WorldWideScience

Sample records for additional neural regions

  1. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  2. Generation of Regionally Specific Neural Progenitor Cells (NPCs) and Neurons from Human Pluripotent Stem Cells (hPSCs).

    Science.gov (United States)

    Cutts, Josh; Brookhouser, Nicholas; Brafman, David A

    2016-01-01

    Neural progenitor cells (NPCs) derived from human pluripotent stem cells (hPSCs) are a multipotent cell population capable of long-term expansion and differentiation into a variety of neuronal subtypes. As such, NPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine. Current methods for the generation of NPCs results in cell populations homogenous for pan-neural markers such as SOX1 and SOX2 but heterogeneous with respect to regional identity. In order to use NPCs and their neuronal derivatives to investigate mechanisms of neurological disorders and develop more physiologically relevant disease models, methods for generation of regionally specific NPCs and neurons are needed. Here, we describe a protocol in which exogenous manipulation of WNT signaling, through either activation or inhibition, during neural differentiation of hPSCs, promotes the formation of regionally homogenous NPCs and neuronal cultures. In addition, we provide methods to monitor and characterize the efficiency of hPSC differentiation to these regionally specific cell identities.

  3. A Neural Region of Abstract Working Memory

    Science.gov (United States)

    Cowan, Nelson; Li, Dawei; Moffitt, Amanda; Becker, Theresa M.; Martin, Elizabeth A.; Saults, J. Scott; Christ, Shawn E.

    2011-01-01

    Over 350 years ago, Descartes proposed that the neural basis of consciousness must be a brain region in which sensory inputs are combined. Using fMRI, we identified at least one such area for working memory, the limited information held in mind, described by William James as the trailing edge of consciousness. Specifically, a region in the left…

  4. Prediction of Shanghai Index based on Additive Legendre Neural Network

    Directory of Open Access Journals (Sweden)

    Yang Bin

    2017-01-01

    Full Text Available In this paper, a novel Legendre neural network model is proposed, namely additive Legendre neural network (ALNN. A new hybrid evolutionary method besed on binary particle swarm optimization (BPSO algorithm and firefly algorithm is proposed to optimize the structure and parameters of ALNN model. Shanghai stock exchange composite index is used to evaluate the performance of ALNN. Results reveal that ALNN performs better than LNN model.

  5. Response of neural reward regions to food cues in autism spectrum disorders

    Directory of Open Access Journals (Sweden)

    Cascio Carissa J

    2012-05-01

    Full Text Available Abstract Background One hypothesis for the social deficits that characterize autism spectrum disorders (ASD is diminished neural reward response to social interaction and attachment. Prior research using established monetary reward paradigms as a test of non-social reward to compare with social reward may involve confounds in the ability of individuals with ASD to utilize symbolic representation of money and the abstraction required to interpret monetary gains. Thus, a useful addition to our understanding of neural reward circuitry in ASD includes a characterization of the neural response to primary rewards. Method We asked 17 children with ASD and 18 children without ASD to abstain from eating for at least four hours before an MRI scan in which they viewed images of high-calorie foods. We assessed the neural reward network for increases in the blood oxygenation level dependent (BOLD signal in response to the food images Results We found very similar patterns of increased BOLD signal to these images in the two groups; both groups showed increased BOLD signal in the bilateral amygdala, as well as in the nucleus accumbens, orbitofrontal cortex, and insula. Direct group comparisons revealed that the ASD group showed a stronger response to food cues in bilateral insula along the anterior-posterior gradient and in the anterior cingulate cortex than the control group, whereas there were no neural reward regions that showed higher activation for controls than for ASD. Conclusion These results suggest that neural response to primary rewards is not diminished but in fact shows an aberrant enhancement in children with ASD.

  6. Generation of Regionally Specified Neural Progenitors and Functional Neurons from Human Embryonic Stem Cells under Defined Conditions

    Directory of Open Access Journals (Sweden)

    Agnete Kirkeby

    2012-06-01

    Full Text Available To model human neural-cell-fate specification and to provide cells for regenerative therapies, we have developed a method to generate human neural progenitors and neurons from human embryonic stem cells, which recapitulates human fetal brain development. Through the addition of a small molecule that activates canonical WNT signaling, we induced rapid and efficient dose-dependent specification of regionally defined neural progenitors ranging from telencephalic forebrain to posterior hindbrain fates. Ten days after initiation of differentiation, the progenitors could be transplanted to the adult rat striatum, where they formed neuron-rich and tumor-free grafts with maintained regional specification. Cells patterned toward a ventral midbrain (VM identity generated a high proportion of authentic dopaminergic neurons after transplantation. The dopamine neurons showed morphology, projection pattern, and protein expression identical to that of human fetal VM cells grafted in parallel. VM-patterned but not forebrain-patterned neurons released dopamine and reversed motor deficits in an animal model of Parkinson's disease.

  7. REGION OF NON-INTEREST BASED DIGITAL IMAGE WATERMARKING USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Bibi Isac

    2011-11-01

    Full Text Available Copyrights protection of digital data become inevitable in current world. Digital watermarks have been recently proposed as secured scheme for copyright protection, authentication, source tracking, and broadcast monitoring of video, audio, text data and digital images. In this paper a method to embed a watermark in region of non-interest (RONI and a method for adaptive calculation of strength factor using neural network are proposed. The embedding and extraction processes are carried out in the transform domain by using Discrete Wavelet Transform (DWT. Finally, the algorithm robustness is tested against noise addition attacks and geometric distortion attacks. The results authenticate that the proposed watermarking algorithm does not degrade the quality of cover image as the watermark is inserted only in region of non-interest and is resistive to attacks.

  8. Regional neural tube closure defined by the Grainy head-like transcription factors.

    Science.gov (United States)

    Rifat, Yeliz; Parekh, Vishwas; Wilanowski, Tomasz; Hislop, Nikki R; Auden, Alana; Ting, Stephen B; Cunningham, John M; Jane, Stephen M

    2010-09-15

    Primary neurulation in mammals has been defined by distinct anatomical closure sites, at the hindbrain/cervical spine (closure 1), forebrain/midbrain boundary (closure 2), and rostral end of the forebrain (closure 3). Zones of neurulation have also been characterized by morphologic differences in neural fold elevation, with non-neural ectoderm-induced formation of paired dorso-lateral hinge points (DLHP) essential for neural tube closure in the cranial and lower spinal cord regions, and notochord-induced bending at the median hinge point (MHP) sufficient for closure in the upper spinal region. Here we identify a unifying molecular basis for these observations based on the function of the non-neural ectoderm-specific Grainy head-like genes in mice. Using a gene-targeting approach we show that deletion of Grhl2 results in failed closure 3, with mutants exhibiting a split-face malformation and exencephaly, associated with failure of neuro-epithelial folding at the DLHP. Loss of Grhl3 alone defines a distinct lower spinal closure defect, also with defective DLHP formation. The two genes contribute equally to closure 2, where only Grhl gene dosage is limiting. Combined deletion of Grhl2 and Grhl3 induces severe rostral and caudal neural tube defects, but DLHP-independent closure 1 proceeds normally in the upper spinal region. These findings provide a molecular basis for non-neural ectoderm mediated formation of the DLHP that is critical for complete neuraxis closure. (c) 2010 Elsevier Inc. All rights reserved.

  9. Sequential neural processes in abacus mental addition: an EEG and FMRI case study.

    Science.gov (United States)

    Ku, Yixuan; Hong, Bo; Zhou, Wenjing; Bodner, Mark; Zhou, Yong-Di

    2012-01-01

    Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes) swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms) after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms). Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7) and bilateral middle frontal gyri (BA 6). These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.

  10. Neural activation in stress-related exhaustion

    DEFF Research Database (Denmark)

    Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Andersson, Micael

    2017-01-01

    The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...... to uphold task performance. Following cognitive training, increased neural activation was observed during 3-back in working memory-related regions, including the striatum, however, low sample size limits any firm conclusions....

  11. Neural activity in the reward-related brain regions predicts implicit self-esteem: A novel validity test of psychological measures using neuroimaging.

    Science.gov (United States)

    Izuma, Keise; Kennedy, Kate; Fitzjohn, Alexander; Sedikides, Constantine; Shibata, Kazuhisa

    2018-03-01

    Self-esteem, arguably the most important attitudes an individual possesses, has been a premier research topic in psychology for more than a century. Following a surge of interest in implicit attitude measures in the 90s, researchers have tried to assess self-esteem implicitly to circumvent the influence of biases inherent in explicit measures. However, the validity of implicit self-esteem measures remains elusive. Critical tests are often inconclusive, as the validity of such measures is examined in the backdrop of imperfect behavioral measures. To overcome this serious limitation, we tested the neural validity of the most widely used implicit self-esteem measure, the implicit association test (IAT). Given the conceptualization of self-esteem as attitude toward the self, and neuroscience findings that the reward-related brain regions represent an individual's attitude or preference for an object when viewing its image, individual differences in implicit self-esteem should be associated with neural signals in the reward-related regions during passive-viewing of self-face (the most obvious representation of the self). Using multi-voxel pattern analysis (MVPA) on functional MRI (fMRI) data, we demonstrate that the neural signals in the reward-related regions were robustly associated with implicit (but not explicit) self-esteem, thus providing unique evidence for the neural validity of the self-esteem IAT. In addition, both implicit and explicit self-esteem were related, although differently, to neural signals in regions involved in self-processing. Our finding highlights the utility of neuroscience methods in addressing fundamental psychological questions and providing unique insights into important psychological constructs. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  12. Co-speech gestures influence neural activity in brain regions associated with processing semantic information.

    Science.gov (United States)

    Dick, Anthony Steven; Goldin-Meadow, Susan; Hasson, Uri; Skipper, Jeremy I; Small, Steven L

    2009-11-01

    Everyday communication is accompanied by visual information from several sources, including co-speech gestures, which provide semantic information listeners use to help disambiguate the speaker's message. Using fMRI, we examined how gestures influence neural activity in brain regions associated with processing semantic information. The BOLD response was recorded while participants listened to stories under three audiovisual conditions and one auditory-only (speech alone) condition. In the first audiovisual condition, the storyteller produced gestures that naturally accompany speech. In the second, the storyteller made semantically unrelated hand movements. In the third, the storyteller kept her hands still. In addition to inferior parietal and posterior superior and middle temporal regions, bilateral posterior superior temporal sulcus and left anterior inferior frontal gyrus responded more strongly to speech when it was further accompanied by gesture, regardless of the semantic relation to speech. However, the right inferior frontal gyrus was sensitive to the semantic import of the hand movements, demonstrating more activity when hand movements were semantically unrelated to the accompanying speech. These findings show that perceiving hand movements during speech modulates the distributed pattern of neural activation involved in both biological motion perception and discourse comprehension, suggesting listeners attempt to find meaning, not only in the words speakers produce, but also in the hand movements that accompany speech.

  13. Modelling innovation performance of European regions using multi-output neural networks.

    Science.gov (United States)

    Hajek, Petr; Henriques, Roberto

    2017-01-01

    Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

  14. Modelling innovation performance of European regions using multi-output neural networks.

    Directory of Open Access Journals (Sweden)

    Petr Hajek

    Full Text Available Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

  15. Forecasting the daily electricity consumption in the Moscow region using artificial neural networks

    Science.gov (United States)

    Ivanov, V. V.; Kryanev, A. V.; Osetrov, E. S.

    2017-07-01

    In [1] we demonstrated the possibility in principle for short-term forecasting of daily volumes of passenger traffic in the Moscow metro with the help of artificial neural networks. During training and predicting, a set of the factors that affect the daily passenger traffic in the subway is passed to the input of the neural network. One of these factors is the daily power consumption in the Moscow region. Therefore, to predict the volume of the passenger traffic in the subway, we must first to solve the problem of forecasting the daily energy consumption in the Moscow region.

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

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

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

  17. Experimental evidence of a chaotic region in a neural pacemaker

    Energy Technology Data Exchange (ETDEWEB)

    Gu, Hua-Guang, E-mail: guhuaguang@tongji.edu.cn [School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092 (China); Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR (China); Jia, Bing [School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092 (China); Chen, Guan-Rong [Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR (China)

    2013-03-15

    In this Letter, we report the finding of period-adding scenarios with chaos in firing patterns, observed in biological experiments on a neural pacemaker, with fixed extra-cellular potassium concentration at different levels and taken extra-cellular calcium concentration as the bifurcation parameter. The experimental bifurcations in the two-dimensional parameter space demonstrate the existence of a chaotic region interwoven with the periodic region thereby forming a period-adding sequence with chaos. The behavior of the pacemaker in this region is qualitatively similar to that of the Hindmarsh–Rose neuron model in a well-known comb-shaped chaotic region in two-dimensional parameter spaces.

  18. Passivity of memristive BAM neural networks with leakage and additive time-varying delays

    Science.gov (United States)

    Wang, Weiping; Wang, Meiqi; Luo, Xiong; Li, Lixiang; Zhao, Wenbing; Liu, Linlin; Ping, Yuan

    2018-02-01

    This paper investigates the passivity of memristive bidirectional associate memory neural networks (MBAMNNs) with leakage and additive time-varying delays. Based on some useful inequalities and appropriate Lyapunov-Krasovskii functionals (LKFs), several delay-dependent conditions for passivity performance are obtained in linear matrix inequalities (LMIs). Moreover, the leakage delays as well as additive delays are considered separately. Finally, numerical simulations are provided to demonstrate the feasibility of the theoretical results.

  19. LOAD FORECASTING FOR POWER SYSTEM PLANNING AND OPERATION USING ARTIFICIAL NEURAL NETWORK AT AL BATINAH REGION OMAN

    Directory of Open Access Journals (Sweden)

    HUSSEIN A. ABDULQADER

    2012-08-01

    Full Text Available Load forecasting is essential part for the power system planning and operation. In this paper the modeling and design of artificial neural network for load forecasting is carried out in a particular region of Oman. Neural network approach helps to reduce the problem associated with conventional method and has the advantage of learning directly from the historical data. The neural network here uses data such as past load; weather information like humidity and temperatures. Once the neural network is trained for the past set of data it can give a prediction of future load. This reduces the capital investment reducing the equipments to be installed. The actual data are taken from the Mazoon Electrical Company, Oman. The data of load for the year 2007, 2008 and 2009 are collected for a particular region called Al Batinah in Oman and trained using neural networks to forecast the future. The main objective is to forecast the amount of electricity needed for better load distribution in the areas of this region in Oman. The load forecasting is done for the year 2010 and is validated for the accuracy.

  20. Neural codes of seeing architectural styles.

    Science.gov (United States)

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

    2017-01-10

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

  1. Transcriptional response of Hoxb genes to retinoid signalling is regionally restricted along the neural tube rostrocaudal axis.

    Science.gov (United States)

    Carucci, Nicoletta; Cacci, Emanuele; Nisi, Paola S; Licursi, Valerio; Paul, Yu-Lee; Biagioni, Stefano; Negri, Rodolfo; Rugg-Gunn, Peter J; Lupo, Giuseppe

    2017-04-01

    During vertebrate neural development, positional information is largely specified by extracellular morphogens. Their distribution, however, is very dynamic due to the multiple roles played by the same signals in the developing and adult neural tissue. This suggests that neural progenitors are able to modify their competence to respond to morphogen signalling and autonomously maintain positional identities after their initial specification. In this work, we take advantage of in vitro culture systems of mouse neural stem/progenitor cells (NSPCs) to show that NSPCs isolated from rostral or caudal regions of the mouse neural tube are differentially responsive to retinoic acid (RA), a pivotal morphogen for the specification of posterior neural fates. Hoxb genes are among the best known RA direct targets in the neural tissue, yet we found that RA could promote their transcription only in caudal but not in rostral NSPCs. Correlating with these effects, key RA-responsive regulatory regions in the Hoxb cluster displayed opposite enrichment of activating or repressing histone marks in rostral and caudal NSPCs. Finally, RA was able to strengthen Hoxb chromatin activation in caudal NSPCs, but was ineffective on the repressed Hoxb chromatin of rostral NSPCs. These results suggest that the response of NSPCs to morphogen signalling across the rostrocaudal axis of the neural tube may be gated by the epigenetic configuration of target patterning genes, allowing long-term maintenance of intrinsic positional values in spite of continuously changing extrinsic signals.

  2. Tracking performance and global stability guaranteed neural control of uncertain hypersonic flight vehicle

    Directory of Open Access Journals (Sweden)

    Tao Teng

    2016-02-01

    Full Text Available In this article, a global adaptive neural dynamic surface control with predefined tracking performance is developed for a class of hypersonic flight vehicles, whose accurate dynamics is hard to obtain. The control scheme developed in this paper overcomes the limitations of neural approximation region by employing a switching mechanism which incorporates an additional robust controller outside the neural approximation region to pull the transient state variables back when they overstep the neural approximation region, such that globally uniformly ultimately bounded stability can be guaranteed. Especially, the developed global adaptive neural control also improves the tracking performance by introducing an error transformation mechanism, such that both transient and steady-state performance can be shaped according to the predefined bounds. Simulation studies on the hypersonic flight vehicle validate that the designed controller has good velocity modulation and velocity stability performance.

  3. Neural crest contributions to the lamprey head

    Science.gov (United States)

    McCauley, David W.; Bronner-Fraser, Marianne

    2003-01-01

    The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

  4. NeuroMEMS: Neural Probe Microtechnologies

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    Sam Musallam

    2008-10-01

    Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

  5. Acute stress evokes sexually dimorphic, stressor-specific patterns of neural activation across multiple limbic brain regions in adult rats.

    Science.gov (United States)

    Sood, Ankit; Chaudhari, Karina; Vaidya, Vidita A

    2018-03-01

    Stress enhances the risk for psychiatric disorders such as anxiety and depression. Stress responses vary across sex and may underlie the heightened vulnerability to psychopathology in females. Here, we examined the influence of acute immobilization stress (AIS) and a two-day short-term forced swim stress (FS) on neural activation in multiple cortical and subcortical brain regions, implicated as targets of stress and in the regulation of neuroendocrine stress responses, in male and female rats using Fos as a neural activity marker. AIS evoked a sex-dependent pattern of neural activation within the cingulate and infralimbic subdivisions of the medial prefrontal cortex (mPFC), lateral septum (LS), habenula, and hippocampal subfields. The degree of neural activation in the mPFC, LS, and habenula was higher in males. Female rats exhibited reduced Fos positive cell numbers in the dentate gyrus hippocampal subfield, an effect not observed in males. We addressed whether the sexually dimorphic neural activation pattern noted following AIS was also observed with the short-term stress of FS. In the paraventricular nucleus of the hypothalamus and the amygdala, FS similar to AIS resulted in robust increases in neural activation in both sexes. The pattern of neural activation evoked by FS was distinct across sexes, with a heightened neural activation noted in the prelimbic mPFC subdivision and hippocampal subfields in females and differed from the pattern noted with AIS. This indicates that the sex differences in neural activation patterns observed within stress-responsive brain regions are dependent on the nature of stressor experience.

  6. Efficient airport detection using region-based fully convolutional neural networks

    Science.gov (United States)

    Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

    2018-04-01

    This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

  7. Anatomically ordered tapping interferes more with one-digit addition than two-digit addition: a dual-task fMRI study.

    Science.gov (United States)

    Soylu, Firat; Newman, Sharlene D

    2016-02-01

    Fingers are used as canonical representations for numbers across cultures. In previous imaging studies, it was shown that arithmetic processing activates neural resources that are known to participate in finger movements. Additionally, in one dual-task study, it was shown that anatomically ordered finger tapping disrupts addition and subtraction more than multiplication, possibly due to a long-lasting effect of early finger counting experiences on the neural correlates and organization of addition and subtraction processes. How arithmetic task difficulty and tapping complexity affect the concurrent performance is still unclear. If early finger counting experiences have bearing on the neural correlates of arithmetic in adults, then one would expect anatomically and non-anatomically ordered tapping to have different interference effects, given that finger counting is usually anatomically ordered. To unravel these issues, we studied how (1) arithmetic task difficulty and (2) the complexity of the finger tapping sequence (anatomical vs. non-anatomical ordering) affect concurrent performance and use of key neural circuits using a mixed block/event-related dual-task fMRI design with adult participants. The results suggest that complexity of the tapping sequence modulates interference on addition, and that one-digit addition (fact retrieval), compared to two-digit addition (calculation), is more affected from anatomically ordered tapping. The region-of-interest analysis showed higher left angular gyrus BOLD response for one-digit compared to two-digit addition, and in no-tapping conditions than dual tapping conditions. The results support a specific association between addition fact retrieval and anatomically ordered finger movements in adults, possibly due to finger counting strategies that deploy anatomically ordered finger movements early in the development.

