at time scales relevant to the behavioral data. Further, we hypothesized that this neural effect will be restricted to the Correct tool-object context wherein action affordances are at a maximum. 64-channel electroencephalography (EEG) was recorded from 16 right-handed subjects while viewing images depicting three classes of tool-object contexts: functionally Correct (e.g. coffee pot-coffee mug), functionally Incorrect (e.g. coffee pot-marker) and Spatial (coffee pot-milk). The Spatial context pairs a tool and object that would not functionally match, but may commonly appear in the same scene. These three contexts were modified by hand interaction: No Hand, Static Hand near the tool, Functional Hand posture and Manipulative Hand posture. The Manipulative posture is convenient for relocating a tool but does not afford a functional engagement of the tool on the target object. Subjects were instructed to visually

  17. On the nature, modeling, and neural bases of social ties.

    Science.gov (United States)

    van Winden, Frans; Stallen, Mirre; Ridderinkhof, K Richard

    2008-01-01

    This chapter addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual ('utility interdependence'). Ties can be positive or negative, and symmetric or asymmetric between individuals. Characteristic of a social tie, as conceived of here, is that it develops over time under the influence of interaction, in contrast with a trait like altruism. Moreover, a tie is not related to strategic behavior such as reputation formation but seen as generated by affective responses. A formalization is presented together with some supportive evidence from behavioral experiments. This is followed by a discussion of related psychological constructs and the presentation of suggestive existing neural findings. To help prepare the grounds for a model-based neural analysis some speculations on the neural networks involved are provided, together with suggestions for future research. Social ties are not only found to be important from an economic viewpoint, it is also shown that they can be modeled and related to neural substrates. By providing an overview of the economic research on social ties and connecting it with the broader behavioral and neuroeconomics literature, the chapter may contribute to the development of a neuroeconomics of social ties.

  18. From neural-based object recognition toward microelectronic eyes

    Science.gov (United States)

    Sheu, Bing J.; Bang, Sa Hyun

    1994-01-01

    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  19. Neural Cell Chip Based Electrochemical Detection of Nanotoxicity

    Directory of Open Access Journals (Sweden)

    Md. Abdul Kafi

    2015-07-01

    Full Text Available Development of a rapid, sensitive and cost-effective method for toxicity assessment of commonly used nanoparticles is urgently needed for the sustainable development of nanotechnology. A neural cell with high sensitivity and conductivity has become a potential candidate for a cell chip to investigate toxicity of environmental influences. A neural cell immobilized on a conductive surface has become a potential tool for the assessment of nanotoxicity based on electrochemical methods. The effective electrochemical monitoring largely depends on the adequate attachment of a neural cell on the chip surfaces. Recently, establishment of integrin receptor specific ligand molecules arginine-glycine-aspartic acid (RGD or its several modifications RGD-Multi Armed Peptide terminated with cysteine (RGD-MAP-C, C(RGD4 ensure farm attachment of neural cell on the electrode surfaces either in their two dimensional (dot or three dimensional (rod or pillar like nano-scale arrangement. A three dimensional RGD modified electrode surface has been proven to be more suitable for cell adhesion, proliferation, differentiation as well as electrochemical measurement. This review discusses fabrication as well as electrochemical measurements of neural cell chip with particular emphasis on their use for nanotoxicity assessments sequentially since inception to date. Successful monitoring of quantum dot (QD, graphene oxide (GO and cosmetic compound toxicity using the newly developed neural cell chip were discussed here as a case study. This review recommended that a neural cell chip established on a nanostructured ligand modified conductive surface can be a potential tool for the toxicity assessments of newly developed nanomaterials prior to their use on biology or biomedical technologies.

  20. Empathic neural responses are modulated by the perceived fairness of others

    Science.gov (United States)

    Singer, Tania; Seymour, Ben; O'Doherty, John P.; Stephan, Klaas E.; Dolan, Raymond J.; Frith, Chris D.

    2009-01-01

    The neural processes underlying empathy are a subject of intense interest within the social neurosciences1-3. However, very little is known about how brain empathic responses are modulated by the affective link between individuals. We show here that empathic responses are modulated by learned preferences, a result consistent with economic models of social preferences4-7. We engaged male and female volunteers in an economic game, in which two confederates played fairly or unfairly, and then measured brain activity with functional magnetic resonance imaging while these same volunteers observed the confederates receiving pain. Both sexes exhibited empathy-related activation in pain-related brain areas (fronto-insular and anterior cingulate cortices) towards fair players. However, these empathy-related responses were significantly reduced in males when observing an unfair person receiving pain. This effect was accompanied by increased activation in reward-related areas, correlated with an expressed desire for revenge. We conclude that in men (at least) empathic responses are shaped by valuation of other people's social behaviour, such that they empathize with fair opponents while favouring the physical punishment of unfair opponents, a finding that echoes recent evidence for altruistic punishment. PMID:16421576

  1. Neural modulation of muscle-tendon control strategy after a single practice session.

    Science.gov (United States)

    Hirayama, Kuniaki; Yanai, Toshimasa; Kanehisa, Hiroaki; Fukunaga, Tetsuo; Kawakami, Yasuo

    2012-08-01

    The purpose of the present study was to examine a hypothesis that the musculotendinous behavior during a propelling action with a countermovement can be altered by a single practice session through modulation of neuromuscular activities. Eight males performed unilateral maximal plantarflexion with (CMJ) and without (noCMJ) countermovement before and after a practice consisting of six sets of three repetitions of unilateral CMJ exercises. Measurements included EMG activities of the triceps surae and tibialis anterior muscles and the fascicle behavior of the gastrocnemius by ultrasonography, and impulse was calculated from the force-time data. The change in tendon length was also estimated. The impulse in CMJ increased after the practice, but that in noCMJ did not. After the practice, the magnitude of fascicle lengthening and shortening in CMJ decreased, which was accompanied by an increase in tendon shortening without change in the ankle joint range of motion. The time lag from the onset of reaction force to that of EMG activities of the triceps surae muscles was shortened after the practice. The results support the hypothesis and indicate that, as a neural modulation through a single practice, the muscle-tendon unit behavior during CMJ can be optimized to improve the performance.

  2. Neural systems and the inhibitory modulation of agonistic behavior: a comparison of mammalian species.

    Science.gov (United States)

    Albert, D J; Walsh, M L

    1984-01-01

    The olfactory bulb, lateral septum, medial accumbens, medial hypothalamus, dorsal and median raphe, and amygdala are known from experiments in rats to participate in the inhibitory modulation of defensiveness and predation but not social aggression. The present paper surveys the influence of these structures in the inhibitory control of these same dimensions of agonistic behavior in other species. The existing evidence suggests that lesions in the lateral septum, medial accumbens, medial hypothalamus, or the dorsal and median raphe (or PCPA-induced depletion or serotonin) induce hyperreactivity to the experimenter in mice, rats, cats, dogs, and humans in every instance where they have been tested with one exception. The exception is that lesions in the medial hypothalamus of mice do not induce heightened reactivity. The same lesions do not cause this dramatic increase in reactivity to the experimenter in gerbils, hamsters, guinea pigs, or rabbits but do heighten some other species typical patterns of defensiveness such as alarm calls and avoidance of contact with conspecifics. Lesions in these same areas in monkeys have not been observed to heighten defensive behaviors. Predatory killing or killing of young conspecifics has been observed in hamsters, mice, rats, and cats in every instance where they have been examined following lesions of the olfactory bulbs, lateral septum, medial accumbens, medial hypothalamus, or the dorsal and median raphe nuclei (or PCPA-induced depletion of serotonin). Social aggression has been decreased with these same lesions in each case where they have been examined except for septal lesions in hamsters which have been reported to heighten social aggression. Across species, the consistency with which lesions of the olfactory region, lateral septum, medial accumbens, medial hypothalamus, and dorsal and median raphe nuclei alter defensiveness and predation but not social aggression supports the inference that neural systems exist which

  3. Adaptive Synchronization of Memristor-based Chaotic Neural Systems

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2014-11-01

    Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.

  4. Hand gesture recognition based on convolutional neural networks

    Science.gov (United States)

    Hu, Yu-lu; Wang, Lian-ming

    2017-11-01

    Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.

  5. RBF neural network based H∞ H∞ H∞ synchronization for ...

    Indian Academy of Sciences (India)

    Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an H ∞ norm constraint. It is shown that finding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved ...

  6. Detecting danger labels with RAM-based neural networks

    DEFF Research Database (Denmark)

    Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.

    1996-01-01

    An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Results...

  7. Artificial Neural Network Based State Estimators Integrated into Kalmtool

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad

    2012-01-01

    In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation...

  8. On the Nature, Modeling, and Neural Bases of Social Ties

    NARCIS (Netherlands)

    F.A.A.M. Winden, van (Frans); M. Stallen (Mirre); K.R. Ridderinkhof (Richard)

    2008-01-01

    textabstractThis paper addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility

  9. Numerical analysis of modeling based on improved Elman neural network.

    Science.gov (United States)

    Jie, Shao; Li, Wang; WeiSong, Zhao; YaQin, Zhong; Malekian, Reza

    2014-01-01

    A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.

  10. A NEURAL NETWORK BASED IRIS RECOGNITION SYSTEM FOR PERSONAL IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Usham Dias

    2010-10-01

    Full Text Available This paper presents biometric personal identification based on iris recognition using artificial neural networks. Personal identification system consists of localization of the iris region, normalization, enhancement and then iris pattern recognition using neural network. In this paper, through results obtained, we have shown that a person’s left and right eye are unique. In this paper, we also show that the network is sensitive to the initial weights and that over-training gives bad results. We also propose a fast algorithm for the localization of the inner and outer boundaries of the iris region. Results of simulations illustrate the effectiveness of the neural system in personal identification. Finally a hardware iris recognition model is proposed and implementation aspects are discussed.

  11. Numerical Analysis of Modeling Based on Improved Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Shao Jie

    2014-01-01

    Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.

  12. Feature extraction for deep neural networks based on decision boundaries

    Science.gov (United States)

    Woo, Seongyoun; Lee, Chulhee

    2017-05-01

    Feature extraction is a process used to reduce data dimensions using various transforms while preserving the discriminant characteristics of the original data. Feature extraction has been an important issue in pattern recognition since it can reduce the computational complexity and provide a simplified classifier. In particular, linear feature extraction has been widely used. This method applies a linear transform to the original data to reduce the data dimensions. The decision boundary feature extraction method (DBFE) retains only informative directions for discriminating among the classes. DBFE has been applied to various parametric and non-parametric classifiers, which include the Gaussian maximum likelihood classifier (GML), the k-nearest neighbor classifier, support vector machines (SVM) and neural networks. In this paper, we apply DBFE to deep neural networks. This algorithm is based on the nonparametric version of DBFE, which was developed for neural networks. Experimental results with the UCI database show improved classification accuracy with reduced dimensionality.

  13. SU-8-based microneedles for in vitro neural applications

    Science.gov (United States)

    Altuna, Ane; Gabriel, Gemma; Menéndez de la Prida, Liset; Tijero, María; Guimerá, Anton; Berganzo, Javier; Salido, Rafa; Villa, Rosa; Fernández, Luis J.

    2010-06-01

    This paper presents novel design, fabrication, packaging and the first in vitro neural activity recordings of SU-8-based microneedles. The polymer SU-8 was chosen because it provides excellent features for the fabrication of flexible and thin probes. A microprobe was designed in order to allow a clean insertion and to minimize the damage caused to neural tissue during in vitro applications. In addition, a tetrode is patterned at the tip of the needle to obtain fine-scale measurements of small neuronal populations within a radius of 100 µm. Impedance characterization of the electrodes has been carried out to demonstrate their viability for neural recording. Finally, probes are inserted into 400 µm thick hippocampal slices, and simultaneous action potentials with peak-to-peak amplitudes of 200-250 µV are detected.

  14. Stereotype-based modulation of person perception.

    Science.gov (United States)

    Quadflieg, Susanne; Flannigan, Natasha; Waiter, Gordon D; Rossion, Bruno; Wig, Gagan S; Turk, David J; Macrae, C Neil

    2011-07-15

    A core social-psychological question is how cultural stereotypes shape our encounters with other people. While there is considerable evidence to suggest that unexpected targets-such as female airline pilots and male nurses-impact the inferential and memorial aspects of person construal, it has yet to be established if early perceptual operations are similarly sensitive to the stereotype-related status of individuals. To explore this issue, the current investigation measured neural activity while participants made social (i.e., sex categorization) and non-social (i.e., dot detection) judgments about men and women portrayed in expected and unexpected occupations. When participants categorized the stimuli according to sex, stereotype-inconsistent targets elicited increased activity in cortical areas associated with person perception and conflict resolution. Comparable effects did not emerge during a non-social judgment task. These findings begin to elucidate how and when stereotypic beliefs modulate the formation of person percepts in the brain. Copyright © 2011 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2014-05-01

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

  16. 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.

  17. A nanoparticle-based epigenetic modulator for efficient gene modulation

    Science.gov (United States)

    Pongkulapa, Thanapat

    Modulation of gene expression through chromatin remodeling involves epigenetic mechanisms, such as histone acetylation. Acetylation is tightly regulated by two classes of enzymes, histone acetyltransferases (HATs) and histone deacetylases (HDACs). Molecules that can regulate these enzymes by altering (activating or inhibiting) their functions have become a valuable tool for understanding cell development and diseases. HAT activators, i.e. N-(4-Chloro-(3-trifluoromethyl)phenyl)-2-ethoxybenzamide (CTB), have shown a therapeutic potential for many diseases, including cancer and neurodegeneration. However, these compounds encounter a solubility and a membrane permeability issue, which restricts their full potential for practical usage, especially for in vivo applications. To address this issue, in this work, we developed a nanoparticle-based HAT activator CTB, named Au-CTB, by incorporating a new CTB analogue onto gold nanoparticles (AuNPs) along with a poly(ethylene glycol) moiety and a nuclear localization signal (NLS) peptide to assist with solubility and membrane permeability. We found that our new CTB analogue and Au-CTB could activate HAT activity. Significantly, an increase in potency to activate HAT activity by Au-CTB proved the effectiveness of using the nanoparticle delivery platform. In addition, the versatility of Au-CTB platform permits the attachment of multiple ligands with tunable ratios on the nanoparticle surface via facile surface functionalization of gold nanoparticles. Due to its high delivery efficiency and versatility, Au-CTB can be a powerful platform for applications in epigenetic regulation of gene expression.

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

    OpenAIRE

    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 cultu...

  19. Neural Network-Based Abstract Generation for Opinions and Arguments

    OpenAIRE

    Wang, Lu; Ling, Wang

    2016-01-01

    We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the encoder to integrate information from an important subset of input. Automatic evaluation indicates that our system outperforms state-of-the-art abstractive and extractive summarization syst...

  20. Improving ECG classification accuracy using an ensemble of neural network modules.

    Directory of Open Access Journals (Sweden)

    Mehrdad Javadi

    Full Text Available This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization.

  1. Neural correlates of attentional and mnemonic processing in event-based prospective memory

    Directory of Open Access Journals (Sweden)

    Justin B Knight

    2010-02-01

    Full Text Available Prospective memory, or memory for realizing delayed intentions, was examined with an event-based paradigm while simultaneously measuring neural activity with high-density EEG recordings. Specifically, the neural substrates of monitoring for an event-based cue were examined, as well as those perhaps associated with the cognitive processes supporting detection of cues and fulfillment of intentions. Participants engaged in a baseline lexical decision task (LDT, followed by a LDT with an embedded prospective memory (PM component. Event-based cues were constituted by color and lexicality (red words. Behavioral data provided evidence that monitoring, or preparatory attentional processes, were used to detect cues. Analysis of the event-related potentials (ERP revealed visual attentional modulations at 140 and 220 ms post-stimulus associated with preparatory attentional processes. In addition, ERP components at 220, 350, and 400 ms post-stimulus were enhanced for intention-related items. Our results suggest preparatory attention may operate by selectively modulating processing of features related to a previously formed event-based intention, as well as provide further evidence for the proposal that dissociable component processes support the fulfillment of delayed intentions.

  2. Neural Spike-Train Analyses of the Speech-Based Envelope Power Spectrum Model

    Directory of Open Access Journals (Sweden)

    Varsha H. Rallapalli

    2016-10-01

    Full Text Available Diagnosing and treating hearing impairment is challenging because people with similar degrees of sensorineural hearing loss (SNHL often have different speech-recognition abilities. The speech-based envelope power spectrum model (sEPSM has demonstrated that the signal-to-noise ratio (SNRENV from a modulation filter bank provides a robust speech-intelligibility measure across a wider range of degraded conditions than many long-standing models. In the sEPSM, noise (N is assumed to: (a reduce S + N envelope power by filling in dips within clean speech (S and (b introduce an envelope noise floor from intrinsic fluctuations in the noise itself. While the promise of SNRENV has been demonstrated for normal-hearing listeners, it has not been thoroughly extended to hearing-impaired listeners because of limited physiological knowledge of how SNHL affects speech-in-noise envelope coding relative to noise alone. Here, envelope coding to speech-in-noise stimuli was quantified from auditory-nerve model spike trains using shuffled correlograms, which were analyzed in the modulation-frequency domain to compute modulation-band estimates of neural SNRENV. Preliminary spike-train analyses show strong similarities to the sEPSM, demonstrating feasibility of neural SNRENV computations. Results suggest that individual differences can occur based on differential degrees of outer- and inner-hair-cell dysfunction in listeners currently diagnosed into the single audiological SNHL category. The predicted acoustic-SNR dependence in individual differences suggests that the SNR-dependent rate of susceptibility could be an important metric in diagnosing individual differences. Future measurements of the neural SNRENV in animal studies with various forms of SNHL will provide valuable insight for understanding individual differences in speech-in-noise intelligibility.

  3. Mechatronic Hydraulic Drive with Regulator, Based on Artificial Neural Network

    Science.gov (United States)

    Burennikov, Y.; Kozlov, L.; Pyliavets, V.; Piontkevych, O.

    2017-06-01

    Mechatronic hydraulic drives, based on variable pump, proportional hydraulics and controllers find wide application in technological machines and testing equipment. Mechatronic hydraulic drives provide necessary parameters of actuating elements motion with the possibility of their correction in case of external loads change. This enables to improve the quality of working operations, increase the capacity of machines. The scheme of mechatronic hydraulic drive, based on the pump, hydraulic cylinder, proportional valve with electrohydraulic control and programmable controller is suggested. Algorithm for the control of mechatronic hydraulic drive to provide necessary pressure change law in hydraulic cylinder is developed. For the realization of control algorithm in the controller artificial neural networks are used. Mathematical model of mechatronic hydraulic drive, enabling to create the training base for adjustment of artificial neural networks of the regulator is developed.

  4. Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network.

    Science.gov (United States)

    Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana M; Dan, Bernard; McIntyre, Joseph; Cheron, Guy

    2014-01-01

    In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

  5. Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network.

    Directory of Open Access Journals (Sweden)

    Ana eBengoetxea

    2014-09-01

    Full Text Available In this study we employed a dynamic recurrent neural network (DRNN in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane. We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others patterns of reciprocal activation operating in orthogonal

  6. Spiking Neural Networks based on OxRAM Synapses for Real-time Unsupervised Spike Sorting

    Directory of Open Access Journals (Sweden)

    Thilo Werner

    2016-11-01

    Full Text Available In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN. The proposed architecture is suitable for hardware implementation by using RRAM technology for the implementation of synapses whose low latency (< 1μs enable real-time spike sorting. This offers promising advantagesto conventional spike sorting techniques for brain-computer interface and neural prosthesis applications. Moreover, the ultralow power consumption of the RRAM synapses of the spiking neural network (nW range may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM as easy to program and low power (< 75 pJ synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intraand extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

  7. Neural-network-based voice-tracking algorithm

    Science.gov (United States)

    Baker, Mary; Stevens, Charise; Chaparro, Brennen; Paschall, Dwayne

    2002-11-01

    A voice-tracking algorithm was developed and tested for the purposes of electronically separating the voice signals of simultaneous talkers. Many individuals suffer from hearing disorders that often inhibit their ability to focus on a single speaker in a multiple speaker environment (the cocktail party effect). Digital hearing aid technology makes it possible to implement complex algorithms for speech processing in both the time and frequency domains. In this work, an average magnitude difference function (AMDF) was performed on mixed voice signals in order to determine the fundamental frequencies present in the signals. A time prediction neural network was trained to recognize normal human voice inflection patterns, including rising, falling, rising-falling, and falling-rising patterns. The neural network was designed to track the fundamental frequency of a single talker based on the training procedure. The output of the neural network can be used to design an active filter for speaker segregation. Tests were done using audio mixing of two to three speakers uttering short phrases. The AMDF function accurately identified the fundamental frequencies present in the signal. The neural network was tested using a single speaker uttering a short sentence. The network accurately tracked the fundamental frequency of the speaker.

  8. Prediction of coal slurry concentration based on artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, J.; Li, Y.; Cheng, J.; Zhou, Z.; Li, S.; Liu, J.; Cen, K. [Zhejiang University, Hangzhou (China)

    2005-12-15

    Based on experimental data of coal slurry, three BP neural network models with 8, 7 and 5 input factors, were set up for predicting the slurry concentration. Three BP neural networks algorithm was Levenberg Marquardt algorithm, and their learning rate was 0.01. The hidden neurons number was settled by practical training effect of the networks. The hidden neurons number of BP model, with 8, 7 and 5 input factors is 27, 30 and 24, respectively. Two data treated methods were tested by seven input factors network model, which proves that the first method is the better one. The mean absolute error of the neural network models with 5, 7 and 8 factors is 0.53%, 0.50% and 0.74%, respectively, while that of the existed regression model is 1.15%. This indicates that the neural network models, especially the 7 factors model, are effective in predicting the slurry. The HGI input neuron in eight input factors model affects the prediction result because of its interference to other input factors. The effect of H and N in coal on the slurry is slight. 8 refs., 7 figs., 3 tabs.

  9. Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

    In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

  10. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  11. Emotional Intent Modulates The Neural Substrates Of Creativity: An fMRI Study of Emotionally Targeted Improvisation in Jazz Musicians.

    Science.gov (United States)

    McPherson, Malinda J; Barrett, Frederick S; Lopez-Gonzalez, Monica; Jiradejvong, Patpong; Limb, Charles J

    2016-01-04

    Emotion is a primary motivator for creative behaviors, yet the interaction between the neural systems involved in creativity and those involved in emotion has not been studied. In the current study, we addressed this gap by using fMRI to examine piano improvisation in response to emotional cues. We showed twelve professional jazz pianists photographs of an actress representing a positive, negative or ambiguous emotion. Using a non-ferromagnetic thirty-five key keyboard, the pianists improvised music that they felt represented the emotion expressed in the photographs. Here we show that activity in prefrontal and other brain networks involved in creativity is highly modulated by emotional context. Furthermore, emotional intent directly modulated functional connectivity of limbic and paralimbic areas such as the amygdala and insula. These findings suggest that emotion and creativity are tightly linked, and that the neural mechanisms underlying creativity may depend on emotional state.

  12. The Neural Bases of Framing Effects in Social Dilemmas

    DEFF Research Database (Denmark)

    Macoveanu, Julian; Ramsøy, Thomas; Skov, Martin

    2015-01-01

    Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remains poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, subjects underwent functional Magnetic Resonance Imaging...... while playing a Prisoners Dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. Social decisions where subjects were affected by the frame engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex and lateral temporal...... gyrus. Among these regions, the engagement of the left hippocampus was further modulated by individual differences in empathy. Social decisions not adhering to the frame were associated with stronger engagement of the angular gyrus and trend increases in lateral orbitofrontal cortex, posterior...

  13. CAMAC 488 module: 68,000 based GPIB interface module

    Energy Technology Data Exchange (ETDEWEB)

    Seino, K.C.

    1985-03-01

    What kind of hardware and software should be used to interface GPIB devices with the existing computer system. One idea was to use a commercially available Multibus card, BLC 8488 from National Semiconductor, whose on-board Z80 would manage the GPIB read/write functions and handshakes. With this card, one could make a hardware system which would consist of (1) CAMAC 080, (2) Multibus crate, (3) M. Shea's M68000, (4) M080, (5) BLC 8488 and (6) memory board. And the software considered for such a hardware package was GAS, which had been an established software package for communication between the ACNET computer system and smart CAMAC modules. However, a second idea was to put everything on a two-wide CAMAC module. The author pursued the second idea and came up with a two-wide CAMAC module called C488. The author describes the hardware - block diagrams, circuit blocks, front panel and hardware tests. He also refers to the software - system, modules and applications.