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

    Science.gov (United States)

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

    2011-11-01

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

  9. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    International Nuclear Information System (INIS)

    Mudraya, I S; Revenko, S V; Khodyreva, L A; Markosyan, T G; Dudareva, A A; Ibragimov, A R; Romich, V V; Kirpatovsky, V I

    2013-01-01

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic – in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  10. Bioimpedance Harmonic Analysis as a Diagnostic Tool to Assess Regional Circulation and Neural Activity

    Science.gov (United States)

    Mudraya, I. S.; Revenko, S. V.; Khodyreva, L. A.; Markosyan, T. G.; Dudareva, A. A.; Ibragimov, A. R.; Romich, V. V.; Kirpatovsky, V. I.

    2013-04-01

    The novel technique based on harmonic analysis of bioimpedance microvariations with original hard- and software complex incorporating a high-resolution impedance converter was used to assess the neural activity and circulation in human urinary bladder and penis in patients with pelvic pain, erectile dysfunction, and overactive bladder. The therapeutic effects of shock wave therapy and Botulinum toxin detrusor injections were evaluated quantitatively according to the spectral peaks at low 0.1 Hz frequency (M for Mayer wave), respiratory (R) and cardiac (C) rhythms with their harmonics. Enhanced baseline regional neural activity identified according to M and R peaks was found to be presumably sympathetic in pelvic pain patients, and parasympathetic - in patients with overactive bladder. Total pulsatile activity and pulsatile resonances found in the bladder as well as in the penile spectrum characterised regional circulation and vascular tone. The abnormal spectral parameters characteristic of the patients with genitourinary diseases shifted to the norm in the cases of efficient therapy. Bioimpedance harmonic analysis seems to be a potent tool to assess regional peculiarities of circulatory and autonomic nervous activity in the course of patient treatment.

  11. Neural dichotomy of word concreteness: a view from functional neuroimaging.

    Science.gov (United States)

    Kumar, Uttam

    2016-02-01

    Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.

  12. Neural mechanisms of social dominance

    Science.gov (United States)

    Watanabe, Noriya; Yamamoto, Miyuki

    2015-01-01

    In a group setting, individuals' perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems' level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation. PMID:26136644

  13. Neural mechanisms of social dominance

    Directory of Open Access Journals (Sweden)

    Noriya eWatanabe

    2015-06-01

    Full Text Available In a group setting, individuals’ perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems’ level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation.

  14. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-12-31

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

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

    Science.gov (United States)

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

    2017-05-01

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

  16. Pax7 lineage contributions to the mammalian neural crest.

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    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  17. Coding of level of ambiguity within neural systems mediating choice.

    Science.gov (United States)

    Lopez-Paniagua, Dan; Seger, Carol A

    2013-01-01

    Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

  18. A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

    Science.gov (United States)

    Kuntanapreeda, S; Fullmer, R R

    1996-01-01

    A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

  19. A regional GNSS-VTEC model over Nigeria using neural networks: A novel approach

    Directory of Open Access Journals (Sweden)

    Daniel Okoh

    2016-01-01

    Full Text Available A neural network model of the Global Navigation Satellite System – vertical total electron content (GNSS-VTEC over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere's (IRI's critical plasma frequency (foF2 parameter as an additional neuron for the network's input layer. The work also explores the effects of using various other input layer neurons like disturbance storm time (DST and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network (NIGNET were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI's foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial performances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.

  20. Molecular regionalization of the developing amphioxus neural tube challenges major partitions of the vertebrate brain.

    Science.gov (United States)

    Albuixech-Crespo, Beatriz; López-Blanch, Laura; Burguera, Demian; Maeso, Ignacio; Sánchez-Arrones, Luisa; Moreno-Bravo, Juan Antonio; Somorjai, Ildiko; Pascual-Anaya, Juan; Puelles, Eduardo; Bovolenta, Paola; Garcia-Fernàndez, Jordi; Puelles, Luis; Irimia, Manuel; Ferran, José Luis

    2017-04-01

    All vertebrate brains develop following a common Bauplan defined by anteroposterior (AP) and dorsoventral (DV) subdivisions, characterized by largely conserved differential expression of gene markers. However, it is still unclear how this Bauplan originated during evolution. We studied the relative expression of 48 genes with key roles in vertebrate neural patterning in a representative amphioxus embryonic stage. Unlike nonchordates, amphioxus develops its central nervous system (CNS) from a neural plate that is homologous to that of vertebrates, allowing direct topological comparisons. The resulting genoarchitectonic model revealed that the amphioxus incipient neural tube is unexpectedly complex, consisting of several AP and DV molecular partitions. Strikingly, comparison with vertebrates indicates that the vertebrate thalamus, pretectum, and midbrain domains jointly correspond to a single amphioxus region, which we termed Di-Mesencephalic primordium (DiMes). This suggests that these domains have a common developmental and evolutionary origin, as supported by functional experiments manipulating secondary organizers in zebrafish and mice.

  1. Rodent Zic Genes in Neural Network Wiring.

    Science.gov (United States)

    Herrera, Eloísa

    2018-01-01

    The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

  2. Region stability analysis and tracking control of memristive recurrent neural network.

    Science.gov (United States)

    Bao, Gang; Zeng, Zhigang; Shen, Yanjun

    2018-02-01

    Memristor is firstly postulated by Leon Chua and realized by Hewlett-Packard (HP) laboratory. Research results show that memristor can be used to simulate the synapses of neurons. This paper presents a class of recurrent neural network with HP memristors. Firstly, it shows that memristive recurrent neural network has more compound dynamics than the traditional recurrent neural network by simulations. Then it derives that n dimensional memristive recurrent neural network is composed of [Formula: see text] sub neural networks which do not have a common equilibrium point. By designing the tracking controller, it can make memristive neural network being convergent to the desired sub neural network. At last, two numerical examples are given to verify the validity of our result. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Brain Region-Dependent Rejection of Neural Precursor Cell Transplants

    Directory of Open Access Journals (Sweden)

    Nina Fainstein

    2018-04-01

    Full Text Available The concept of CNS as an immune-privileged site has been challenged by the occurrence of immune surveillance and allogeneic graft rejection in the brain. Here we examined whether the immune response to allogeneic neural grafts is determined by the site of implantation in the CNS. Dramatic regional differences were observed between immune responses to allogeneic neural precursor/stem cell (NPC grafts in the striatum vs. the hippocampus. Striatal grafts were heavily infiltrated with IBA-1+ microglia/macrophages and CD3+ T cells and completely rejected. In contrast, hippocampal grafts exhibited milder IBA-1+ cell infiltration, were not penetrated efficiently by CD3+ cells, and survived efficiently for at least 2 months. To evaluate whether the hippocampal protective effect is universal, astrocytes were then transplanted. Allogeneic astrocyte grafts elicited a vigorous rejection process from the hippocampus. CD200, a major immune-inhibitory signal, plays an important role in protecting grafts from rejection. Indeed, CD200 knock out NPC grafts were rejected more efficiently than wild type NPCs from the striatum. However, lack of CD200 expression did not elicit NPC graft rejection from the hippocampus. In conclusion, the hippocampus has partial immune-privilege properties that are restricted to NPCs and are CD200-independent. The unique hippocampal milieu may be protective for allogeneic NPC grafts, through host-graft interactions enabling sustained immune-regulatory properties of transplanted NPCs. These findings have implications for providing adequate immunosuppression in clinical translation of cell therapy.

  4. A one-layer recurrent neural network for constrained nonconvex optimization.

    Science.gov (United States)

    Li, Guocheng; Yan, Zheng; Wang, Jun

    2015-01-01

    In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

  5. Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Donghui Hu

    2017-01-01

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

  6. Tectonic modeling of Konya-Beysehir Region (Turkey using cellular neural networks

    Directory of Open Access Journals (Sweden)

    D. Aydogan

    2007-06-01

    Full Text Available In this paper, to separate regional-residual anomaly maps and to detect borders of buried geological bodies, we applied the Cellular Neural Network (CNN approach to gravity and magnetic anomaly maps. CNN is a stochastic image processing technique, based optimization of templates, which imply relationships of neighborhood pixels in 2-Dimensional (2D potential anomalies. Here, CNN performance in geophysics, tested by various synthetic examples and the results are compared to classical methods such as boundary analysis and second vertical derivatives. After we obtained satisfactory results in synthetic models, we applied CNN to Bouguer anomaly map of Konya-Beysehir Region, which has complex tectonic structure with various fault combinations. We evaluated CNN outputs and 2D/3D models, which are constructed using forward and inversion methods. Then we presented a new tectonic structure of Konya-Beysehir Region. We have denoted (F1, F2, …, F7 and (Konya1, Konya2 faults according to our evaluations of CNN outputs. Thus, we have concluded that CNN is a compromising stochastic image processing technique in geophysics.

  7. Restriction of neural precursor ability to respond to Nurr1 by early regional specification.

    Directory of Open Access Journals (Sweden)

    Chiara Soldati

    Full Text Available During neural development, spatially regulated expression of specific transcription factors is crucial for central nervous system (CNS regionalization, generation of neural precursors (NPs and subsequent differentiation of specific cell types within defined regions. A critical role in dopaminergic differentiation in the midbrain (MB has been assigned to the transcription factor Nurr1. Nurr1 controls the expression of key genes involved in dopamine (DA neurotransmission, e.g. tyrosine hydroxylase (TH and the DA transporter (DAT, and promotes the dopaminergic phenotype in embryonic stem cells. We investigated whether cells derived from different areas of the mouse CNS could be directed to differentiate into dopaminergic neurons in vitro by forced expression of the transcription factor Nurr1. We show that Nurr1 overexpression can promote dopaminergic cell fate specification only in NPs obtained from E13.5 ganglionic eminence (GE and MB, but not in NPs isolated from E13.5 cortex (CTX and spinal cord (SC or from the adult subventricular zone (SVZ. Confirming previous studies, we also show that Nurr1 overexpression can increase the generation of TH-positive neurons in mouse embryonic stem cells. These data show that Nurr1 ability to induce a dopaminergic phenotype becomes restricted during CNS development and is critically dependent on the region of NPs derivation. Our results suggest that the plasticity of NPs and their ability to activate a dopaminergic differentiation program in response to Nurr1 is regulated during early stages of neurogenesis, possibly through mechanisms controlling CNS regionalization.

  8. Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun [College of Medicine, Seoul National University, Seoul (Korea, Republic of)

    2004-07-01

    Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9{+-}4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7{+-} 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder.

  9. Regional cerebral glucose metabolic changes in oculopalatal myoclonus: implication for neural pathways, underlying the disorder

    International Nuclear Information System (INIS)

    Cho, Sang Soo; Moon, So Young; Kim, Ji Soo; Kim, Sang Eun

    2004-01-01

    Palatal myoclonus (PM) is characterized by rhythmic involuntary jerky movements of the soft palate of the throat. When associated with eye movements, it is called oculopalatal myoclonus (OPM). Ordinary PM is characterized by hypertrophic olivary degeneration, a trans-synaptic degeneration following loss of neuronal input to the inferior olivary nucleus due to an interruption of the Guillain-Mollaret triangle usually by a hemorrhage. However, the neural pathways underlying the disorder are uncertain. In an attempt to understand the pathologic neural pathways, we examined the metabolic correlates of this tremulous condition. Brain FDG PET scans were acquired in 8 patients with OPM (age, 49.9±4.6 y: all males: 7 with pontine hemorrhage, 1 with diffuse brainstem infarction) and age-matched 50 healthy males (age, 50.7± 9.0) and the regional glucose metabolism compared using SPM99. For group analysis, the hemispheres containing lesions were assigned to the right side of the brain. Patients with OPM had significant hypometabolism in the ipsilateral (to the lesion) brainstem and superior temporal and parahippocampal gyri (P < 0.05 corrected, k = 100). By contrast, there was significant hypermetabolism in the contralateral middle and inferior temporal gyri, thalamus, middle frontal gyrus and precuneus (P < 0.05 corrected, k=l00). Our data demonstrate the distinct metabolic changes between several ipsilateral and contralateral brain regions (hypometabolism vs. hypermetabolism) in patients with OPM. This may provide clues for understanding the neural pathways underlying the disorder

  10. Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

    Directory of Open Access Journals (Sweden)

    Aline Regina Walkoff

    2017-10-01

    Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

  11. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.

    Science.gov (United States)

    Bandeira Diniz, João Otávio; Bandeira Diniz, Pedro Henrique; Azevedo Valente, Thales Levi; Corrêa Silva, Aristófanes; de Paiva, Anselmo Cardoso; Gattass, Marcelo

    2018-03-01

    The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for

  12. Neural activity in the posterior superior temporal region during eye contact perception correlates with autistic traits.

    Science.gov (United States)

    Hasegawa, Naoya; Kitamura, Hideaki; Murakami, Hiroatsu; Kameyama, Shigeki; Sasagawa, Mutsuo; Egawa, Jun; Endo, Taro; Someya, Toshiyuki

    2013-08-09

    The present study investigated the relationship between neural activity associated with gaze processing and autistic traits in typically developed subjects using magnetoencephalography. Autistic traits in 24 typically developed college students with normal intelligence were assessed using the Autism Spectrum Quotient (AQ). The Minimum Current Estimates method was applied to estimate the cortical sources of magnetic responses to gaze stimuli. These stimuli consisted of apparent motion of the eyes, displaying direct or averted gaze motion. Results revealed gaze-related brain activations in the 150-250 ms time window in the right posterior superior temporal sulcus (pSTS), and in the 150-450 ms time window in medial prefrontal regions. In addition, the mean amplitude in the 150-250 ms time window in the right pSTS region was modulated by gaze direction, and its activity in response to direct gaze stimuli correlated with AQ score. pSTS activation in response to direct gaze is thought to be related to higher-order social processes. Thus, these results suggest that brain activity linking eye contact and social signals is associated with autistic traits in a typical population. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. A one-layer recurrent neural network for constrained nonsmooth invex optimization.

    Science.gov (United States)

    Li, Guocheng; Yan, Zheng; Wang, Jun

    2014-02-01

    Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Neural mechanisms of mindfulness and meditation: Evidence from neuroimaging studies

    Institute of Scientific and Technical Information of China (English)

    William; R; Marchand

    2014-01-01

    Mindfulness is the dispassionate,moment-by-moment awareness of sensations,emotions and thoughts.Mindfulness-based interventions are being increasingly used for stress,psychological well being,coping with chronic illness as well as adjunctive treatments for psychiatric disorders.However,the neural mechanisms associated with mindfulness have not been well characterized.Recent functional and structural neuroimaging studies are beginning to provide insights into neural processes associated with the practice of mindfulness.A review of this literature revealed compelling evidence that mindfulness impacts the function of the medial cortex and associated default mode network as well as insula and amygdala.Additionally,mindfulness practice appears to effect lateral frontal regions and basal ganglia,at least in some cases.Structural imaging studies are consistent with these findings and also indicate changes in the hippocampus.While many questions remain unanswered,the current literature provides evidence of brain regions and networks relevant for understanding neural processes associated with mindfulness.

  15. Neural overlap in processing music and speech

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

    2015-01-01

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

  16. Predicting mastitis in dairy cows using neural networks and generalized additive models

    DEFF Research Database (Denmark)

    Anantharama Ankinakatte, Smitha; Norberg, Elise; Løvendahl, Peter

    2013-01-01

    The aim of this paper is to develop and compare methods for early detection of oncoming mastitis with automated recorded data. The data were collected at the Danish Cattle Research Center (Tjele, Denmark). As indicators of mastitis, electrical conductivity (EC), somatic cell scores (SCS), lactate...... that combines residual components into a score to improve the model. To develop and verify the model, the data are randomly divided into training and validation data sets. To predict the occurrence of mastitis, neural network models (NNs) and generalized additive models (GAMs) are developed using the training...... classification with all indicators, using individual residuals rather than factor scores. When SCS is excluded, GAMs shows better classification result when milk yield is also excluded. In conclusion, the study shows that NNs and GAMs are similar in their ability to detect mastitis, a sensitivity of almost 75...

  17. Fluid region segmentation in OCT images based on convolution neural network

    Science.gov (United States)

    Liu, Dong; Liu, Xiaoming; Fu, Tianyu; Yang, Zhou

    2017-07-01

    In the retinal image, characteristics of fluid have great significance for diagnosis in eye disease. In the clinical, the segmentation of fluid is usually conducted manually, but is time-consuming and the accuracy is highly depend on the expert's experience. In this paper, we proposed a segmentation method based on convolution neural network (CNN) for segmenting the fluid from fundus image. The B-scans of OCT are segmented into layers, and patches from specific region with annotation are used for training. After the data set being divided into training set and test set, network training is performed and a good segmentation result is obtained, which has a significant advantage over traditional methods such as threshold method.

  18. Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

    Science.gov (United States)

    Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

    2014-12-24

    This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

  19. The Neural Cell Adhesion Molecule NCAM2/OCAM/RNCAM, a Close Relative to NCAM

    DEFF Research Database (Denmark)

    Kulahin, Nikolaj; Walmod, Peter

    2008-01-01

    molecule (NCAM) is a well characterized, ubiquitously expressed CAM that is highly expressed in the nervous system. In addition to mediating cell adhesion, NCAM participates in a multitude of cellular events, including survival, migration, and differentiation of cells, outgrowth of neurites, and formation......Cell adhesion molecules (CAMs) constitute a large class of plasma membrane-anchored proteins that mediate attachment between neighboring cells and between cells and the surrounding extracellular matrix (ECM). However, CAMs are more than simple mediators of cell adhesion. The neural cell adhesion...... and plasticity of synapses. NCAM shares an overall sequence identity of approximately 44% with the neural cell adhesion molecule 2 (NCAM2), a protein also known as olfactory cell adhesion molecule (OCAM) and Rb-8 neural cell adhesion molecule (RNCAM), and the region-for-region sequence homology between the two...

  20. Probing many-body localization with neural networks

    Science.gov (United States)

    Schindler, Frank; Regnault, Nicolas; Neupert, Titus

    2017-06-01

    We show that a simple artificial neural network trained on entanglement spectra of individual states of a many-body quantum system can be used to determine the transition between a many-body localized and a thermalizing regime. Specifically, we study the Heisenberg spin-1/2 chain in a random external field. We employ a multilayer perceptron with a single hidden layer, which is trained on labeled entanglement spectra pertaining to the fully localized and fully thermal regimes. We then apply this network to classify spectra belonging to states in the transition region. For training, we use a cost function that contains, in addition to the usual error and regularization parts, a term that favors a confident classification of the transition region states. The resulting phase diagram is in good agreement with the one obtained by more conventional methods and can be computed for small systems. In particular, the neural network outperforms conventional methods in classifying individual eigenstates pertaining to a single disorder realization. It allows us to map out the structure of these eigenstates across the transition with spatial resolution. Furthermore, we analyze the network operation using the dreaming technique to show that the neural network correctly learns by itself the power-law structure of the entanglement spectra in the many-body localized regime.