  14. Modulation Based on Probability Density Functions

    Science.gov (United States)

    Williams, Glenn L.

    2009-01-01

    A proposed method of modulating a sinusoidal carrier signal to convey digital information involves the use of histograms representing probability density functions (PDFs) that characterize samples of the signal waveform. The method is based partly on the observation that when a waveform is sampled (whether by analog or digital means) over a time interval at least as long as one half cycle of the waveform, the samples can be sorted by frequency of occurrence, thereby constructing a histogram representing a PDF of the waveform during that time interval.

  15. Phase retrieval based on pupil scanning modulation

    Science.gov (United States)

    Dou, Jiantai; Gao, Zhishan; Ma, Jun; Yuan, Caojin; Yang, Zhongming; Claus, Daniel; Zhang, Tianyu

    2017-08-01

    The pupil scanning modulation is a maneuverable method for retrieving the phase of the complex-valued object. It is based on changing the extent of the illumination function using an adaptive aperture. The apertures are fixed on the same border or point of intersection that ensures the location of the aperture. We sequentially increase the size of the aperture and guarantee the necessary overlap between adjacent object fields. An improved algorithm including the adaptive raised-power estimation constraint and gradient-descent step is proposed to accelerate convergence and avoid stagnation during iterations. Both the simulation and experiment have been conducted to verify the feasibility of this method.

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

    Science.gov (United States)

    Gulati, Sandeep

    1991-01-01

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

  17. Neural Net Gains Estimation Based on an Equivalent Model

    Directory of Open Access Journals (Sweden)

    Karen Alicia Aguilar Cruz

    2016-01-01

    Full Text Available A model of an Equivalent Artificial Neural Net (EANN describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN. The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB the factors based on the functional error and the reference signal built with the past information of the system.

  18. Chinese Sentence Classification Based on Convolutional Neural Network

    Science.gov (United States)

    Gu, Chengwei; Wu, Ming; Zhang, Chuang

    2017-10-01

    Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

  19. Neural cell image segmentation method based on support vector machine

    Science.gov (United States)

    Niu, Shiwei; Ren, Kan

    2015-10-01

    In the analysis of neural cell images gained by optical microscope, accurate and rapid segmentation is the foundation of nerve cell detection system. In this paper, a modified image segmentation method based on Support Vector Machine (SVM) is proposed to reduce the adverse impact caused by low contrast ratio between objects and background, adherent and clustered cells' interference etc. Firstly, Morphological Filtering and OTSU Method are applied to preprocess images for extracting the neural cells roughly. Secondly, the Stellate Vector, Circularity and Histogram of Oriented Gradient (HOG) features are computed to train SVM model. Finally, the incremental learning SVM classifier is used to classify the preprocessed images, and the initial recognition areas identified by the SVM classifier are added to the library as the positive samples for training SVM model. Experiment results show that the proposed algorithm can achieve much better segmented results than the classic segmentation algorithms.

  20. Parametric Jominy profiles predictor based on neural networks

    Directory of Open Access Journals (Sweden)

    Valentini, R.

    2005-12-01

    Full Text Available The paper presents a method for the prediction of the Jominy hardness profiles of steels for microalloyed Boron steel which is based on neural networks. The Jominy profile has been parameterized and the parameters, which are a sort of "compact representation" of the profile itself, are linked to the steel chemical composition through a neural network. Numerical results are presented and discussed.

    El trabajo presenta un método de estimación de perfiles de dureza Jominy para aceros microaleados al boro basado en redes neuronales. Los parámetros de perfil Jominy, que constituyen una especie de "representación compacta" del perfil mismo, son determinados y puestos en relación con la composición química del acero mediante una red neuronal. Los resultados numéricos son expuestos y discutidos.

  1. Neural Online Filtering Based on Preprocessed Calorimeter Data

    CERN Document Server

    Torres, R C; The ATLAS collaboration; de Simas Filho, E F; De Seixas, J M

    2009-01-01

    Aiming at coping with LHC high event rate, the ATLAS collaboration has been designing a sophisticated three-level online triggering system. A significant number of interesting events decays into electrons, which have to be identified from a huge background noise. This work proposes a high-efficient L2 electron / jet discrimination algorithm based on artificial neural processing fed from preprocessed calorimeter information. The feature extraction part of the proposed system provides a ring structure for data description. Energy normalization is later applied to the rings, making the proposed system usable for a broad energy spectrum. Envisaging data compaction, Principal Component Analysis and Principal Component of Discrimination are compared in terms of both compaction rates and classification efficiency. For the pattern recognition section, an artificial neural network was employed. The proposed algorithm was able to achieve an electron detection efficiency of 96% for a false alarm of 7%.

  2. Vehicle Sideslip Angle Estimation Based on General Regression Neural Network

    Directory of Open Access Journals (Sweden)

    Wang Wei

    2016-01-01

    Full Text Available Aiming at the accuracy of estimation of vehicle’s mass center sideslip angle, an estimation method of slip angle based on general regression neural network (GRNN and driver-vehicle closed-loop system has been proposed: regarding vehicle’s sideslip angle as time series mapping of yaw speed and lateral acceleration; using homogeneous design project to optimize the training samples; building the mapping relationship among sideslip angle, yaw speed, and lateral acceleration; at the same time, using experimental method to measure vehicle’s sideslip angle to verify validity of this method. Estimation results of neural network and real vehicle experiment show the same changing tendency. The mean of error is within 10% of test result’s amplitude. Results show GRNN can estimate vehicle’s sideslip angle correctly. It can offer a reference to the application of vehicle’s stability control system on vehicle’s state estimation.

  3. Stereo vision calibration based on GMDH neural network.

    Science.gov (United States)

    Chen, Bingwen; Wang, Wenwei; Qin, Qianqing

    2012-03-01

    In order to improve the accuracy and stability of stereo vision calibration, a novel stereo vision calibration approach based on the group method of data handling (GMDH) neural network is presented. Three GMDH neural networks are utilized to build a spatial mapping relationship adaptively in individual dimension. In the process of modeling, the Levenberg-Marquardt optimization algorithm is introduced as an interior criterion to train each partial model, and the corrected Akaike's information criterion is introduced as an exterior criterion to evaluate these models. Experiments demonstrate that the proposed approach is stable and able to calibrate three-dimensional (3D) locations more accurately and learn the stereo mapping models adaptively. It is a convenient way to calibrate the stereo vision without specialized knowledge of stereo vision. © 2012 Optical Society of America

  4. A Gain-Scheduling PI Control Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Stefania Tronci

    2017-01-01

    Full Text Available This paper presents a gain-scheduling design technique that relies upon neural models to approximate plant behaviour. The controller design is based on generic model control (GMC formalisms and linearization of the neural model of the process. As a result, a PI controller action is obtained, where the gain depends on the state of the system and is adapted instantaneously on-line. The algorithm is tested on a nonisothermal continuous stirred tank reactor (CSTR, considering both single-input single-output (SISO and multi-input multi-output (MIMO control problems. Simulation results show that the proposed controller provides satisfactory performance during set-point changes and disturbance rejection.

  5. A comparison between the neural correlates of laser and electric pain stimulation and their modulation by expectation.

    Science.gov (United States)

    Hird, E J; Jones, A K P; Talmi, D; El-Deredy, W

    2018-01-01

    Pain is modulated by expectation. Event-related potential (ERP) studies of the influence of expectation on pain typically utilise laser heat stimulation to provide a controllable nociceptive-specific stimulus. Painful electric stimulation has a number of practical advantages, but is less nociceptive-specific. We compared the modulation of electric versus laser-evoked pain by expectation, and their corresponding pain-evoked and anticipatory ERPs. We developed understanding of recognised methods of laser and electric stimulation. We tested whether pain perception and neural activity induced by electric stimulation was modulated by expectation, whether this expectation elicited anticipatory neural correlates, and how these measures compared to those associated with laser stimulation by eliciting cue-evoked expectations of high and low pain in a within-participant design. Despite sensory and affective differences between laser and electric pain, intensity ratings and pain-evoked potentials were modulated equivalently by expectation, though ERPs only correlated with pain ratings in the laser pain condition. Anticipatory correlates differentiated pain intensity expectation to laser but not electric pain. Previous studies show that laser-evoked potentials are modulated by expectation. We extend this by showing electric pain-evoked potentials are equally modulated by expectation, within the same participants. We also show a difference between the pain types in anticipation. Though laser-evoked potentials express a stronger relationship with pain perception, both laser and electric stimulation may be used to study the modulation of pain-evoked potentials by expectation. Anticipatory-evoked potentials are elicited by both pain types, but they may reflect different processes. Copyright © 2017. Published by Elsevier B.V.

  6. Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

    National Research Council Canada - National Science Library

    Raheel Zafar; Sarat C Dass; Aamir Saeed Malik

    2017-01-01

    .... In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion...

  7. Using fuzzy logic to integrate neural networks and knowledge-based systems

    Science.gov (United States)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  8. Significantly High Modulation Efficiency of Compact Graphene Modulator Based on Silicon Waveguide.

    Science.gov (United States)

    Shu, Haowen; Su, Zhaotang; Huang, Le; Wu, Zhennan; Wang, Xingjun; Zhang, Zhiyong; Zhou, Zhiping

    2018-01-17

    We theoretically and experimentally demonstrate a significantly large modulation efficiency of a compact graphene modulator based on a silicon waveguide using the electro refractive effect of graphene. The modulation modes of electro-absorption and electro-refractive can be switched with different applied voltages. A high extinction ratio of 25 dB is achieved in the electro-absorption modulation mode with a driving voltage range of 0 V to 1 V. For electro-refractive modulation, the driving voltage ranges from 1 V to 3 V with a 185-pm spectrum shift. The modulation efficiency of 1.29 V · mm with a 40-μm interaction length is two orders of magnitude higher than that of the first reported graphene phase modulator. The realisation of phase and intensity modulation with graphene based on a silicon waveguide heralds its potential application in optical communication and optical interconnection systems.

  9. Multi-dimensional modulations of alpha and gamma cortical dynamics following mindfulness-based cognitive therapy in Major Depressive Disorder

    NARCIS (Netherlands)

    Schoenberg, P.L.; Speckens, A.E.M.

    2015-01-01

    To illuminate candidate neural working mechanisms of Mindfulness-Based Cognitive Therapy (MBCT) in the treatment of recurrent depressive disorder, parallel to the potential interplays between modulations in electro-cortical dynamics and depressive symptom severity and self-compassionate experience.

  10. Gap Junction–mediated Cell–Cell Communication Modulates Mouse Neural Crest Migration

    OpenAIRE

    Huang, G.Y.; Cooper, E.S.; Waldo, K.; Kirby, M L; Gilula, N B; Lo, C.W.

    1998-01-01

    Previous studies showed that conotruncal heart malformations can arise with the increase or decrease in α1 connexin function in neural crest cells. To elucidate the possible basis for the quantitative requirement for α1 connexin gap junctions in cardiac development, a neural crest outgrowth culture system was used to examine migration of neural crest cells derived from CMV43 transgenic embryos overexpressing α1 connexins, and from α1 connexin knockout (KO) mice and FC transgenic mice expressi...

  11. A developmental perspective on the neural bases of human empathy.

    Science.gov (United States)

    Tousignant, Béatrice; Eugène, Fanny; Jackson, Philip L

    2017-08-01

    While empathy has been widely studied in philosophical and psychological literatures, recent advances in social neuroscience have shed light on the neural correlates of this complex interpersonal phenomenon. In this review, we provide an overview of brain imaging studies that have investigated the neural substrates of human empathy. Based on existing models of the functional architecture of empathy, we review evidence of the neural underpinnings of each main component, as well as their development from infancy. Although early precursors of affective sharing and self-other distinction appear to be present from birth, recent findings also suggest that even higher-order components of empathy such as perspective-taking and emotion regulation demonstrate signs of development during infancy. This merging of developmental and social neuroscience literature thus supports the view that ontogenic development of empathy is rooted in early infancy, well before the emergence of verbal abilities. With age, the refinement of top-down mechanisms may foster more appropriate empathic responses, thus promoting greater altruistic motivation and prosocial behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Active terahertz wave modulator based on molybdenum disulfide

    Science.gov (United States)

    Liu, Xin; Zhang, Bo; Wang, Guocui; Wang, Wei; Ji, Hongyu; Shen, Jingling

    2017-11-01

    A high-efficiency active terahertz wave modulator based on a molybdenum disulfide (MoS2)/germanium (Ge) structure was investigated. Spectrally broadband modulation of the THz transmission was obtained using optical control over the frequency range from 0.2 to 2.6 THz. The MoS2 monolayer structure on germanium demonstrated enhancement of the terahertz modulation depth when compared with those of bare Ge and the graphene/Ge structures. The results show that the MoS2-based modulator demonstrated even higher modulation efficiency than the graphene-based device. The modulation enhancement mechanism that originated from increased conductivity was analyzed. The optical modulation properties of the MoS2/Ge device show tremendous promise for applications in terahertz modulation and switching.

  13. Real-Time Inverse Optimal Neural Control for Image Based Visual Servoing with Nonholonomic Mobile Robots

    Directory of Open Access Journals (Sweden)

    Carlos López-Franco

    2015-01-01

    Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.

  14. Expression, crystallization and preliminary X-ray analysis of extracellular modules of the neural cell-adhesion molecules NCAM and L1

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Kasper, Christina; Gajhede, Michael

    2004-01-01

    Recombinant proteins consisting of either the four or five amino-terminal immunoglobulin (Ig) modules of the rat neural cell-adhesion molecule NCAM or the whole extracellular part [six Ig and five fibronectin type III (F3) modules] of mouse L1 have been expressed in Drosophila S2 cells...

  15. Fast neural-net based fake track rejection

    CERN Document Server

    De Cian, Michel; Seyfert, Paul; Stahl, Sascha

    2017-01-01

    A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, is fast enough to fit into the CPU time budget of the software trigger farm. It allows reducing the fake rate and consequently the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment.

  16. EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    F. Arce

    2017-09-01

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

  17. The neural and computational bases of semantic cognition.

    Science.gov (United States)

    Ralph, Matthew A Lambon; Jefferies, Elizabeth; Patterson, Karalyn; Rogers, Timothy T

    2017-01-01

    Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of this ability, leading to a new framework that we term controlled semantic cognition (CSC). CSC offers solutions to long-standing queries in philosophy and cognitive science, and yields a convergent framework for understanding the neural and computational bases of healthy semantic cognition and its dysfunction in brain disorders.

  18. Efficient Lane Detection Based on Artificial Neural Networks

    Science.gov (United States)

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

    2017-09-01

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

  19. A Usability Study of Interactive Web-Based Modules

    Science.gov (United States)

    Girard, Tulay; Pinar, Musa

    2011-01-01

    This research advances the understanding of the usability of marketing case study modules in the area of interactive web-based technologies through the assignment of seven interactive case modules in a Principles of Marketing course. The case modules were provided for marketing students by the publisher, McGraw Hill Irwin, of the…

  20. Prediction of the moderator temperature field in a heavy water reactor based on a cellular neural network

    Directory of Open Access Journals (Sweden)

    S.O. Starkov

    2017-06-01

    Full Text Available Reactors with heavy water coolants and moderators have been used extensively in today's power industry. Monitoring of the moderator condition plays an important role in ensuring normal operation of a power plant. A cellular neural network, the architecture of which has been adapted for hardware implementation, is proposed for use in a system for prediction of the heavy water moderator temperature. A reactor model composed in accordance with the CANDU Darlington heavy water reactor design was used to form the training sample collection and to control correct operation of the neural network structure. The sample components for the adjustment and configuration of the network topology include key parameters that characterize the energy generation process in the core. The paper considers the feasibility of the temperature prediction only for the calandria's central cross-section. To solve this problem, the cellular neural network architecture has been designed, and major parts of the digital computational element and methods for their implementation based on an FPLD have also been developed. The method is described for organizing an optical coupling between individual neural modules within the network, which enables not only the restructuring of the topology in the training process, but also the assignment of priorities for the propagation of the information signals of neurons depending on the activity in a situation analysis at the neural network structure inlet. Asynchronous activation of cells was used based on an oscillating fractal network, the basis for which was a modified ring oscillator. The efficiency of training the proposed architecture using stochastic diffusion search algorithms is evaluated. A comparative analysis of the model behavior and the results of the neural network operation have shown that the use of the neural network approach is effective in safety systems of power plants.

  1. KNN BASED CLASSIFICATION OF DIGITAL MODULATED SIGNALS

    Directory of Open Access Journals (Sweden)

    Sajjad Ahmed Ghauri

    2016-11-01

    Full Text Available Demodulation process without the knowledge of modulation scheme requires Automatic Modulation Classification (AMC. When receiver has limited information about received signal then AMC become essential process. AMC finds important place in the field many civil and military fields such as modern electronic warfare, interfering source recognition, frequency management, link adaptation etc. In this paper we explore the use of K-nearest neighbor (KNN for modulation classification with different distance measurement methods. Five modulation schemes are used for classification purpose which is Binary Phase Shift Keying (BPSK, Quadrature Phase Shift Keying (QPSK, Quadrature Amplitude Modulation (QAM, 16-QAM and 64-QAM. Higher order cummulants (HOC are used as an input feature set to the classifier. Simulation results shows that proposed classification method provides better results for the considered modulation formats.

  2. Neural Processing of Calories in Brain Reward Areas Can be Modulated by Reward Sensitivity.

    Science.gov (United States)

    van Rijn, Inge; Griffioen-Roose, Sanne; de Graaf, Cees; Smeets, Paul A M

    2015-01-01

    A food's reward value is dependent on its caloric content. Furthermore, a food's acute reward value also depends on hunger state. The drive to obtain rewards (reward sensitivity), however, differs between individuals. Here, we assessed the association between brain responses to calories in the mouth and trait reward sensitivity in different hunger states. Firstly, we assessed this in data from a functional neuroimaging study (van Rijn et al., 2015), in which participants (n = 30) tasted simple solutions of a non-caloric sweetener with or without a non-sweet carbohydrate (maltodextrin) during hunger and satiety. Secondly, we expanded these analyses to regular drinks by assessing the same relationship in data from a study in which soft drinks sweetened with either sucrose or a non-caloric sweetener were administered during hunger (n = 18) (Griffioen-Roose et al., 2013). First, taste activation by the non-caloric solution/soft drink was subtracted from that by the caloric solution/soft drink to eliminate sweetness effects and retain activation induced by calories. Subsequently, this difference in taste activation was correlated with reward sensitivity as measured with the BAS drive subscale of the Behavioral Activation System (BAS) questionnaire. When participants were hungry and tasted calories from the simple solution, brain activation in the right ventral striatum (caudate), right amygdala and anterior cingulate cortex (bilaterally) correlated negatively with BAS drive scores. In contrast, when participants were satiated, taste responses correlated positively with BAS drive scores in the left caudate. These results were not replicated for soft drinks. Thus, neural responses to oral calories from maltodextrin were modulated by reward sensitivity in reward-related brain areas. This was not the case for sucrose. This may be due to the direct detection of maltodextrin, but not sucrose in the oral cavity. Also, in a familiar beverage, detection of calories per se may be

  3. Stock Price Prediction Based on Procedural Neural Networks

    OpenAIRE

    Jiuzhen Liang; Wei Song; Mei Wang

    2011-01-01

    We present a spatiotemporal model, namely, procedural neural networks for stock price prediction. Compared with some successful traditional models on simulating stock market, such as BNN (backpropagation neural networks, HMM (hidden Markov model) and SVM (support vector machine)), the procedural neural network model processes both spacial and temporal information synchronously without slide time window, which is typically used in the well-known recurrent neural networks. Two differen...

  4. The quality of adolescents' peer relationships modulates neural sensitivity to risk taking

    National Research Council Canada - National Science Library

    Telzer, Eva H; Fuligni, Andrew J; Lieberman, Matthew D; Miernicki, Michelle E; Galván, Adriana

    2015-01-01

    .... In the current 2-year three-wave longitudinal study, we examined how chronic levels of peer conflict relate to risk taking behaviorally and neurally, and whether this is modified by high-quality peer relationships...

  5. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

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

    Science.gov (United States)

    Torbati, Nima; Ayatollahi, Ahmad; Kermani, Ali

    2014-01-01

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

  7. Video-based face recognition via convolutional neural networks

    Science.gov (United States)

    Bao, Tianlong; Ding, Chunhui; Karmoshi, Saleem; Zhu, Ming

    2017-06-01

    Face recognition has been widely studied recently while video-based face recognition still remains a challenging task because of the low quality and large intra-class variation of video captured face images. In this paper, we focus on two scenarios of video-based face recognition: 1)Still-to-Video(S2V) face recognition, i.e., querying a still face image against a gallery of video sequences; 2)Video-to-Still(V2S) face recognition, in contrast to S2V scenario. A novel method was proposed in this paper to transfer still and video face images to an Euclidean space by a carefully designed convolutional neural network, then Euclidean metrics are used to measure the distance between still and video images. Identities of still and video images that group as pairs are used as supervision. In the training stage, a joint loss function that measures the Euclidean distance between the predicted features of training pairs and expanding vectors of still images is optimized to minimize the intra-class variation while the inter-class variation is guaranteed due to the large margin of still images. Transferred features are finally learned via the designed convolutional neural network. Experiments are performed on COX face dataset. Experimental results show that our method achieves reliable performance compared with other state-of-the-art methods.

  8. Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.

    Science.gov (United States)

    Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao

    2017-05-05

    This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.

  9. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

    Mar 1, 2012 ... Abstract. In this study, an artificial neural network (ANN) application of load frequency control. (LFC) of a Multi-Area power system by using a neural network controller is presented. The comparison between a conventional Proportional Integral (PI) controller and the proposed artificial neural networks ...

  10. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  11. Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

    Science.gov (United States)

    Fu, H C; Xu, Y Y; Chang, H Y

    1999-12-01

    Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

  12. Theme-Based Bidisciplinary Chemistry Laboratory Modules

    Science.gov (United States)

    Leber, Phyllis A.; Szczerbicki, Sandra K.

    1996-12-01

    A thematic approach to each of the two introductory chemistry laboratory sequences, general and organic chemistry, not only provides an element of cohesion but also stresses the role that chemistry plays as the "central science" and emphasizes the intimate link between chemistry and other science disciplines. Thus, in general chemistry the rubric "Environmental Chemistry" affords connections to the geosciences, whereas experiments on the topic of "Plant Assays" bridge organic chemistry and biology. By establishing links with other science departments, the theme-based laboratory experiments will satisfy the following multidisciplinary criteria: (i) to demonstrate the general applicability of core methodologies to the sciences, (ii) to help students relate concepts to a broader multidisciplinary context, (iii) to foster an attitude of both independence and cooperation that can transcend the teaching laboratory to the research arena, and (iv) to promote greater cooperation and interaction between the science departments. Fundamentally, this approach has the potential to impact the chemistry curriculum significantly by including student decision-making in the experimental process. Furthermore, the incorporation of GC-MS, a powerful tool for separation and identification as well as a state-of-the-art analytical technique, in the modules will enhance the introductory general and organic chemistry laboratory sequences by making them more instrument-intensive and by providing a reliable and reproducible means of obtaining quantitative analyses. Each multifaceted module has been designed to meet the following criteria: (i) a synthetic protocol including full spectral characterization of products, (ii) quantitative and statistical analyses of data, and (iii) construction of a database of results. The database will provide several concrete functions. It will foster the idea that science is a continuous incremental process building on the results of earlier experimentalists

  13. Neural network based feed-forward high density associative memory

    Science.gov (United States)

    Daud, T.; Moopenn, A.; Lamb, J. L.; Ramesham, R.; Thakoor, A. P.

    1987-01-01

    A novel thin film approach to neural-network-based high-density associative memory is described. The information is stored locally in a memory matrix of passive, nonvolatile, binary connection elements with a potential to achieve a storage density of 10 to the 9th bits/sq cm. Microswitches based on memory switching in thin film hydrogenated amorphous silicon, and alternatively in manganese oxide, have been used as programmable read-only memory elements. Low-energy switching has been ascertained in both these materials. Fabrication and testing of memory matrix is described. High-speed associative recall approaching 10 to the 7th bits/sec and high storage capacity in such a connection matrix memory system is also described.