  1. Neural overlap in processing music and speech.

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

    2015-03-19

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  2. Abnormal regional spontaneous neural activity in visual pathway in retinal detachment patients: a resting-state functional MRI study

    Directory of Open Access Journals (Sweden)

    Huang X

    2017-11-01

    Full Text Available Xin Huang,1,2,* Dan Li,3,* Hai-Jun Li,3 Yu-Lin Zhong,1 Shelby Freeberg,4 Jing Bao,1 Xian-Jun Zeng,3 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Jiangxi Province Clinical Ophthalmology Institute, Nanchang, Jiangxi, People’s Republic of China; 2Department of Ophthalmology, Eye Center, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, Hubei, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 4Department of Ophthalmology, University of Florida, Gainesville, FL, USA *These authors contributed equally to this work Objective: The aim of the study was to investigate changes of brain neural homogeneity in retinal detachment (RD patients using the regional homogeneity (ReHo method to understand their relationships with clinical features. Materials and methods: A total of 30 patients with RD (16 men and 14 women, and 30 healthy controls (HCs (16 men and 14 women closely matched in age and sex were recruited. Resting-state functional magnetic resonance imaging scans were performed for all subjects. The ReHo method was used to investigate the brain regional neural homogeneity. Patients with RD were distinguished from HCs by receiver operating characteristic curve. The relationships between the mean ReHo signal values in many brain regions and clinical features in RD patients were calculated by Pearson correlation analysis. Results: Compared with HCs, RD patients had significantly decreased ReHo values in the right occipital lobe, right superior temporal gyrus, bilateral cuneus and left middle frontal gyrus. Moreover, we found that the mean ReHo signal of the bilateral cuneus showed positive relationships with the duration of the RD (r=0.392, P=0.032. Conclusion: The RD patients showed brain neural homogeneity dysfunction in the visual pathway, which may underline the pathological mechanism

  3. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    Directory of Open Access Journals (Sweden)

    Sheng Wang

    2015-07-01

    Full Text Available Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields, to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

  4. Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

    Science.gov (United States)

    Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

    2018-04-19

    White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

  5. Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition.

    Science.gov (United States)

    Bast, Tobias; Pezze, Marie; McGarrity, Stephanie

    2017-10-01

    We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, that is, deficient GABA transmission, within the prefrontal cortex and the hippocampus, for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity, and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function). Moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support the proposition that prefrontal and hippocampal neural disinhibition

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

    Science.gov (United States)

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

    2015-06-01

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

  7. On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

    Science.gov (United States)

    Mizutani, Eiji; Demmel, James W

    2003-01-01

    This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

  8. Financial Incentives Differentially Regulate Neural Processing of Positive and Negative Emotions during Value-Based Decision-Making

    Directory of Open Access Journals (Sweden)

    Anne M. Farrell

    2018-02-01

    Full Text Available Emotional and economic incentives often conflict in decision environments. To make economically desirable decisions then, deliberative neural processes must be engaged to regulate automatic emotional reactions. In this functional magnetic resonance imaging (fMRI study, we evaluated how fixed wage (FW incentives and performance-based (PB financial incentives, in which pay is proportional to outcome, differentially regulate positive and negative emotional reactions to hypothetical colleagues that conflicted with the economics of available alternatives. Neural activity from FW to PB incentive contexts decreased for positive emotional stimuli but increased for negative stimuli in middle temporal, insula, and medial prefrontal regions. In addition, PB incentives further induced greater responses to negative than positive emotional decisions in the frontal and anterior cingulate regions involved in emotion regulation. Greater response to positive than negative emotional features in these regions also correlated with lower frequencies of economically desirable choices. Our findings suggest that whereas positive emotion regulation involves a reduction of responses in valence representation regions, negative emotion regulation additionally engages brain regions for deliberative processing and signaling of incongruous events.

  9. Financial Incentives Differentially Regulate Neural Processing of Positive and Negative Emotions during Value-Based Decision-Making.

    Science.gov (United States)

    Farrell, Anne M; Goh, Joshua O S; White, Brian J

    2018-01-01

    Emotional and economic incentives often conflict in decision environments. To make economically desirable decisions then, deliberative neural processes must be engaged to regulate automatic emotional reactions. In this functional magnetic resonance imaging (fMRI) study, we evaluated how fixed wage (FW) incentives and performance-based (PB) financial incentives, in which pay is proportional to outcome, differentially regulate positive and negative emotional reactions to hypothetical colleagues that conflicted with the economics of available alternatives. Neural activity from FW to PB incentive contexts decreased for positive emotional stimuli but increased for negative stimuli in middle temporal, insula, and medial prefrontal regions. In addition, PB incentives further induced greater responses to negative than positive emotional decisions in the frontal and anterior cingulate regions involved in emotion regulation. Greater response to positive than negative emotional features in these regions also correlated with lower frequencies of economically desirable choices. Our findings suggest that whereas positive emotion regulation involves a reduction of responses in valence representation regions, negative emotion regulation additionally engages brain regions for deliberative processing and signaling of incongruous events.

  10. Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands

    Directory of Open Access Journals (Sweden)

    Jon Touryan

    2017-07-01

    Full Text Available A growing number of studies use the combination of eye-tracking and electroencephalographic (EEG measures to explore the neural processes that underlie visual perception. In these studies, fixation-related potentials (FRPs are commonly used to quantify early and late stages of visual processing that follow the onset of each fixation. However, FRPs reflect a mixture of bottom-up (sensory-driven and top-down (goal-directed processes, in addition to eye movement artifacts and unrelated neural activity. At present there is little consensus on how to separate this evoked response into its constituent elements. In this study we sought to isolate the neural sources of target detection in the presence of eye movements and over a range of concurrent task demands. Here, participants were asked to identify visual targets (Ts amongst a grid of distractor stimuli (Ls, while simultaneously performing an auditory N-back task. To identify the discriminant activity, we used independent components analysis (ICA for the separation of EEG into neural and non-neural sources. We then further separated the neural sources, using a modified measure-projection approach, into six regions of interest (ROIs: occipital, fusiform, temporal, parietal, cingulate, and frontal cortices. Using activity from these ROIs, we identified target from non-target fixations in all participants at a level similar to other state-of-the-art classification techniques. Importantly, we isolated the time course and spectral features of this discriminant activity in each ROI. In addition, we were able to quantify the effect of cognitive load on both fixation-locked potential and classification performance across regions. Together, our results show the utility of a measure-projection approach for separating task-relevant neural activity into meaningful ROIs within more complex contexts that include eye movements.

  11. Transformed Neural Pattern Reinstatement during Episodic Memory Retrieval.

    Science.gov (United States)

    Xiao, Xiaoqian; Dong, Qi; Gao, Jiahong; Men, Weiwei; Poldrack, Russell A; Xue, Gui

    2017-03-15

    Contemporary models of episodic memory posit that remembering involves the reenactment of encoding processes. Although encoding-retrieval similarity has been consistently reported and linked to memory success, the nature of neural pattern reinstatement is poorly understood. Using high-resolution fMRI on human subjects, our results obtained clear evidence for item-specific pattern reinstatement in the frontoparietal cortex, even when the encoding-retrieval pairs shared no perceptual similarity. No item-specific pattern reinstatement was found in the ventral visual cortex. Importantly, the brain regions and voxels carrying item-specific representation differed significantly between encoding and retrieval, and the item specificity for encoding-retrieval similarity was smaller than that for encoding or retrieval, suggesting different nature of representations between encoding and retrieval. Moreover, cross-region representational similarity analysis suggests that the encoded representation in the ventral visual cortex was reinstated in the frontoparietal cortex during retrieval. Together, these results suggest that, in addition to reinstatement of the originally encoded pattern in the brain regions that perform encoding processes, retrieval may also involve the reinstatement of a transformed representation of the encoded information. These results emphasize the constructive nature of memory retrieval that helps to serve important adaptive functions. SIGNIFICANCE STATEMENT Episodic memory enables humans to vividly reexperience past events, yet how this is achieved at the neural level is barely understood. A long-standing hypothesis posits that memory retrieval involves the faithful reinstatement of encoding-related activity. We tested this hypothesis by comparing the neural representations during encoding and retrieval. We found strong pattern reinstatement in the frontoparietal cortex, but not in the ventral visual cortex, that represents visual details. Critically

  12. Maternal neural responses to infant cries and faces: relationships with substance use

    Directory of Open Access Journals (Sweden)

    Nicole eLandi

    2011-06-01

    Full Text Available Substance abuse in pregnant and recently postpartum women is a major public health concern because of effects on the infant and on the ability of the adult to care for the infant. In addition to the negative health effects of teratogenic substances on fetal development, substance use can contribute to difficulties associated with the social and behavioral aspects of parenting. Neural circuits associated with parenting behavior overlap with circuits involved in addiction (e.g., frontal, striatal and limbic systems and thus may be co-opted for the craving/reward cycle associated with substance use and abuse and be less available for parenting. The current study investigates the degree to which neural circuits associated with parenting are disrupted in mothers who are substance-using. Specifically, we used functional magnetic resonance imaging to examine the neural response to emotional infant cues (faces and cries in substance-using compared to non-using mothers. In response to both faces (of varying emotional valence and cries (of varying distress levels, substance-using mothers evidenced reduced neural activation in regions that have been previously implicated in reward and motivation as well as regions involved in cognitive control. Specifically, in response to faces, substance users showed reduced activation in prefrontal regions, including the dorsolateral and ventromedial prefrontal cortex, as well as visual processing (occipital lobes and limbic regions (parahippocampus and amygdala. Similarly, in response to infant cries substance-using mothers showed reduced activation relative to non-using mothers in prefrontal regions, auditory sensory processing regions, insula and limbic regions (parahippocampus and amygdala. These findings suggest that infant stimuli may be less salient for substance-using mothers, and such reduced saliency may impair developing infant-caregiver attachment and the ability of mothers to respond appropriately to their

  13. Categorical and continuous - disentangling the neural correlates of the carry effect in multi-digit addition

    Directory of Open Access Journals (Sweden)

    Dressel Katharina

    2010-11-01

    Full Text Available Abstract Background Recently it was suggested that the carry effect observed in addition involves both categorical and continuous processing characteristics. Methods In the present study, we aimed at identifying the specific neural correlates associated with processing either categorical or continuous aspects of the carry effect in an fMRI study on multi-digit addition. Results In line with our expectations, we observed two distinct parts of the fronto-parietal network subserving numerical cognition to be associated with either one of these two characteristics. On the one hand, the categorical aspect of the carry effect was associated with left-hemispheric language areas and the basal ganglia probably reflecting increased demands on procedural and problem solving processes. Complementarily, the continuous aspect of the carry effect was associated with increased intraparietal activation indicating increasing demands on magnitude processing as well as place-value integration with increasing unit sum. Conclusions In summary, the findings suggest representations and processes underlying the carry effect in multi-digit addition to be more complex and interactive than assumed previously.

  14. The Neural Basis of Typewriting: A Functional MRI Study.

    Science.gov (United States)

    Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki

    2015-01-01

    To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

  15. The Neural Basis of Typewriting: A Functional MRI Study.

    Directory of Open Access Journals (Sweden)

    Yuichi Higashiyama

    Full Text Available To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.

  16. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  17. Neural dissociations in attitude strength: Distinct regions of cingulate cortex track ambivalence and certainty.

    Science.gov (United States)

    Luttrell, Andrew; Stillman, Paul E; Hasinski, Adam E; Cunningham, William A

    2016-04-01

    People's behaviors are often guided by valenced responses to objects in the environment. Beyond positive and negative evaluations, attitudes research has documented the importance of attitude strength--qualities of an attitude that enhance or attenuate its impact and durability. Although neuroscience research has extensively investigated valence, little work exists on other related variables like metacognitive judgments about one's attitudes. It remains unclear, then, whether the various indicators of attitude strength represent a single underlying neural process or whether they reflect independent processes. To examine this, we used functional MRI (fMRI) to identify the neural correlates of attitude strength. Specifically, we focus on ambivalence and certainty, which represent metacognitive judgments that people can make about their evaluations. Although often correlated, prior neuroscience research suggests that these 2 attributes may have distinct neural underpinnings. We investigate this by having participants make evaluative judgments of visually presented words while undergoing fMRI. After scanning, participants rated the degree of ambivalence and certainty they felt regarding their attitudes toward each word. We found that these 2 judgments corresponded to distinct brain regions' activity during the process of evaluation. Ambivalence corresponded to activation in anterior cingulate cortex, dorsomedial prefrontal cortex, and posterior cingulate cortex. Certainty, however, corresponded to activation in unique areas of the precuneus/posterior cingulate cortex. These results support a model treating ambivalence and certainty as distinct, though related, attitude strength variables, and we discuss implications for both attitudes and neuroscience research. (c) 2016 APA, all rights reserved).

  18. Neural substrates of sublexical processing for spelling.

    Science.gov (United States)

    DeMarco, Andrew T; Wilson, Stephen M; Rising, Kindle; Rapcsak, Steven Z; Beeson, Pélagie M

    2017-01-01

    We used fMRI to examine the neural substrates of sublexical phoneme-grapheme conversion during spelling in a group of healthy young adults. Participants performed a writing-to-dictation task involving irregular words (e.g., choir), plausible nonwords (e.g., kroid), and a control task of drawing familiar geometric shapes (e.g., squares). Written production of both irregular words and nonwords engaged a left-hemisphere perisylvian network associated with reading/spelling and phonological processing skills. Effects of lexicality, manifested by increased activation during nonword relative to irregular word spelling, were noted in anterior perisylvian regions (posterior inferior frontal gyrus/operculum/precentral gyrus/insula), and in left ventral occipito-temporal cortex. In addition to enhanced neural responses within domain-specific components of the language network, the increased cognitive demands associated with spelling nonwords engaged domain-general frontoparietal cortical networks involved in selective attention and executive control. These results elucidate the neural substrates of sublexical processing during written language production and complement lesion-deficit correlation studies of phonological agraphia. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Additional protocols and regional cooperation on peaceful uses of nuclear energy in northeast Asia

    Energy Technology Data Exchange (ETDEWEB)

    Choe, Kwan Kyoo [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    2004-07-01

    The main object of this article is to clarify the relations between the implementation of the Protocols Additional to Safeguards Agreement (hereinafter referred to as the Additional Protocols) and the feasibility of the regional cooperation on peaceful uses of nuclear energy in Northeast Asia (NEA). The regionalism has a strong tendency to be based in advance on regional cooperation. The regionalism has three main structural elements in its definition: geographical proximity, cultural resemblance, and cooperative attitudes among all the countries concerned. The Additional Protocols allow the IAEA to access to more detailed information and nuclear activities of a State party. The aspect that the Additional Protocols could increase the nuclear transparency will result in ultimately promoting the confidence among the regional nations concerned.

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

    Directory of Open Access Journals (Sweden)

    Sylvia A. Morelli

    2013-05-01

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

  1. Neuron cell positioning on polystyrene in culture by silver-negative ion implantation and region control of neural outgrowth

    International Nuclear Information System (INIS)

    Tsuji, Hiroshi; Sato, Hiroko; Baba, Takahiro; Ikemura, Shin'ichi; Gotoh, Yasuhito; Ishikawa, Junzo

    2000-01-01

    A new method to control the position of neuron cell attachment and extension region of neural outgrowth has been developed by using a pattering ion implantation with silver-negative ions into polystyrene dishes. This technique offers a promising method to form an artificially designed neural network in cell culture in vitro. Silver-negative ions were implanted into non-treated polystyrene dishes (NTPS) at conditions of 20 keV and 3x10 15 ions/cm 2 through a pattering mask, which had as many as 67 slits of 60 μm in width and 4 mm in length with a spacing of 60 μm. For cell culture in vitro, nerve cells of PC-12h (rat adrenal phechromocytoma) were used because they respond to a nerve growth factor (NGF). In the first 2 days in culture without NGF, we observed a selective cell attachment only to the ion-implanted region in patterning Ag - implanted polystyrene sample (p-Ag/NTPS). In another 2 days in culture with NGF, the nerve cells expanded neurites only over the ion-implanted region. For collagen-coated p-Ag/NTPS sample of which collagen was coated after the ion implantation (Collagen/p-Ag/NTPS), most nerve cells were also attached on the ion-implanted region. However, neurites expanded in both ion-implanted and unimplanted regions. The contact angle of NTPS decreased after the ion implantation from 86 deg. to 74 deg. . The region selectivity of neuron attachment and neurite extension is considered to be due to contact angle lowering by the ion implantation as radiation effect on the surface

  2. Neural correlates of socioeconomic status in the developing human brain.

    Science.gov (United States)

    Noble, Kimberly G; Houston, Suzanne M; Kan, Eric; Sowell, Elizabeth R

    2012-07-01

    Socioeconomic disparities in childhood are associated with remarkable differences in cognitive and socio-emotional development during a time when dramatic changes are occurring in the brain. Yet, the neurobiological pathways through which socioeconomic status (SES) shapes development remain poorly understood. Behavioral evidence suggests that language, memory, social-emotional processing, and cognitive control exhibit relatively large differences across SES. Here we investigated whether volumetric differences could be observed across SES in several neural regions that support these skills. In a sample of 60 socioeconomically diverse children, highly significant SES differences in regional brain volume were observed in the hippocampus and the amygdala. In addition, SES × age interactions were observed in the left superior temporal gyrus and left inferior frontal gyrus, suggesting increasing SES differences with age in these regions. These results were not explained by differences in gender, race or IQ. Likely mechanisms include differences in the home linguistic environment and exposure to stress, which may serve as targets for intervention at a time of high neural plasticity. © 2012 Blackwell Publishing Ltd.

  3. A Study on Regional Frequency Analysis using Artificial Neural Network - the Sumjin River Basin

    Science.gov (United States)

    Jeong, C.; Ahn, J.; Ahn, H.; Heo, J. H.

    2017-12-01

    Regional frequency analysis means to make up for shortcomings in the at-site frequency analysis which is about a lack of sample size through the regional concept. Regional rainfall quantile depends on the identification of hydrologically homogeneous regions, hence the regional classification based on hydrological homogeneous assumption is very important. For regional clustering about rainfall, multidimensional variables and factors related geographical features and meteorological figure are considered such as mean annual precipitation, number of days with precipitation in a year and average maximum daily precipitation in a month. Self-Organizing Feature Map method which is one of the artificial neural network algorithm in the unsupervised learning techniques solves N-dimensional and nonlinear problems and be shown results simply as a data visualization technique. In this study, for the Sumjin river basin in South Korea, cluster analysis was performed based on SOM method using high-dimensional geographical features and meteorological factor as input data. then, for the results, in order to evaluate the homogeneity of regions, the L-moment based discordancy and heterogeneity measures were used. Rainfall quantiles were estimated as the index flood method which is one of regional rainfall frequency analysis. Clustering analysis using SOM method and the consequential variation in rainfall quantile were analyzed. This research was supported by a grant(2017-MPSS31-001) from Supporting Technology Development Program for Disaster Management funded by Ministry of Public Safety and Security(MPSS) of the Korean government.

  4. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  5. Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

    Science.gov (United States)

    Xu, Long-Chun; Zhang, Gang; Zou, Yue; Zhang, Min-Feng; Zhang, Dong-Sheng; Ma, Hua; Zhao, Wen-Bo; Zhang, Guang-Yu

    2017-10-13

    The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

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

    Directory of Open Access Journals (Sweden)

    Tatia M.C. Lee

    2015-11-01

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

  7. A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

    Science.gov (United States)

    Cona, F; Zavaglia, M; Massimini, M; Rosanova, M; Ursino, M

    2011-08-01

    Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs. Copyright © 2011 Elsevier Inc. All rights reserved.