  14. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2017-08-01

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  15. Quantum neural network based machine translator for Hindi to English.

    Science.gov (United States)

    Narayan, Ravi; Singh, V P; Chakraverty, S

    2014-01-01

    This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

  16. Manganese oxide microswitch for electronic memory based on neural networks

    Science.gov (United States)

    Ramesham, R.; Daud, T.; Moopenn, A.; Thakoor, A. P.; Khanna, S. K.

    1989-01-01

    A solid-state, resistance tailorable, programmable-once, binary, nonvolatile memory switch based on manganese oxide thin films is reported. MnO(x) exhibits irreversible memory switching from conducting (on) to insulating (off) state, with the off and on resistance ratio of greater than 10,000. The switching mechanism is current-triggered chemical transformation of a conductive MnO(2-Delta) to an insulating Mn2O3 state. The energy required for switching is of the order of 4-20 nJ/sq micron. The low switching energy, stability of the on and off states, and tailorability of the on state resistance make these microswitches well suited as programmable binary synapses in electronic associative memories based on neural network models.

  17. The Neural Correlates of Similarity- and Rule-based Generalization.

    Science.gov (United States)

    Milton, Fraser; Bealing, Pippa; Carpenter, Kathryn L; Bennattayallah, Abdelmalek; Wills, Andy J

    2017-01-01

    The idea that there are multiple learning systems has become increasingly influential in recent years, with many studies providing evidence that there is both a quick, similarity-based or feature-based system and a more effortful rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby [Shanks, D. R., & Darby, R. J. Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405-415, 1998]. Unlike previous related studies employing other procedures, rule generalization in the Shanks-Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the pFC and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons, but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed.

  18. Deep Neural Network Based Demand Side Short Term Load Forecasting

    Directory of Open Access Journals (Sweden)

    Seunghyoung Ryu

    2016-12-01

    Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

  19. The Dissolved Oxygen Prediction Method Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Zhong Xiao

    2017-01-01

    Full Text Available The dissolved oxygen (DO is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture’s dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, curve fitting (CF, autoregression (AR, grey model (GM, and support vector machines (SVM, the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease.

  20. Intelligent reservoir operation system based on evolving artificial neural networks

    Science.gov (United States)

    Chaves, Paulo; Chang, Fi-John

    2008-06-01

    We propose a novel intelligent reservoir operation system based on an evolving artificial neural network (ANN). Evolving means the parameters of the ANN model are identified by the GA evolutionary optimization technique. Accordingly, the ANN model should represent the operational strategies of reservoir operation. The main advantages of the Evolving ANN Intelligent System (ENNIS) are as follows: (i) only a small number of parameters to be optimized even for long optimization horizons, (ii) easy to handle multiple decision variables, and (iii) the straightforward combination of the operation model with other prediction models. The developed intelligent system was applied to the operation of the Shihmen Reservoir in North Taiwan, to investigate its applicability and practicability. The proposed method is first built to a simple formulation for the operation of the Shihmen Reservoir, with single objective and single decision. Its results were compared to those obtained by dynamic programming. The constructed network proved to be a good operational strategy. The method was then built and applied to the reservoir with multiple (five) decision variables. The results demonstrated that the developed evolving neural networks improved the operation performance of the reservoir when compared to its current operational strategy. The system was capable of successfully simultaneously handling various decision variables and provided reasonable and suitable decisions.

  1. Comparison Of Power Quality Disturbances Classification Based On Neural Network

    Directory of Open Access Journals (Sweden)

    Nway Nway Kyaw Win

    2015-07-01

    Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.

  2. Environmental testing of CIS based modules

    Energy Technology Data Exchange (ETDEWEB)

    Willett, D.

    1995-11-01

    This report describes environmental testing of Siemen`s CIS modules. Charts and diagrams are presented on data concerning: temporary power loss of laminated mini-modules; the 50 thermal cycle test; the 10 humidity freeze cycle test; results after 1000 hours of exposure to damp heat; and interconnect test structures in damp heat testing. It is concluded that moisture ingress causes permanent increases in the series resistance of modules, and that improved packaging is needed for better high humidity reliability. Also, dry dark heat caused temporary power losses which were recovered in sunlight.

  3. Performance of Skutterudite-Based Modules

    Science.gov (United States)

    Nie, G.; Suzuki, S.; Tomida, T.; Sumiyoshi, A.; Ochi, T.; Mukaiyama, K.; Kikuchi, M.; Guo, J. Q.; Yamamoto, A.; Obara, H.

    2017-05-01

    Due to their excellent thermoelectric (TE) performance, skutterudite materials have been selected by many laboratories and companies for development of TE modules to recover power from waste heat at high temperatures (300°C to 600°C). After years of effort, we have developed reliable n- and p-type skutterudite materials showing maximum figure of merit ( ZT) of 1.0 at 550°C and 0.75 at 450°C, respectively. In this work, we systematically investigated the performance of a module made using these two kinds of skutterudite. We demonstrate ˜7.2% conversion efficiency for temperature of 600°C at the hot side of the module and 50°C at the cold side, and show that the module had excellent stability in the high-temperature environment. Further improving the TE performance of our skutterudites, the conversion efficiency reached ˜8.5% under the same condition.

  4. Human avoidance and approach learning: evidence for overlapping neural systems and experiential avoidance modulation of avoidance neurocircuitry.

    Science.gov (United States)

    Schlund, Michael W; Magee, Sandy; Hudgins, Caleb D

    2011-12-01

    Adaptive functioning is thought to reflect a balance between approach and avoidance neural systems with imbalances often producing pathological forms of avoidance. Yet little evidence is available in healthy adults demonstrating a balance between approach and avoidance neural systems and modulation in avoidance neurocircuitry by vulnerability factors for avoidance. Consequently, we used functional magnetic resonance imaging (fMRI) to compare changes in brain activation associated with human avoidance and approach learning and modulation of avoidance neurocircuitry by experiential avoidance. fMRI tracked trial-by-trial increases in activation while adults learned through trial and error an avoidance response that prevented money loss and an approach response that produced money gain. Avoidance and approach cues elicited similar experience-dependent increases in activation in a fronto-limbic-striatal network. Positive and negative reinforcing outcomes (i.e., money gain and avoidance of loss) also elicited similar increases in activation in frontal and striatal regions. Finally, increased experiential avoidance and self-punishment coping was associated with decreased activation in medial/superior frontal regions, anterior cingulate, amygdala and hippocampus. These findings suggest avoidance and approach learning recruit a similar fronto-limbic-striatal network in healthy adults. Increased experiential avoidance also appears to be associated with reduced frontal and limbic reactivity in avoidance, establishing an important link between maladaptive avoidance coping and altered responses in avoidance neurocircuitry. Copyright © 2011 Elsevier B.V. All rights reserved.

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

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  7. Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

    Science.gov (United States)

    Han, Zhongyi; Wei, Benzheng; Leung, Stephanie; Nachum, Ilanit Ben; Laidley, David; Li, Shuo

    2018-02-15

    Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.

  8. Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhisheng Zhang

    2016-01-01

    Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

  9. Multivariate Cryptography Based on Clipped Hopfield Neural Network.

    Science.gov (United States)

    Wang, Jia; Cheng, Lee-Ming; Su, Tong

    2018-02-01

    Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in space. The Diffie-Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography. The efficiency and security of our proposed new public key cryptosystem CHNN-MVC are simulated and found to be NP-hard. The proposed algorithm will strengthen multivariate public key cryptosystems and allows hardware realization practicality.

  10. ART-Based Neural Networks for Multi-label Classification

    Science.gov (United States)

    Sapozhnikova, Elena P.

    Multi-label classification is an active and rapidly developing research area of data analysis. It becomes increasingly important in such fields as gene function prediction, text classification or web mining. This task corresponds to classification of instances labeled by multiple classes rather than just one. Traditionally, it was solved by learning independent binary classifiers for each class and combining their outputs to obtain multi-label predictions. Alternatively, a classifier can be directly trained to predict a label set of an unknown size for each unseen instance. Recently, several direct multi-label machine learning algorithms have been proposed. This paper presents a novel approach based on ART (Adaptive Resonance Theory) neural networks. The Fuzzy ARTMAP and ARAM algorithms were modified in order to improve their multi-label classification performance and were evaluated on benchmark datasets. Comparison of experimental results with the results of other multi-label classifiers shows the effectiveness of the proposed approach.

  11. Neurally based measurement and evaluation of environmental noise

    CERN Document Server

    Soeta, Yoshiharu

    2015-01-01

    This book deals with methods of measurement and evaluation of environmental noise based on an auditory neural and brain-oriented model. The model consists of the autocorrelation function (ACF) and the interaural cross-correlation function (IACF) mechanisms for signals arriving at the two ear entrances. Even when the sound pressure level of a noise is only about 35 dBA, people may feel annoyed due to the aspects of sound quality. These aspects can be formulated by the factors extracted from the ACF and IACF. Several examples of measuring environmental noise—from outdoor noise such as that of aircraft, traffic, and trains, and indoor noise such as caused by floor impact, toilets, and air-conditioning—are demonstrated. According to the noise measurement and evaluation, applications for sound design are discussed. This book provides an excellent resource for students, researchers, and practitioners in a wide range of fields, such as the automotive, railway, and electronics industries, and soundscape, architec...

  12. Deep neural network and noise classification-based speech enhancement

    Science.gov (United States)

    Shi, Wenhua; Zhang, Xiongwei; Zou, Xia; Han, Wei

    2017-07-01

    In this paper, a speech enhancement method using noise classification and Deep Neural Network (DNN) was proposed. Gaussian mixture model (GMM) was employed to determine the noise type in speech-absent frames. DNN was used to model the relationship between noisy observation and clean speech. Once the noise type was determined, the corresponding DNN model was applied to enhance the noisy speech. GMM was trained with mel-frequency cepstrum coefficients (MFCC) and the parameters were estimated with an iterative expectation-maximization (EM) algorithm. Noise type was updated by spectrum entropy-based voice activity detection (VAD). Experimental results demonstrate that the proposed method could achieve better objective speech quality and smaller distortion under stationary and non-stationary conditions.

  13. Intelligent control based on fuzzy logic and neural net theory

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  14. Optical phase modulation based on directly modulated reflection-mode OIL-VCSEL.

    Science.gov (United States)

    Guo, Peng; Sun, Tao; Yang, Weijian; Parekh, Devang; Zhang, Cheng; Xie, Xiaopeng; Chang-Hasnain, Connie J; Xu, Anshi; Chen, Zhangyuan

    2013-09-23

    Optical phase modulation based on directly modulated reflection-mode optically injection-locked VCSEL is investigated based on standard OIL rate equations and reflection-mode OIL model. The phase information of both static and dynamic state is simulated. The difference of static state phase information between transmission- and reflection-mode OIL is numerically analyzed. With specific OIL parameters, the output power of directly modulated OIL-VCSEL remains constant and phase deviation of 0.934π rad is obtained. Results show that a directly modulated OIL-VCSEL can function as a key component in QPSK or 8PSK transmitters. Preliminary 2.5 Gb/s PSK modulation characteristic is demonstrated experimentally.

  15. Comparison of Artificial Neural Networks and GIS Based Solar Analysis for Solar Potential Estimation

    Science.gov (United States)

    Konakoǧlu, Berkant; Usta, Ziya; Cömert, Çetin; Gökalp, Ertan

    2016-04-01

    Nowadays, estimation of solar potential plays an important role in planning process for sustainable cities. The use of solar panels, which produces electricity directly from the sun, has become popular in accordance with developing technologies. Since the use of solar panels enables the users to decrease costs and increase yields, the use of solar panels will be more popular in the future. Production of electricity is not convenient for all circumstances. Shading effects, massive clouds and rainy weather are some factors that directly affect the production of electricity from solar energy. Hence, before the installation of solar panels, it is crucial to conduct spatial analysis and estimate the solar potential of the place that the solar panel will be installed. There are several approaches to determine the solar potential. Examination of the applications in the literature reveals that the applications conducted for determining the solar potential are divided into two main categories. Solar potential is estimated either by using artificial neural network approach in which statistical parameters such as the duration of sun shine, number of clear days, solar radiation etc. are used, or by spatial analysis conducted in GIS approaches in which spatial parameters such as, latitude, longitude, slope, aspect etc. are used. In the literature, there are several studies that use both approaches but the literature lacks of a study related to the comparison of these approaches. In this study, Karadeniz Technical University campus has been selected as study area. Monthly average values of the number of clear sky days, air temperature, atmospheric pressure, relative humidity, sunshine duration and solar radiation parameters obtained for the years between 2005 and 2015 will be used to perform artificial neural network analysis to estimate the solar potential of the study area. The solar potential will also be estimated by using GIS-based solar analysis modules. The results of

  16. Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

    Science.gov (United States)

    Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

    2017-12-01

    We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

  17. Vision-Based Fall Detection with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Adrián Núñez-Marcos

    2017-01-01

    Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

  18. Adaptive PID control based on orthogonal endocrine neural networks.

    Science.gov (United States)

    Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D

    2016-12-01

    A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Web based educational tool for neural network robot control

    Directory of Open Access Journals (Sweden)

    Jure Čas

    2007-05-01

    Full Text Available Abstract— This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC and the graphical user interface (GUI. Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI, running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote’s user. Index Terms—LabVIEW; Matlab/Simulink; Neural network control; remote educational tool; robotics

  20. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Electric load variations can happen independently in both units. Both neural controllers are trained with the back propagation-through-time algorithm. Use of a neural network to model the dynamic system is avoided by introducing the Jacobian matrices of the system in the back propagation chain used in controller training.

  1. A neural network based approach to social touch classification

    NARCIS (Netherlands)

    van Wingerden, Siewart; Uebbing, Tobias J.; Jung, Merel Madeleine; Poel, Mannes

    2014-01-01

    Touch is an important interaction modality in social interaction, for instance touch can communicate emotions and can intensify emotions communicated by other modalities. In this paper we explore the use of Neural Networks for the classification of touch. The exploration and assessment of Neural

  2. Purely sequence trained neural networks for ASR based on lattice free MMI (Author’s Manuscript)

    Science.gov (United States)

    2016-09-08

    Purely sequence-trained neural networks for ASR based on lattice-free MMI Daniel Povey1,2, Vijayaditya Peddinti1, Daniel Galvez3, Pegah Ghahrmani1...we describe a method to perform sequence- discriminative training of neural network acoustic models with- out the need for frame-level cross-entropy... neural network outputs at one third the standard frame rate. These changes en- able us to perform the computation for the forward-backward algorithm

  3. Multiagent Intrusion Detection Based on Neural Network Detectors and Artificial Immune System

    OpenAIRE

    Vaitsekhovich, L.; Golovko, V; Rubanau, V.

    2009-01-01

    In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP. The KDD-99 dataset was used for experiments performing. The experimental results show that such int...

  4. Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer

    Directory of Open Access Journals (Sweden)

    Youtao Gao

    2017-01-01

    Full Text Available In order to improve the accuracy of the dynamical model used in the orbit determination of the Lagrangian navigation satellites, the nonlinear perturbations acting on Lagrangian navigation satellites are estimated by a neural network. A neural network based state observer is applied to autonomously determine the orbits of Lagrangian navigation satellites using only satellite-to-satellite range. This autonomous orbit determination method does not require linearizing the dynamical mode. There is no need to calculate the transition matrix. It is proved that three satellite-to-satellite ranges are needed using this method; therefore, the navigation constellation should include four Lagrangian navigation satellites at least. Four satellites orbiting on the collinear libration orbits are chosen to construct a constellation which is used to demonstrate the utility of this method. Simulation results illustrate that the stable error of autonomous orbit determination is about 10 m. The perturbation can be estimated by the neural network.

  5. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2017-10-01

    Full Text Available In this study, we present the Ensemble Convolutional Neural Network (ECNN, an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86.

  6. Design and Implementation of Multicarrier Modulation Systems Based on Cosine-modulated Filter Banks

    Directory of Open Access Journals (Sweden)

    Ling Zhuang

    2014-02-01

    Full Text Available Transmultiplexer systems based filter bank have certain advantages compared with existing multicarrier modulation (MCM systems based discrete Fourier transform (DFT. In this paper, a new cosine-modulated filter bank (CMFB with the performance of perfect reconstruction was designed and applied to the transmultiplexer based MCM. The prototype filter of the CMFB was designed based on semi-sinusoidal window. Simulation results show that its performance in symbol error rate (SER and peak to average power ration (PAPR based on filter bank system are improved, compared to the classical orthogonal frequency-division multiplexing (OFDM system.

  7. Energy detection UWB system based on pulse width modulation

    Directory of Open Access Journals (Sweden)

    Song Cui

    2014-05-01

    Full Text Available A new energy detection ultra-wideband system based on pulse width modulation is proposed. The bit error rate (BER performance of this new system is slightly worst than that of a pulse position modulation (PPM system in additive white Gaussian noise channels. In multipath channels, this system does not suffer from cross-modulation interference as PPM, so it can achieve better BER performance than PPM when cross-modulation interference occurs. In addition, when synchronisation errors occur, this system is more robust than PPM.

  8. Graphene-based THz modulator analyzed by equivalent circuit model

    DEFF Research Database (Denmark)

    Xiao, Binggang; Chen, Jing; Xie, Zhiyi

    2016-01-01

    A terahertz (THz) modulator based on graphene is proposed and analysed by use of equivalent transmission line of a homogeneous mediumand the local anisotropic model of the graphene conductivity. The result calculated by the equivalent circuit is consistent with that obtained byFresnel transfer...... matrices. For the modulator proposed here, when the frequency of carrier wave is 0.6 THz, the theoretical analysis indicatesthat the modulation bandwidth is 55.5 kHz and the modulation depth is 81.3% for voltage change from 0 to 50 V...

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

    Science.gov (United States)

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

    2013-01-01

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

  10. Cognitive flexibility modulates maturation and music-training-related changes in neural sound discrimination.

    Science.gov (United States)

    Saarikivi, Katri; Putkinen, Vesa; Tervaniemi, Mari; Huotilainen, Minna

    2016-07-01

    Previous research has demonstrated that musicians show superior neural sound discrimination when compared to non-musicians, and that these changes emerge with accumulation of training. Our aim was to investigate whether individual differences in executive functions predict training-related changes in neural sound discrimination. We measured event-related potentials induced by sound changes coupled with tests for executive functions in musically trained and non-trained children aged 9-11 years and 13-15 years. High performance in a set-shifting task, indexing cognitive flexibility, was linked to enhanced maturation of neural sound discrimination in both musically trained and non-trained children. Specifically, well-performing musically trained children already showed large mismatch negativity (MMN) responses at a young age as well as at an older age, indicating accurate sound discrimination. In contrast, the musically trained low-performing children still showed an increase in MMN amplitude with age, suggesting that they were behind their high-performing peers in the development of sound discrimination. In the non-trained group, in turn, only the high-performing children showed evidence of an age-related increase in MMN amplitude, and the low-performing children showed a small MMN with no age-related change. These latter results suggest an advantage in MMN development also for high-performing non-trained individuals. For the P3a amplitude, there was an age-related increase only in the children who performed well in the set-shifting task, irrespective of music training, indicating enhanced attention-related processes in these children. Thus, the current study provides the first evidence that, in children, cognitive flexibility may influence age-related and training-related plasticity of neural sound discrimination. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

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

    Directory of Open Access Journals (Sweden)

    Yuhan Chen

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

  12. A Project-Based Biologically-Inspired Robotics Module

    Science.gov (United States)

    Crowder, R. M.; Zauner, K.-P.

    2013-01-01

    The design of any robotic system requires input from engineers from a variety of technical fields. This paper describes a project-based module, "Biologically-Inspired Robotics," that is offered to Electronics and Computer Science students at the University of Southampton, U.K. The overall objective of the module is for student groups to…

  13. Computer-Based Self-Instructional Modules. Final Technical Report.

    Science.gov (United States)

    Weinstock, Harold

    Reported is a project involving seven chemists, six mathematicians, and six physicists in the production of computer-based, self-study modules for use in introductory college courses in chemistry, physics, and mathematics. These modules were designed to be used by students and instructors with little or no computer backgrounds, in institutions…

  14. CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Munro, Troy [Multiscale Thermal-Physics Lab, Department of Mechanical and Aerospace Engineering, Utah State University, 4130 Old Main Hill, Logan, Utah 84322 (United States); Laboratory of Soft Matter and Biophysics, Department of Physics and Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Heverlee (Belgium); Liu, Liwang; Glorieux, Christ [Laboratory of Soft Matter and Biophysics, Department of Physics and Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Heverlee (Belgium); Ban, Heng [Multiscale Thermal-Physics Lab, Department of Mechanical and Aerospace Engineering, Utah State University, 4130 Old Main Hill, Logan, Utah 84322 (United States)

    2016-06-07

    As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based—peak intensity, peak wavelength; shape-based—integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensity-modulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of

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

    Science.gov (United States)

    Hermer-Vazquez, Linda

    2008-04-01

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

  16. The quality of adolescents’ peer relationships modulates neural sensitivity to risk taking

    Science.gov (United States)

    Fuligni, Andrew J.; Lieberman, Matthew D.; Miernicki, Michelle E.; Galván, Adriana

    2015-01-01

    Adolescents' peer culture plays a key role in the development and maintenance of risk-taking behavior. Despite recent advances in developmental neuroscience suggesting that peers may increase neural sensitivity to rewards, we know relatively little about how the quality of peer relations impact adolescent risk taking. In the current 2-year three-wave longitudinal study, we examined how chronic levels of peer conflict relate to risk taking behaviorally and neurally, and whether this is modified by high-quality peer relationships. Forty-six adolescents completed daily diaries assessing peer conflict across 2 years as well as a measure of peer support. During a functional brain scan, adolescents completed a risk-taking task. Behaviorally, peer conflict was associated with greater risk-taking behavior, especially for adolescents reporting low peer support. High levels of peer support buffered this association. At the neural level, peer conflict was associated with greater activation in the striatum and insula, especially among adolescents reporting low peer support, whereas this association was buffered for adolescents reporting high peer support. Results are consistent with the stress-buffering model of social relationships and underscore the importance of the quality of adolescents’ peer relationships for their risk taking. PMID:24795443

  17. The quality of adolescents' peer relationships modulates neural sensitivity to risk taking.

    Science.gov (United States)

    Telzer, Eva H; Fuligni, Andrew J; Lieberman, Matthew D; Miernicki, Michelle E; Galván, Adriana

    2015-03-01

    Adolescents' peer culture plays a key role in the development and maintenance of risk-taking behavior. Despite recent advances in developmental neuroscience suggesting that peers may increase neural sensitivity to rewards, we know relatively little about how the quality of peer relations impact adolescent risk taking. In the current 2-year three-wave longitudinal study, we examined how chronic levels of peer conflict relate to risk taking behaviorally and neurally, and whether this is modified by high-quality peer relationships. Forty-six adolescents completed daily diaries assessing peer conflict across 2 years as well as a measure of peer support. During a functional brain scan, adolescents completed a risk-taking task. Behaviorally, peer conflict was associated with greater risk-taking behavior, especially for adolescents reporting low peer support. High levels of peer support buffered this association. At the neural level, peer conflict was associated with greater activation in the striatum and insula, especially among adolescents reporting low peer support, whereas this association was buffered for adolescents reporting high peer support. Results are consistent with the stress-buffering model of social relationships and underscore the importance of the quality of adolescents' peer relationships for their risk taking. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  18. Modeling Current Transfer from PV Modules Based on Meteorological Data

    Energy Technology Data Exchange (ETDEWEB)

    Hacke, Peter; Smith, Ryan; Kurtz, Sarah; Jordan, Dirk; Wohlgemuth, John

    2016-11-21

    Current transferred from the active cell circuit to ground in modules undergoing potential-induced degradation (PID) stress is analyzed with respect to meteorological data. Duration and coulombs transferred as a function of whether the module is wet (from dew or rain) or the extent of uncondensed surface humidity are quantified based on meteorological indicators. With this, functions predicting the mode and rate of coulomb transfer are developed for use in estimating the relative PID stress associated with temperature, moisture, and system voltage in any climate. Current transfer in a framed crystalline silicon module is relatively high when there is no condensed water on the module, whereas current transfer in a thin-film module held by edge clips is not, and displays a greater fraction of coulombs transferred when wet compared to the framed module in the natural environment.

  19. Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling.

    Science.gov (United States)

    Struzyna, Laura A; Adewole, Dayo O; Gordián-Vélez, Wisberty J; Grovola, Michael R; Burrell, Justin C; Katiyar, Kritika S; Petrov, Dmitriy; Harris, James P; Cullen, D Kacy

    2017-05-31

    Functional recovery rarely occurs following injury or disease-induced degeneration within the central nervous system (CNS) due to the inhibitory environment and the limited capacity for neurogenesis. We are developing a strategy to simultaneously address neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative "living scaffolds" capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium

  20. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  1. Neural-Fuzzy model Based Steel Pipeline Multiple Cracks Classification

    Science.gov (United States)

    Elwalwal, Hatem Mostafa; Mahzan, Shahruddin Bin Hj.; Abdalla, Ahmed N.

    2017-10-01

    While pipes are cheaper than other means of transportation, this cost saving comes with a major price: pipes are subject to cracks, corrosion etc., which in turn can cause leakage and environmental damage. In this paper, Neural-Fuzzy model for multiple cracks classification based on Lamb Guide Wave. Simulation results for 42 sample were collected using ANSYS software. The current research object to carry on the numerical simulation and experimental study, aiming at finding an effective way to detection and the localization of cracks and holes defects in the main body of pipeline. Considering the damage form of multiple cracks and holes which may exist in pipeline, to determine the respective position in the steel pipe. In addition, the technique used in this research a guided lamb wave based structural health monitoring method whereas piezoelectric transducers will use as exciting and receiving sensors by Pitch-Catch method. Implementation of simple learning mechanism has been developed specially for the ANN for fuzzy the system represented.

  2. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  3. Neural Signature of Value-Based Sensorimotor Prioritization in Humans.

    Science.gov (United States)

    Blangero, Annabelle; Kelly, Simon P

    2017-11-01

    value biases in sensorimotor decision making have been widely studied, little is known about the neural processes that set these biases in place beforehand. Here, we report the discovery of a transient, spatially selective neural signal in humans that encodes the relative value of competing decision alternatives and strongly predicts behavioral value biases in decisions made ∼500 ms later. Follow-up manipulations of value differential, reward valence, response modality, sensory features, and time constraints establish that the signal reflects an active, feature- and effector-general preparatory mechanism for value-based prioritization. Copyright © 2017 the authors 0270-6474/17/3710725-13$15.00/0.

  4. Associating a product with a luxury brand label modulates neural reward processing and favors choices in materialistic individuals.

    Science.gov (United States)

    Audrin, Catherine; Ceravolo, Leonardo; Chanal, Julien; Brosch, Tobias; Sander, David

    2017-11-23

    The present study investigated the extent to which luxury vs. non-luxury brand labels (i.e., extrinsic cues) randomly assigned to items and preferences for these items impact choice, and how this impact may be moderated by materialistic tendencies (i.e., individual characteristics). The main objective was to investigate the neural correlates of abovementioned effects using functional magnetic resonance imaging. Behavioural results showed that the more materialistic people are, the more they choose and like items labelled with luxury brands. Neuroimaging results revealed the implication of a neural network including the dorsolateral and ventromedial prefrontal cortex and the orbitofrontal cortex that was modulated by the brand label and also by the participants' preference. Most importantly, items with randomly assigned luxurious brand labels were preferentially chosen by participants and triggered enhanced signal in the caudate nucleus. This effect increased linearly with materialistic tendencies. Our results highlight the impact of brand-item association, although random in our study, and materialism on preference, relying on subparts of the brain valuation system for the integration of extrinsic cues, preferences and individual characteristics.

  5. Modulation of calcium-induced cell death in human neural stem cells by the novel peptidylarginine deiminase-AIF pathway.

    Science.gov (United States)

    U, Kin Pong; Subramanian, Venkataraman; Nicholas, Antony P; Thompson, Paul R; Ferretti, Patrizia

    2014-06-01

    PADs (peptidylarginine deiminases) are calcium-dependent enzymes that change protein-bound arginine to citrulline (citrullination/deimination) affecting protein conformation and function. PAD up-regulation following chick spinal cord injury has been linked to extensive tissue damage and loss of regenerative capability. Having found that human neural stem cells (hNSCs) expressed PAD2 and PAD3, we studied PAD function in these cells and investigated PAD3 as a potential target for neuroprotection by mimicking calcium-induced secondary injury responses. We show that PAD3, rather than PAD2 is a modulator of cell growth/death and that PAD activity is not associated with caspase-3-dependent cell death, but is required for AIF (apoptosis inducing factor)-mediated apoptosis. PAD inhibition prevents association of PAD3 with AIF and AIF cleavage required for its translocation to the nucleus. Finally, PAD inhibition also hinders calcium-induced cytoskeleton disassembly and association of PAD3 with vimentin, that we show to be associated also with AIF; together this suggests that PAD-dependent cytoskeleton disassembly may play a role in AIF translocation to the nucleus. This is the first study highlighting a role of PAD activity in balancing hNSC survival/death, identifying PAD3 as an important upstream regulator of calcium-induced apoptosis, which could be targeted to reduce neural loss, and shedding light on the mechanisms involved. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

  6. 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

    2015-08-01

    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. © FASEB.

  7. A common oxytocin receptor gene (OXTR) polymorphism modulates intranasal oxytocin effects on the neural response to social cooperation in humans.

    Science.gov (United States)

    Feng, C; Lori, A; Waldman, I D; Binder, E B; Haroon, E; Rilling, J K

    2015-09-01

    Intranasal oxytocin (OT) can modulate social-emotional functioning and related brain activity in humans. Consequently, OT has been discussed as a potential treatment for psychiatric disorders involving social behavioral deficits. However, OT effects are often heterogeneous across individuals. Here we explore individual differences in OT effects on the neural response to social cooperation as a function of the rs53576 polymorphism of the oxytocin receptor gene (OXTR). Previously, we conducted a double-blind, placebo-controlled study in which healthy men and women were randomized to treatment with intranasal OT or placebo. Afterwards, they were imaged with functional magnetic resonance imaging while playing an iterated Prisoner's Dilemma Game with same-sex partners. Within the left ventral caudate nucleus, intranasal OT treatment increased activation to reciprocated cooperation in men, but tended to decrease activation in women. Here, we show that these sex differences in OT effects are specific to individuals with the rs53576 GG genotype, and are not found for other genotypes (rs53576 AA/AG). Thus, OT may increase the reward or salience of positive social interactions for male GG homozygotes, while decreasing those processes for female GG homozygotes. These results suggest that rs53576 genotype is an important variable to consider in future investigations of the clinical efficacy of intranasal OT treatment. © 2015 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  8. Neural network predicts sequence of TP53 gene based on DNA chip

    DEFF Research Database (Denmark)

    Spicker, J.S.; Wikman, F.; Lu, M.L.

    2002-01-01

    We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero...

  9. A comparative performance evaluation of neural network based approach for sentiment classification of online reviews

    Directory of Open Access Journals (Sweden)

    G. Vinodhini

    2016-01-01

    Full Text Available The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest that ensemble based machine learning methods provide better performance in classification. Artificial neural networks (ANNs are rarely being investigated in the literature of sentiment classification. This paper compares neural network based sentiment classification methods (back propagation neural network (BPN, probabilistic neural network (PNN & homogeneous ensemble of PNN (HEN using varying levels of word granularity as features for feature level sentiment classification. They are validated using a dataset of product reviews collected from the Amazon reviews website. An empirical analysis is done to compare results of ANN based methods with two statistical individual methods. The methods are evaluated using five different quality measures and results show that the homogeneous ensemble of the neural network method provides better performance. Among the two neural network approaches used, probabilistic neural networks (PNNs outperform in classifying the sentiment of the product reviews. The integration of neural network based sentiment classification methods with principal component analysis (PCA as a feature reduction technique provides superior performance in terms of training time also.

  10. Neural bases of ingroup altruistic motivation in soccer fans

    National Research Council Canada - National Science Library

    Tiago Bortolini; Patrícia Bado; Sebastian Hoefle; Annerose Engel; Roland Zahn; Ricardo de Oliveira Souza; Jean-Claude Dreher; Jorge Moll

    2017-01-01

    .... Here, we used soccer fandom as an ecological framing of group membership to investigate the neural mechanisms underlying ingroup altruistic behaviour in male fans using event-related functional magnetic resonance...

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

    National Research Council Canada - National Science Library

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

    2012-01-01

    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...

  12. A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

    Science.gov (United States)

    Hong, Xia

    2006-07-01

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

  13. Expectation violation and attention to pain jointly modulate neural gain in somatosensory cortex

    DEFF Research Database (Denmark)

    Fardo, Francesca; Auksztulewicz, Ryszard; Allen, Micah

    2017-01-01

    The neural processing and experience of pain are influenced by both expectations and attention. For example, the amplitude of event-related pain responses is enhanced by both novel and unexpected pain, and by moving the focus of attention towards a painful stimulus. Under predictive coding...... be mapped onto changes in effective connectivity between or within specific neuronal populations, using a canonical microcircuit (CMC) model of hierarchical processing. We thus implemented a CMC within dynamic causal modelling (DCM) for magnetoencephalography in human subjects, to investigate how...

  14. Classification-based Financial Markets Prediction using Deep Neural Networks

    OpenAIRE

    Dixon, Matthew; Klabjan, Diego; Bang, Jin Hoon

    2016-01-01

    Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to algorithmic trading has not been previously researched, partly because of their computational complexity. This paper describes the applicat...

  15. SMS service self-based module

    OpenAIRE

    Gascons Gómez, Christian

    2008-01-01

    El SMS Service Module fue hecho para ser incorporado en cualquier aplicación informática de gestión. Está pensado para ser instalado en las aplicaciones de empresas que cuentan con una cartera de clientes lo suficientemente grande como para automatizar todo el sistema de comunicación con sus abonados mediante el envío de mensajes SMS. Lo que el poseedor de ésta aplicación conseguirá es no tener que preocuparse por emitir ningún tipo de aviso puntual a sus clientes en caso de...

  16. Artificial Neural Network-Based System for PET Volume Segmentation

    Directory of Open Access Journals (Sweden)

    Mhd Saeed Sharif

    2010-01-01

    Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.

  17. Quantum-based algorithm for optimizing artificial neural networks.

    Science.gov (United States)

    Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang

    2013-08-01

    This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

  18. Didactic Strategy Discussion Based on Artificial Neural Networks Results.

    Science.gov (United States)

    Andina, D.; Bermúdez-Valbuena, R.

    2009-04-01

    Artificial Neural Networks (ANNs) are a mathematical model of the main known characteristics of biological brian dynamics. ANNs inspired in biological reality have been useful to design machines that show some human-like behaviours. Based on them, many experimentes have been succesfully developed emulating several biologial neurons characteristics, as learning how to solve a given problem. Sometimes, experimentes on ANNs feedback to biology and allow advances in understanding the biological brian behaviour, allowing the proposal of new therapies for medical problems involving neurons performing. Following this line, the author present results on artificial learning on ANN, and interpret them aiming to reinforce one of this two didactic estrategies to learn how to solve a given difficult task: a) To train with clear, simple, representative examples and feel confidence in brian generalization capabilities to achieve succes in more complicated cases. b) To teach with a set of difficult cases of the problem feeling confidence that the brian will efficiently solve the rest of cases if it is able to solve the difficult ones. Results may contribute in the discussion of how to orientate the design innovative succesful teaching strategies in the education field.

  19. Animal Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Tibor Trnovszky

    2017-01-01

    Full Text Available In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA, Linear Discriminant Analysis (LDA, Local Binary Patterns Histograms (LBPH and Support Vector Machine (SVM are tested and compared with proposed convolutional neural network (CNN for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class. The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

  20. Toward Content Based Image Retrieval with Deep Convolutional Neural Networks.

    Science.gov (United States)

    Sklan, Judah E S; Plassard, Andrew J; Fabbri, Daniel; Landman, Bennett A

    2015-03-19

    Content-based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and eventually, improve patient care. Recent advances in database capacity, algorithm efficiency, and deep Convolutional Neural Networks (dCNN), a machine learning technique, have enabled great CBIR success for general photographic images. Here, we investigate applying the leading ImageNet CBIR technique to clinically acquired medical images captured by the Vanderbilt Medical Center. Briefly, we (1) constructed a dCNN with four hidden layers, reducing dimensionality of an input scaled to 128×128 to an output encoded layer of 4×384, (2) trained the network using back-propagation 1 million random magnetic resonance (MR) and computed tomography (CT) images, (3) labeled an independent set of 2100 images, and (4) evaluated classifiers on the projection of the labeled images into manifold space. Quantitative results were disappointing (averaging a true positive rate of only 20%); however, the data suggest that improvements would be possible with more evenly distributed sampling across labels and potential re-grouping of label structures. This prelimainry effort at automated classification of medical images with ImageNet is promising, but shows that more work is needed beyond direct adaptation of existing techniques.

  1. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  2. 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.

  3. Neural Online Filtering Based on Preprocessed Calorimeter Data

    CERN Document Server

    Torres, R C; The ATLAS collaboration; Simas Filho, E F; De Seixas, J M

    2009-01-01

    Among LHC detectors, ATLAS aims at coping with such high event rate by designing a three-level online triggering system. The first level trigger output will be ~75 kHz. This level will mark the regions where relevant events were found. The second level will validate LVL1 decision by looking only at the approved data using full granularity. At the level two output, the event rate will be reduced to ~2 kHz. Finally, the third level will look at full event information and a rate of ~200 Hz events is expected to be approved, and stored in persistent media for further offline analysis. Many interesting events decay into electrons, which have to be identified from the huge background noise (jets). This work proposes a high-efficient LVL2 electron / jet discrimination system based on neural networks fed from preprocessed calorimeter information. The feature extraction part of the proposed system performs a ring structure of data description. A set of concentric rings centered at the highest energy cell is generated ...

  4. Neural bases of goal-directed implicit learning.

    Science.gov (United States)

    Rostami, Maryam; Hosseini, S M Hadi; Takahashi, Makoto; Sugiura, Motoaki; Kawashima, Ryuta

    2009-10-15

    Several neuropsychological and neuroimaging studies have been performed to clarify the neural bases of implicit learning, but the question of which brain regions are involved in different forms of implicit learning, including goal-directed learning and habit learning, has not yet been resolved. The present study sought to clarify the mechanisms of goal-directed implicit learning by examining the sugar production factory (SPF) task in conjunction with functional magnetic resonance imaging (fMRI). Several brain regions were identified that contribute to learning in the SPF task. Significant learning-related decreases in brain activity were found in the right inferior parietal lobule (IPL), left superior frontal gyrus, right medial frontal gyrus, cerebellar vermis, and left inferior frontal gyrus, while significant learning-related increases in activity were observed in the right inferior frontal gyrus, left precenteral gyrus and, left precuneus. Among these regions, we speculate that the IPL and medial frontal gyrus may specifically be involved in the early stage of goal-directed implicit learning. We also attempted to investigate the role of the striatum, which has a significant role in habit learning, during learning of the SPF task. The results of ROI analysis showed no learning-related change in the activity of the striatum. Although some of the observed learning-related activations in this study have also been previously reported in neuroimaging studies of habit learning, the possibility that specific brain regions involved in goal-direct implicit learning cannot be excluded.

  5. Illicit material detector based on gas sensors and neural networks

    Science.gov (United States)

    Grimaldi, Vincent; Politano, Jean-Luc

    1997-02-01

    In accordance with its missions, le Centre de Recherches et d'Etudes de la Logistique de la Police Nationale francaise (CREL) has been conducting research for the past few years targeted at detecting drugs and explosives. We have focused our approach of the underlying physical and chemical detection principles on solid state gas sensors, in the hope of developing a hand-held drugs and explosives detector. The CREL and Laboratory and Scientific Services Directorate are research partners for this project. Using generic hydrocarbon, industrially available, metal oxide sensors as illicit material detectors, requires usage precautions. Indeed, neither the product's concentrations, nor even the products themselves, belong to the intended usage specifications. Therefore, the CREL is currently investigating two major research topics: controlling the sensor's environment: with environmental control we improve the detection of small product concentration; determining detection thresholds: both drugs and explosives disseminate low gas concentration. We are attempting to quantify the minimal concentration which triggers detection. In the long run, we foresee a computer-based tool likely to detect a target gas in a noisy atmosphere. A neural network is the suitable tool for interpreting the response of heterogeneous sensor matrix. This information processing structure, alongside with proper sensor environment control, will lessen the repercussions of common MOS sensor sensitivity characteristic dispersion.

  6. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  7. A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application.

    Science.gov (United States)

    Onumanyi, A J; Onwuka, E N; Aibinu, A M; Ugweje, O C; Salami, M J E

    2014-01-01

    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application.

  8. Neural network based adaptive control for nonlinear dynamic regimes

    Science.gov (United States)

    Shin, Yoonghyun

    Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

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

    Science.gov (United States)

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

    2015-02-01

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

  10. Neural Representation of Working Memory Content Is Modulated by Visual Attentional Demand.

    Science.gov (United States)

    Kiyonaga, Anastasia; Dowd, Emma Wu; Egner, Tobias

    2017-12-01

    Recent theories assert that visual working memory (WM) relies on the same attentional resources and sensory substrates as visual attention to external stimuli. Behavioral studies have observed competitive tradeoffs between internal (i.e., WM) and external (i.e., visual) attentional demands, and neuroimaging studies have revealed representations of WM content as distributed patterns of activity within the same cortical regions engaged by perception of that content. Although a key function of WM is to protect memoranda from competing input, it remains unknown how neural representations of WM content are impacted by incoming sensory stimuli and concurrent attentional demands. Here, we investigated how neural evidence for WM information is affected when attention is occupied by visual search-at varying levels of difficulty-during the delay interval of a WM match-to-sample task. Behavioral and fMRI analyses suggested that WM maintenance was impacted by the difficulty of a concurrent visual task. Critically, multivariate classification analyses of category-specific ventral visual areas revealed a reduction in decodable WM-related information when attention was diverted to a visual search task, especially when the search was more difficult. This study suggests that the amount of available attention during WM maintenance influences the detection of sensory WM representations.

  11. Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

    Science.gov (United States)

    Xia, Youshen; Wang, Jun

    2015-07-01

    This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Neural-networks-based feedback linearization versus model predictive control of continuous alcoholic fermentation process

    Energy Technology Data Exchange (ETDEWEB)

    Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)

    2005-10-01

    In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)

  13. Asteroid! An Event-Based Science Module. Teacher's Guide. Astronomy Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school earth science or general science teachers to help their students learn scientific literacy through event-based science. Unlike traditional curricula, the event- based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork,…

  14. Asteroid! An Event-Based Science Module. Student Edition. Astronomy Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school students to learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research, hands-on investigations, and…

  15. Oil Spill! An Event-Based Science Module. Student Edition. Oceanography Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school students to learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research, hands-on investigations, and…

  16. Oil Spill!: An Event-Based Science Module. Teacher's Guide. Oceanography Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school earth science or general science teachers to help their students learn scientific literacy through event-based science. Unlike traditional curricula, the event- based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork,…

  17. Volcano!: An Event-Based Science Module. Teacher's Guide. Geology Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school earth science teachers to help their students learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research,…

  18. Volcano!: An Event-Based Science Module. Student Edition. Geology Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school students to learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research, hands-on investigations, and…

  19. Hurricane! An Event-Based Science Module. Student Edition. Meteorology Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school students to learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research, hands-on investigations, and…

  20. Hurricane!: An Event-Based Science Module. Teacher's Guide. Meteorology Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school earth science teachers to help their students learn about problems with hurricanes and scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning,…

  1. Fraud! An Event-Based Science Module. Student Edition. Chemistry Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school students to learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork, independent research, hands-on investigations, and…

  2. Fraud! An Event-Based Science Module. Teacher's Guide. Chemistry Module.

    Science.gov (United States)

    Wright, Russell G.

    This book is designed for middle school life science or physical science teachers to help their students learn scientific literacy through event-based science. Unlike traditional curricula, the event-based earth science module is a student-centered, interdisciplinary, inquiry-oriented program that emphasizes cooperative learning, teamwork,…

  3. PENGEMBANGAN MODUL BERORIENTASI PROBLEM BASED LEARNING BERBANTUAN APLIKASI ANDROID

    Directory of Open Access Journals (Sweden)

    Anggih Alfiantara

    2017-02-01

    Full Text Available Peningkatan kualitas pembelajaran dapat dilakukan dari berbagai variabel pembelajaran, salah satunya berupa modul sebagai variabel bahan ajar. Pengembangan modul berbantuan android application mobile berorientasi Problem Based Learning diharapkan dapat meningkatkan aktivitas dan hasil belajar. Penelitian pengembangan ini bertujuan untuk mengetahui validitas modul berorientasi Problem Based Learning dan untuk mendapatkan respon dari pengguna. Desain penelitian yang digunakan yaitu four-D Models yang dimodifikasi. Pengumpulan data menggunakan lembar validasi dan metode angket. Kelayakan awal bahan ajar ditentukan oleh ahli materi dan ahli media menggunakan teknik deskriptif persentase. Kelayakan akhir bahan ajar ditentukan berdasarkan hasil pengujian. Data hasil penelitian dianalisis menggunakan metode analisis deskriptif kuantitatif. Secara kuantitatif, data hasil penelitian dianalisis dengan cara menghitung rerata skor dan menentukan kriteria pada interval kelas tertentu. Uji validitas modul memperoleh nilai rata-rata sebesar 3,196, dengan persentase skor rata-rata 79,905%. Data skor perolehan angket tanggapan siswa sebesar 3,21, dan angket tanggapan guru sebesar 3,40, sehingga modul ini terbukti memenuhi kriteria layak yang didukung dengan respon baik dari pengguna. Berdasarkan hasil analisis data dapat disimpulkan bahwa modul ini dinyatakan valid dan mendapat respon baik dari pengguna sehingga dapat digunakan sebagai sumber belajar.Improving the quality of learning can be done from a variety of learning variables, one of them is a variable module as teaching materials. Module development with android application oriented Problem Based Learning is expected to increase activity and learning outcomes. This development study aims to determine the validity of the module oriented problem based learning and getting response from users. The study design used is four-D Models were modified. Data collection is using validation sheet and questionnaires

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

    Science.gov (United States)

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

    2012-01-01

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

  5. Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.

    Science.gov (United States)

    Li, Ning; Cao, Jinde

    2016-03-01

    This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.

  6. Forecast of consumer behaviour based on neural networks models comparison

    Directory of Open Access Journals (Sweden)

    Michael Štencl

    2012-01-01

    Full Text Available The aim of this article is comparison of accuracy level of forecasted values of several artificial neural network models. The comparison is performed on datasets of Czech household consumption values. Several statistical models often resolve this task with more or fewer restrictions. In previous work where models’ input conditions were not so strict and model with missing data was used (the time series didn’t contain many values we have obtained comparably good results with artificial neural networks. Two views – practical and theoretical, motivate the purpose of this study. Forecasting models for medium term prognosis of the main trends of Czech household consumption is part of the faculty research design grant MSM 6215648904/03/02 (Sub-task 5.3 which defines the practical purpose. Testing of nonlinear autoregressive artificial neural network model compared with feed-forward neural network and radial basis function neural network defines the theoretical purpose. The performance metrics of the models were evaluated using a combination of common error metrics, namely Correlation Coefficient and Mean Square Error, together with the number of epochs and/or main prediction error.

  7. Neural-network-based fuzzy logic decision systems

    Science.gov (United States)

    Kulkarni, Arun D.; Giridhar, G. B.; Coca, Praveen

    1994-10-01

    During the last few years there has been a large and energetic upswing in research efforts aimed at synthesizing fuzzy logic with neural networks. This combination of neural networks and fuzzy logic seems natural because the two approaches generally attack the design of `intelligent' system from quite different angles. Neural networks provide algorithms for learning, classification, and optimization whereas fuzzy logic often deals with issues such as reasoning in a high (semantic or linguistic) level. Consequently the two technologies complement each other. In this paper, we combine neural networks with fuzzy logic techniques. We propose an artificial neural network (ANN) model for a fuzzy logic decision system. The model consists of six layers. The first three layers map the input variables to fuzzy set membership functions. The last three layers implement the decision rules. The model learns the decision rules using a supervised gradient descent procedure. As an illustration we considered two examples. The first example deals with pixel classification in multispectral satellite images. In our second example we used the fuzzy decision system to analyze data from magnetic resonance imaging (MRI) scans for tissue classification.