  8. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

    Science.gov (United States)

    Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  9. Multistability of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing; Cao, Jinde

    2015-11-01

    The problem of coexistence and dynamical behaviors of multiple equilibrium points is addressed for a class of memristive Cohen-Grossberg neural networks with non-monotonic piecewise linear activation functions and time-varying delays. By virtue of the fixed point theorem, nonsmooth analysis theory and other analytical tools, some sufficient conditions are established to guarantee that such n-dimensional memristive Cohen-Grossberg neural networks can have 5(n) equilibrium points, among which 3(n) equilibrium points are locally exponentially stable. It is shown that greater storage capacity can be achieved by neural networks with the non-monotonic activation functions introduced herein than the ones with Mexican-hat-type activation function. In addition, unlike most existing multistability results of neural networks with monotonic activation functions, those obtained 3(n) locally stable equilibrium points are located both in saturated regions and unsaturated regions. The theoretical findings are verified by an illustrative example with computer simulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Assessment of synchronous neural activities revealed by regional homogeneity in individuals with acute eye pain: a resting-state functional magnetic resonance imaging study

    Directory of Open Access Journals (Sweden)

    Tang L

    2018-04-01

    Full Text Available Li-Yuan Tang,1,* Hai-Jun Li,2,* Xin Huang,1 Jing Bao,1 Zubin Sethi,3 Lei Ye,1 Qing Yuan,1 Pei-Wen Zhu,1 Nan Jiang,1 Gui-Ping Gao,1 Yi Shao1 1Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China; 2Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China; 3The Department of Medicine, University of Miami, Coral Gables, FL, USA *These authors contributed equally to this work Objective: Previous neuroimaging studies have demonstrated that pain-related diseases are associated with brain function and anatomical abnormalities, whereas altered synchronous neural activity in acute eye pain (EP patients has not been investigated. The purpose of this study was to explore whether or not synchronous neural activity changes were measured with the regional homogeneity (ReHo method in acute EP patients.Methods: A total of 20 patients (15 males and 5 females with EP and 20 healthy controls (HCs consisting of 15 and 5 age-, sex-, and education-matched males and females, respectively, underwent resting-state functional magnetic resonance imaging. The ReHo method was applied to assess synchronous neural activity changes.Results: Compared with HCs, acute EP patients had significantly lower ReHo values in the left precentral/postcentral gyrus (Brodmann area [BA]3/4, right precentral/postcentral gyrus (BA3/4, and left middle frontal gyrus (BA6. In contrast, higher ReHo values in acute EP patients were observed in the left superior frontal gyrus (BA11, right inferior parietal lobule (BA39/40, and left precuneus (BA7. However, no relationship was found between the mean ReHo signal values of the different areas and clinical manifestations, which included both the duration and degree of pain in EP patients.Conclusion: Our study highlighted that acute EP patients showed altered synchronous neural activities in many brain regions, including somatosensory regions. These

  11. The neural basis of testable and non-testable beliefs.

    Directory of Open Access Journals (Sweden)

    Jonathon R Howlett

    Full Text Available Beliefs about the state of the world are an important influence on both normal behavior and psychopathology. However, understanding of the neural basis of belief processing remains incomplete, and several aspects of belief processing have only recently been explored. Specifically, different types of beliefs may involve fundamentally different inferential processes and thus recruit distinct brain regions. Additionally, neural processing of truth and falsity may differ from processing of certainty and uncertainty. The purpose of this study was to investigate the neural underpinnings of assessment of testable and non-testable propositions in terms of truth or falsity and the level of certainty in a belief. Functional magnetic resonance imaging (fMRI was used to study 14 adults while they rated propositions as true or false and also rated the level of certainty in their judgments. Each proposition was classified as testable or non-testable. Testable propositions activated the DLPFC and posterior cingulate cortex, while non-testable statements activated areas including inferior frontal gyrus, superior temporal gyrus, and an anterior region of the superior frontal gyrus. No areas were more active when a proposition was accepted, while the dorsal anterior cingulate was activated when a proposition was rejected. Regardless of whether a proposition was testable or not, certainty that the proposition was true or false activated a common network of regions including the medial prefrontal cortex, caudate, posterior cingulate, and a region of middle temporal gyrus near the temporo-parietal junction. Certainty in the truth or falsity of a non-testable proposition (a strong belief without empirical evidence activated the insula. The results suggest that different brain regions contribute to the assessment of propositions based on the type of content, while a common network may mediate the influence of beliefs on motivation and behavior based on the level of

  12. Regionally-specified second trimester fetal neural stem cells reveals differential neurogenic programming.

    Directory of Open Access Journals (Sweden)

    Yiping Fan

    Full Text Available Neural stem/progenitor cells (NSC have the potential for treatment of a wide range of neurological diseases such as Parkinson Disease and multiple sclerosis. Currently, NSC have been isolated only from hippocampus and subventricular zone (SVZ of the adult brain. It is not known whether NSC can be found in all parts of the developing mid-trimester central nervous system (CNS when the brain undergoes massive transformation and growth. Multipotent NSC from the mid-trimester cerebra, thalamus, SVZ, hippocampus, thalamus, cerebellum, brain stem and spinal cord can be derived and propagated as clonal neurospheres with increasing frequencies with increasing gestations. These NSC can undergo multi-lineage differentiation both in vitro and in vivo, and engraft in a developmental murine model. Regionally-derived NSC are phenotypically distinct, with hippocampal NSC having a significantly higher neurogenic potential (53.6% over other sources (range of 0%-27.5%, p<0.004. Whole genome expression analysis showed differential gene expression between these regionally-derived NSC, which involved the Notch, epidermal growth factor as well as interleukin pathways. We have shown the presence of phenotypically-distinct regionally-derived NSC from the mid-trimester CNS, which may reflect the ontological differences occurring within the CNS. Aside from informing on the role of such cells during fetal growth, they may be useful for different cellular therapy applications.

  13. Neural correlates of HIV risk feelings.

    Science.gov (United States)

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

    2015-04-01

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

  14. Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network

    International Nuclear Information System (INIS)

    Xu, Bo; Dan, Han-Cheng; Li, Liang

    2017-01-01

    Highlights: • Pavement temperature prediction model is presented with improved BP neural network. • Dynamic and static methods are presented to predict pavement temperature. • Pavement temperature can be excellently predicted in next 3 h. - Abstract: Ice cover on pavement threatens traffic safety, and pavement temperature is the main factor used to determine whether the wet pavement is icy or not. In this paper, a temperature prediction model of the pavement in winter is established by introducing an improved Back Propagation (BP) neural network model. Before the application of the BP neural network model, many efforts were made to eliminate chaos and determine the regularity of temperature on the pavement surface (e.g., analyze the regularity of diurnal and monthly variations of pavement temperature). New dynamic and static prediction methods are presented by improving the algorithms to intelligently overcome the prediction inaccuracy at the change point of daily temperature. Furthermore, some scenarios have been compared for different dates and road sections to verify the reliability of the prediction model. According to the analysis results, the daily pavement temperatures can be accurately predicted for the next 3 h from the time of prediction by combining the dynamic and static prediction methods. The presented method in this paper can provide technical references for temperature prediction of the pavement and the development of an early-warning system for icy pavements in cold regions.

  15. Metabolic neural mapping in neonatal rats

    International Nuclear Information System (INIS)

    DiRocco, R.J.; Hall, W.G.

    1981-01-01

    Functional neural mapping by 14 C-deoxyglucose autoradiography in adult rats has shown that increases in neural metabolic rate that are coupled to increased neurophysiological activity are more evident in axon terminals and dendrites than neuron cell bodies. Regions containing architectonically well-defined concentrations of terminals and dendrites (neuropil) have high metabolic rates when the neuropil is physiologically active. In neonatal rats, however, we find that regions containing well-defined groupings of neuron cell bodies have high metabolic rates in 14 C-deoxyglucose autoradiograms. The striking difference between the morphological appearance of 14 C-deoxyglucose autoradiograms obtained from neonatal and adult rats is probably related to developmental changes in morphometric features of differentiating neurons, as well as associated changes in type and locus of neural work performed

  16. Chondroitin sulfate effects on neural stem cell differentiation.

    Science.gov (United States)

    Canning, David R; Brelsford, Natalie R; Lovett, Neil W

    2016-01-01

    We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

  17. Inter-progenitor pool wiring: An evolutionarily conserved strategy that expands neural circuit diversity.

    Science.gov (United States)

    Suzuki, Takumi; Sato, Makoto

    2017-11-15

    Diversification of neuronal types is key to establishing functional variations in neural circuits. The first critical step to generate neuronal diversity is to organize the compartmental domains of developing brains into spatially distinct neural progenitor pools. Neural progenitors in each pool then generate a unique set of diverse neurons through specific spatiotemporal specification processes. In this review article, we focus on an additional mechanism, 'inter-progenitor pool wiring', that further expands the diversity of neural circuits. After diverse types of neurons are generated in one progenitor pool, a fraction of these neurons start migrating toward a remote brain region containing neurons that originate from another progenitor pool. Finally, neurons of different origins are intermingled and eventually form complex but precise neural circuits. The developing cerebral cortex of mammalian brains is one of the best examples of inter-progenitor pool wiring. However, Drosophila visual system development has revealed similar mechanisms in invertebrate brains, suggesting that inter-progenitor pool wiring is an evolutionarily conserved strategy that expands neural circuit diversity. Here, we will discuss how inter-progenitor pool wiring is accomplished in mammalian and fly brain systems. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Hierarchical modeling of molecular energies using a deep neural network

    Science.gov (United States)

    Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

    2018-06-01

    We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

  19. Altered neural reward and loss processing and prediction error signalling in depression

    Science.gov (United States)

    Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela

    2015-01-01

    Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763

  20. Neural Correlates of Task Cost for Stance Control with an Additional Motor Task: Phase-Locked Electroencephalogram Responses

    Science.gov (United States)

    Hwang, Ing-Shiou; Huang, Cheng-Ya

    2016-01-01

    With appropriate reallocation of central resources, the ability to maintain an erect posture is not necessarily degraded by a concurrent motor task. This study investigated the neural control of a particular postural-suprapostural procedure involving brain mechanisms to solve crosstalk between posture and motor subtasks. Participants completed a single posture task and a dual-task while concurrently conducting force-matching and maintaining a tilted stabilometer stance at a target angle. Stabilometer movements and event-related potentials (ERPs) were recorded. The added force-matching task increased the irregularity of postural response rather than the size of postural response prior to force-matching. In addition, the added force-matching task during stabilometer stance led to marked topographic ERP modulation, with greater P2 positivity in the frontal and sensorimotor-parietal areas of the N1-P2 transitional phase and in the sensorimotor-parietal area of the late P2 phase. The time-frequency distribution of the ERP primary principal component revealed that the dual-task condition manifested more pronounced delta (1–4 Hz) and beta (13–35 Hz) synchronizations but suppressed theta activity (4–8 Hz) before force-matching. The dual-task condition also manifested coherent fronto-parietal delta activity in the P2 period. In addition to a decrease in postural regularity, this study reveals spatio-temporal and temporal-spectral reorganizations of ERPs in the fronto-sensorimotor-parietal network due to the added suprapostural motor task. For a particular set of postural-suprapostural task, the behavior and neural data suggest a facilitatory role of autonomous postural response and central resource expansion with increasing interregional interactions for task-shift and planning the motor-suprapostural task. PMID:27010634

  1. Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

    Science.gov (United States)

    Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

    2017-11-15

    The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

  3. Neural regions supporting lexical processing of objects and actions: A case series analysis

    Directory of Open Access Journals (Sweden)

    Bonnie L Breining

    2014-04-01

    Full Text Available Introduction. Linking semantic representations to lexical items is an important cognitive process for both producing and comprehending language. Past research has suggested that the bilateral anterior temporal lobes are critical for this process (e.g. Patterson, Nestor, & Rogers, 2007. However, the majority of studies focused on object concepts alone, ignoring actions. The few that considered actions suggest that the temporal poles are not critical for their processing (e.g. Kemmerer et al., 2010. In this case series, we investigated the neural substrates of linking object and action concepts to lexical labels by correlating the volume of defined regions of interest with behavioral performance on picture-word verification and picture naming tasks of individuals with primary progressive aphasia (PPA. PPA is a neurodegenerative condition with heterogeneous neuropathological causes, characterized by increasing language deficits for at least two years in the face of relatively intact cognitive function in other domains (Gorno-Tempini et al., 2011. This population displays appropriate heterogeneity of performance and focal atrophy for investigating the neural substrates involved in lexical semantic processing of objects and actions. Method. Twenty-one individuals with PPA participated in behavioral assessment within six months of high resolution anatomical MRI scans. Behavioral assessments consisted of four tasks: picture-word verification and picture naming of objects and actions. Performance on these assessments was correlated with brain volume measured using atlas-based analysis in twenty regions of interest that are commonly atrophied in PPA and implicated in language processing. Results. Impaired performance for all four tasks significantly correlated with atrophy in the right superior temporal pole, left anterior middle temporal gyrus, and left fusiform gyrus. No regions were identified in which volume correlated with performance for both

  4. Activity patterns of cultured neural networks on micro electrode arrays

    NARCIS (Netherlands)

    Rutten, Wim; van Pelt, J.

    2001-01-01

    A hybrid neuro-electronic interface is a cell-cultured micro electrode array, acting as a neural information transducer for stimulation and/or recording of neural activity in the brain or the spinal cord (ventral motor region or dorsal sensory region). It consists of an array of micro electrodes on

  5. Adolescents' emotional competence is associated with parents' neural sensitivity to emotions.

    Science.gov (United States)

    Telzer, Eva H; Qu, Yang; Goldenberg, Diane; Fuligni, Andrew J; Galván, Adriana; Lieberman, Matthew D

    2014-01-01

    An essential component of youths' successful development is learning to appropriately respond to emotions, including the ability to recognize, identify, and describe one's feelings. Such emotional competence is thought to arise through the parent-child relationship. Yet, the mechanisms by which parents transmit emotional competence to their children are difficult to measure because they are often implicit, idiosyncratic, and not easily articulated by parents or children. In the current study, we used a multifaceted approach that went beyond self-report measures and examined whether parental neural sensitivity to emotions predicted their child's emotional competence. Twenty-two adolescent-parent dyads completed an fMRI scan during which they labeled the emotional expressions of negatively valenced faces. Results indicate that parents who recruited the amygdala, VLPFC, and brain regions involved in mentalizing (i.e., inferring others' emotional states) had adolescent children with greater emotional competence. These results held after controlling for parents' self-reports of emotional expressivity and adolescents' self-reports of the warmth and support of their parent relationships. In addition, adolescents recruited neural regions involved in mentalizing during affect labeling, which significantly mediated the associated between parental neural sensitivity and adolescents' emotional competence, suggesting that youth are modeling or referencing their parents' emotional profiles, thereby contributing to better emotional competence.

  6. Adolescents’ emotional competence is associated with parents’ neural sensitivity to emotions

    Directory of Open Access Journals (Sweden)

    Eva H Telzer

    2014-07-01

    Full Text Available An essential component of youths’ successful development is learning to appropriately respond to emotions, including the ability to recognize, identify, and describe one’s feelings. Such emotional competence is thought to arise through the parent-child relationship. Yet, the mechanisms by which parents transmit emotional competence to their children are difficult to measure because they are often implicit, idiosyncratic, and not easily articulated by parents or children. In the current study, we used a multifaceted approach that went beyond self-report measures and examined whether parental neural sensitivity to emotions predicted their child’s emotional competence. Twenty-two adolescent-parent dyads completed an fMRI scan during which they labeled the emotional expressions of negatively valenced faces. Results indicate that parents who recruited the amygdala, VLPFC, and brain regions involved in mentalizing (i.e., inferring others’ emotional states had adolescent children with greater emotional competence. These results held after controlling for parents’ self-reports of emotional expressivity and adolescents’ self-reports of the warmth and support of their parent relationships. In addition, adolescents recruited neural regions involved in mentalizing during affect labeling, which significantly mediated the associated between parental neural sensitivity and adolescents’ emotional competence, suggesting that youth are modeling or referencing their parents’ emotional profiles, thereby contributing to better emotional competence.

  7. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  8. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  9. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  10. Computerized detection of multiple sclerosis candidate regions based on a level set method using an artificial neural network

    International Nuclear Information System (INIS)

    Kuwazuru, Junpei; Magome, Taiki; Arimura, Hidetaka; Yamashita, Yasuo; Oki, Masafumi; Toyofuku, Fukai; Kakeda, Shingo; Yamamoto, Daisuke

    2010-01-01

    Yamamoto et al. developed the system for computer-aided detection of multiple sclerosis (MS) candidate regions. In a level set method in their proposed method, they employed the constant threshold value for the edge indicator function related to a speed function of the level set method. However, it would be appropriate to adjust the threshold value to each MS candidate region, because the edge magnitudes in MS candidates differ from each other. Our purpose of this study was to develop a computerized detection of MS candidate regions in MR images based on a level set method using an artificial neural network (ANN). To adjust the threshold value for the edge indicator function in the level set method to each true positive (TP) and false positive (FP) region, we constructed the ANN. The ANN could provide the suitable threshold value for each candidate region in the proposed level set method so that TP regions can be segmented and FP regions can be removed. Our proposed method detected MS regions at a sensitivity of 82.1% with 0.204 FPs per slice and similarity index of MS candidate regions was 0.717 on average. (author)

  11. Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey by artificial neural networks

    Directory of Open Access Journals (Sweden)

    M. Ercanoglu

    2005-01-01

    Full Text Available Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index were used. To obtain maps of these parameters, Digital Elevation Model (DEM and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi. Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij value. Much of the areal extents of the landslides (87.2% were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.

  12. Discrete Neural Signatures of Basic Emotions.

    Science.gov (United States)

    Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

    2016-06-01

    Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. The murine homeobox gene Msx-3 shows highly restricted expression in the developing neural tube.

    Science.gov (United States)

    Shimeld, S M; McKay, I J; Sharpe, P T

    1996-04-01

    The mouse homeobox-genes Msx-1 and Msx-2 are expressed in several areas of the developing embryo, including the neural tube, neural crest, facial processes and limb buds. Here we report the characterisation of a third mouse Msx gene, which we designate Msx-3. The embryonic expression of Msx-3 was found to differ from that of Msx-1 and -2 in that it was confined to the dorsal neural tube. In embryos with 5-8 somites a segmental pattern of expression was observed in the hindbrain, with rhombomeres 3 and 5 lacking Msx-3 while other rhombomeres expressed Msx-3. This pattern was transient, however, such that in embryos with 18 or more somites expression was continuous throughout the dorsal hindbrain and anterior dorsal spinal cord. Differentiation of dorsal cell types in the neural tube can be induced by addition of members of the Tgf-beta family. Additionally, Msx-1 and -2 have been shown to be activated by addition of the Tgf-beta family member Bmp-4. To determine if Bmp-4 could activate Msx-3, we incubated embryonic hindbrain explants with exogenous Bmp-4. The dorsal expression of Msx-3 was seen to expand into more ventral regions of the neurectoderm in Bmp-4-treated cultures, implying that Bmp-4 may be able to mimic an in vivo signal that induces Msx-3.