  8. Cultural modulation of the neural correlates of emotional pain perception: the role of other-focusedness.

    Science.gov (United States)

    Cheon, Bobby K; Im, Dong-Mi; Harada, Tokiko; Kim, Ji-Sook; Mathur, Vani A; Scimeca, Jason M; Parrish, Todd B; Park, Hyunwook; Chiao, Joan Y

    2013-06-01

    Cultures vary in the extent to which they emphasize group members to habitually attend to the needs, perspectives, and internal experiences of others compared to the self. Here we examined the influence that collectivistic and individualistic cultural environments may play on the engagement of the neurobiological processes that underlie the perception and processing of emotional pain. Using cross-cultural fMRI, Korean and Caucasian-American participants passively viewed scenes of others in situations of emotional pain and distress. Regression analyses revealed that the value of other-focusedness was associated with heightened neural response within the affective pain matrix (i.e. anterior cingulate cortex and insula) to a greater extent for Korean relative to Caucasian-American participants. These findings suggest that mindsets promoting attunement to the subjective experience of others may be especially critical for pain-related and potentially empathic processing within collectivistic relative to individualistic cultural environments. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. The influence of the diffusion module to determination of two substrate concentrations by articial neural network

    Directory of Open Access Journals (Sweden)

    Linas Litvinas

    2015-09-01

    Full Text Available The essential part of amperometric biosensor is an enzyme. It should be selective, i.e., react only with certain substrate. The selectivity of enzyme reduces the set of possible to use enzymes. This paper demonstrates that non selective enzymes (reacting with two substrates can be used to determine concentrations of two substrates. For this purpose the steady-state current of two double biosensors was measured. The currents were used as input for an artificial neural network to determine concentrations of the substrates. The proposed approach was approved as the relative error of determined concentrations was relatively small. Paper analyses the influence of biosensor parameters to error values. The recommendations to error values minimisation were obtained.DOI: 10.15181/csat.v3i2.1109 

  10. PPARs Expression in Adult Mouse Neural Stem Cells: Modulation of PPARs during Astroglial Differentiaton of NSC

    Directory of Open Access Journals (Sweden)

    A. Cimini

    2007-01-01

    Full Text Available PPAR isotypes are involved in the regulation of cell proliferation, death, and differentiation, with different roles and mechanisms depending on the specific isotype and ligand and on the differentiated, undifferentiated, or transformed status of the cell. Differentiation stimuli are integrated by key transcription factors which regulate specific sets of specialized genes to allow proliferative cells to exit the cell cycle and acquire specialized functions. The main differentiation programs known to be controlled by PPARs both during development and in the adult are placental differentiation, adipogenesis, osteoblast differentiation, skin differentiation, and gut differentiation. PPARs may also be involved in the differentiation of macrophages, brain, and breast. However, their functions in this cell type and organs still awaits further elucidation. PPARs may be involved in cell proliferation and differentiation processes of neural stem cells (NSC. To this aim, in this work the expression of the three PPAR isotypes and RXRs in NSC has been investigated.

  11. Percutaneous autonomic neural modulation: A novel technique to treat cardiac arrhythmia

    Energy Technology Data Exchange (ETDEWEB)

    DeSimone, Christopher V.; Madhavan, Malini [Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, MN (United States); Venkatachalam, Kalpathi L. [Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Jacksonville, FL (United States); Knudson, Mark B. [Mayo Clinic, Rochester, MN (United States); EnteroMedics, EnteroMedics, St. Paul, MN (United States); Asirvatham, Samuel J., E-mail: asirvatham.samuel@mayo.edu [Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, MN (United States); Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN (United States)

    2013-05-15

    Ablation and anti-arrhythmic medications have shown promise but have been met with varying success and unwanted side effects such as myocardial injury, arrhythmias, and morbidity from invasive surgical intervention. The answer to improving efficacy of ablation may include modulation of the cardiac aspect of the autonomic nervous system. Our lab has developed a novel approach and device to navigate the oblique sinus and to use DC current and saline/alcohol irrigation to selectively stimulate and block the autonomic ganglia found on the epicardial side of the heart. This novel approach minimizes myocardial damage from thermal injury and provides a less invasive and targeted approach. For feasibility, proof-of-concept, and safety monitoring, we carried out canine studies to test this novel application. Our results suggest a safer and less invasive way of modulating arrhythmogenic substrate that may lead to improved treatment of AF in humans.

  12. Neural substrates of impulsive decision making modulated by modafinil in alcohol-dependent patients.

    Science.gov (United States)

    Schmaal, L; Goudriaan, A E; Joos, L; Dom, G; Pattij, T; van den Brink, W; Veltman, D J

    2014-10-01

    Impulsive decision making is a hallmark of frequently occurring addiction disorders including alcohol dependence (AD). Therefore, ameliorating impulsive decision making is a promising target for the treatment of AD. Previous studies have shown that modafinil enhances cognitive control functions in various psychiatric disorders. However, the effects of modafinil on delay discounting and its underlying neural correlates have not been investigated as yet. The aim of the current study was to investigate the effects of modafinil on neural correlates of impulsive decision making in abstinent AD patients and healthy control (HC) subjects. A randomized, double-blind, placebo-controlled, within-subjects cross-over study using functional magnetic resonance imaging (fMRI) was conducted in 14 AD patients and 16 HC subjects. All subjects participated in two fMRI sessions in which they either received a single dose of placebo or 200 mg of modafinil 2 h before the session. During fMRI, subjects completed a delay-discounting task to measure impulsive decision making. Modafinil improved impulsive decision making in AD pateints, which was accompanied by enhanced recruitment of frontoparietal regions and reduced activation of the ventromedial prefrontal cortex. Moreover, modafinil-induced enhancement of functional connectivity between the superior frontal gyrus and ventral striatum was specifically associated with improvement in impulsive decision making. These findings indicate that modafinil can improve impulsive decision making in AD patients through an enhanced coupling of prefrontal control regions and brain regions coding the subjective value of rewards. Therefore, the current study supports the implementation of modafinil in future clinical trials for AD.

  13. Neural mechanisms influencing interlimb coordination during locomotion in humans: presynaptic modulation of forearm H-reflexes during leg cycling.

    Directory of Open Access Journals (Sweden)

    Tsuyoshi Nakajima

    Full Text Available Presynaptic inhibition of transmission between Ia afferent terminals and alpha motoneurons (Ia PSI is a major control mechanism associated with soleus H-reflex modulation during human locomotion. Rhythmic arm cycling suppresses soleus H-reflex amplitude by increasing segmental Ia PSI. There is a reciprocal organization in the human nervous system such that arm cycling modulates H-reflexes in leg muscles and leg cycling modulates H-reflexes in forearm muscles. However, comparatively little is known about mechanisms subserving the effects from leg to arm. Using a conditioning-test (C-T stimulation paradigm, the purpose of this study was to test the hypothesis that changes in Ia PSI underlie the modulation of H-reflexes in forearm flexor muscles during leg cycling. Subjects performed leg cycling and static activation while H-reflexes were evoked in forearm flexor muscles. H-reflexes were conditioned with either electrical stimuli to the radial nerve (to increase Ia PSI; C-T interval  = 20 ms or to the superficial radial (SR nerve (to reduce Ia PSI; C-T interval  = 37-47 ms. While stationary, H-reflex amplitudes were significantly suppressed by radial nerve conditioning and facilitated by SR nerve conditioning. Leg cycling suppressed H-reflex amplitudes and the amount of this suppression was increased with radial nerve conditioning. SR conditioning stimulation removed the suppression of H-reflex amplitude resulting from leg cycling. Interestingly, these effects and interactions on H-reflex amplitudes were observed with subthreshold conditioning stimulus intensities (radial n., ∼0.6×MT; SR n., ∼ perceptual threshold that did not have clear post synaptic effects. That is, did not evoke reflexes in the surface EMG of forearm flexor muscles. We conclude that the interaction between leg cycling and somatosensory conditioning of forearm H-reflex amplitudes is mediated by modulation of Ia PSI pathways. Overall our results support a

  14. Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

    National Research Council Canada - National Science Library

    Dan Yang; Hailin Mu; Zengbing Xu; Zhigang Wang; Cancan Yi; Changming Liu

    2017-01-01

    ...) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL...

  15. The performance of immune-based neural network with financial time series prediction

    Directory of Open Access Journals (Sweden)

    Dhiya Al-Jumeily

    2015-12-01

    Full Text Available This paper presents the use of immune-based neural networks that include multilayer perceptron (MLP and functional neural network for the prediction of financial time series signals. Extensive simulations for the prediction of one- and five-steps-ahead of stationary and non-stationary time series were performed which indicate that immune-based neural networks in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return and rapid convergence over MLPs.

  16. Single satellite beam scanning positioning based on Neural Network BP algorithm

    Directory of Open Access Journals (Sweden)

    Li Yongwei

    2017-01-01

    Full Text Available In this paper, the principle of single line positioning based on beam scanning and the neural network algorithm are analysing, and the neural network BP algorithm is applying to the single satellite positioning. At the same time, this paper presents a new algorithm based on electron beam (MEO for the single scan positioning (Middle Earth orbit. Finally, through theoretical analysis and simulation, it is proving that the neural network BP algorithm of single satellite beam scanning is feasible in fast positioning.

  17. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  18. Neural Circuitry Based on Single Electron Transistors and Single Electron Memories

    Directory of Open Access Journals (Sweden)

    Aïmen BOUBAKER

    2014-05-01

    Full Text Available In this paper, we propose and explain a neural circuitry based on single electron transistors ‘SET’ which can be used in classification and recognition. We implement, after that, a Winner-Take-All ‘WTA’ neural network with lateral inhibition architecture. The original idea of this work is reflected, first, in the proposed new single electron memory ‘SEM’ design by hybridising two promising Single Electron Memory ‘SEM’ and the MTJ/Ring memory and second, in modeling and simulation results of neural memory based on SET. We prove the charge storage in quantum dot in two types of memories.

  19. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

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

    Science.gov (United States)

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

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

  1. [Working Temperature Predication of Artificial Heart Based on Neural Network].

    Science.gov (United States)

    Li, Qilei; Yang, Ming; Ou, Wenchu; Meng, Fan; Xu, Zihao; Xu, Liang

    2015-03-01

    The purpose of this paper is to achieve a measurement of temperature prediction for artificial heart without sensor, for which the research briefly describes the application of back propagation neural network as well as the optimized, by genetic algorithm, BP network. Owing to the limit of environment after the artificial heart implanted, detectable parameters out of body are taken advantage of to predict the working temperature of the pump. Lastly, contrast is made to demonstrate the prediction result between BP neural network and genetically optimized BP network, by which indicates that the probability is 1.84% with the margin of error more than 1%.

  2. Modulation of DNA base excision repair during neuronal differentiation

    DEFF Research Database (Denmark)

    Sykora, Peter; Yang, Jenq-Lin; Ferrarelli, Leslie K

    2013-01-01

    Neurons are terminally differentiated cells with a high rate of metabolism and multiple biological properties distinct from their undifferentiated precursors. Previous studies showed that nucleotide excision DNA repair is downregulated in postmitotic muscle cells and neurons. Here, we characterize...... DNA damage susceptibility and base excision DNA repair (BER) capacity in undifferentiated and differentiated human neural cells. The results show that undifferentiated human SH-SY5Y neuroblastoma cells are less sensitive to oxidative damage than their differentiated counterparts, in part because...

  3. Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network

    National Research Council Canada - National Science Library

    Ningbo Zhao; Zhiming Li

    2017-01-01

    In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids...

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

    Science.gov (United States)

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

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

  5. A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

    Science.gov (United States)

    Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

    2018-01-02

    In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

  6. Enhanced Neural Cell Adhesion and Neurite Outgrowth on Graphene-Based Biomimetic Substrates

    Science.gov (United States)

    Lee, Jong Ho; Kang, Seok Hee; Hwang, Eun Young; Hwang, Yu-Shik; Lee, Mi Hee; Park, Jong-Chul

    2014-01-01

    Neural cell adhesion and neurite outgrowth were examined on graphene-based biomimetic substrates. The biocompatibility of carbon nanomaterials such as graphene and carbon nanotubes (CNTs), that is, single-walled and multiwalled CNTs, against pheochromocytoma-derived PC-12 neural cells was also evaluated by quantifying metabolic activity (with WST-8 assay), intracellular oxidative stress (with ROS assay), and membrane integrity (with LDH assay). Graphene films were grown by using chemical vapor deposition and were then coated onto glass coverslips by using the scooping method. Graphene sheets were patterned on SiO2/Si substrates by using photolithography and were then covered with serum for a neural cell culture. Both types of CNTs induced significant dose-dependent decreases in the viability of PC-12 cells, whereas graphene exerted adverse effects on the neural cells just at over 62.5 ppm. This result implies that graphene and CNTs, even though they were the same carbon-based nanomaterials, show differential influences on neural cells. Furthermore, graphene-coated or graphene-patterned substrates were shown to substantially enhance the adhesion and neurite outgrowth of PC-12 cells. These results suggest that graphene-based substrates as biomimetic cues have good biocompatibility as well as a unique surface property that can enhance the neural cells, which would open up enormous opportunities in neural regeneration and nanomedicine. PMID:24592382

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

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E. P.

    2014-01-01

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

  8. Research on quasi-dynamic calibration model of plastic sensitive element based on neural networks

    Science.gov (United States)

    Wang, Fang; Kong, Deren; Yang, Lixia; Zhang, Zouzou

    2017-08-01

    Quasi-dynamic calibration accuracy of the plastic sensitive element depends on the accuracy of the fitting model between pressure and deformation. By using the excellent nonlinear mapping ability of RBF (Radial Basis Function) neural network, a calibration model is established which use the peak pressure as the input and use the deformation of the plastic sensitive element as the output in this paper. The calibration experiments of a batch of copper cylinders are carried out on the quasi-dynamic pressure calibration device, which pressure range is within the range of 200MPa to 700MPa. The experiment data are acquired according to the standard pressure monitoring system. The network train and study are done to quasi dynamic calibration model based on neural network by using MATLAB neural network toolbox. Taking the testing samples as the research object, the prediction accuracy of neural network model is compared with the exponential fitting model and the second-order polynomial fitting model. The results show that prediction of the neural network model is most close to the testing samples, and the accuracy of prediction model based on neural network is better than 0.5%, respectively one order higher than the second-order polynomial fitting model and two orders higher than the exponential fitting model. The quasi-dynamic calibration model between pressure peak and deformation of plastic sensitive element, which is based on neural network, provides important basis for creating higher accuracy quasi-dynamic calibration table.

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

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E P

    2014-01-01

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

  10. High Speed PAM -8 Optical Interconnects with Digital Equalization based on Neural Network

    DEFF Research Database (Denmark)

    Gaiarin, Simone; Pang, Xiaodan; Ozolins, Oskars

    2016-01-01

    We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission.......We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission....

  11. How right is left? Handedness modulates neural responses during morphosyntactic processing.

    Science.gov (United States)

    Grey, Sarah; Tanner, Darren; van Hell, Janet G

    2017-08-15

    Most neurocognitive models of language processing generally assume population-wide homogeneity in the neural mechanisms used during language comprehension, yet individual differences are known to influence these neural mechanisms. In this study, we focus on handedness as an individual difference hypothesized to affect language comprehension. Left-handers and right-handers with a left-handed blood relative, or familial sinistrals, are hypothesized to process language differently than right-handers with no left-handed relatives (Hancock and Bever, 2013; Ullman, 2004). Yet, left-handers are often excluded from neurocognitive language research, and familial sinistrality in right-handers is often not taken into account. In the current study we used event-related potentials to test morphosyntactic processing in three groups that differed in their handedness profiles: left-handers (LH), right-handers with a left-handed blood relative (RH FS+), and right-handers with no reported left-handed blood relative (RH FS-; both right-handed groups were previously tested by Tanner and Van Hell, 2014). Results indicated that the RH FS- group showed only P600 responses during morphosyntactic processing whereas the LH and RH FS+ groups showed biphasic N400-P600 patterns. N400s in LH and RH FS+ groups are consistent with theories that associate left-handedness (self or familial) with increased reliance on lexical/semantic mechanisms during language processing. Inspection of individual-level results illustrated that variability in RH FS- individuals' morphosyntactic processing was remarkably low: most individuals were P600-dominant. In contrast, LH and RH FS+ individuals showed marked variability in brain responses, which was similar for both groups: half of individuals were N400-dominant and half were P600-dominant. Our findings have implications for neurocognitive models of language that have been largely formulated around data from only right-handers without accounting for familial

  12. ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.

    Science.gov (United States)

    Cao, Renzhi; Freitas, Colton; Chan, Leong; Sun, Miao; Jiang, Haiqing; Chen, Zhangxin

    2017-10-17

    With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language "ProLan" to the protein function language "GOLan", and build a neural machine translation model based on recurrent neural networks to translate "ProLan" language to "GOLan" language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

  13. Reward-based training of recurrent neural networks for cognitive and value-based tasks

    Science.gov (United States)

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2017-01-01

    Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

  14. Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

    Science.gov (United States)

    Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

    2007-01-01

    In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

  15. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

    Full Text Available Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR. Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.

  16. RBF neural network based H∞ synchronization for unknown chaotic ...

    Indian Academy of Sciences (India)

    MS received 9 February 2010; accepted 24 May 2010. Abstract. In this paper, we propose a new H∞ synchronization strategy, called a. Radial Basis Function Neural Network H∞ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the pro- posed framework, a ...

  17. An artificial neural network based fast radiative transfer model for ...

    Indian Academy of Sciences (India)

    In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in ...

  18. An artificial neural network based fast radiative transfer model for ...

    Indian Academy of Sciences (India)

    the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of ... in construction, purpose and design and already in use are used. The fast RT model is able to ... porates measurements from various instruments in comparison with other ...

  19. The neural bases of framing effects in social dilemmas

    DEFF Research Database (Denmark)

    Macoveanu, Julian; Ramsøy, Thomas Z.; Skov, Martin

    2016-01-01

    Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remain poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, participants underwent functional MRI while playing a p...

  20. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    environments. The system developed includes a feature extractor and a modular neural network. The feature extractor consists of two stages. In the first stage ... environments is script/language identification (Muthusamy et al 1994; Hochberg et al 1997). ... In order to take advantage of the learning and generalization abilities ...

  1. A breathing circuit alarm system based on neural networks.

    Science.gov (United States)

    Orr, J A; Westenskow, D R

    1994-03-01

    The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms. We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm. In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring. Neural networks may be useful in creating intelligent anesthesia alarm systems.

  2. Guanidinium-based "molecular glues" for modulation of biomolecular functions.

    Science.gov (United States)

    Mogaki, Rina; Hashim, P K; Okuro, Kou; Aida, Takuzo

    2017-10-30

    Molecular adhesion based on multivalent interactions plays essential roles in various biological processes. Hence, "molecular glues" that can adhere to biomolecules may modulate biomolecular functions and therefore can be applied to therapeutics. This tutorial review describes design strategies for developing adhesive motifs for biomolecules based on multivalent interactions. We highlight a guanidinium ion-based salt-bridge as a key interaction for adhesion to biomolecules and discuss the application of molecular glues for manipulation of biomolecular assemblies, drug delivery systems, and modulation of biomolecular functions.

  3. Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

    Science.gov (United States)

    Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei

    2018-02-01

    This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

  4. Staying cool when things get hot: Emotion regulation modulates neural mechanisms of memory encoding

    Directory of Open Access Journals (Sweden)

    Jasmeet P Hayes

    2010-12-01

    Full Text Available During times of emotional stress, individuals often engage in emotion regulation to reduce the experiential and physiological impact of negative emotions. Interestingly, emotion regulation strategies also influence memory encoding of the event. Cognitive reappraisal is associated with enhanced memory while expressive suppression is associated with impaired explicit memory of the emotional event. However, the mechanism by which these emotion regulation strategies affect memory is unclear. We used event-related fMRI to investigate the neural mechanisms that give rise to memory formation during emotion regulation. Twenty-five participants viewed negative pictures while alternately engaging in cognitive reappraisal, expressive suppression, or passive viewing. As part of the subsequent memory design, participants returned to the laboratory two weeks later for a surprise memory test. Behavioral results showed a reduction in negative affect and a retention advantage for reappraised stimuli relative to the other conditions. Imaging results showed that successful encoding during reappraisal was uniquely associated with greater co-activation of the left inferior frontal gyrus, amygdala and hippocampus, suggesting a possible role for elaborative encoding of negative memories. This study provides neurobehavioral evidence that engaging in cognitive reappraisal is advantageous to both affective and mnemonic processes.

  5. Dietary restraint and impulsivity modulate neural responses to food in adolescents with obesity and healthy adolescents.

    Science.gov (United States)

    Hofmann, Johannes; Ardelt-Gattinger, Elisabeth; Paulmichl, Katharina; Weghuber, Daniel; Blechert, Jens

    2015-11-01

    Despite alarming prevalence rates, surprisingly little is known about neural mechanisms underlying eating behavior in juveniles with obesity. To simulate reactivity to modern food environments, event-related potentials (ERP) to appetizing food images (relative to control images) were recorded in adolescents with obesity and healthy adolescents. Thirty-four adolescents with obesity (patients) and 24 matched healthy control adolescents watched and rated standardized food and object images during ERP recording. Personality (impulsivity) and eating styles (trait craving and dietary restraint) were assessed as potential moderators. Food relative to object images triggered larger early (P100) and late (P300) ERPs. More impulsive individuals had considerably larger food-specific P100 amplitudes in both groups. Controls with higher restraint scores showed reduced food-specific P300 amplitudes and subjective palatability ratings whereas patients with higher restraint scores showed increased P300 and palatability ratings. This first ERP study in adolescents with obesity and controls revealed impulsivity as a general risk factor in the current obesogenic environment by increasing food-cue salience. Dietary restraint showed paradoxical effects in patients, making them more vulnerable to visual food-cues. Salutogenic therapeutic approaches that deemphasize strict dietary restraint and foster healthy food choice might reduce such paradoxical effects. © 2015 The Obesity Society.

  6. Neural mechanisms of mood-induced modulation of reality monitoring in schizophrenia.

    Science.gov (United States)

    Subramaniam, Karuna; Ranasinghe, Kamalini G; Mathalon, Daniel; Nagarajan, Srikantan; Vinogradov, Sophia

    2017-06-01

    Reality monitoring is the ability to accurately distinguish the source of self-generated information from externally-presented information. Although people with schizophrenia (SZ) show impaired reality monitoring, nothing is known about how mood state influences this higher-order cognitive process. Accordingly, we induced positive, neutral and negative mood states to test how different mood states modulate subsequent reality monitoring performance. Our findings indicate that mood affected reality monitoring performance in HC and SZ participants in both similar and dissociable ways. Only a positive mood facilitated task performance in Healthy Control (HC) subjects, whereas a negative mood facilitated task performance in SZ subjects. Yet, when both HC and SZ participants were in a positive mood, they recruited medial prefrontal cortex (mPFC) to bias better subsequent self-generated item identification, despite the fact that mPFC signal was reduced in SZ participants. Additionally, in SZ subjects, negative mood states also modulated left and right dorsal mPFC signal to bias better externally-presented item identification. Together our findings reveal that although the mPFC is hypoactive in SZ participants, mPFC signal plays a functional role in mood-cognition interactions during both positive and negative mood states to facilitate subsequent reality monitoring decision-making. Copyright © 2017. Published by Elsevier Ltd.

  7. Ingroup/outgroup membership modulates fairness consideration: neural signatures from ERPs and EEG oscillations.

    Science.gov (United States)

    Wang, Yiwen; Zhang, Zhen; Bai, Liying; Lin, Chongde; Osinsky, Roman; Hewig, Johannes

    2017-01-04

    Previous studies have shown that ingroup/outgroup membership influences individual's fairness considerations. However, it is not clear yet how group membership influences brain activity when a recipient evaluates the fairness of asset distribution. In this study, subjects participated as recipients in an Ultimatum Game with alleged members of both an experimentally induced ingroup and outgroup. They either received extremely unequal, moderately unequal, or equal offers from proposers while electroencephalogram was recorded. Behavioral results showed that the acceptance rates for unequal offers were higher when interacting with ingroup partners than with outgroup partners. Analyses of event related potentials revealed that proposers' group membership modulated offer evaluation at earlier processing stages. Feedback-related negativity was more negative for extremely and moderately unequal offers compared to equal offers in the ingroup interaction whereas it did not show differential responses to different offers in the outgroup interaction. Analyses of event related oscillations revealed that the theta power (4-6 Hz) was larger for moderately unequal offers than equal offers in the ingroup interaction whereas it did not show differential responses to different offers in the outgroup interaction. Thus, early mechanisms of fairness evaluation are strongly modulated by the ingroup/outgroup membership of the interaction partner.