  14. Neural network for nonsmooth pseudoconvex optimization with general convex constraints.

    Science.gov (United States)

    Bian, Wei; Ma, Litao; Qin, Sitian; Xue, Xiaoping

    2018-05-01

    In this paper, a one-layer recurrent neural network is proposed for solving a class of nonsmooth, pseudoconvex optimization problems with general convex constraints. Based on the smoothing method, we construct a new regularization function, which does not depend on any information of the feasible region. Thanks to the special structure of the regularization function, we prove the global existence, uniqueness and "slow solution" character of the state of the proposed neural network. Moreover, the state solution of the proposed network is proved to be convergent to the feasible region in finite time and to the optimal solution set of the related optimization problem subsequently. In particular, the convergence of the state to an exact optimal solution is also considered in this paper. Numerical examples with simulation results are given to show the efficiency and good characteristics of the proposed network. In addition, some preliminary theoretical analysis and application of the proposed network for a wider class of dynamic portfolio optimization are included. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Rhesus monkey neural stem cell transplantation promotes neural regeneration in rats with hippocampal lesions

    Directory of Open Access Journals (Sweden)

    Li-juan Ye

    2016-01-01

    Full Text Available Rhesus monkey neural stem cells are capable of differentiating into neurons and glial cells. Therefore, neural stem cell transplantation can be used to promote functional recovery of the nervous system. Rhesus monkey neural stem cells (1 × 105 cells/μL were injected into bilateral hippocampi of rats with hippocampal lesions. Confocal laser scanning microscopy demonstrated that green fluorescent protein-labeled transplanted cells survived and grew well. Transplanted cells were detected at the lesion site, but also in the nerve fiber-rich region of the cerebral cortex and corpus callosum. Some transplanted cells differentiated into neurons and glial cells clustering along the ventricular wall, and integrated into the recipient brain. Behavioral tests revealed that spatial learning and memory ability improved, indicating that rhesus monkey neural stem cells noticeably improve spatial learning and memory abilities in rats with hippocampal lesions.

  16. Neural reactivation links unconscious thought to decision-making performance.

    Science.gov (United States)

    Creswell, John David; Bursley, James K; Satpute, Ajay B

    2013-12-01

    Brief periods of unconscious thought (UT) have been shown to improve decision making compared with making an immediate decision (ID). We reveal a neural mechanism for UT in decision making using blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Participants (N = 33) encoded information on a set of consumer products (e.g. 48 attributes describing four different cars), and we manipulated whether participants (i) consciously thought about this information (conscious thought), (ii) completed a difficult 2-back working memory task (UT) or (iii) made an immediate decision about the consumer products (ID) in a within-subjects blocked design. To differentiate UT neural activity from 2-back working memory neural activity, participants completed an independent 2-back task and this neural activity was subtracted from neural activity occurring during the UT 2-back task. Consistent with a neural reactivation account, we found that the same regions activated during the encoding of complex decision information (right dorsolateral prefrontal cortex and left intermediate visual cortex) continued to be activated during a subsequent 2-min UT period. Moreover, neural reactivation in these regions was predictive of subsequent behavioral decision-making performance after the UT period. These results provide initial evidence for post-encoding unconscious neural reactivation in facilitating decision making.

  17. Separate neural mechanisms underlie choices and strategic preferences in risky decision making.

    Science.gov (United States)

    Venkatraman, Vinod; Payne, John W; Bettman, James R; Luce, Mary Frances; Huettel, Scott A

    2009-05-28

    Adaptive decision making in real-world contexts often relies on strategic simplifications of decision problems. Yet, the neural mechanisms that shape these strategies and their implementation remain largely unknown. Using an economic decision-making task, we dissociate brain regions that predict specific choices from those predicting an individual's preferred strategy. Choices that maximized gains or minimized losses were predicted by functional magnetic resonance imaging activation in ventromedial prefrontal cortex or anterior insula, respectively. However, choices that followed a simplifying strategy (i.e., attending to overall probability of winning) were associated with activation in parietal and lateral prefrontal cortices. Dorsomedial prefrontal cortex, through differential functional connectivity with parietal and insular cortex, predicted individual variability in strategic preferences. Finally, we demonstrate that robust decision strategies follow from neural sensitivity to rewards. We conclude that decision making reflects more than compensatory interaction of choice-related regions; in addition, specific brain systems potentiate choices depending on strategies, traits, and context.

  18. miR-137 forms a regulatory loop with nuclear receptor TLX and LSD1 in neural stem cells.

    Science.gov (United States)

    Sun, GuoQiang; Ye, Peng; Murai, Kiyohito; Lang, Ming-Fei; Li, Shengxiu; Zhang, Heying; Li, Wendong; Fu, Chelsea; Yin, Jason; Wang, Allen; Ma, Xiaoxiao; Shi, Yanhong

    2011-11-08

    miR-137 is a brain-enriched microRNA. Its role in neural development remains unknown. Here we show that miR-137 has an essential role in controlling embryonic neural stem cell fate determination. miR-137 negatively regulates cell proliferation and accelerates neural differentiation of embryonic neural stem cells. In addition, we show that the histone lysine-specific demethylase 1 (LSD1), a transcriptional co-repressor of nuclear receptor TLX, is a downstream target of miR-137. In utero electroporation of miR-137 in embryonic mouse brains led to premature differentiation and outward migration of the transfected cells. Introducing a LSD1 expression vector lacking the miR-137 recognition site rescued miR-137-induced precocious differentiation. Furthermore, we demonstrate that TLX, an essential regulator of neural stem cell self-renewal, represses the expression of miR-137 by recruiting LSD1 to the genomic regions of miR-137. Thus, miR-137 forms a feedback regulatory loop with TLX and LSD1 to control the dynamics between neural stem cell proliferation and differentiation during neural development.

  19. Empathy and aversion: the neural signature of mentalizing in Tourette syndrome.

    Science.gov (United States)

    Eddy, C M; Cavanna, A E; Hansen, P C

    2017-02-01

    Previous studies suggest that adults with Tourette syndrome (TS) can respond unconventionally on tasks involving social cognition. We therefore hypothesized that these patients would exhibit different neural responses to healthy controls in response to emotionally salient expressions of human eyes. Twenty-five adults with TS and 25 matched healthy controls were scanned using fMRI during the standard version of the Reading the Mind in the Eyes Task which requires mental state judgements, and a novel comparison version requiring judgements about age. During prompted mental state recognition, greater activity was apparent in TS within left orbitofrontal cortex, posterior cingulate, right amygdala and right temporo-parietal junction (TPJ), while reduced activity was apparent in regions including left inferior parietal cortex. Age judgement elicited greater activity in TS within precuneus, medial prefrontal and temporal regions involved in mentalizing. The interaction between group and task revealed differential activity in areas including right inferior frontal gyrus. Task-related activity in the TPJ covaried with global ratings of the urge to tic. While recognizing mental states, adults with TS exhibit greater activity than controls in brain areas involved in the processing of negative emotion, in addition to reduced activity in regions associated with the attribution of agency. In addition, increased recruitment of areas involved in mental state reasoning is apparent in these patients when mentalizing is not a task requirement. Our findings highlight differential neural reactivity in response to emotive social cues in TS, which may interact with tic expression.

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

    Science.gov (United States)

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

    2018-06-01

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

  1. Normalization as a canonical neural computation

    Science.gov (United States)

    Carandini, Matteo; Heeger, David J.

    2012-01-01

    There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

  2. A two-layer recurrent neural network for nonsmooth convex optimization problems.

    Science.gov (United States)

    Qin, Sitian; Xue, Xiaoping

    2015-06-01

    In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.

  3. Forecasting solar proton event with artificial neural network

    Science.gov (United States)

    Gong, J.; Wang, J.; Xue, B.; Liu, S.; Zou, Z.

    Solar proton event (SPE), relatively rare but popular in solar maximum, can bring hazard situation to spacecraft. As a special event, SPE always accompanies flare, which is also called proton flare. To produce such an eruptive event, large amount energy must be accumulated within the active region. So we can investigate the character of the active region and its evolving trend, together with other such as cm radio emission and soft X-ray background to evaluate the potential of SEP in chosen area. In order to summarize the omen of SPEs in the active regions behind the observed parameters, we employed AI technology. Full connecting neural network was chosen to fulfil this job. After constructing the network, we train it with 13 parameters that was able to exhibit the character of active regions and their evolution trend. More than 80 sets of event parameter were defined to teach the neural network to identify whether an active region was potential of SPE. Then we test this model with a data base consisting SPE and non-SPE cases that was not used to train the neural network. The result showed that 75% of the choice by the model was right.

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

    Directory of Open Access Journals (Sweden)

    Lucy Alba-Ferrara

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

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

    Science.gov (United States)

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

    2014-02-01

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

  6. Distinct pathways of neural coupling for different basic emotions.

    Science.gov (United States)

    Tettamanti, Marco; Rognoni, Elena; Cafiero, Riccardo; Costa, Tommaso; Galati, Dario; Perani, Daniela

    2012-01-16

    Emotions are complex events recruiting distributed cortical and subcortical cerebral structures, where the functional integration dynamics within the involved neural circuits in relation to the nature of the different emotions are still unknown. Using fMRI, we measured the neural responses elicited by films representing basic emotions (fear, disgust, sadness, happiness). The amygdala and the associative cortex were conjointly activated by all basic emotions. Furthermore, distinct arrays of cortical and subcortical brain regions were additionally activated by each emotion, with the exception of sadness. Such findings informed the definition of three effective connectivity models, testing for the functional integration of visual cortex and amygdala, as regions processing all emotions, with domain-specific regions, namely: i) for fear, the frontoparietal system involved in preparing adaptive motor responses; ii) for disgust, the somatosensory system, reflecting protective responses against contaminating stimuli; iii) for happiness: medial prefrontal and temporoparietal cortices involved in understanding joyful interactions. Consistently with these domain-specific models, the results of the effective connectivity analysis indicate that the amygdala is involved in distinct functional integration effects with cortical networks processing sensorimotor, somatosensory, or cognitive aspects of basic emotions. The resulting effective connectivity networks may serve to regulate motor and cognitive behavior based on the quality of the induced emotional experience. Copyright © 2011. Published by Elsevier Inc.

  7. Neural Markers in Pediatric Bipolar Disorder and Familial Risk for Bipolar Disorder.

    Science.gov (United States)

    Wiggins, Jillian Lee; Brotman, Melissa A; Adleman, Nancy E; Kim, Pilyoung; Wambach, Caroline G; Reynolds, Richard C; Chen, Gang; Towbin, Kenneth; Pine, Daniel S; Leibenluft, Ellen

    2017-01-01

    Bipolar disorder (BD) is highly heritable. Neuroimaging studies comparing unaffected youth at high familial risk for BD (i.e., those with a first-degree relative with the disorder; termed "high-risk" [HR]) to "low-risk" (LR) youth (i.e., those without a first-degree relative with BD) and to patients with BD may help identify potential brain-based markers associated with risk (i.e., regions where HR+BD≠LR), resilience (HR≠BD+LR), or illness (BD≠HR+LR). During functional magnetic resonance imaging (fMRI), 99 youths (i.e., adolescents and young adults) aged 9.8 to 24.8 years (36 BD, 22 HR, 41 LR) performed a task probing face emotion labeling, previously shown to be impaired behaviorally in youth with BD and HR youth. We found three patterns of results. Candidate risk endophenotypes (i.e., where BD and HR shared deficits) included dysfunction in higher-order face processing regions (e.g., middle temporal gyrus, dorsolateral prefrontal cortex). Candidate resilience markers and disorder sequelae (where HR and BD, respectively, show unique alterations relative to the other two groups) included different patterns of neural responses across other regions mediating face processing (e.g., fusiform), executive function (e.g., inferior frontal gyrus), and social cognition (e.g., default network, superior temporal sulcus, temporo-parietal junction). If replicated in longitudinal studies and with additional populations, neural patterns suggesting risk endophenotypes could be used to identify individuals at risk for BD who may benefit from prevention measures. Moreover, information about risk and resilience markers could be used to develop novel treatments that recruit neural markers of resilience and attenuate neural patterns associated with risk. Clinical trial registration information-Studies of Brain Function and Course of Illness in Pediatric Bipolar Disorder and Child and Adolescent Bipolar Disorder Brain Imaging and Treatment Study; http://clinicaltrials.gov/; NCT

  8. The neural basis of monitoring goal progress

    Directory of Open Access Journals (Sweden)

    Yael eBenn

    2014-09-01

    Full Text Available The neural basis of progress monitoring has received relatively little attention compared to other sub-processes that are involved in goal directed behavior such as motor control and response inhibition. Studies of error-monitoring have identified the dorsal anterior cingulate cortex (dACC as a structure that is sensitive to conflict detection, and triggers corrective action. However, monitoring goal progress involves monitoring correct as well as erroneous events over a period of time. In the present research, 20 healthy participants underwent fMRI while playing a game that involved monitoring progress towards either a numerical or a visuo-spatial target. The findings confirmed the role of the dACC in detecting situations in which the current state may conflict with the desired state, but also revealed activations in the frontal and parietal regions, pointing to the involvement of processes such as attention and working memory in monitoring progress over time. In addition, activation of the cuneus was associated with monitoring progress towards a specific target presented in the visual modality. This is the first time that activation in this region has been linked to higher-order processing of goal-relevant information, rather than low-level anticipation of visual stimuli. Taken together, these findings identify the neural substrates involved in monitoring progress over time, and how these extend beyond activations observed in conflict and error monitoring.

  9. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

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

  10. A neural network approach to the study of internal energy flow in molecular systems

    International Nuclear Information System (INIS)

    Sumpter, B.G.; Getino, C.; Noid, D.W.

    1992-01-01

    Neural networks are used to develop a new technique for efficient analysis of data obtained from molecular-dynamics calculations and is applied to the study of mode energy flow in molecular systems. The methodology is based on teaching an appropriate neural network the relationship between phase-space points along a classical trajectory and mode energies for stretch, bend, and torsion vibrations. Results are discussed for reactive and nonreactive classical trajectories of hydrogen peroxide (H 2 O 2 ) on a semiempirical potential-energy surface. The neural-network approach is shown to produce reasonably accurate values for the mode energies, with average errors between 1% and 12%, and is applicable to any region within the 24-dimensional phase space of H 2 O 2 . In addition, the generic knowledge learned by the neural network allows calculations to be made for other molecular systems. Results are discussed for a series of tetratomic molecules: H 2 X 2 , X=C, N, O, Si, S, or Se, and preliminary results are given for energy flow predictions in macromolecules

  11. Risk factors of neural tube defects: A reality of Batna region in Algeria

    Directory of Open Access Journals (Sweden)

    Romyla Bourouba

    2018-07-01

    Full Text Available Background: Neural tube defects (NTDs are severe birth defects, with genetic and/or environmental risk factors. Aim: The objective of this study was to analyze data on NTDs cases at the Batna Maternity Hospital and to investigate some environmental and two genetic risk factors suspected in the etiology of NTDs. Subjects and methods: This study was conducted on 82 healthy participants and 48 mothers with an NTD child. Peripheral blood samples were collected, in EDTA tubes and frozen at −20 °C until DNA extraction by conventional method. Genetic analysis of methylene tetrahydrofolate reductase C677T polymorphism was determined by real time PCR, while cystathionine-beta-synthase 844 insertion was investigated by traditional PCR. Chi-square analyses were used to evaluate differences in the distribution of data. The odds-ratio was also calculated. A P-value less than 0.05 were significant. Results: The incidence of NTD in Batna region was 1.58 per 1000 births. The rate of NTD was significantly higher in females than males, highest affected NTD newborn’s was observed in mothers aged between 25 and 29 years and the consanguinity among all NTD cases was 30%. Data showed no significant association of NTDs with personal education, obesity, diabetes, but regarding folic acid consumption, about 86% of NTD’s mothers in our region didn’t take pre-conceptional supplementation with this vitamin .Genetic factors results didn't show a significant association of NTDs with specific mutations of the variant C677T MTHFR, and no gene-gene interaction of CBS insertion and C677T polymorphism was found, despite a significant difference in heterozygote frequency of CBS 844ins68 genotype between NTD’s mothers and controls, OR: 2.85(1.18–6.88. Conclusion: NTD represents a real public health problem in Batna, Algeria. Various genetic and/or nutritional factors are implicated, although the mechanism is not clear. We suggest that further research should continue

  12. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

  13. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

  14. Direct adaptive control using feedforward neural networks

    OpenAIRE

    Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

    2003-01-01

    ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...

  15. Extracting Neural Oscillation Signatures of Laser-Induced Nociception in Pain-Related Regions in Rats

    Directory of Open Access Journals (Sweden)

    Xuezhu Li

    2017-10-01

    Full Text Available Previous studies have shown that multiple brain regions are involved in pain perception and pain-related neural processes by forming a functionally connected pain network. It is still unclear how these pain-related brain areas actively work together to generate the experience of pain. To get a better insight into the pain network, we implanted electrodes in four pain-related areas of rats including the anterior cingulate cortex (ACC, orbitofrontal cortex (OFC, primary somatosensory cortex (S1 and periaqueductal gray (PAG. We analyzed the pattern of local field potential (LFP oscillations under noxious laser stimulations and innoxious laser stimulations. A high-dimensional feature matrix was built based on the LFP characters for both experimental conditions. Generalized linear models (GLMs were trained to classify recorded LFPs under noxious vs. innoxious condition. We found a general power decrease in α and β bands and power increase in γ band in the recorded areas under noxious condition. After noxious laser stimulation, there was a consistent change in LFP power and correlation in all four brain areas among all 13 rats. With GLM classifiers, noxious laser trials were distinguished from innoxious laser trials with high accuracy (86% using high-dimensional LFP features. This work provides a basis for further research to examine which aspects (e.g., sensory, motor or affective processes of noxious stimulation should drive distinct neural activity across the pain network.

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

    Science.gov (United States)

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

    2012-01-01

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

  17. Wnt/Yes-Associated Protein Interactions During Neural Tissue Patterning of Human Induced Pluripotent Stem Cells.

    Science.gov (United States)

    Bejoy, Julie; Song, Liqing; Zhou, Yi; Li, Yan

    2018-04-01

    Human induced pluripotent stem cells (hiPSCs) have special ability to self-assemble into neural spheroids or mini-brain-like structures. During the self-assembly process, Wnt signaling plays an important role in regional patterning and establishing positional identity of hiPSC-derived neural progenitors. Recently, the role of Wnt signaling in regulating Yes-associated protein (YAP) expression (nuclear or cytoplasmic), the pivotal regulator during organ growth and tissue generation, has attracted increasing interests. However, the interactions between Wnt and YAP expression for neural lineage commitment of hiPSCs remain poorly explored. The objective of this study is to investigate the effects of Wnt signaling and YAP expression on the cellular population in three-dimensional (3D) neural spheroids derived from hiPSCs. In this study, Wnt signaling was activated using CHIR99021 for 3D neural spheroids derived from human iPSK3 cells through embryoid body formation. Our results indicate that Wnt activation induces nuclear localization of YAP and upregulates the expression of HOXB4, the marker for hindbrain/spinal cord. By contrast, the cells exhibit more rostral forebrain neural identity (expression of TBR1) without Wnt activation. Cytochalasin D was then used to induce cytoplasmic YAP and the results showed the decreased HOXB4 expression. In addition, the incorporation of microparticles in the neural spheroids was investigated for the perturbation of neural patterning. This study may indicate the bidirectional interactions of Wnt signaling and YAP expression during neural tissue patterning, which have the significance in neurological disease modeling, drug screening, and neural tissue regeneration.

  18. A Recurrent Neural Network for Nonlinear Fractional Programming

    Directory of Open Access Journals (Sweden)

    Quan-Ju Zhang

    2012-01-01

    Full Text Available This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.

  19. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  20. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  1. Neural Correlates of Attentional Flexibility during Approach and Avoidance Motivation

    Science.gov (United States)

    Calcott, Rebecca D.; Berkman, Elliot T.

    2015-01-01

    Dynamic, momentary approach or avoidance motivational states have downstream effects on eventual goal success and overall well being, but there is still uncertainty about how those states affect the proximal neurocognitive processes (e.g., attention) that mediate the longer-term effects. Attentional flexibility, or the ability to switch between different attentional foci, is one such neurocognitive process that influences outcomes in the long run. The present study examined how approach and avoidance motivational states affect the neural processes involved in attentional flexibility using fMRI with the aim of determining whether flexibility operates via different neural mechanisms under these different states. Attentional flexibility was operationalized as subjects’ ability to switch between global and local stimulus features. In addition to subjects’ motivational state, the task context was manipulated by varying the ratio of global to local trials in a block in light of recent findings about the moderating role of context on motivation-related differences in attentional flexibility. The neural processes involved in attentional flexibility differ under approach versus avoidance states. First, differences in the preparatory activity in key brain regions suggested that subjects’ preparedness to switch was influenced by motivational state (anterior insula) and the interaction between motivation and context (superior temporal gyrus, inferior parietal lobule). Additionally, we observed motivation-related differences the anterior cingulate cortex during switching. These results provide initial evidence that motivation-induced behavioral changes may arise via different mechanisms in approach versus avoidance motivational states. PMID:26000735

  2. The neural correlates of gist-based true and false recognition

    Science.gov (United States)

    Gutchess, Angela H.; Schacter, Daniel L.