  8. Neural mechanisms of mood-induced modulation of reality monitoring in schizophrenia

    Science.gov (United States)

    Subramaniam, Karuna; Ranasinghe, Kamalini G.; Mathalon, Daniel; Nagarajan, Srikantan; Vinogradov, Sophia

    2017-01-01

    Reality monitoring is the ability to accurately distinguish the source of self-generated information from externally-presented information. Although people with schizophrenia (SZ) show impaired reality monitoring, nothing is known about how mood state influences this higher-order cognitive process. Accordingly, we induced positive, neutral and negative mood states to test how different mood states modulate subsequent reality monitoring performance. Our findings indicate that mood affected reality monitoring performance in HC and SZ participants in both similar and dissociable ways. Only a positive mood facilitated task performance in Healthy Control (HC) subjects, whereas a negative mood facilitated task performance in SZ subjects. Yet, when both HC and SZ participants were in a positive mood, they recruited medial prefrontal cortex (mPFC) to bias better subsequent self-generated item identification, despite the fact that mPFC signal was reduced in SZ participants. Additionally, in SZ subjects, negative mood states also modulated left and right dorsal mPFC signal to bias better externally-presented item identification. Together our findings reveal that although the mPFC is hypoactive in SZ participants, mPFC signal plays a functional role in mood–cognition interactions during both positive and negative mood states to facilitate subsequent reality monitoring decision-making. PMID:28162778

  9. Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

    Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.

  10. [Robustness analysis of adaptive neural network model based on spike timing-dependent plasticity].

    Science.gov (United States)

    Chen, Yunzhi; Xu, Guizhi; Zhou, Qian; Guo, Miaomiao; Guo, Lei; Wan, Xiaowei

    2015-02-01

    To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.

  11. Global exponential almost periodicity of a delayed memristor-based neural networks.

    Science.gov (United States)

    Chen, Jiejie; Zeng, Zhigang; Jiang, Ping

    2014-12-01

    In this paper, the existence, uniqueness and stability of almost periodic solution for a class of delayed memristor-based neural networks are studied. By using a new Lyapunov function method, the neural network that has a unique almost periodic solution, which is globally exponentially stable is proved. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for delayed memristor-based neural networks with periodic coefficients (or constant coefficients). The obtained results are helpful to design the global exponential stability of almost periodic oscillatory memristor-based neural networks. Three numerical examples and simulations are also given to show the feasibility of our results. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Misalignment compensation in spatial light modulator based optical filtering techniques

    CERN Document Server

    Agour, Mostafa; von Kopylow, Christoph; Bergmann, Ralf B

    2012-01-01

    A new method for the compensation of misalignment in the spatial light modulator based optical linear filtering techniques is presented. It is based on the correlation of the wave fields generated across the input and the output planes of filtering setups. Experimental results are given to demonstrate the effectiveness of the method.

  13. Orthonormal bases for anisotropic α-modulation spaces

    DEFF Research Database (Denmark)

    Rasmussen, Kenneth Niemann

    2012-01-01

    In this article we construct orthonormal bases for bi-variate anisotropic α-modulation spaces. The construction is based on generating a nice anisotropic α-covering and using carefully selected tensor products of univariate brushlet functions with regards to this covering. As an application, we s...

  14. Orthonormal bases for anisotropic α-modulation spaces

    DEFF Research Database (Denmark)

    Rasmussen, Kenneth Niemann

    In this article we construct orthonormal bases for bi-variate anisotropic α-modulation spaces. The construction is based on generating a nice anisotropic α-covering and using carefully selected tensor products of univariate brushlet functions with regards to this covering. As an application, we s...

  15. An Integrated, Problem-Based Learning Material: The "Satellite" Module

    Science.gov (United States)

    Selcuk, Gamze Sezgin; Emiroglu, Handan Byacioglu; Tarakci, Mehmet; Ozel, Mustafa

    2011-01-01

    The purpose of this study is to introduce a problem-based learning material, the Satellite Module, that has integrated some of the subjects included in the disciplines of physics and mathematics at an introductory level in undergraduate education. The reason why this modular and problem-based material has been developed is to enable students to…

  16. Functional recordings from awake, behaving rodents through a microchannel based regenerative neural interface

    Science.gov (United States)

    Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur

    2015-02-01

    group of awake and behaving animals. These unique findings provide preliminary evidence that efferent, volitional motor potentials can be recorded from the microchannel-based peripheral neural interface; a critical requirement for any neural interface intended to facilitate direct neural control of external technologies.

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

    Science.gov (United States)

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

    2017-08-11

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

  18. PDF-1 neuropeptide signaling modulates a neural circuit for mate-searching behavior in C. elegans.

    Science.gov (United States)

    Barrios, Arantza; Ghosh, Rajarshi; Fang, Chunhui; Emmons, Scott W; Barr, Maureen M

    2012-12-01

    Appetitive behaviors require complex decision making that involves the integration of environmental stimuli and physiological needs. C. elegans mate searching is a male-specific exploratory behavior regulated by two competing needs: food and reproductive appetite. We found that the pigment dispersing factor receptor (PDFR-1) modulates the circuit that encodes the male reproductive drive that promotes male exploration following mate deprivation. PDFR-1 and its ligand, PDF-1, stimulated mate searching in the male, but not in the hermaphrodite. pdf-1 was required in the gender-shared interneuron AIM, and the receptor acted in internal and external environment-sensing neurons of the shared nervous system (URY, PQR and PHA) to produce mate-searching behavior. Thus, the pdf-1 and pdfr-1 pathway functions in non-sex-specific neurons to produce a male-specific, goal-oriented exploratory behavior. Our results indicate that secretin neuropeptidergic signaling is involved in regulating motivational internal states.

  19. Effectiveness of Discovery Learning-Based Transformation Geometry Module

    Science.gov (United States)

    Febriana, R.; Haryono, Y.; Yusri, R.

    2017-09-01

    Development of transformation geometry module is conducted because the students got difficulties to understand the existing book. The purpose of the research was to find out the effectiveness of discovery learning-based transformation geometry module toward student’s activity. Model of the development was Plomp model consisting preliminary research, prototyping phase and assessment phase. The research was focused on assessment phase where it was to observe the designed product effectiveness. The instrument was observation sheet. The observed activities were visual activities, oral activities, listening activities, mental activities, emotional activities and motor activities. Based on the result of the research, it is found that visual activities, learning activities, writing activities, the student’s activity is in the criteria very effective. It can be concluded that the use of discovery learning-based transformation geometry module use can increase the positive student’s activity and decrease the negative activity.

  20. Modulation of neural activities by enhanced local selection in the processing of compound stimuli.

    Science.gov (United States)

    Han, Shihui; He, Xun

    2003-08-01

    The global precedence effect refers to the findings that responses are faster to a global structure than to its local parts and local responses are slowed by incongruent global information. We recorded high-density event-related potentials (ERPs) to study the role of enhanced local selection in the global precedence effect. Hierarchical stimuli were compound letters in which the local letters were either identical (homogeneous stimuli) or the central local letter was brighter than (bright stimuli) or different in color from the others (red stimuli). Subjects were asked to attend to the pop-out local letter of the red and bright stimuli during the local task whereas there was no such instruction for the homogeneous stimuli. Top-down attention to the pop-out local item weakened the global reaction time advantage and the interference effect. The enhanced local selection decreased the amplitude of an occipito-temporal negativity between 240-360 msec but increased the amplitude of a frontal-central negativity between 260-320 msec related to local processing. The incongruency between global and local letters enlarged the posterior N2 in the local condition and this effect was eliminated by enhanced local selection. These effects were evident regardless of whether the pop-out local letter was defined by color or luminance difference. The results support the proposal that distinct neural mechanisms over the posterior and anterior areas are engaged in the selection process that contributes to local processing of compound stimuli. Copyright 2003 Wiley-Liss, Inc.

  1. Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

    Science.gov (United States)

    Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

    2018-01-01

    In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

  2. Convolutional neural network-based data page classification for holographic memory.

    Science.gov (United States)

    Shimobaba, Tomoyoshi; Kuwata, Naoki; Homma, Mizuha; Takahashi, Takayuki; Nagahama, Yuki; Sano, Marie; Hasegawa, Satoki; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Takada, Naoki; Ito, Tomoyoshi

    2017-09-10

    We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multilayer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. When data pages are randomly laterally shifted, the MLP was found to have a classification accuracy of 93.02%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is 2 orders of magnitude better than the MLP.

  3. Passivity of memristor-based BAM neural networks with different memductance and uncertain delays.

    Science.gov (United States)

    Anbuvithya, R; Mathiyalagan, K; Sakthivel, R; Prakash, P

    2016-08-01

    This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.

  4. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  5. Neural network based adaptive output feedback control: Applications and improvements

    Science.gov (United States)

    Kutay, Ali Turker

    Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in

  6. Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization

    CERN Document Server

    Castillo, Oscar; Kacprzyk, Janusz

    2015-01-01

    This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...

  7. Radial Basis Function Neural Network-based PID model for functional electrical stimulation system control.

    Science.gov (United States)

    Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong

    2009-01-01

    Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.

  8. Fuzzy Input Information-based Rational Ordering of Curriculum Modules

    Directory of Open Access Journals (Sweden)

    A. S. Domnikov

    2015-01-01

    Full Text Available All modern e-learning systems and standards support a module-based structure of the training materials. It means that independent and rather closed modules of training material form the courses of study. As compared to the unstructured arrangement of training material, the modulebased structure has a number of apparent advantages. In particular, it is highly flexible and allows a reuse of educational modules as a part of various courses and trainings.The main cohort of theoretical researches in synthesis of e-learning course structure concerns a problem of designing a module-based structure. The important problem of rational ordering of the curriculum modules is investigated insufficiently. This is the second article of the cycle related to the rational ranking of the e-course modules. The work supposes that initial information is set as a fuzzy graph of preferences, which formalizes expert’s subjective information on precedence of module pairs. It is required to find a linear order, which is a good approximant of the initial structure of preferences.The article offers a simple way for transforming values of membership function to lead an adjacency matrix of the fuzzy graph to a probabilistic calibration condition. It investigates the rational ordering methods capable to process matrixes of pair comparisons with probabilistic calibration and justifies an application of the ordering method, which uses a dominating function as a ranking factor. The proposed way to solve the task is based on decomposition of a fuzzy set into the levels and generations of a set of the minimum level that possesses acyclic properties. It is shown that this task is reduced to the search of such a shift of tops, which has the smallest possible potential among all the shifts of objects.

  9. Discrimination task reveals differences in neural bases of tinnitus and hearing impairment.

    Directory of Open Access Journals (Sweden)

    Fatima T Husain

    Full Text Available We investigated auditory perception and cognitive processing in individuals with chronic tinnitus or hearing loss using functional magnetic resonance imaging (fMRI. Our participants belonged to one of three groups: bilateral hearing loss and tinnitus (TIN, bilateral hearing loss without tinnitus (HL, and normal hearing without tinnitus (NH. We employed pure tones and frequency-modulated sweeps as stimuli in two tasks: passive listening and active discrimination. All subjects had normal hearing through 2 kHz and all stimuli were low-pass filtered at 2 kHz so that all participants could hear them equally well. Performance was similar among all three groups for the discrimination task. In all participants, a distributed set of brain regions including the primary and non-primary auditory cortices showed greater response for both tasks compared to rest. Comparing the groups directly, we found decreased activation in the parietal and frontal lobes in the participants with tinnitus compared to the HL group and decreased response in the frontal lobes relative to the NH group. Additionally, the HL subjects exhibited increased response in the anterior cingulate relative to the NH group. Our results suggest that a differential engagement of a putative auditory attention and short-term memory network, comprising regions in the frontal, parietal and temporal cortices and the anterior cingulate, may represent a key difference in the neural bases of chronic tinnitus accompanied by hearing loss relative to hearing loss alone.

  10. Neural network based PWM AC chopper fed induction motor drive

    Directory of Open Access Journals (Sweden)

    Venkatesan Jamuna

    2009-01-01

    Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .

  11. Memristor-based neural networks: Synaptic versus neuronal stochasticity

    Directory of Open Access Journals (Sweden)

    Rawan Naous

    2016-11-01

    Full Text Available In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

  12. Vocal learning in elephants: neural bases and adaptive context.

    Science.gov (United States)

    Stoeger, Angela S; Manger, Paul

    2014-10-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. Memristor-based neural networks: Synaptic versus neuronal stochasticity

    KAUST Repository

    Naous, Rawan

    2016-11-02

    In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.

  14. Glaucoma detection based on deep convolutional neural network.

    Science.gov (United States)

    Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

    2015-08-01

    Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.

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

    Science.gov (United States)

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

    2017-09-01

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

  16. A new fiber-optic microphone based on waveguide modulator

    Science.gov (United States)

    Zhang, Chengmei; Zhen, Shenglai; Zhang, Bo; Ai, Fei; Zhang, Shuangxi; Jiang, Chao; Yu, Benli

    2009-11-01

    A new fiber-optic microphone was demonstrated theoretically and experimentally in this paper. The microphone is based on Mach-Zehnder and Sagnac interferometers, which comprise an amplified spontaneous emission (ASE) light source, a conventional single-mode fiber, a fiber reflector and two 3dB couplers. As two light paths have the same optical length but travel different sequence paths in this hybrid interferometer, the beams in different paths pass through the sensing fiber at different times and the phase signals differ from each other. Utilizing the two light paths interfered and fiber waveguide modulator replaced by piezoelectric ceramic (PZT) modulation, to implement the direct acquiring of weak voice signals. Adoption of the ASE light source and the single-mode fiber as sensing fiber decreases the system cost. The application of the fiber waveguide modulator overcomes the limitation in high frequency and nonlinear effect of PZT modulation, improves the flexibility of the system and the frequency response range. Phase shifts of the two interfered beams, which is caused by the slowly varying environmental parameter, is equal to eliminate the influence from outside effectively. In this system, the signal demodulation circuit based on weak voice signal is simpler than the PGC demodulation circuit. The experimental results of the fiber-optic microphone based on waveguide modulator have been demonstrated that the simple circuit demodulation for the weak voice signal is feasible.

  17. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  18. Aptamer-based modulation of blood coagulation.

    Science.gov (United States)

    Mayer, G; Rohrbach, F; Pötzsch, B; Müller, J

    2011-11-01

    Nucleic acid based aptamers are single-stranded oligonucleotide ligands isolated from random libraries by an in-vitro selection procedure. Through the formation of unique three-dimensional structures, aptamers are able to selectively interact with a variety of target molecules and are therefore also promising candidates for the development of anticoagulant drugs. While thrombin represents the most prominent enzymatic target in this field, also aptamers directed against other coagulation proteins and proteases have been identified with some currently being tested in clinical trials. In this review, we summarize recent developments in the design and evaluation of aptamers for anticoagulant therapy and research.

  19. Estradiol modulates neural response to conspecific and heterospecific song in female house sparrows: An in vivo positron emission tomography study.

    Science.gov (United States)

    Lattin, Christine R; Stabile, Frank A; Carson, Richard E

    2017-01-01

    Although there is growing evidence that estradiol modulates female perception of male sexual signals, relatively little research has focused on female auditory processing. We used in vivo 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) imaging to examine the neuronal effects of estradiol and conspecific song in female house sparrows (Passer domesticus). We assessed brain glucose metabolism, a measure of neuronal activity, in females with empty implants, estradiol implants, and empty implants ~1 month after estradiol implant removal. Females were exposed to conspecific or heterospecific songs immediately prior to imaging. The activity of brain regions involved in auditory perception did not differ between females with empty implants exposed to conspecific vs. heterospecific song, but neuronal activity was significantly reduced in females with estradiol implants exposed to heterospecific song. Furthermore, our within-individual design revealed that changes in brain activity due to high estradiol were actually greater several weeks after peak hormone exposure. Overall, this study demonstrates that PET imaging is a powerful tool for assessing large-scale changes in brain activity in living songbirds, and suggests that after breeding is done, specific environmental and physiological cues are necessary for estradiol-stimulated females to lose the selectivity they display in neural response to conspecific song.

  20. Estradiol modulates neural response to conspecific and heterospecific song in female house sparrows: An in vivo positron emission tomography study.

    Directory of Open Access Journals (Sweden)

    Christine R Lattin

    Full Text Available Although there is growing evidence that estradiol modulates female perception of male sexual signals, relatively little research has focused on female auditory processing. We used in vivo 18F-fluorodeoxyglucose (18F-FDG positron emission tomography (PET imaging to examine the neuronal effects of estradiol and conspecific song in female house sparrows (Passer domesticus. We assessed brain glucose metabolism, a measure of neuronal activity, in females with empty implants, estradiol implants, and empty implants ~1 month after estradiol implant removal. Females were exposed to conspecific or heterospecific songs immediately prior to imaging. The activity of brain regions involved in auditory perception did not differ between females with empty implants exposed to conspecific vs. heterospecific song, but neuronal activity was significantly reduced in females with estradiol implants exposed to heterospecific song. Furthermore, our within-individual design revealed that changes in brain activity due to high estradiol were actually greater several weeks after peak hormone exposure. Overall, this study demonstrates that PET imaging is a powerful tool for assessing large-scale changes in brain activity in living songbirds, and suggests that after breeding is done, specific environmental and physiological cues are necessary for estradiol-stimulated females to lose the selectivity they display in neural response to conspecific song.

  1. Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

    Science.gov (United States)

    Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Kofman, I. S.; Cassady, K.; Yuan, P.; De Dios, Y. E.; Gadd, N.; Riascos, R. F.; Wood, S. J.; hide

    2017-01-01

    We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that are conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. We have collected data on several crewmembers and preliminary findings will be presented. Eventual comparison to results from our parallel bed rest study will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe.

  2. Expert music performance: cognitive, neural, and developmental bases.

    Science.gov (United States)

    Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

    2015-01-01

    In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. © 2015 Elsevier B.V. All rights reserved.

  3. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    OpenAIRE

    Bo Fan; Zhixin Yang; Wei Xu; Xianbo Wang

    2014-01-01

    Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Tho...

  4. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  5. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  6. Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

    Science.gov (United States)

    Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim

    2009-12-01

    An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.

  7. Modulation of neural activity during observational learning of actions and their sequential orders.

    Science.gov (United States)

    Frey, Scott H; Gerry, Valerie E

    2006-12-20

    How does the brain transform perceptual representations of others' actions into motor representations that can be used to guide behavior? Here we used functional magnetic resonance imaging to record human brain activity while subjects watched others construct multipart objects under varied task demands. We find that relative to resting baseline, passive action observation increases activity within inferior frontal and parietal cortices implicated in action encoding (mirror system) and throughout a distributed network of areas involved in motor representation, including dorsal premotor cortex, pre-supplementary motor area, cerebellum, and basal ganglia (experiments 1 and 2). Relative to passive observation, these same areas show increased activity when subjects observe with the intention to subsequently reproduce component actions using the demonstrated sequential procedures (experiment 1). Observing the same actions with the intention of reproducing component actions, but without the requirement to use the demonstrated sequential procedure, increases activity in the same regions, although to a lesser degree (experiment 2). These findings demonstrate that when attempting to learn behaviors through observation, the observers' intentions modulate responses in a widely distributed network of cortical and subcortical regions implicated previously in action encoding and/or motor representation. Among these regions, only activity within the right intraparietal sulcus predicts the accuracy with which observed procedures are subsequently performed. Successful formation of motor representations of sequential procedures through observational learning is dependent on computations implemented within this parietal region.

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

    Science.gov (United States)

    Tsukiura, Takashi

    2012-01-01

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

  9. A SOLUTION TO THE DOUBLE DUMMY CONTRACT BRIDGE PROBLEM INFLUENCED BY SUPERVISED LEARNING MODULE ADAPTED BY ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    M. Dharmalingam

    2014-10-01

    Full Text Available Contract Bridge is an intellectual game which motivates multiple skills and application of prior experience and knowledge, as no player knows accurately what moves other players are capable of making. The Bridge is a game played in the presence of imperfect information, yet its strategies must be well formulated, since the outcome at any intermediate stage is solely based on the choices made during the immediately preceding phase. In this paper, we train an Artificial Neural Network architecture using sample deals and use it to estimate the number of tricks to be taken by one pair of bridge players, which is the main challenge in the Double Dummy Bridge Problem. We focus on Back Propagation Neural Network Architecture with Back Propagation Algorithm with Sigmoidal transfer functions. We used two approaches namely, High – Card Point Count System and Distribution Point Method during the bidding phase of Contract Bridge. We experimented with two sigmoidal transfer functions namely, Log Sigmoid transfer function and the Hyperbolic Tangent Sigmoid function. Results reveal that the later performs better giving lower mean squared error on the output.

  10. Expression, crystallization and preliminary X-ray analysis of the extracellular Ig modules I-IV and F3 modules I-III of the neural cell-adhesion molecule L1

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Kasper, Christina; Kristensen, Ole

    2005-01-01

    Four amino-terminal immunoglobulin (Ig) modules and three fibronectin type III (F3) modules of the mouse neural cell-adhesion molecule L1 have been expressed in Drosophila S2 cells. The Ig modules I-IV of L1 crystallized in a trigonal space group, with unit-cell parameters a = b = 239.6, c = 99.3 A......, and the crystals diffracted X-rays to a resolution of about 3.5 A. The F3 modules I-III of L1 crystallized in a tetragonal space group, with unit-cell parameters a = b = 80.1, c = 131 A, and the crystals diffracted X-rays to 2.8 A resolution. This is a step towards the structure determination of the multimodular...

  11. Lifetime Prediction of IGBT Modules based on Linear Damage Accumulation

    DEFF Research Database (Denmark)

    Choi, Uimin; Blaabjerg, Frede; Ma, Ke

    2017-01-01

    In this paper, the lifetime prediction of power device modules based on the linear damage accumulation in conjunction with real mission profile assessment is studied. Four tests are performed under two superimposed power cycling conditions using an advanced power cycling test setup with 600 V, 30...

  12. Teaching Students about Plagiarism Using a Web-Based Module

    Science.gov (United States)

    Stetter, Maria Earman

    2013-01-01

    The following research delivered a web-based module about plagiarism and paraphrasing to avoid plagiarism in both a blended method, with live instruction paired with web presentation for 105 students, and a separate web-only method for 22 other students. Participants were graduates and undergraduates preparing to become teachers, the majority of…

  13. Lifelong Learning: Web-Based Information Literacy Module for Merchandisers

    Science.gov (United States)

    Hines, Jean D.; Frey, Diane K.; Swinker, Mary E.

    2005-01-01

    Universities are strategically positioned to serve as a vital impetus in developing pre-professionals' lifelong learning skills. The development of a Web portal, InfoWIZARD, a tool for integrating information literacy and information technology in problem-based research assignments is described in this article. InfoWIZARD includes 20 modules in…

  14. Digital Pulse Modulation Amplifier (PMA) systems based on PEDEC control

    DEFF Research Database (Denmark)

    Nielsen, Karsten

    1999-01-01

    The paper extends previous research and presents a suite of novel high efficiency digital PMA topologies based on Pulse Edge Delay Error Correction (PEDEC). The practical results are very encouraging, showing that digital modulator performance is maintained throughout the subsequent power...... conversion. The topologies are believed to be the first implemented digital PMA systems including effective power stage error correction....

  15. The impact of coaching module based on teaching games for ...

    African Journals Online (AJOL)

    The study aimed was to assess the effectiveness of coaching module based on Teaching Games for Understanding (TGfU) towards the performance of school netball players. Samples consisted of 14 netball players in selected Malaysian secondary schools. The players had gone through four-weeks of training using the ...