    2012-01-01

    When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

  3. Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loids Pollution Based on Kriging Interpolation and BP Neural Network

    Directory of Open Access Journals (Sweden)

    Zhenyi Jia

    2017-12-01

    Full Text Available Soil pollution by metal(loids resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As and cadmium (Cd pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loids in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid pollution.

  4. Estimation of neural energy in microelectrode signals

    Science.gov (United States)

    Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

    2004-09-01

    We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

  5. Neural codes of seeing architectural styles

    OpenAIRE

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

    2017-01-01

    Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people′s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding sugges...

  6. Neural tube closure depends on expression of Grainyhead-like 3 in multiple tissues.

    Science.gov (United States)

    De Castro, Sandra C P; Hirst, Caroline S; Savery, Dawn; Rolo, Ana; Lickert, Heiko; Andersen, Bogi; Copp, Andrew J; Greene, Nicholas D E

    2018-03-15

    Failure of neural tube closure leads to neural tube defects (NTDs), common congenital abnormalities in humans. Among the genes whose loss of function causes NTDs in mice, Grainyhead-like3 (Grhl3) is essential for spinal neural tube closure, with null mutants exhibiting fully penetrant spina bifida. During spinal neurulation Grhl3 is initially expressed in the surface (non-neural) ectoderm, subsequently in the neuroepithelial component of the neural folds and at the node-streak border, and finally in the hindgut endoderm. Here, we show that endoderm-specific knockout of Grhl3 causes late-arising spinal NTDs, preceded by increased ventral curvature of the caudal region which was shown previously to suppress closure of the spinal neural folds. This finding supports the hypothesis that diminished Grhl3 expression in the hindgut is the cause of spinal NTDs in the curly tail, carrying a hypomorphic Grhl3 allele. Complete loss of Grhl3 function produces a more severe phenotype in which closure fails earlier in neurulation, before the stage of onset of expression in the hindgut of wild-type embryos. This implicates additional tissues and NTD mechanisms in Grhl3 null embryos. Conditional knockout of Grhl3 in the neural plate and node-streak border has minimal effect on closure, suggesting that abnormal function of surface ectoderm, where Grhl3 transcripts are first detected, is primarily responsible for early failure of spinal neurulation in Grhl3 null embryos. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  7. Common neural substrates for visual working memory and attention.

    Science.gov (United States)

    Mayer, Jutta S; Bittner, Robert A; Nikolić, Danko; Bledowski, Christoph; Goebel, Rainer; Linden, David E J

    2007-06-01

    Humans are severely limited in their ability to memorize visual information over short periods of time. Selective attention has been implicated as a limiting factor. Here we used functional magnetic resonance imaging to test the hypothesis that this limitation is due to common neural resources shared by visual working memory (WM) and selective attention. We combined visual search and delayed discrimination of complex objects and independently modulated the demands on selective attention and WM encoding. Participants were presented with a search array and performed easy or difficult visual search in order to encode one or three complex objects into visual WM. Overlapping activation for attention-demanding visual search and WM encoding was observed in distributed posterior and frontal regions. In the right prefrontal cortex and bilateral insula blood oxygen-level-dependent activation additively increased with increased WM load and attentional demand. Conversely, several visual, parietal and premotor areas showed overlapping activation for the two task components and were severely reduced in their WM load response under the condition with high attentional demand. Regions in the left prefrontal cortex were selectively responsive to WM load. Areas selectively responsive to high attentional demand were found within the right prefrontal and bilateral occipital cortex. These results indicate that encoding into visual WM and visual selective attention require to a high degree access to common neural resources. We propose that competition for resources shared by visual attention and WM encoding can limit processing capabilities in distributed posterior brain regions.

  8. Measurements of additional X-ray flux in South Atlantic magnetic anomaly region

    International Nuclear Information System (INIS)

    Martin, I.M.

    1968-01-01

    The purpose of this study is the calculation of the additional X-ray flux (20 - 150 KeV), produced by electron precipitation in the South Atlantic anomaly region. The kind of detector and the technique employed in the observations of this flux, utilizing stratospheric balloons as a means of transport of the payload across the anomaly region, are described. The results of two balloon launchins in Sao Jose dos Campos in July 1968, with the expected flux, are compared. (author) [pt

  9. The neural correlates of internal and external comparisons: an fMRI study.

    Science.gov (United States)

    Wen, Xue; Xiang, Yanhui; Cant, Jonathan S; Wang, Tingting; Cupchik, Gerald; Huang, Ruiwang; Mo, Lei

    2017-01-01

    Many previous studies have suggested that various comparisons rely on the same cognitive and neural mechanisms. However, little attention has been paid to exploring the commonalities and differences between the internal comparison based on concepts or rules and the external comparison based on perception. In the present experiment, moral beauty comparison and facial beauty comparison were selected as the representatives of internal comparison and external comparison, respectively. Functional magnetic resonance imaging (fMRI) was used to record brain activity while participants compared the level of moral beauty of two scene drawings containing moral acts or the level of facial beauty of two face photos. In addition, a physical size comparison task with the same stimuli as the beauty comparison was included. We observed that both the internal moral beauty comparison and external facial beauty comparison obeyed a typical distance effect and this behavioral effect recruited a common frontoparietal network involved in comparisons of simple physical magnitudes such as size. In addition, compared to external facial beauty comparison, internal moral beauty comparison induced greater activity in more advanced and complex cortical regions, such as the bilateral middle temporal gyrus and middle occipital gyrus, but weaker activity in the putamen, a subcortical region. Our results provide novel neural evidence for the comparative process and suggest that different comparisons may rely on both common cognitive processes as well as distinct and specific cognitive components.

  10. NEURAL METHODS FOR THE FINANCIAL PREDICTION

    OpenAIRE

    Jerzy Balicki; Piotr Dryja; Waldemar Korłub; Piotr Przybyłek; Maciej Tyszka; Marcin Zadroga; Marcin Zakidalski

    2016-01-01

    Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

  11. NEURAL METHODS FOR THE FINANCIAL PREDICTION

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2016-06-01

    Full Text Available Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

  12. Transcriptional profiling of adult neural stem-like cells from the human brain.

    Directory of Open Access Journals (Sweden)

    Cecilie Jonsgar Sandberg

    Full Text Available There is a great potential for the development of new cell replacement strategies based on adult human neural stem-like cells. However, little is known about the hierarchy of cells and the unique molecular properties of stem- and progenitor cells of the nervous system. Stem cells from the adult human brain can be propagated and expanded in vitro as free floating neurospheres that are capable of self-renewal and differentiation into all three cell types of the central nervous system. Here we report the first global gene expression study of adult human neural stem-like cells originating from five human subventricular zone biopsies (mean age 42, range 33-60. Compared to adult human brain tissue, we identified 1,189 genes that were significantly up- and down-regulated in adult human neural stem-like cells (1% false discovery rate. We found that adult human neural stem-like cells express stem cell markers and have reduced levels of markers that are typical of the mature cells in the nervous system. We report that the genes being highly expressed in adult human neural stem-like cells are associated with developmental processes and the extracellular region of the cell. The calcium signaling pathway and neuroactive ligand-receptor interactions are enriched among the most differentially regulated genes between adult human neural stem-like cells and adult human brain tissue. We confirmed the expression of 10 of the most up-regulated genes in adult human neural stem-like cells in an additional sample set that included adult human neural stem-like cells (n = 6, foetal human neural stem cells (n = 1 and human brain tissues (n = 12. The NGFR, SLITRK6 and KCNS3 receptors were further investigated by immunofluorescence and shown to be heterogeneously expressed in spheres. These receptors could potentially serve as new markers for the identification and characterisation of neural stem- and progenitor cells or as targets for manipulation of cellular

  13. Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

    Science.gov (United States)

    Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

    2017-02-10

    Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

  14. Functional neural networks underlying response inhibition in adolescents and adults.

    Science.gov (United States)

    Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

    2007-07-19

    This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

  15. Neural control of vascular reactions: impact of emotion and attention.

    Science.gov (United States)

    Okon-Singer, Hadas; Mehnert, Jan; Hoyer, Jana; Hellrung, Lydia; Schaare, Herma Lina; Dukart, Juergen; Villringer, Arno

    2014-03-19

    This study investigated the neural regions involved in blood pressure reactions to negative stimuli and their possible modulation by attention. Twenty-four healthy human subjects (11 females; age = 24.75 ± 2.49 years) participated in an affective perceptual load task that manipulated attention to negative/neutral distractor pictures. fMRI data were collected simultaneously with continuous recording of peripheral arterial blood pressure. A parametric modulation analysis examined the impact of attention and emotion on the relation between neural activation and blood pressure reactivity during the task. When attention was available for processing the distractor pictures, negative pictures resulted in behavioral interference, neural activation in brain regions previously related to emotion, a transient decrease of blood pressure, and a positive correlation between blood pressure response and activation in a network including prefrontal and parietal regions, the amygdala, caudate, and mid-brain. These effects were modulated by attention; behavioral and neural responses to highly negative distractor pictures (compared with neutral pictures) were smaller or diminished, as was the negative blood pressure response when the central task involved high perceptual load. Furthermore, comparing high and low load revealed enhanced activation in frontoparietal regions implicated in attention control. Our results fit theories emphasizing the role of attention in the control of behavioral and neural reactions to irrelevant emotional distracting information. Our findings furthermore extend the function of attention to the control of autonomous reactions associated with negative emotions by showing altered blood pressure reactions to emotional stimuli, the latter being of potential clinical relevance.

  16. Effects of modality on the neural correlates of encoding processes supporting recollection and familiarity

    Science.gov (United States)

    Gottlieb, Lauren J.; Rugg, Michael D.

    2011-01-01

    Prior research has demonstrated that the neural correlates of successful encoding (“subsequent memory effects”) partially overlap with neural regions selectively engaged by the on-line demands of the study task. The primary goal of the present experiment was to determine whether this overlap is associated solely with encoding processes supporting later recollection, or whether overlapping subsequent memory and study condition effects are also evident when later memory is familiarity-based. Subjects (N = 17) underwent fMRI scanning while studying a series of visually and auditorily presented words. Memory for the words was subsequently tested with a modified Remember/Know procedure. Auditorily selective subsequent familiarity effects were evident in bilateral temporal regions that also responded preferentially to auditory items. Although other interpretations are possible, these findings suggest that overlap between study condition-selective subsequent memory effects and regions selectively sensitive to study demands is not uniquely associated with later recollection. In addition, modality-independent subsequent memory effects were identified in several cortical regions. In every case, the effects were greatest for later recollected items, and smaller for items later recognized on the basis of familiarity. The implications of this quantitative dissociation for dual-process models of recognition memory are discussed. PMID:21852431

  17. Exploring the spatio-temporal neural basis of face learning

    Science.gov (United States)

    Yang, Ying; Xu, Yang; Jew, Carol A.; Pyles, John A.; Kass, Robert E.; Tarr, Michael J.

    2017-01-01

    Humans are experts at face individuation. Although previous work has identified a network of face-sensitive regions and some of the temporal signatures of face processing, as yet, we do not have a clear understanding of how such face-sensitive regions support learning at different time points. To study the joint spatio-temporal neural basis of face learning, we trained subjects to categorize two groups of novel faces and recorded their neural responses using magnetoencephalography (MEG) throughout learning. A regression analysis of neural responses in face-sensitive regions against behavioral learning curves revealed significant correlations with learning in the majority of the face-sensitive regions in the face network, mostly between 150–250 ms, but also after 300 ms. However, the effect was smaller in nonventral regions (within the superior temporal areas and prefrontal cortex) than that in the ventral regions (within the inferior occipital gyri (IOG), midfusiform gyri (mFUS) and anterior temporal lobes). A multivariate discriminant analysis also revealed that IOG and mFUS, which showed strong correlation effects with learning, exhibited significant discriminability between the two face categories at different time points both between 150–250 ms and after 300 ms. In contrast, the nonventral face-sensitive regions, where correlation effects with learning were smaller, did exhibit some significant discriminability, but mainly after 300 ms. In sum, our findings indicate that early and recurring temporal components arising from ventral face-sensitive regions are critically involved in learning new faces. PMID:28570739

  18. Neural Mechanisms Underlying Risk and Ambiguity Attitudes.

    Science.gov (United States)

    Blankenstein, Neeltje E; Peper, Jiska S; Crone, Eveline A; van Duijvenvoorde, Anna C K

    2017-11-01

    Individual differences in attitudes to risk (a taste for risk, known probabilities) and ambiguity (a tolerance for uncertainty, unknown probabilities) differentially influence risky decision-making. However, it is not well understood whether risk and ambiguity are coded differently within individuals. Here, we tested whether individual differences in risk and ambiguity attitudes were reflected in distinct neural correlates during choice and outcome processing of risky and ambiguous gambles. To these ends, we developed a neuroimaging task in which participants ( n = 50) chose between a sure gain and a gamble, which was either risky or ambiguous, and presented decision outcomes (gains, no gains). From a separate task in which the amount, probability, and ambiguity level were varied, we estimated individuals' risk and ambiguity attitudes. Although there was pronounced neural overlap between risky and ambiguous gambling in a network typically related to decision-making under uncertainty, relatively more risk-seeking attitudes were associated with increased activation in valuation regions of the brain (medial and lateral OFC), whereas relatively more ambiguity-seeking attitudes were related to temporal cortex activation. In addition, although striatum activation was observed during reward processing irrespective of a prior risky or ambiguous gamble, reward processing after an ambiguous gamble resulted in enhanced dorsomedial PFC activation, possibly functioning as a general signal of uncertainty coding. These findings suggest that different neural mechanisms reflect individual differences in risk and ambiguity attitudes and that risk and ambiguity may impact overt risk-taking behavior in different ways.

  19. Common and distinct neural mechanisms of attentional switching and response conflict.

    Science.gov (United States)

    Kim, Chobok; Johnson, Nathan F; Gold, Brian T

    2012-08-21

    The human capacities for overcoming prepotent actions and flexibly switching between tasks represent cornerstones of cognitive control. Functional neuroimaging has implicated a diverse set of brain regions contributing to each of these cognitive control processes. However, the extent to which attentional switching and response conflict draw on shared or distinct neural mechanisms remains unclear. The current study examined the neural correlates of response conflict and attentional switching using event-related functional magnetic resonance imaging (fMRI) and a fully randomized 2×2 design. We manipulated an arrow-word version of the Stroop task to measure conflict and switching in the context of a single task decision, in response to a common set of stimuli. Under these common conditions, both behavioral and imaging data showed significant main effects of conflict and switching but no interaction. However, conjunction analyses identified frontal regions involved in both switching and response conflict, including the dorsal anterior cingulate cortex (dACC) and left inferior frontal junction. In addition, connectivity analyses demonstrated task-dependent functional connectivity patterns between dACC and inferior temporal cortex for attentional switching and between dACC and posterior parietal cortex for response conflict. These results suggest that the brain makes use of shared frontal regions, but can dynamically modulate the connectivity patterns of some of those regions, to deal with attentional switching and response conflict. Copyright © 2012 Elsevier B.V. All rights reserved.

  20. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    OpenAIRE

    Wu, Zhonghua; Lu, Jingchao; Shi, Jingping; Liu, Yang; Zhou, Qing

    2017-01-01

    This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) tech...

  1. IAEA safeguards and the additional protocol in the Eurasia Region

    International Nuclear Information System (INIS)

    Murakami, K.

    2001-01-01

    Developing and implementing safeguards against misuse of nuclear material and facilities has always been the Agency's main activities. Like the nuclear non-proliferation regime itself, the development of the safeguards system has been an evolutionary process. The first safeguards inspection was carried out in 1962 (in Norway). In the sixties, the basic concepts behind safeguards were developed (INFCIRC/26, adopted in 1961, for some of you it might still have a familiar ring) and the number of inspections and types of facilities inspected grew slowly. With the advent of INFCIRC/66/Rev. 2, a more complete, albeit limited, system of safeguards covering nuclear material, equipment and facilities emerged. But the quantum leap came, of course, wit the entry into force of the NPT. Today, the IAEA has 224 safeguards agreements in force with 140 States. Nearly all of these States are NPT States. In the Eurasia Region, particularly the Newly Independent States (NIS) significant achievements have been made in the Safeguards Implementation. States with nuclear activities have the SG Agreement in force. Some states are already signing the Additional Protocol and it is in force in two of these States in the NIS region. Much progress has been made in the area of nuclear material and accountancy through the IAEA Coordinated Technical Support Programme (CTSP). The programme was organized to co-ordinate the donor states activities and has been successful for the last seven years in providing assistance in the area of nuclear legislation establishment of the State System of Accountancy of nuclear material (SSAC) and other related areas. Improvement is still foreseen in these areas, particularly as more states in the region will be signing and implementing the Additional Protocols

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

    Science.gov (United States)

    Chen, Shiyang; Hu, Xiaoping P

    2018-03-20

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

  3. Evaluation of therapeutic effectiveness of neural transplantation using PET imaging technique

    International Nuclear Information System (INIS)

    Inaji, Motoki

    2004-01-01

    Neural transplantation is expected as an eradicative treatment of intractable central neural disease. In addition to behavioral observations, the recent development of the in vivo imaging technique also enabled to assess functions of neural graft in living subjects. Then we performed the PET scans using the unilateral 6-OHDA-lesioned rats in order to assess the pre- and post-synaptic functions in the striatum after transplantation of fetal dopaminergic neurons. As a result of PET scan, the images of [11C]PE2I, tracer of dopamine transporter, showed increased accumulation in the region which corresponded to the transplanted site after the graft. Because dopamine transporter exists on the cytoplasma membrane of axonal terminal, the accumulation of [11C]PE2I was regarded as a market of survival and maturation of transplanted cells. Also the images of [11C]raclopride, tracer of dopamine D2 receptor, revealed that up-regulation of D2 receptors normalized 4 weeks after transplantation. [11C]Raclopride was considered a marker of change of secondary dopaminergic environment. We believed that assessments with PET bring us much information, and it will increasingly contribute to a development of the regenerative medicine. (author)

  4. Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

    Science.gov (United States)

    Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

    2017-12-26

    Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

  5. Asymmetric continuous-time neural networks without local traps for solving constraint satisfaction problems.

    Directory of Open Access Journals (Sweden)

    Botond Molnár

    Full Text Available There has been a long history of using neural networks for combinatorial optimization and constraint satisfaction problems. Symmetric Hopfield networks and similar approaches use steepest descent dynamics, and they always converge to the closest local minimum of the energy landscape. For finding global minima additional parameter-sensitive techniques are used, such as classical simulated annealing or the so-called chaotic simulated annealing, which induces chaotic dynamics by addition of extra terms to the energy landscape. Here we show that asymmetric continuous-time neural networks can solve constraint satisfaction problems without getting trapped in non-solution attractors. We concentrate on a model solving Boolean satisfiability (k-SAT, which is a quintessential NP-complete problem. There is a one-to-one correspondence between the stable fixed points of the neural network and the k-SAT solutions and we present numerical evidence that limit cycles may also be avoided by appropriately choosing the parameters of the model. This optimal parameter region is fairly independent of the size and hardness of instances, this way parameters can be chosen independently of the properties of problems and no tuning is required during the dynamical process. The model is similar to cellular neural networks already used in CNN computers. On an analog device solving a SAT problem would take a single operation: the connection weights are determined by the k-SAT instance and starting from any initial condition the system searches until finding a solution. In this new approach transient chaotic behavior appears as a natural consequence of optimization hardness and not as an externally induced effect.