  16. Expression, crystallization and preliminary X-ray analysis of the extracellular Ig modules I–IV and F3 modules I–III of the neural cell-adhesion molecule L1

    Energy Technology Data Exchange (ETDEWEB)

    Kulahin, Nikolaj, E-mail: kulahin@plab.ku.dk [Protein Laboratory, Institute of Molecular Pathology, Panum Institute, Blegdamsvej 3C, DK-2200 Copenhagen (Denmark); Biostructural Research, Department of Medicinal Chemistry, Danish University of Pharmaceutical Sciences, Universitetsparken 2, DK-2100 Copenhagen (Denmark); Kasper, Christina; Kristensen, Ole; Kastrup, Jette Sandholm [Biostructural Research, Department of Medicinal Chemistry, Danish University of Pharmaceutical Sciences, Universitetsparken 2, DK-2100 Copenhagen (Denmark); Berezin, Vladimir; Bock, Elisabeth [Protein Laboratory, Institute of Molecular Pathology, Panum Institute, Blegdamsvej 3C, DK-2200 Copenhagen (Denmark); Gajhede, Michael [Biostructural Research, Department of Medicinal Chemistry, Danish University of Pharmaceutical Sciences, Universitetsparken 2, DK-2100 Copenhagen (Denmark); Protein Laboratory, Institute of Molecular Pathology, Panum Institute, Blegdamsvej 3C, DK-2200 Copenhagen (Denmark)

    2005-09-01

    Mouse L1 modules Ig I–IV and F3 I–III were crystallized. The crystals diffracted X-rays to 3.5 and 2.8 Å resolution, respectively. Four amino-terminal immunoglobulin (Ig) modules and three fibronectin type III (F3) modules of the mouse neural cell-adhesion molecule L1 have been expressed in Drosophila S2 cells. The Ig modules I–IV of L1 crystallized in a trigonal space group, with unit-cell parameters a = b = 239.6, c = 99.3 Å, and the crystals diffracted X-rays to a resolution of about 3.5 Å. The F3 modules I–III of L1 crystallized in a tetragonal space group, with unit-cell parameters a = b = 80.1, c = 131 Å, and the crystals diffracted X-rays to 2.8 Å resolution. This is a step towards the structure determination of the multimodular constructs of the neural cell-adhesion molecule L1 in order to understand the function of L1 on a structural basis.

  17. Neural-net based real-time economic dispatch for thermal power plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  18. Advanced obstacle avoidance for a laser based wheelchair using optimised Bayesian neural networks.

    Science.gov (United States)

    Trieu, Hoang T; Nguyen, Hung T; Willey, Keith

    2008-01-01

    In this paper we present an advanced method of obstacle avoidance for a laser based intelligent wheelchair using optimized Bayesian neural networks. Three neural networks are designed for three separate sub-tasks: passing through a door way, corridor and wall following and general obstacle avoidance. The accurate usable accessible space is determined by including the actual wheelchair dimensions in a real-time map used as inputs to each networks. Data acquisitions are performed separately to collect the patterns required for specified sub-tasks. Bayesian frame work is used to determine the optimal neural network structure in each case. Then these networks are trained under the supervision of Bayesian rule. Experiment results showed that compare to the VFH algorithm our neural networks navigated a smoother path following a near optimum trajectory.

  19. MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2015-01-01

    Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

  20. A Predictive Neural Network-Based Cascade Control for pH Reactors

    Directory of Open Access Journals (Sweden)

    Mujahed AlDhaifallah

    2016-01-01

    Full Text Available This paper is concerned with the development of predictive neural network-based cascade control for pH reactors. The cascade structure consists of a master control loop (fuzzy proportional-integral and a slave one (predictive neural network. The master loop is chosen to be more accurate but slower than the slave one. The strong features found in cascade structure have been added to the inherent features in model predictive neural network. The neural network is used to alleviate modeling difficulties found with pH reactor and to predict its behavior. The parameters of predictive algorithm are determined using an optimization algorithm. The effectiveness and feasibility of the proposed design have been demonstrated using MatLab.

  1. Experimental method to predict avalanches based on neural networks

    Directory of Open Access Journals (Sweden)

    V. V. Zhdanov

    2016-01-01

    Full Text Available The article presents results of experimental use of currently available statistical methods to classify the avalanche‑dangerous precipitations and snowfalls in the Kishi Almaty river basin. The avalanche service of Kazakhstan uses graphical methods for prediction of avalanches developed by I.V. Kondrashov and E.I. Kolesnikov. The main objective of this work was to develop a modern model that could be used directly at the avalanche stations. Classification of winter precipitations into dangerous snowfalls and non‑dangerous ones was performed by two following ways: the linear discriminant function (canonical analysis and artificial neural networks. Observational data on weather and avalanches in the gorge Kishi Almaty in the gorge Kishi Almaty were used as a training sample. Coefficients for the canonical variables were calculated by the software «Statistica» (Russian version 6.0, and then the necessary formula had been constructed. The accuracy of the above classification was 96%. Simulator by the authors L.N. Yasnitsky and F.М. Cherepanov was used to learn the neural networks. The trained neural network demonstrated 98% accuracy of the classification. Prepared statistical models are recommended to be tested at the snow‑avalanche stations. Results of the tests will be used for estimation of the model quality and its readiness for the operational work. In future, we plan to apply these models for classification of the avalanche danger by the five‑point international scale.

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

    Science.gov (United States)

    Kolossa, Antonio; Kopp, Bruno; Fingscheidt, Tim

    2015-02-01

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

  3. Boosting feature selection for Neural Network based regression.

    Science.gov (United States)

    Bailly, Kevin; Milgram, Maurice

    2009-01-01

    The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high dimensionality of the input (number of image pixels) and the large variety of morphologies and illumination. We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of Haar-like wavelets which are well known to be adapted to face image processing. To achieve the feature selection, a new Fuzzy Functional Criterion (FFC) is introduced which is able to evaluate the link between a feature and the output without any estimation of the joint probability density function as in the Mutual Information. The boosting strategy uses this criterion at each step: features are evaluated by the FFC using weights on examples computed from the error produced by the neural network trained at the previous step. Tests are carried out on the commonly used Pointing 04 database and compared with three state-of-the-art methods. We also evaluate the accuracy of the estimation on FacePix, a database with a high angular resolution. Our method is compared positively to a Convolutional Neural Network, which is well known to incorporate feature extraction in its first layers.

  4. Study Modules for Calculus-Based General Physics. [Includes Modules 3-5: Planar Motion; Newton's Laws; and Vector Multiplication].

    Science.gov (United States)

    Fuller, Robert G., Ed.; And Others

    This is part of a series of 42 Calculus Based Physics (CBP) modules totaling about 1,000 pages. The modules include study guides, practice tests, and mastery tests for a full-year individualized course in calculus-based physics based on the Personalized System of Instruction (PSI). The units are not intended to be used without outside materials;…

  5. Risk assessment of logistics outsourcing based on BP neural network

    Science.gov (United States)

    Liu, Xiaofeng; Tian, Zi-you

    The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.

  6. Age estimation of facial image based on convolution neural network

    Science.gov (United States)

    Meng, Xiaodong; Wang, Yifeng; Zheng, Haihong

    2017-07-01

    Age is an inherent biological characteristic of human and is reflected in facial images to a certain extent. A method for estimating age from a facial image by combining CNN (Convolution Neural Network) with SVR (Support Vector Regression) is proposed. First, a deep CNN is trained to automatically extract age features from facial images and classify them into variant age groups. Then different SVRs are trained for each age group to estimate the age of a facial image. The experimental results show that a lower MAE (Mean Absolute Error) of age estimation on MORPH database is obtained.

  7. Evolving Neural Turing Machines for Reward-based Learning

    DEFF Research Database (Denmark)

    Greve, Rasmus Boll; Jacobsen, Emil Juul; Risi, Sebastian

    2016-01-01

    and integrating new information without losing previously acquired skills. Here we build on recent work by Graves et al. [5] who extended the capabilities of an ANN by combining it with an external memory bank trained through gradient descent. In this paper, we introduce an evolvable version of their Neural...... version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE....

  8. Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

    Science.gov (United States)

    Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

    2018-01-01

    In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

  9. A PACS-based interactive teaching module for radiologic sciences.

    Science.gov (United States)

    Sinha, S; Sinha, U; Kangarloo, H; Huang, H K

    1992-07-01

    This article describes an interactive teaching module, linked to a picture archiving and communications system (PACS) data base, for teaching radiology. The module is currently tailored to MR images but can be adapted to any other imaging technique. An algorithm has been developed that allows the use of MR images acquired with routine clinical protocols and stored in the data base to yield, in real time, images at any other arbitrary TE and TR. In the browse mode, the user can study either the effect of different scan parameters or clinical cases on synthesized or acquired images. The quiz mode has multiple-choice questions and answers, accompanied by images. In the teaching mode, the instructor has access to the clinical data base and WRITE privileges for setting up the browse or quiz mode. The module achieves considerable flexibility when linked to the PACS, with access to all archived images and the ability to subsequently synthesize MR images at arbitrary TE and TR values in real time. The module is also "dynamic" in character, in that the instructor can easily add new cases and comments to the teaching files, both to enhance its clinical aspects and to reflect advances in technology.

  10. The neural bases of key competencies of emotional intelligence.

    Science.gov (United States)

    Krueger, Frank; Barbey, Aron K; McCabe, Kevin; Strenziok, Maren; Zamboni, Giovanna; Solomon, Jeffrey; Raymont, Vanessa; Grafman, Jordan

    2009-12-29

    Emotional intelligence (EI) refers to a set of competencies that are essential features of human social life. Although the neural substrates of EI are virtually unknown, it is well established that the prefrontal cortex (PFC) plays a crucial role in human social-emotional behavior. We studied a unique sample of combat veterans from the Vietnam Head Injury Study, which is a prospective, long-term follow-up study of veterans with focal penetrating head injuries. We administered the Mayer-Salovey-Caruso Emotional Intelligence Test as a valid standardized psychometric measure of EI behavior to examine two key competencies of EI: (i) Strategic EI as the competency to understand emotional information and to apply it for the management of the self and of others and (ii) Experiential EI as the competency to perceive emotional information and to apply it for the integration into thinking. The results revealed that key competencies underlying EI depend on distinct neural PFC substrates. First, ventromedial PFC damage diminishes Strategic EI, and therefore, hinders the understanding and managing of emotional information. Second, dorsolateral PFC damage diminishes Experiential EI, and therefore, hinders the perception and integration of emotional information. In conclusion, EI should be viewed as complementary to cognitive intelligence and, when considered together, provide a more complete understanding of human intelligence.

  11. Automatic localization of vertebrae based on convolutional neural networks

    Science.gov (United States)

    Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie

    2015-03-01

    Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.

  12. Stereo Matching Based on Immune Neural Network in Abdomen Reconstruction

    Directory of Open Access Journals (Sweden)

    Huan Liu

    2015-01-01

    Full Text Available Stereo feature matching is a technique that finds an optimal match in two images from the same entity in the three-dimensional world. The stereo correspondence problem is formulated as an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. A novel intelligent biological network (Bio-Net, which involves the human B-T cells immune system into neural network, is proposed in this study in order to learn the robust relationship between the input feature points and the output matched points. A model from input-output data (left reference point-right target point is established. In the experiments, the abdomen reconstructions for different-shape mannequins are then performed by means of the proposed method. The final results are compared and analyzed, which demonstrate that the proposed approach greatly outperforms the single neural network and the conventional matching algorithm in precise. Particularly, as far as time cost and efficiency, the proposed method exhibits its significant promising and potential for improvement. Hence, it is entirely considered as an effective and feasible alternative option for stereo matching.

  13. Artificial neural network based particle size prediction of polymeric nanoparticles.

    Science.gov (United States)

    Youshia, John; Ali, Mohamed Ehab; Lamprecht, Alf

    2017-10-01

    Particle size of nanoparticles and the respective polydispersity are key factors influencing their biopharmaceutical behavior in a large variety of therapeutic applications. Predicting these attributes would skip many preliminary studies usually required to optimize formulations. The aim was to build a mathematical model capable of predicting the particle size of polymeric nanoparticles produced by a pharmaceutical polymer of choice. Polymer properties controlling the particle size were identified as molecular weight, hydrophobicity and surface activity, and were quantified by measuring polymer viscosity, contact angle and interfacial tension, respectively. A model was built using artificial neural network including these properties as input with particle size and polydispersity index as output. The established model successfully predicted particle size of nanoparticles covering a range of 70-400nm prepared from other polymers. The percentage bias for particle prediction was 2%, 4% and 6%, for the training, validation and testing data, respectively. Polymer surface activity was found to have the highest impact on the particle size followed by viscosity and finally hydrophobicity. Results of this study successfully highlighted polymer properties affecting particle size and confirmed the usefulness of artificial neural networks in predicting the particle size and polydispersity of polymeric nanoparticles. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Image Finder Mobile Application Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2017-04-01

    Full Text Available Nowadays taking photos via mobile phone has become a very important part of everyone’s life. Almost each and every person who has a smart phone also has thousands of photos in their mobile device. At times it becomes very difficult to find a particular photo from thousands of photos, and it takes time. This research was done to come up with an innovative solution that could solve this problem. The solution will allow the user to find the required photo by simply drawing a sketch on the objects in the required picture, for example a tree or car, etc. Two types of supervised Artificial Neural Networks are used for this purpose; one is trained to identify the handmade sketches and other is trained to identify the images. The proposed approach introduces a mechanism to relate the sketches with the images by matching them after training. The experimentation results for testing the trained neural networks reached 100% for the sketches, and 84% for the images of two objects as a case study.

  15. Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

    Science.gov (United States)

    Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

    2017-10-01

    Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

  16. Neural network based real-time reconstruction of KSTAR magnetic equilibria with Bayesian-based preprocessing

    Science.gov (United States)

    Joung, Semin; Kwak, Sehyun; Ghim, Y.-C.

    2017-10-01

    Obtaining plasma shapes during tokamak discharges requires real-time estimation of magnetic configuration using Grad-Shafranov solver such as EFIT. Since off-line EFIT is computationally intensive and the real-time reconstructions do not agree with the results of off-line EFIT within our desired accuracy, we use a neural network to generate an off-line-quality equilibrium in real time. To train the neural network (two hidden layers with 30 and 20 nodes for each layer), we create database consisting of the magnetic signals and off-line EFIT results from KSTAR as inputs and targets, respectively. To compensate drifts in the magnetic signals originated from electronic circuits, we develop a Bayesian-based two-step real-time correction method. Additionally, we infer missing inputs, i.e. when some of inputs to the network are not usable, using Gaussian process coupled with Bayesian model. The likelihood of this model is determined based on the Maxwell's equations. We find that our network can withstand at least up to 20% of input errors. Note that this real-time reconstruction scheme is not yet implemented for KSTAR operation.

  17. Prediction of annual water consumption in Guangdong Province based on Bayesian neural network

    Science.gov (United States)

    Tian, Tao; Xue, Huifeng

    2017-06-01

    In the context of the implementation of the most stringent water resources management system, the role of water demand forecasting for regional water resources management is becoming increasingly significant. Based on the analysis of the influencing factors of water consumption in Guangdong Province, we made the forecast index system of annual water consumption, and constructed the forecast model of annual water consumption of BP neural network, then optimized the regularization BP neural network in utilization rate of water. The results showed that the average absolute percentage error of Bayesian neural network prediction model and BP neural network prediction model is 0.70% and 0.46% respectively. BP neural network model by Bayesian regularization is more ability to improve the accuracy of about 0.24%, more in line with the regional annual water demand forecast high precision requirements. Take the planning index value of Guangdong Province’s thirteen five plan into Bayesian neural network forecasting model, and its forecast value is 45.432 billion cubic meters, which will reach 456.04 billion cubic meters of red water in Guangdong Province in 2020.

  18. Study on Optimized Elman Neural Network Classification Algorithm Based on PLS and CA

    Science.gov (United States)

    Zhao, Dean; Shen, Tian; Zhao, Yuyan

    2014-01-01

    High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion. PMID:25165470

  19. Age and gender modulate the neural circuitry supporting facial emotion processing in adults with major depressive disorder.

    Science.gov (United States)

    Briceño, Emily M; Rapport, Lisa J; Kassel, Michelle T; Bieliauskas, Linas A; Zubieta, Jon-Kar; Weisenbach, Sara L; Langenecker, Scott A

    2015-03-01

    Emotion processing, supported by frontolimbic circuitry known to be sensitive to the effects of aging, is a relatively understudied cognitive-emotional domain in geriatric depression. Some evidence suggests that the neurophysiological disruption observed in emotion processing among adults with major depressive disorder (MDD) may be modulated by both gender and age. Therefore, the present study investigated the effects of gender and age on the neural circuitry supporting emotion processing in MDD. Cross-sectional comparison of fMRI signal during performance of an emotion processing task. Outpatient university setting. One hundred adults recruited by MDD status, gender, and age. Participants underwent fMRI while completing the Facial Emotion Perception Test. They viewed photographs of faces and categorized the emotion perceived. Contrast for fMRI was of face perception minus animal identification blocks. Effects of depression were observed in precuneus and effects of age in a number of frontolimbic regions. Three-way interactions were present between MDD status, gender, and age in regions pertinent to emotion processing, including frontal, limbic, and basal ganglia. Young women with MDD and older men with MDD exhibited hyperactivation in these regions compared with their respective same-gender healthy comparison (HC) counterparts. In contrast, older women and younger men with MDD exhibited hypoactivation compared to their respective same-gender HC counterparts. This the first study to report gender- and age-specific differences in emotion processing circuitry in MDD. Gender-differential mechanisms may underlie cognitive-emotional disruption in older adults with MDD. The present findings have implications for improved probes into the heterogeneity of the MDD syndrome. Copyright © 2015 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  20. Environmental CO2 inhibits Caenorhabditis elegans egg-laying by modulating olfactory neurons and evokes widespread changes in neural activity

    Science.gov (United States)

    Fenk, Lorenz A.; de Bono, Mario

    2015-01-01

    Carbon dioxide (CO2) gradients are ubiquitous and provide animals with information about their environment, such as the potential presence of prey or predators. The nematode Caenorhabditis elegans avoids elevated CO2, and previous work identified three neuron pairs called “BAG,” “AFD,” and “ASE” that respond to CO2 stimuli. Using in vivo Ca2+ imaging and behavioral analysis, we show that C. elegans can detect CO2 independently of these sensory pathways. Many of the C. elegans sensory neurons we examined, including the AWC olfactory neurons, the ASJ and ASK gustatory neurons, and the ASH and ADL nociceptors, respond to a rise in CO2 with a rise in Ca2+. In contrast, glial sheath cells harboring the sensory endings of C. elegans’ major chemosensory neurons exhibit strong and sustained decreases in Ca2+ in response to high CO2. Some of these CO2 responses appear to be cell intrinsic. Worms therefore may couple detection of CO2 to that of other cues at the earliest stages of sensory processing. We show that C. elegans persistently suppresses oviposition at high CO2. Hermaphrodite-specific neurons (HSNs), the executive neurons driving egg-laying, are tonically inhibited when CO2 is elevated. CO2 modulates the egg-laying system partly through the AWC olfactory neurons: High CO2 tonically activates AWC by a cGMP-dependent mechanism, and AWC output inhibits the HSNs. Our work shows that CO2 is a more complex sensory cue for C. elegans than previously thought, both in terms of behavior and neural circuitry. PMID:26100886

  1. Pulse count modulation based biphasic current stimulator for retinal prosthesis and in vitro experiment using rd1 mouse.

    Science.gov (United States)

    Sungjin Oh; Jae-Hyun Ahn; Jongyoon Shin; Hyoungho Ko; Yong-Sook Goo; Dong-Il Dan Cho

    2014-01-01

    For a retinal prosthesis, retinal nerve cells are electrically stimulated by current pulses. Typically, the amplitude of the current pulses is modulated to control the amount of injected charges. However, a high spatial resolution can be difficult to achieve with this amplitude modulation method, because the neural response spreads more widely as the amplitude of the current pulses is increased. In this paper, a biphasic current stimulator integrated circuit (IC) using a new modulation method called, the pulse count modulation, is proposed. In the pulse count modulation method, the amplitude and the width of the current pulses are fixed, and the amount of injected charges is controlled by the number of applied current pulses in a base period. The proposed stimulator IC is fabricated by a 0.35 μm bipolar-CMOS-DMOS (BCDMOS) technology. The operation and performance of the stimulator IC are evaluated in an in vitro experiment environment with rd 1 mice. It is shown that a higher spatial resolution can be achieved compared with the amplitude modulation method.

  2. H∞state estimation of stochastic memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

    2018-03-01

    This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Stability and synchronization of memristor-based fractional-order delayed neural networks.

    Science.gov (United States)

    Chen, Liping; Wu, Ranchao; Cao, Jinde; Liu, Jia-Bao

    2015-11-01

    Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Pinning synchronization of memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Yang, Zhanyu; Luo, Biao; Liu, Derong; Li, Yueheng

    2017-09-01

    In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Module-based multiscale simulation of angiogenesis in skeletal muscle

    Directory of Open Access Journals (Sweden)

    Mac Gabhann Feilim

    2011-04-01

    Full Text Available Abstract Background Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem. Results We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation. Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis. Conclusions This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions.

  6. Module-based multiscale simulation of angiogenesis in skeletal muscle.

    Science.gov (United States)

    Liu, Gang; Qutub, Amina A; Vempati, Prakash; Mac Gabhann, Feilim; Popel, Aleksander S

    2011-04-04

    Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem. We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis. This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions.

  7. Triangulating the neural, psychological, and economic bases of guilt aversion

    Science.gov (United States)

    Chang, Luke J.; Smith, Alec; Dufwenberg, Martin; Sanfey, Alan G.

    2011-01-01

    Why do people often choose to cooperate when they can better serve their interests by acting selfishly? One potential mechanism is that the anticipation of guilt can motivate cooperative behavior. We utilize a formal model of this process in conjunction with fMRI to identify brain regions that mediate cooperative behavior while participants decided whether or not to honor a partner’s trust. We observed increased activation in the insula, supplementary motor area, dorsolateral prefrontal cortex (PFC), and temporal parietal junction when participants were behaving consistent with our model, and found increased activity in the ventromedial PFC, dorsomedial PFC, and nucleus accumbens when they chose to abuse trust and maximize their financial reward. This study demonstrates that a neural system previously implicated in expectation processing plays a critical role in assessing moral sentiments that in turn can sustain human cooperation in the face of temptation. PMID:21555080

  8. Glomerulus Classification and Detection Based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jaime Gallego

    2018-01-01

    Full Text Available Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI, showing robustness while reducing false positive and false negative detections.

  9. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  10. High power fuel cell simulator based on artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)

    2010-11-15

    Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)

  11. Culture and social support: neural bases and biological impact.

    Science.gov (United States)

    Sherman, David K; Kim, Heejung S; Taylor, Shelley E

    2009-01-01

    Social support is an effective means by which people cope with stressful events, and consequently, it beneficially affects health and well-being. Yet there are profound cultural differences in the effectiveness of different types of support and how people use their support networks. In this paper, we examine research on the impact of culture on social support, the neural underpinnings of social support, and how cultural differences in social support seeking are manifested biologically. We focus on cultural factors that may affect individuals' decisions to seek or not to seek social support and how culture moderates the impact of support seeking on biological and psychological health outcomes. We also examine recent research on the interaction between genes and culture in social support use. Discussion centers on the importance of developing an overarching framework of social support that integrates health psychology, cultural psychology, social neuroscience, and genetics.

  12. Feature Fusion Based on Convolutional Neural Network for SAR ATR

    Directory of Open Access Journals (Sweden)

    Chen Shi-Qi

    2017-01-01

    Full Text Available Recent breakthroughs in algorithms related to deep convolutional neural networks (DCNN have stimulated the development of various of signal processing approaches, where the specific application of Automatic Target Recognition (ATR using Synthetic Aperture Radar (SAR data has spurred widely attention. Inspired by the more efficient distributed training such as inception architecture and residual network, a new feature fusion structure which jointly exploits all the merits of each version is proposed to reduce the data dimensions and the complexity of computation. The detailed procedure presented in this paper consists of the fused features, which make the representation of SAR images more distinguishable after the extraction of a set of features from DCNN, followed by a trainable classifier. In particular, the obtained results on the 10-class benchmark data set demonstrate that the presented architecture can achieve remarkable classification performance to the current state-of-the-art methods.