  6. Artificial neural networks a practical course

    CERN Document Server

    da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco

    2017-01-01

    This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

  7. What Neural Substrates Trigger the Adept Scientific Pattern Discovery by Biologists?

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yong-Ju

    2011-04-01

    This study investigated the neural correlates of experts and novices during biological object pattern detection using an fMRI approach in order to reveal the neural correlates of a biologist's superior pattern discovery ability. Sixteen healthy male participants (8 biologists and 8 non-biologists) volunteered for the study. Participants were shown fifteen series of organism pictures and asked to detect patterns amid stimulus pictures. Primary findings showed significant activations in the right middle temporal gyrus and inferior parietal lobule amongst participants in the biologist (expert) group. Interestingly, the left superior temporal gyrus was activated in participants from the non-biologist (novice) group. These results suggested that superior pattern discovery ability could be related to a functional facilitation of the parieto-temporal network, which is particularly driven by the right middle temporal gyrus and inferior parietal lobule in addition to the recruitment of additional brain regions. Furthermore, the functional facilitation of the network might actually pertain to high coherent processing skills and visual working memory capacity. Hence, study results suggested that adept scientific thinking ability can be detected by neuronal substrates, which may be used as criteria for developing and evaluating a brain-based science curriculum and test instrument.

  8. 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

  9. The Neural Basis of Social Influence in a Dictator Decision

    Directory of Open Access Journals (Sweden)

    Zhenyu Wei

    2017-12-01

    Full Text Available Humans tend to reduce inequitable distributions. Previous neuroimaging studies have shown that inequitable decisions are related to brain regions that associated with negative emotion and signaling conflict. In the highly complex human social environment, our opinions and behaviors can be affected by social information. In current study, we used a modified dictator game to investigate the effect of social influence on making an equitable decision. We found that the choices of participants in present task was influenced by the choices of peers. However, participants’ decisions were influenced by equitable rather than inequitable group choices. fMRI results showed that brain regions that related to norm violation and social conflict were related to the inequitable social influence. The neural responses in the dorsomedial prefrontal cortex, rostral cingulate zone, and insula predicted subsequent conforming behavior in individuals. Additionally, psychophysiological interaction analysis revealed that the interconnectivity between the dorsal striatum and insula was elevated in advantageous inequity influence versus no-social influence conditions. We found decreased functional connectivity between the medial prefrontal cortex and insula, supplementary motor area, posterior cingulate gyrus and dorsal anterior cingulate cortex in the disadvantageous inequity influence versus no-social influence conditions. This suggests that a disadvantageous inequity influence may decrease the functional connectivity among brain regions that are related to reward processes. Thus, the neural mechanisms underlying social influence in an equitable decision may be similar to those implicated in social norms and reward processing.

  10. The Neural Basis of Social Influence in a Dictator Decision.

    Science.gov (United States)

    Wei, Zhenyu; Zhao, Zhiying; Zheng, Yong

    2017-01-01

    Humans tend to reduce inequitable distributions. Previous neuroimaging studies have shown that inequitable decisions are related to brain regions that associated with negative emotion and signaling conflict. In the highly complex human social environment, our opinions and behaviors can be affected by social information. In current study, we used a modified dictator game to investigate the effect of social influence on making an equitable decision. We found that the choices of participants in present task was influenced by the choices of peers. However, participants' decisions were influenced by equitable rather than inequitable group choices. fMRI results showed that brain regions that related to norm violation and social conflict were related to the inequitable social influence. The neural responses in the dorsomedial prefrontal cortex, rostral cingulate zone, and insula predicted subsequent conforming behavior in individuals. Additionally, psychophysiological interaction analysis revealed that the interconnectivity between the dorsal striatum and insula was elevated in advantageous inequity influence versus no-social influence conditions. We found decreased functional connectivity between the medial prefrontal cortex and insula, supplementary motor area, posterior cingulate gyrus and dorsal anterior cingulate cortex in the disadvantageous inequity influence versus no-social influence conditions. This suggests that a disadvantageous inequity influence may decrease the functional connectivity among brain regions that are related to reward processes. Thus, the neural mechanisms underlying social influence in an equitable decision may be similar to those implicated in social norms and reward processing.

  11. Statistical modelling of neural networks in γ-spectrometry applications

    International Nuclear Information System (INIS)

    Vigneron, V.; Martinez, J.M.; Morel, J.; Lepy, M.C.

    1995-01-01

    Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio 235 U/( 235 U + 236 U + 238 U). The usual method consider a limited number of Γ-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K α X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium 235 U and 238 U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs

  12. Racial bias in neural empathic responses to pain.

    Directory of Open Access Journals (Sweden)

    Luis Sebastian Contreras-Huerta

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

  13. Racial Bias in Neural Empathic Responses to Pain

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2015-12-01

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

  15. Neural network modeling for near wall turbulent flow

    International Nuclear Information System (INIS)

    Milano, Michele; Koumoutsakos, Petros

    2002-01-01

    A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

  16. Convolutional over Recurrent Encoder for Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Dakwale Praveen

    2017-06-01

    Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.

  17. The neural response in short-term visual recognition memory for perceptual conjunctions.

    Science.gov (United States)

    Elliott, R; Dolan, R J

    1998-01-01

    Short-term visual memory has been widely studied in humans and animals using delayed matching paradigms. The present study used positron emission tomography (PET) to determine the neural substrates of delayed matching to sample for complex abstract patterns over a 5-s delay. More specifically, the study assessed any differential neural response associated with remembering individual perceptual properties (color only and shape only) compared to conjunction between these properties. Significant activations associated with short-term visual memory (all memory conditions compared to perceptuomotor control) were observed in extrastriate cortex, medial and lateral parietal cortex, anterior cingulate, inferior frontal gyrus, and the thalamus. Significant deactivations were observed throughout the temporal cortex. Although the requirement to remember color compared to shape was associated with subtly different patterns of blood flow, the requirement to remember perceptual conjunctions between these features was not associated with additional specific activations. These data suggest that visual memory over a delay of the order of 5 s is mainly dependent on posterior perceptual regions of the cortex, with the exact regions depending on the perceptual aspect of the stimuli to be remembered.

  18. A neural measure of behavioral engagement: task-residual low-frequency blood oxygenation level-dependent activity in the precuneus.

    Science.gov (United States)

    Zhang, Sheng; Li, Chiang-Shan Ray

    2010-01-15

    Brain imaging has provided a useful tool to examine the neural processes underlying human cognition. A critical question is whether and how task engagement influences the observed regional brain activations. Here we highlighted this issue and derived a neural measure of task engagement from the task-residual low-frequency blood oxygenation level-dependent (BOLD) activity in the precuneus. Using independent component analysis, we identified brain regions in the default circuit - including the precuneus and medial prefrontal cortex (mPFC) - showing greater activation during resting as compared to task residuals in 33 individuals. Time series correlations with the posterior cingulate cortex as the seed region showed that connectivity with the precuneus was significantly stronger during resting as compared to task residuals. We hypothesized that if the task-residual BOLD activity in the precuneus reflects engagement, it should account for a certain amount of variance in task-related regional brain activation. In an additional experiment of 59 individuals performing a stop signal task, we observed that the fractional amplitude of low-frequency fluctuation (fALFF) of the precuneus but not the mPFC accounted for approximately 10% of the variance in prefrontal activation related to attentional monitoring and response inhibition. Taken together, these results suggest that task-residual fALFF in the precuneus may be a potential indicator of task engagement. This measurement may serve as a useful covariate in identifying motivation-independent neural processes that underlie the pathogenesis of a psychiatric or neurological condition.

  19. Morphological covariance in anatomical MRI scans can identify discrete neural pathways in the brain and their disturbances in persons with neuropsychiatric disorders.

    Science.gov (United States)

    Bansal, Ravi; Hao, Xuejun; Peterson, Bradley S

    2015-05-01

    using DTI data from a different set of 20 healthy adults (10 males, mean age 29.7 ± 7.7 years). The PCA identified portions of structures that covaried across the brain, the eigenvalues measuring the magnitude of the covariation in morphology along the respective eigenvectors. Our results showed that the eigenvectors, and the DTI fibers tracked from their associated brain regions, corresponded with known neural pathways in the brain. In addition, the eigenvectors that captured morphological covariation across regions, and the principal components along those eigenvectors, identified neural pathways with aberrant morphological features associated with TS. These findings suggest that covariations in brain morphology can identify aberrant neural pathways in specific neuropsychiatric disorders. Copyright © 2015. Published by Elsevier Inc.

  20. Rapid generation of sub-type, region-specific neurons and neural networks from human pluripotent stem cell-derived neurospheres

    Directory of Open Access Journals (Sweden)

    Aynun N. Begum

    2015-11-01

    Full Text Available Stem cell-based neuronal differentiation has provided a unique opportunity for disease modeling and regenerative medicine. Neurospheres are the most commonly used neuroprogenitors for neuronal differentiation, but they often clump in culture, which has always represented a challenge for neurodifferentiation. In this study, we report a novel method and defined culture conditions for generating sub-type or region-specific neurons from human embryonic and induced pluripotent stem cells derived neurosphere without any genetic manipulation. Round and bright-edged neurospheres were generated in a supplemented knockout serum replacement medium (SKSRM with 10% CO2, which doubled the expression of the NESTIN, PAX6 and FOXG1 genes compared with those cultured with 5% CO2. Furthermore, an additional step (AdSTEP was introduced to fragment the neurospheres and facilitate the formation of a neuroepithelial-type monolayer that we termed the “neurosphederm”. The large neural tube-type rosette (NTTR structure formed from the neurosphederm, and the NTTR expressed higher levels of the PAX6, SOX2 and NESTIN genes compared with the neuroectoderm-derived neuroprogenitors. Different layers of cortical, pyramidal, GABAergic, glutamatergic, cholinergic neurons appeared within 27 days using the neurosphederm, which is a shorter period than in traditional neurodifferentiation-protocols (42–60 days. With additional supplements and timeline dopaminergic and Purkinje neurons were also generated in culture too. Furthermore, our in vivo results indicated that the fragmented neurospheres facilitated significantly better neurogenesis in severe combined immunodeficiency (SCID mouse brains compared with the non-fragmented neurospheres. Therefore, this neurosphere-based neurodifferentiation protocol is a valuable tool for studies of neurodifferentiation, neuronal transplantation and high throughput screening assays.

  1. Neural correlates of own- and other-race face recognition in children: a functional near-infrared spectroscopy study.

    Science.gov (United States)

    Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2014-01-15

    The present study used the functional Near-infrared Spectroscopy (fNIRS) methodology to investigate the neural correlates of elementary school children's own- and other-race face processing. An old-new paradigm was used to assess children's recognition ability of own- and other-race faces. FNIRS data revealed that other-race faces elicited significantly greater [oxy-Hb] changes than own-race faces in the right middle frontal gyrus and inferior frontal gyrus regions (BA9) and the left cuneus (BA18). With increased age, the [oxy-Hb] activity differences between own- and other-race faces, or the neural other-race effect (NORE), underwent significant changes in these two cortical areas: at younger ages, the neural response to the other-race faces was modestly greater than that to the own-race faces, but with increased age, the neural response to the own-race faces became increasingly greater than that to the other-race faces. Moreover, these areas had strong regional functional connectivity with a swath of the cortical regions in terms of the neural other-race effect that also changed with increased age. We also found significant and positive correlations between the behavioral other-race effect (reaction time) and the neural other-race effect in the right middle frontal gyrus and inferior frontal gyrus regions (BA9). These results taken together suggest that children, like adults, devote different amounts of neural resources to processing own- and other-race faces, but the size and direction of the neural other-race effect and associated functional regional connectivity change with increased age. © 2013.

  2. Cannabis abstinence during treatment and one-year follow-up: relationship to neural activity in men.

    Science.gov (United States)

    Kober, Hedy; DeVito, Elise E; DeLeone, Cameron M; Carroll, Kathleen M; Potenza, Marc N

    2014-09-01

    Cannabis is among the most frequently abused substances in the United States. Cognitive control is a contributory factor in the maintenance of substance-use disorders and may relate to treatment response. Therefore, we assessed whether cognitive-control-related neural activity before treatment differs between treatment-seeking cannabis-dependent and healthy individuals and relates to cannabis-abstinence measures during treatment and 1-year follow-up. Cannabis-dependent males (N=20) completed a functional magnetic resonance imaging (fMRI) cognitive-control (Stroop) task before a 12-week randomized controlled trial of cognitive-behavioral therapy and/or contingency management. A healthy-comparison group (N=20) also completed the fMRI task. Cannabis use was assessed by urine toxicology and self-report during treatment, and by self-report across a 1-year follow-up period (N=18). The cannabis-dependent group displayed diminished Stroop-related neural activity relative to the healthy-comparison group in multiple regions, including those strongly implicated in cognitive-control and addiction-related processes (eg, dorsolateral prefrontal cortex and ventral striatum). The groups did not differ significantly in response times (cannabis-dependent, N=12; healthy-comparison, N=14). Within the cannabis-dependent group, greater Stroop-related activity in regions including the dorsal anterior cingulate cortex was associated with less cannabis use during treatment. Greater activity in regions including the ventral striatum was associated with less cannabis use during 1-year posttreatment follow-up. These data suggest that lower cognitive-control-related neural activity in classic 'control' regions (eg, dorsolateral prefrontal cortex and dorsal anterior cingulate) and classic 'salience/reward/learning' regions (eg, ventral striatum) differentiates cannabis-dependent individuals from healthy individuals and relates to less abstinence within-treatment and during long-term follow

  3. Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

    Science.gov (United States)

    Mundell, Nathan A; Labosky, Patricia A

    2011-02-01

    Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

  4. Using neural networks to describe tracer correlations

    Directory of Open Access Journals (Sweden)

    D. J. Lary

    2004-01-01

    Full Text Available Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.. In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE which has continuously observed CH4  (but not N2O from 1991 till the present. The neural network Fortran code used is available for download.

  5. Classification of Company Performance using Weighted Probabilistic Neural Network

    Science.gov (United States)

    Yasin, Hasbi; Waridi Basyiruddin Arifin, Adi; Warsito, Budi

    2018-05-01

    Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the company's performance modeling with the WPNN model has a very high accuracy that reaches 100%.

  6. Neural substrates of decision-making.

    Science.gov (United States)

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

    2016-06-01

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

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

    Science.gov (United States)

    Rossion, Bruno; Jacques, Corentin; Jonas, Jacques

    2018-02-26

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

  8. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

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

    Science.gov (United States)

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

    2018-03-01

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

  10. Braided Multi-Electrode Probes (BMEPs) for Neural Interfaces

    Science.gov (United States)

    Kim, Tae Gyo

    Although clinical use of invasive neural interfaces is very limited, due to safety and reliability concerns, the potential benefits of their use in brain machine interfaces (BMIs) seem promising and so they have been widely used in the research field. Microelectrodes as invasive neural interfaces are the core tool to record neural activities and their failure is a critical issue for BMI systems. Possible sources of this failure are neural tissue motions and their interactions with stiff electrode arrays or probes fixed to the skull. To overcome these tissue motion problems, we have developed novel braided multi-electrode probes (BMEPs). By interweaving ultra-fine wires into a tubular braid structure, we obtained a highly flexible multi-electrode probe. In this thesis we described BMEP designs and how to fabricate BMEPs, and explore experiments to show the advantages of BMEPs through a mechanical compliance comparison and a chronic immunohistological comparison with single 50microm nichrome wires used as a reference electrode type. Results from the mechanical compliance test showed that the bodies of BMEPs have 4 to 21 times higher compliance than the single 50microm wire and the tethers of BMEPs were 6 to 96 times higher compliance, depending on combinations of the wire size (9.6microm or 12.7microm), the wire numbers (12 or 24), and the length of tether (3, 5 or 10 mm). Results from the immunohistological comparison showed that both BMEPs and 50microm wires anchored to the skull caused stronger tissue reactions than unanchored BMEPs and 50microm wires, and 50microm wires caused stronger tissue reactions than BMEPs. In in-vivo tests with BMEPs, we succeeded in chronic recordings from the spinal cord of freely jumping frogs and in acute recordings from the spinal cord of decerebrate rats during air stepping which was evoked by mesencephalic locomotor region (MLR) stimulation. This technology may provide a stable and reliable neural interface to spinal cord

  11. Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

    Science.gov (United States)

    Baumann, Oliver; Vromen, Joyce M G; Cheung, Allen; McFadyen, Jessica; Ren, Yudan; Guo, Christine C

    2018-01-01

    We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

  12. Differential neural substrates of working memory and cognitive skill learning in healthy young volunteers

    International Nuclear Information System (INIS)

    Cho, Sang Soo; Lee, Eun Ju; Yoon, Eun Jin; Kim, Yu Kyeong; Lee, Won Woo; Kim, Sang Eun

    2005-01-01

    It is known that different neural circuits are involved in working memory and cognitive skill learning that represent explicit and implicit memory functions, respectively. In the present study, we investigated the metabolic correlates of working memory and cognitive skill learning with correlation analysis of FDG PET images. Fourteen right-handed healthy subjects (age, 24 ± 2 yr; 5 males and 9 females) underwent brain FDG PET and neuropsychological testing. Two-back task and weather prediction task were used for the evaluation of working memory and cognitive skill learning, respectively, Correlation between regional glucose metabolism and cognitive task performance was examined using SPM99. A significant positive correlation between 2-back task performance and regional glucose metabolism was found in the prefrontal regions and superior temporal gyri bilaterally. In the first term of weather prediction task the task performance correlated positively with glucose metabolism in the bilateral prefrontal areas, left middle temporal and posterior cingulate gyri, and left thalamus. In the second and third terms of the task, the correlation found in the prefrontal areas, superior temporal and anterior cingulate gyri bilaterally, right insula, left parahippocampal gyrus, and right caudate nucleus. We identified the neural substrates that are related with performance of working memory and cognitive skill learning. These results indicate that brain regions associated with the explicit memory system are recruited in early periods of cognitive skill learning, but additional brain regions including caudate nucleus are involved in late periods of cognitive skill learning

  13. Differential neural substrates of working memory and cognitive skill learning in healthy young volunteers

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Sang Soo; Lee, Eun Ju; Yoon, Eun Jin; Kim, Yu Kyeong; Lee, Won Woo; Kim, Sang Eun [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of)

    2005-07-01

    It is known that different neural circuits are involved in working memory and cognitive skill learning that represent explicit and implicit memory functions, respectively. In the present study, we investigated the metabolic correlates of working memory and cognitive skill learning with correlation analysis of FDG PET images. Fourteen right-handed healthy subjects (age, 24 {+-} 2 yr; 5 males and 9 females) underwent brain FDG PET and neuropsychological testing. Two-back task and weather prediction task were used for the evaluation of working memory and cognitive skill learning, respectively, Correlation between regional glucose metabolism and cognitive task performance was examined using SPM99. A significant positive correlation between 2-back task performance and regional glucose metabolism was found in the prefrontal regions and superior temporal gyri bilaterally. In the first term of weather prediction task the task performance correlated positively with glucose metabolism in the bilateral prefrontal areas, left middle temporal and posterior cingulate gyri, and left thalamus. In the second and third terms of the task, the correlation found in the prefrontal areas, superior temporal and anterior cingulate gyri bilaterally, right insula, left parahippocampal gyrus, and right caudate nucleus. We identified the neural substrates that are related with performance of working memory and cognitive skill learning. These results indicate that brain regions associated with the explicit memory system are recruited in early periods of cognitive skill learning, but additional brain regions including caudate nucleus are involved in late periods of cognitive skill learning.