  13. Forecasting stochastic neural network based on financial empirical mode decomposition.

    Science.gov (United States)

    Wang, Jie; Wang, Jun

    2017-06-01

    In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD is a processing technique introduced to extract all the oscillatory modes embedded in a series, and the STNN model is established for considering the weight of occurrence time of the historical data. The linear regression performs the predictive availability of the proposed model, and the effectiveness of EMD-STNN is revealed clearly through comparing the predicted results with the traditional models. Moreover, a new evaluated method (q-order multiscale complexity invariant distance) is applied to measure the predicted results of real stock index series, and the empirical results show that the proposed model indeed displays a good performance in forecasting stock market fluctuations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

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

    2013-12-01

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

  15. Two-photon excitation based photochemistry and neural imaging

    Science.gov (United States)

    Hatch, Kevin Andrew

    Two-photon microscopy is a fluorescence imaging technique which provides distinct advantages in three-dimensional cellular and molecular imaging. The benefits of this technology may extend beyond imaging capabilities through exploitation of the quantum processes responsible for fluorescent events. This study utilized a two-photon microscope to investigate a synthetic photoreactive collagen peptidomimetic, which may serve as a potential material for tissue engineering using the techniques of two-photon photolysis and two-photon polymerization. The combination of these techniques could potentially be used to produce a scaffold for the vascularization of engineered three-dimensional tissues in vitro to address the current limitations of tissue engineering. Additionally, two-photon microscopy was used to observe the effects of the application of the neurotransmitter dopamine to the mushroom body neural structures of Drosophila melanogaster to investigate dopamine's connection to cognitive degeneration.

  16. Water Diagnosis in Shrimp Aquaculture based on Neural Network

    Science.gov (United States)

    Carbajal Hernández, J. J.; Sánchez Fernández, L. P.

    2007-05-01

    In many countries, the shrimp aquaculture has not advanced computational systems to supervise the artificial habitat of the farms and laboratories. A computational system of this type helps significantly to improve the environmental conditions and to elevate the production and its quality. The main idea of this study is the creation of a system using an artificial neural network (ANN), which can help to recognize patterns of problems and their evolution in shrimp aquaculture, and thus to respond with greater rapidity against the negative effects. Bad control on the shrimp artificial habitat produces organisms with high stress and as consequence losses in their defenses. It generate low nutrition, low reproduction or worse still, they prearrange to acquire lethal diseases. The proposed system helps to control this problem. Environmental variables as pH, temperature, salinity, dissolved oxygen and turbidity have an important effect in the suitable growth of the shrimps and influence in their health. However, the exact mathematical model of this relationship is unspecified; an ANN is useful for establishing a relationship between these variables and to classify a status that describes a problem into the farm. The data classification is made to recognize and to quantify two states within the pool: a) Normal: Everything is well. b) Risk: One, some or all environmental variables are outside of the allowed interval, which generates problems. The neural network will have to recognize the state and to quantify it, in others words, how normal or risky it is, which allows finding trend of the water quality. A study was developed for designing a software tool that allows recognizing the status of the water quality and control problems for the environment into the pond.

  17. Neural Fuzzy Inference System-Based Weather Prediction Model and Its Precipitation Predicting Experiment

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2014-11-01

    Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.

  18. Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Shawq Malik Mehibs

    2017-12-01

    Full Text Available Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

  19. Interest in politics modulates neural activity in the amygdala and ventral striatum.

    Science.gov (United States)

    Gozzi, Marta; Zamboni, Giovanna; Krueger, Frank; Grafman, Jordan

    2010-11-01

    Studies on political participation have found that a person's interest in politics contributes to the likelihood that he or she will be involved in the political process. Here, we looked at whether or not interest in politics affects patterns of brain activity when individuals think about political matters. Using functional magnetic resonance imaging (fMRI), we scanned individuals (either interested or uninterested in politics based on a self-report questionnaire) while they were expressing their agreement or disagreement with political opinions. After scanning, participants were asked to rate each political opinion presented in the scanner for emotional valence and emotional intensity. Behavioral results showed that those political opinions participants agreed with were perceived as more emotionally intense and more positive by individuals interested in politics relative to individuals uninterested in politics. In addition, individuals interested in politics showed greater activation in the amygdala and the ventral striatum (ventral putamen) relative to individuals uninterested in politics when reading political opinions in accordance with their own views. This study shows that having an interest in politics elicits activations in emotion- and reward-related brain areas even when simply agreeing with written political opinions. © 2010 Wiley-Liss, Inc.

  20. Educating the blind brain: a panorama of neural bases of vision and of training programs in organic neurovisual deficits

    Directory of Open Access Journals (Sweden)

    Olivier A. Coubard

    2014-12-01

    Full Text Available Vision is a complex function, which is achieved by movements of the eyes to properly foveate targets at any location in 3D space and to continuously refresh neural information in the different visual pathways. The visual system involves five routes originating in the retinas but varying in their destination within the brain: the occipital cortex, but also the superior colliculus, the pretectum, the supra-chiasmatic nucleus, the nucleus of the optic tract and terminal dorsal, medial and lateral nuclei. Visual pathway architecture obeys systematization in sagittal and transversal planes so that visual information from left/right and upper/lower hemi-retinas, corresponding respectively to right/left and lower/upper visual fields, is processed ipsilaterally and ipsialtitudinally to hemi-retinas in left/right hemispheres and upper/lower fibers. Organic neurovisual deficits may occur at any level of this circuitry from the optic nerve to subcortical and cortical destinations, resulting in low or high-level visual deficits. In this didactic review article, we provide a panorama of the neural bases of eye movements and visual systems, and of related neurovisual deficits. Additionally, we briefly review the different schools of rehabilitation of organic neurovisual deficits, and show that whatever the emphasis is put on action or perception, benefits may be observed at both motor and perceptual levels. Given the extent of its neural bases in the brain, vision in its motor and perceptual aspects is also a useful tool to assess and modulate central nervous system in general.

  1. Feature-based attention modulates direction-selective hemodynamic activity within human MT.

    Science.gov (United States)

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

    2011-12-01

    Attending to the spatial location or to nonspatial features of a stimulus modulates neural activity in cortical areas that process its perceptual attributes. The feature-based attentional selection of the direction of a moving stimulus is associated with increased firing of individual neurons tuned to the direction of the movement in area V5/MT, while responses of neurons tuned to opposite directions are suppressed. However, it is not known how these multiplicatively scaled responses of individual neurons tuned to different motion-directions are integrated at the population level, in order to facilitate the processing of stimuli that match the perceptual goals. Using functional magnetic resonance imaging (fMRI) the present study revealed that attending to the movement direction of a dot field enhances the response in a number of areas including the human MT region (hMT) as a function of the coherence of the stimulus. Attending the opposite direction, however, lead to a suppressed response in hMT that was inversely correlated with stimulus-coherence. These findings demonstrate that the multiplicative scaling of single-neuron responses by feature-based attention results in an enhanced direction-selective population response within those cortical modules that processes the physical attributes of the attended stimuli. Our results provide strong support for the validity of the "feature similarity gain model" on the integrated population response as quantified by parametric fMRI in humans. Copyright © 2011 Wiley Periodicals, Inc.

  2. Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon; Stoustrup, Jakob

    2003-01-01

    This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training sam...... control can be achieved by interpolating between each controller.In this paper, we propose to use the Youla-Jabr-Bongiorno-Kucera parameterization to achieve a smooth scheduling between the operating points with certain stability guarantees....

  3. An Artificial Neural Network Based Short-term Dynamic Prediction of Algae Bloom

    Directory of Open Access Journals (Sweden)

    Yao Junyang

    2014-06-01

    Full Text Available This paper proposes a method of short-term prediction of algae bloom based on artificial neural network. Firstly, principal component analysis is applied to water environmental factors in algae bloom raceway ponds to get main factors that influence the formation of algae blooms. Then, a model of short-term dynamic prediction based on neural network is built with the current chlorophyll_a values as input and the chlorophyll_a values in the next moment as output to realize short-term dynamic prediction of algae bloom. Simulation results show that the model can realize short-term prediction of algae bloom effectively.

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

    Science.gov (United States)

    Frank, Florian; Weigel, Robert

    2011-09-01

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

  5. Prediction of Palm Oil-Based Methyl Ester Biodiesel Density Using Artificial Neural Networks

    Science.gov (United States)

    Baroutian, Saeid; Kheireddine Aroua, Mohamed; Raman, Abdul Aziz Abdul; Meriam Nik Sulaiman, Nik

    In this study, a new approach based on Artificial Neural Networks (ANNs) has been designed to estimate the density of pure palm oil-based methyl ester biodiesel. The experimental density data measured at various temperatures from 14 to 90°C at 1°C intervals were used to train the networks. The present research, applied a three layer back propagation neural network with seven neurons in the hidden layer. The results from the network are in good agreement with the measured data and the average absolute percent deviation is 0.29%. The results of ANNs have also been compared with the results of empirical and theoretical estimations.

  6. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    Science.gov (United States)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  7. Exploring Skills-Based Competencies Through Geriatric Care Management Modules.

    Science.gov (United States)

    Costley, Alex W

    2016-01-01

    As educational competencies in gerontology continue to evolve, skills-based competencies (from beginner to expert level) endure as the hallmarks of the field. This study explored the impact of exposure to "active learning" modules for the practice of professional geriatric care management (GCM) on beginning gerontology students and their ability to acquire core knowledge and more advanced skills related to assessment and counseling community-based older adults. Working with a group of "nontraditional" students, many having previous caregiving experience and working with older adults in direct care and allied health care roles, evaluation of these modules show that early exposure to "advanced" professional practice can be an effective approach for introducing higher-level competencies to beginning gerontology students.

  8. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion

    Directory of Open Access Journals (Sweden)

    Lijun Zhang

    2018-02-01

    Full Text Available Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.

  9. Almost Periodic Dynamics for Memristor-Based Shunting Inhibitory Cellular Neural Networks with Leakage Delays

    Directory of Open Access Journals (Sweden)

    Lin Lu

    2016-01-01

    Full Text Available We investigate a class of memristor-based shunting inhibitory cellular neural networks with leakage delays. By applying a new Lyapunov function method, we prove that the neural network which has a unique almost periodic solution is globally exponentially stable. Moreover, the theoretical findings of this paper on the almost periodic solution are applied to prove the existence and stability of periodic solution for memristor-based shunting inhibitory cellular neural networks with leakage delays and periodic coefficients. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Wu (2011 and Chen and Cao (2002.

  10. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Research on the image of sweeping robot based on the Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Song Chang

    2017-01-01

    Full Text Available Based on the theory of Artificial Neural Network and Kansei Engineering, the image of sweeping robots are formed using the content analysis method, and propose four kinds of sweeping robot as the experimental samples, which have a strong influence on the market. The image questionnaires are compiled by the semantic differences methods. 200 office workers, half men and half women, are chose as the survey respondents. And use SPSS statistical software for data analysis. Afterwards, the BP Artificial Neural Network model is established by Matlab based on the questionnaire results, and the optimized design scheme with image feature combination for sweeping robot products is generated on the basis of BP Artificial Neural Network model. This study construct the emotional demands on the image level, and carry out experiments and statistical analysis, which lays a solid foundation for the study of product image in theory and approach.

  12. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  13. Prediction of essential proteins based on overlapping essential modules.

    Science.gov (United States)

    Zhao, Bihai; Wang, Jianxin; Li, Min; Wu, Fang-Xiang; Pan, Yi

    2014-12-01

    Many computational methods have been proposed to identify essential proteins by using the topological features of interactome networks. However, the precision of essential protein discovery still needs to be improved. Researches show that majority of hubs (essential proteins) in the yeast interactome network are essential due to their involvement in essential complex biological modules and hubs can be classified into two categories: date hubs and party hubs. In this study, combining with gene expression profiles, we propose a new method to predict essential proteins based on overlapping essential modules, named POEM. In POEM, the original protein interactome network is partitioned into many overlapping essential modules. The frequencies and weighted degrees of proteins in these modules are employed to decide which categories does a protein belong to? The comparative results show that POEM outperforms the classical centrality measures: Degree Centrality (DC), Information Centrality (IC), Eigenvector Centrality (EC), Subgraph Centrality (SC), Betweenness Centrality (BC), Closeness Centrality (CC), Edge Clustering Coefficient Centrality (NC), and two newly proposed essential proteins prediction methods: PeC and CoEWC. Experimental results indicate that the precision of predicting essential proteins can be improved by considering the modularity of proteins and integrating gene expression profiles with network topological features.

  14. Pairwise domain adaptation module for CNN-based 2-D/3-D registration.

    Science.gov (United States)

    Zheng, Jiannan; Miao, Shun; Jane Wang, Z; Liao, Rui

    2018-04-01

    Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical. Therefore, deep learning-based 2-D/3-D registration methods are often trained with synthetically generated data, and a performance gap is often observed when testing the trained model on clinical data. We propose a pairwise domain adaptation (PDA) module to adapt the model trained on source domain (i.e., synthetic data) to target domain (i.e., clinical data) by learning domain invariant features with only a few paired real and synthetic data. The PDA module is designed to be flexible for different deep learning-based 2-D/3-D registration frameworks, and it can be plugged into any pretrained CNN model such as a simple Batch-Norm layer. The proposed PDA module has been quantitatively evaluated on two clinical applications using different frameworks of deep networks, demonstrating its significant advantages of generalizability and flexibility for 2-D/3-D medical image registration when a small number of paired real-synthetic data can be obtained.

  15. Single-hidden-layer feed-forward quantum neural network based on Grover learning.

    Science.gov (United States)

    Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

    2013-09-01

    In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohammad S. Islam

    2017-01-01

    Full Text Available Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs for robust movement decoding of Parkinson’s disease (PD and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value at about 0.729±0.16 for decoding movement from the resting state and about 0.671±0.14 for decoding left and right visually cued movements.

  17. A prediction method for the wax deposition rate based on a radial basis function neural network

    Directory of Open Access Journals (Sweden)

    Ying Xie

    2017-06-01

    Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

  18. Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.

    Science.gov (United States)

    Wang, Leimin; Shen, Yi; Zhang, Guodong

    Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.

  19. Working with interpreters: an interactive Web-based learning module.

    Science.gov (United States)

    Kalet, Adina; Gany, Francesca; Senter, Lindsay

    2002-09-01

    Medical students are presented with unique challenges when they care for patients with limited English proficiency. Students must learn a complex set of skills needed to care for patients across cultural and language barriers and to understand the impact of their own attitudes and beliefs about caring for these patients. We developed and piloted a multimedia interactive Web-based module aimed at teaching students effective strategies for working with interpreters and diverse patient populations, and at raising their awareness of important legal, ethical, and cultural issues. First the learner completes a 37-multiple-choice-question (MCQ) pre-test that assesses attitudes, factual knowledge, and ability to analyze written clinical scenarios relevant to the module's content. Learners are then shown a series of professionally produced video vignettes, which reflect diverse patient populations, interpreters, and effectiveness of interpretation strategies (e.g., a Russian-speaking woman with chest pain whose daughter interprets, a medical student interpreting for a Chinese-speaking man using herbal medication, a Haitian woman told of an abnormal mammogram through a trained simultaneous interpreter). In each case, learners submit short answers to on-screen questions analyzing the effectiveness of the interpretation strategies demonstrated. Immediate feedback is given comparing student responses with those of experts. At any time during the module, the learners may view video commentary by legal, ethics, and cultural experts, or access a glossary and Web site links. Students conclude the module by again taking the MCQ test. A final screen compares their pre- and post-MCQ test responses and shows best answers, allowing them to assess their learning. The learners also complete a survey, providing personal cultural information and feedback on the module. All 160 first-year medical students completed the module and evaluated its effectiveness this year. On average, students

  20. A neural network based reputation bootstrapping approach for service selection

    Science.gov (United States)

    Wu, Quanwang; Zhu, Qingsheng; Li, Peng

    2015-10-01

    With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

  1. Neural bases of recommendations differ according to social network structure.

    Science.gov (United States)

    O'Donnell, Matthew Brook; Bayer, Joseph B; Cascio, Christopher N; Falk, Emily B

    2017-01-01

    Ideas spread across social networks, but not everyone is equally positioned to be a successful recommender. Do individuals with more opportunities to connect otherwise unconnected others-high information brokers-use their brains differently than low information brokers when making recommendations? We test the hypothesis that those with more opportunities for information brokerage may use brain systems implicated in considering the thoughts, perspectives, and mental states of others (i.e. 'mentalizing') more when spreading ideas. We used social network analysis to quantify individuals' opportunities for information brokerage. This served as a predictor of activity within meta-analytically defined neural regions associated with mentalizing (dorsomedial prefrontal cortex, temporal parietal junction, medial prefrontal cortex, /posterior cingulate cortex, middle temporal gyrus) as participants received feedback about peer opinions of mobile game apps. Higher information brokers exhibited more activity in this mentalizing network when receiving divergent peer feedback and updating their recommendation. These data support the idea that those in different network positions may use their brains differently to perform social tasks. Different social network positions might provide more opportunities to engage specific psychological processes. Or those who tend to engage such processes more may place themselves in systematically different network positions. These data highlight the value of integrating levels of analysis, from brain networks to social networks. © The Author (2017). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  2. Automatic Pavement Crack Recognition Based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Li Li

    2014-02-01

    Full Text Available A feasible pavement crack detection system plays an important role in evaluating the road condition and providing the necessary road maintenance. In this paper, a back propagation neural network (BPNN is used to recognize pavement cracks from images. To improve the recognition accuracy of the BPNN, a complete framework of image processing is proposed including image preprocessing and crack information extraction. In this framework, the redundant image information is reduced as much as possible and two sets of feature parameters are constructed to classify the crack images. Then a BPNN is adopted to distinguish pavement images between linear and alligator cracks to acquire high recognition accuracy. Besides, the linear cracks can be further classified into transversal and longitudinal cracks according to the direction angle. Finally, the proposed method is evaluated on the data of 400 pavement images obtained by the Automatic Road Analyzer (ARAN in Northern China and the results show that the proposed method seems to be a powerful tool for pavement crack recognition. The rates of correct classification for alligator, transversal and longitudinal cracks are 97.5%, 100% and 88.0%, respectively. Compared to some previous studies, the method proposed in this paper is effective for all three kinds of cracks and the results are also acceptable for engineering application.

  3. 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.

  4. Classification of Two Comic Books based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Miki UENO

    2017-03-01

    Full Text Available Unphotographic images are the powerful representations described various situations. Thus, understanding intellectual products such as comics and picture books is one of the important topics in the field of artificial intelligence. Hence, stepwise analysis of a comic story, i.e., features of a part of the image, information features, features relating to continuous scene etc., was pursued. Especially, the length and each scene of four-scene comics are limited so as to ensure a clear interpretation of the contents.In this study, as the first step in this direction, the problem to classify two four-scene comics by the same artists were focused as the example. Several classifiers were constructed by utilizing a Convolutional Neural Network(CNN, and the results of classification by a human annotator and by a computational method were compared.From these experiments, we have clearly shown that CNN is efficient way to classify unphotographic gray scaled images and found that characteristic features of images to classify incorrectly.

  5. Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment

    Directory of Open Access Journals (Sweden)

    Garima Singh

    2011-01-01

    Full Text Available With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN has been proposed for voltage stability margin estimation which is an indication of the power system's proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance-based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6-bus and IEEE 30-bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.

  6. Erythropoietin modulates neural and cognitive processing of emotional information in biomarker models of antidepressant drug action in depressed patients

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla W; Favaron, Elisa; Hafizi, Sepehr

    2010-01-01

    Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The curren...... study investigates the effects of Epo on the neural and cognitive response to emotional facial expressions in depressed patients.......Erythropoietin (Epo) has neuroprotective and neurotrophic effects, and may be a novel therapeutic agent in the treatment of psychiatric disorders. We have demonstrated antidepressant-like effects of Epo on the neural and cognitive processing of facial expressions in healthy volunteers. The current...

  7. An Artificial Neural Network Based Robot Controller that Uses Rat’s Brain Signals

    Directory of Open Access Journals (Sweden)

    Marsel Mano

    2013-04-01

    Full Text Available Brain machine interface (BMI has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.

  8. 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.

  9. A chaos secure communication scheme based on multiplication modulation

    Science.gov (United States)

    Fallahi, Kia; Leung, Henry

    2010-02-01

    A secure spread spectrum communication scheme using multiplication modulation is proposed. The proposed system multiplies the message by chaotic signal. The scheme does not need to know the initial condition of the chaotic signals and the receiver is based on an extended Kalman filter (EKF). This signal encryption scheme lends itself to cheap implementation and can therefore be used effectively for ensuring security and privacy in commercial consumer electronics products. To illustrate the effectiveness of the proposed scheme, a numerical example based on Genesio-Tesi system and also Chen dynamical system is presented and the results are compared.

  10. CLASSIFICATION OF NEURAL NETWORK FOR TECHNICAL CONDITION OF TURBOFAN ENGINES BASED ON HYBRID ALGORITHM

    Directory of Open Access Journals (Sweden)

    Valentin Potapov

    2016-12-01

    Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.

  11. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

    Science.gov (United States)

    Paul, R R; Mukherjee, A; Dutta, P K; Banerjee, S; Pal, M; Chatterjee, J; Chaudhuri, K; Mukkerjee, K

    2005-01-01

    Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions. PMID:16126873

  12. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  13. Phase diagram of silicon using a DFT-based neural network potential

    Science.gov (United States)

    Andreussi, Oliviero; Behler, Joerg; Parrinello, Michele

    2008-03-01

    The phase diagram of silicon is computed by means of Classical Molecular Dynamics. A recently developed [Behler and Parrinello, Phys. Rev. Lett. 98 146401 (2007)] neural-network potential based on Density Functional Theory calculations in the Local Density Approximation is used. This potential was shown to be several orders of magnitude faster than corresponding LDA-DFT calculations, while the accuracy is essentially maintained. Results on the liquid-solid coexistence curve are in good agreement with ab-initio calculations and demonstrate the quality of the neural-network potential.

  14. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  15. Neural network based data-driven predictor: Case study on clinker ...

    African Journals Online (AJOL)

    Soft sensors are key solutions in process industries. Important parameters which are difficult or cost a lot to measure can be predicted using soft sensors. In this paper neural network based clinker quality predictor is developed. The predictor genuinely estimates LSF, SM, AM and C3S values. There is a time delay while ...

  16. Wind Turbine Fault Detection based on Artificial Neural Network Analysis of SCADA Data

    DEFF Research Database (Denmark)

    Herp, Jürgen; S. Nadimi, Esmaeil

    2015-01-01

    Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. We are proposing a fault detection method based on Artificial Neural Networks (ANN) and the recordings from Supervisory Control and Data Acquisition (SCADA) systems installed in wind...

  17. Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

    African Journals Online (AJOL)

    In this study, an oil-fired boiler system is modeled as a multivariable plant with two inputs (feed water rate and oil-fired flow rate) and two outputs (steam temperature and pressure). The plant parameters are modeled using artificial neural network, based on experimental data collected directly from the physical plant.

  18. Image Classification System Based on Cortical Representations and Unsupervised Neural Network Learning

    NARCIS (Netherlands)

    Petkov, Nikolay

    1995-01-01

    A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input

  19. Identification of children's activity type with accelerometer-based neural networks

    NARCIS (Netherlands)

    Vries, S.I. de; Engels, M.; Garre, F.G.

    2011-01-01

    Purpose: The study's purpose was to identify children's physical activity type using artificial neural network (ANN) models based on uniaxial or triaxial accelerometer data from the hip or the ankle. Methods: Fifty-eight children (31 boys and 27 girls, age range = 9-12 yr) performed the following

  20. Cryptanalysis of a cryptographic scheme based on delayed chaotic neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Yang Jiyun [Department of Computer Science and Engineering, Chongqing University, Chongqing 400044 (China)], E-mail: yangjy@cqu.edu.cn; Liao Xiaofeng [Department of Computer Science and Engineering, Chongqing University, Chongqing 400044 (China); Key Laboratory of Optoelectric Technology and Systems, Ministry of Education (China); Yu Wenwu [Department of Mathematics, Southeast University, Nanjing 210096 (China); Wong Kwokwo [Department of Computer Engineering and Information Technology, City University of Hong Kong (Hong Kong); Wei Jun [Zhunyi Medical College, Zhunyi 563000, Guizhou (China)

    2009-04-30

    Recently, Yu et al. presented a new cryptographic scheme based on delayed chaotic neural networks. In this letter, a fundamental flaw in Yu's scheme is described. By means of chosen plaintext attack, the secret keystream used can easily be obtained.