  14. Modelling the continuous cooling transformation diagram of engineering steels using neural networks. Part I. Phase regions

    Energy Technology Data Exchange (ETDEWEB)

    Wolk, P.J. van der; Wang, J. [Delft Univ. of Technology (Netherlands); Sietsma, J.; Zwaag, S. van der [Delft Univ. of Technology, Lab. for Materials Science (Netherlands)

    2002-12-01

    A neural network model for the calculation of the phase regions of the continuous cooling transformation (CCT) diagram of engineering steels has been developed. The model is based on experimental CCT diagrams of 459 low-alloy steels, and calculates the CCT diagram as a function of composition and austenitisation temperature. In considering the composition, 9 alloying elements are taken into account. The model reproduces the original diagrams rather accurately, with deviations that are not larger than the average experimental inaccuracy of the experimental diagrams. Therefore, it can be considered an adequate alternative to the experimental determination of the CCT diagram of a certain steel within the composition range used. The effects of alloying elements can be quantified, either individually or in combination, with the model. Nonlinear composition dependencies are observed. (orig.)

  15. Neural Language Processing in Adolescent First-Language Learners: Longitudinal Case Studies in American Sign Language.

    Science.gov (United States)

    Ferjan Ramirez, Naja; Leonard, Matthew K; Davenport, Tristan S; Torres, Christina; Halgren, Eric; Mayberry, Rachel I

    2016-03-01

    One key question in neurolinguistics is the extent to which the neural processing system for language requires linguistic experience during early life to develop fully. We conducted a longitudinal anatomically constrained magnetoencephalography (aMEG) analysis of lexico-semantic processing in 2 deaf adolescents who had no sustained language input until 14 years of age, when they became fully immersed in American Sign Language. After 2 to 3 years of language, the adolescents' neural responses to signed words were highly atypical, localizing mainly to right dorsal frontoparietal regions and often responding more strongly to semantically primed words (Ferjan Ramirez N, Leonard MK, Torres C, Hatrak M, Halgren E, Mayberry RI. 2014. Neural language processing in adolescent first-language learners. Cereb Cortex. 24 (10): 2772-2783). Here, we show that after an additional 15 months of language experience, the adolescents' neural responses remained atypical in terms of polarity. While their responses to less familiar signed words still showed atypical localization patterns, the localization of responses to highly familiar signed words became more concentrated in the left perisylvian language network. Our findings suggest that the timing of language experience affects the organization of neural language processing; however, even in adolescence, language representation in the human brain continues to evolve with experience. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Uncovering the neural mechanisms underlying learning from tests.

    Directory of Open Access Journals (Sweden)

    Xiaonan L Liu

    Full Text Available People learn better when re-study opportunities are replaced with tests. While researchers have begun to speculate on why testing is superior to study, few studies have directly examined the neural underpinnings of this effect. In this fMRI study, participants engaged in a study phase to learn arbitrary word pairs, followed by a cued recall test (recall second half of pair when cued with first word of pair, re-study of each pair, and finally another cycle of cued recall tests. Brain activation patterns during the first test (recall of the studied pairs predicts performance on the second test. Importantly, while subsequent memory analyses of encoding trials also predict later accuracy, the brain regions involved in predicting later memory success are more extensive for activity during retrieval (testing than during encoding (study. Those additional regions that predict subsequent memory based on their activation at test but not at encoding may be key to understanding the basis of the testing effect.

  17. Neural markers of loss aversion in resting-state brain activity.

    Science.gov (United States)

    Canessa, Nicola; Crespi, Chiara; Baud-Bovy, Gabriel; Dodich, Alessandra; Falini, Andrea; Antonellis, Giulia; Cappa, Stefano F

    2017-02-01

    Neural responses in striatal, limbic and somatosensory brain regions track individual differences in loss aversion, i.e. the higher sensitivity to potential losses compared with equivalent gains in decision-making under risk. The engagement of structures involved in the processing of aversive stimuli and experiences raises a further question, i.e. whether the tendency to avoid losses rather than acquire gains represents a transient fearful overreaction elicited by choice-related information, or rather a stable component of one's own preference function, reflecting a specific pattern of neural activity. We tested the latter hypothesis by assessing in 57 healthy human subjects whether the relationship between behavioral and neural loss aversion holds at rest, i.e. when the BOLD signal is collected during 5minutes of cross-fixation in the absence of an explicit task. Within the resting-state networks highlighted by a spatial group Independent Component Analysis (gICA), we found a significant correlation between strength of activity and behavioral loss aversion in the left ventral striatum and right posterior insula/supramarginal gyrus, i.e. the very same regions displaying a pattern of neural loss aversion during explicit choices. Cross-study analyses confirmed that this correlation holds when voxels identified by gICA are used as regions of interest in task-related activity and vice versa. These results suggest that the individual degree of (neural) loss aversion represents a stable dimension of decision-making, which reflects in specific metrics of intrinsic brain activity at rest possibly modulating cortical excitability at choice. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Artificial neural networks application for horizontal and vertical forecasting radionuclides transport

    International Nuclear Information System (INIS)

    Khil'ko, O.S.; Kovalenko, V.I.; Kundas, S.P.

    2010-01-01

    Artificial neural networks approach for horizontal and vertical radionuclide transport forecasting was proposed. Runoff factors analysis was considered. Additional artificial neural network structures for physical-chemical properties recognition were used. (authors)

  19. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  20. Neural substrates of sexual desire in individuals with problematic hypersexual behavior

    Directory of Open Access Journals (Sweden)

    Ji-Woo eSeok

    2015-11-01

    Full Text Available Studies on the characteristics of individuals with hypersexual disorder have been accumulating due to increasing concerns about problematic hypersexual behavior (PHB. Currently, relatively little is known about the underlying behavioral and neural mechanisms of sexual desire. Our study aimed to investigate the neural correlates of sexual desire with event-related functional magnetic resonance imaging (fMRI. Twenty-three individuals with PHB and 22 age-matched healthy controls were scanned while they passively viewed sexual and nonsexual stimuli. The subjects’ levels of sexual desire were assessed in response to each sexual stimulus. Relative to controls, individuals with PHB experienced more frequent and enhanced sexual desire during exposure to sexual stimuli. Greater activation was observed in the caudate nucleus, inferior parietal lobe, dorsal anterior cingulate gyrus, thalamus, and dorsolateral prefrontal cortex in the PHB group than in the control group. In addition, the hemodynamic patterns in the activated areas differed between the groups. Consistent with the findings of brain imaging studies of substance and behavior addiction, individuals with the behavioral characteristics of PHB and enhanced desire exhibited altered activation in the prefrontal cortex and subcortical regions. In conclusion, our results will help to characterize the behaviors and associated neural mechanisms of individuals with PHB.

  1. Neural dynamics of morphological processing in spoken word comprehension: Laterality and automaticity

    Directory of Open Access Journals (Sweden)

    Caroline M. Whiting

    2013-11-01

    Full Text Available Rapid and automatic processing of grammatical complexity is argued to take place during speech comprehension, engaging a left-lateralised fronto-temporal language network. Here we address how neural activity in these regions is modulated by the grammatical properties of spoken words. We used combined magneto- and electroencephalography (MEG, EEG to delineate the spatiotemporal patterns of activity that support the recognition of morphologically complex words in English with inflectional (-s and derivational (-er affixes (e.g. bakes, baker. The mismatch negativity (MMN, an index of linguistic memory traces elicited in a passive listening paradigm, was used to examine the neural dynamics elicited by morphologically complex words. Results revealed an initial peak 130-180 ms after the deviation point with a major source in left superior temporal cortex. The localisation of this early activation showed a sensitivity to two grammatical properties of the stimuli: 1 the presence of morphological complexity, with affixed words showing increased left-laterality compared to non-affixed words; and 2 the grammatical category, with affixed verbs showing greater left-lateralisation in inferior frontal gyrus compared to affixed nouns (bakes vs. beaks. This automatic brain response was additionally sensitive to semantic coherence (the meaning of the stem vs. the meaning of the whole form in fronto-temporal regions. These results demonstrate that the spatiotemporal pattern of neural activity in spoken word processing is modulated by the presence of morphological structure, predominantly engaging the left-hemisphere’s fronto-temporal language network, and does not require focused attention on the linguistic input.

  2. Neural correlates underlying musical semantic memory.

    Science.gov (United States)

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

    2009-07-01

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

  3. Neural activity, neural connectivity, and the processing of emotionally valenced information in older adults: links with life satisfaction.

    Science.gov (United States)

    Waldinger, Robert J; Kensinger, Elizabeth A; Schulz, Marc S

    2011-09-01

    This study examines whether differences in late-life well-being are linked to how older adults encode emotionally valenced information. Using fMRI with 39 older adults varying in life satisfaction, we examined how viewing positive and negative images would affect activation and connectivity of an emotion-processing network. Participants engaged most regions within this network more robustly for positive than for negative images, but within the PFC this effect was moderated by life satisfaction, with individuals higher in satisfaction showing lower levels of activity during the processing of positive images. Participants high in satisfaction showed stronger correlations among network regions-particularly between the amygdala and other emotion processing regions-when viewing positive, as compared with negative, images. Participants low in satisfaction showed no valence effect. Findings suggest that late-life satisfaction is linked with how emotion-processing regions are engaged and connected during processing of valenced information. This first demonstration of a link between neural recruitment and late-life well-being suggests that differences in neural network activation and connectivity may account for the preferential encoding of positive information seen in some older adults.

  4. Using imagination to understand the neural basis of episodic memory

    Science.gov (United States)

    Hassabis, Demis; Kumaran, Dharshan; Maguire, Eleanor A.

    2008-01-01

    Functional MRI (fMRI) studies investigating the neural basis of episodic memory recall, and the related task of thinking about plausible personal future events, have revealed a consistent network of associated brain regions. Surprisingly little, however, is understood about the contributions individual brain areas make to the overall recollective experience. In order to examine this, we employed a novel fMRI paradigm where subjects had to imagine fictitious experiences. In contrast to future thinking, this results in experiences that are not explicitly temporal in nature or as reliant on self-processing. By using previously imagined fictitious experiences as a comparison for episodic memories, we identified the neural basis of a key process engaged in common, namely scene construction, involving the generation, maintenance and visualisation of complex spatial contexts. This was associated with activations in a distributed network, including hippocampus, parahippocampal gyrus, and retrosplenial cortex. Importantly, we disambiguated these common effects from episodic memory-specific responses in anterior medial prefrontal cortex, posterior cingulate cortex and precuneus. These latter regions may support self-schema and familiarity processes, and contribute to the brain's ability to distinguish real from imaginary memories. We conclude that scene construction constitutes a common process underlying episodic memory and imagination of fictitious experiences, and suggest it may partially account for the similar brain networks implicated in navigation, episodic future thinking, and the default mode. We suggest that further brain regions are co-opted into this core network in a task-specific manner to support functions such as episodic memory that may have additional requirements. PMID:18160644

  5. Generating regionalized neuronal cells from pluripotency, a step-by-step protocol

    Directory of Open Access Journals (Sweden)

    Agnete eKirkeby

    2013-01-01

    Full Text Available Human pluripotent stem cells possess the potential to generate cells for regenerative therapies in patients with neurodegenerative diseases, and constitute an excellent cell source for studying human neural development and disease modeling. Protocols for neural differentiation of human pluripotent stem cells have undergone significant progress during recent years, allowing for rapid and synchronized neural conversion. Differentiation procedures can further be combined with accurate and efficient positional patterning to yield regionalized neural progenitors and subtype-specific neurons corresponding to different parts of the developing human brain. Here, we present a step-by-step protocol for neuralization and regionalization of human pluripotent cells for transplantation studies or in vitro analysis.

  6. Neural evidence that human emotions share core affective properties.

    Science.gov (United States)

    Wilson-Mendenhall, Christine D; Barrett, Lisa Feldman; Barsalou, Lawrence W

    2013-06-01

    Research on the "emotional brain" remains centered around the idea that emotions like fear, happiness, and sadness result from specialized and distinct neural circuitry. Accumulating behavioral and physiological evidence suggests, instead, that emotions are grounded in core affect--a person's fluctuating level of pleasant or unpleasant arousal. A neuroimaging study revealed that participants' subjective ratings of valence (i.e., pleasure/displeasure) and of arousal evoked by various fear, happiness, and sadness experiences correlated with neural activity in specific brain regions (orbitofrontal cortex and amygdala, respectively). We observed these correlations across diverse instances within each emotion category, as well as across instances from all three categories. Consistent with a psychological construction approach to emotion, the results suggest that neural circuitry realizes more basic processes across discrete emotions. The implicated brain regions regulate the body to deal with the world, producing the affective changes at the core of emotions and many other psychological phenomena.

  7. Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads

    Energy Technology Data Exchange (ETDEWEB)

    Pai, Ping-Feng [Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan (China)

    2006-09-15

    Because of the privatization of electricity in many countries, load forecasting has become one of the most crucial issues in the planning and operations of electric utilities. In addition, accurate regional load forecasting can provide the transmission and distribution operators with more information. The hybrid ellipsoidal fuzzy system was originally designed to solve control and pattern recognition problems. The main objective of this investigation is to develop a hybrid ellipsoidal fuzzy system for time series forecasting (HEFST) and apply the proposed model to forecast regional electricity loads in Taiwan. Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model. Subsequently, numerical data taken from the existing literature is used to demonstrate the forecasting performance of the HEFST model. Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model. Thus, the HEFST model is a valid and promising alternative for forecasting regional electricity loads. (author)

  8. Intranasal oxytocin modulates neural functional connectivity during human social interaction.

    Science.gov (United States)

    Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

    2018-02-10

    Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

  9. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  10. Therapeutic physical exercise in neural injury: friend or foe?

    Science.gov (United States)

    Park, Kanghui; Lee, Seunghoon; Hong, Yunkyung; Park, Sookyoung; Choi, Jeonghyun; Chang, Kyu-Tae; Kim, Joo-Heon; Hong, Yonggeun

    2015-12-01

    [Purpose] The intensity of therapeutic physical exercise is complex and sometimes controversial in patients with neural injuries. This review assessed whether therapeutic physical exercise is beneficial according to the intensity of the physical exercise. [Methods] The authors identified clinically or scientifically relevant articles from PubMed that met the inclusion criteria. [Results] Exercise training can improve body strength and lead to the physiological adaptation of skeletal muscles and the nervous system after neural injuries. Furthermore, neurophysiological and neuropathological studies show differences in the beneficial effects of forced therapeutic exercise in patients with severe or mild neural injuries. Forced exercise alters the distribution of muscle fiber types in patients with neural injuries. Based on several animal studies, forced exercise may promote functional recovery following cerebral ischemia via signaling molecules in ischemic brain regions. [Conclusions] This review describes several types of therapeutic forced exercise and the controversy regarding the therapeutic effects in experimental animals versus humans with neural injuries. This review also provides a therapeutic strategy for physical therapists that grades the intensity of forced exercise according to the level of neural injury.

  11. A recurrent neural network based on projection operator for extended general variational inequalities.

    Science.gov (United States)

    Liu, Qingshan; Cao, Jinde

    2010-06-01

    Based on the projection operator, a recurrent neural network is proposed for solving extended general variational inequalities (EGVIs). Sufficient conditions are provided to ensure the global convergence of the proposed neural network based on Lyapunov methods. Compared with the existing neural networks for variational inequalities, the proposed neural network is a modified version of the general projection neural network existing in the literature and capable of solving the EGVI problems. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed neural network.

  12. Neural Activations of Guided Imagery and Music in Negative Emotional Processing: A Functional MRI Study.

    Science.gov (United States)

    Lee, Sang Eun; Han, Yeji; Park, HyunWook

    2016-01-01

    The Bonny Method of Guided Imagery and Music uses music and imagery to access and explore personal emotions associated with episodic memories. Understanding the neural mechanism of guided imagery and music (GIM) as combined stimuli for emotional processing informs clinical application. We performed functional magnetic resonance imaging (fMRI) to demonstrate neural mechanisms of GIM for negative emotional processing when personal episodic memory is recalled and re-experienced through GIM processes. Twenty-four healthy volunteers participated in the study, which used classical music and verbal instruction stimuli to evoke negative emotions. To analyze the neural mechanism, activated regions associated with negative emotional and episodic memory processing were extracted by conducting volume analyses for the contrast between GIM and guided imagery (GI) or music (M). The GIM stimuli showed increased activation over the M-only stimuli in five neural regions associated with negative emotional and episodic memory processing, including the left amygdala, left anterior cingulate gyrus, left insula, bilateral culmen, and left angular gyrus (AG). Compared with GI alone, GIM showed increased activation in three regions associated with episodic memory processing in the emotional context, including the right posterior cingulate gyrus, bilateral parahippocampal gyrus, and AG. No neural regions related to negative emotional and episodic memory processing showed more activation for M and GI than for GIM. As a combined multimodal stimulus, GIM may increase neural activations related to negative emotions and episodic memory processing. Findings suggest a neural basis for GIM with personal episodic memories affecting cortical and subcortical structures and functions. © the American Music Therapy Association 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  13. Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions

    Directory of Open Access Journals (Sweden)

    Irina Popova

    2013-08-01

    Full Text Available Very-low-frequency/ low-frequency (VLF/LF sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself. To train a neural network, we first create a so-called ‘training set’. The ‘teacher’ specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007, and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed.

  14. Neural representations of emotion are organized around abstract event features.

    Science.gov (United States)

    Skerry, Amy E; Saxe, Rebecca

    2015-08-03

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

  15. Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation

    Directory of Open Access Journals (Sweden)

    Pei-Chen Lo

    2013-01-01

    Full Text Available This paper reports the results of our investigation of the effects of Chan meditation on brain electrophysiological behaviors from the viewpoint of spatially nonlinear interdependence among regional neural networks. Particular emphasis is laid on the alpha-dominated EEG (electroencephalograph. Continuous-time wavelet transform was adopted to detect the epochs containing substantial alpha activities. Nonlinear interdependence quantified by similarity index S(X∣Y, the influence of source signal Y on sink signal X, was applied to the nonlinear dynamical model in phase space reconstructed from multichannel EEG. Experimental group involved ten experienced Chan-Meditation practitioners, while control group included ten healthy subjects within the same age range, yet, without any meditation experience. Nonlinear interdependence among various cortical regions was explored for five local neural-network regions, frontal, posterior, right-temporal, left-temporal, and central regions. In the experimental group, the inter-regional interaction was evaluated for the brain dynamics under three different stages, at rest (stage R, pre-meditation background recording, in Chan meditation (stage M, and the unique Chakra-focusing practice (stage C. Experimental group exhibits stronger interactions among various local neural networks at stages M and C compared with those at stage R. The intergroup comparison demonstrates that Chan-meditation brain possesses better cortical inter-regional interactions than the resting brain of control group.

  16. Culture of Mouse Neural Stem Cell Precursors

    OpenAIRE

    Currle, D. Spencer; Hu, Jia Sheng; Kolski-Andreaco, Aaron; Monuki, Edwin S.

    2007-01-01

    Primary neural stem cell cultures are useful for studying the mechanisms underlying central nervous system development. Stem cell research will increase our understanding of the nervous system and may allow us to develop treatments for currently incurable brain diseases and injuries. In addition, stem cells should be used for stem cell research aimed at the detailed study of mechanisms of neural differentiation and transdifferentiation and the genetic and environmental signals that direct the...

  17. Neural networks for predicting breeding values and genetic gains

    Directory of Open Access Journals (Sweden)

    Gabi Nunes Silva

    2014-12-01

    Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

  18. Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

    OpenAIRE

    Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

    2018-01-01

    Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

  19. Efficient and rapid derivation of primitive neural stem cells and generation of brain subtype neurons from human pluripotent stem cells.

    Science.gov (United States)

    Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C

    2013-11-01

    Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

  20. The neural circuits that generate tics in Tourette's syndrome.

    Science.gov (United States)

    Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

    2011-12-01

    The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico

  1. The behavioural patterns and neural correlates of concrete and abstract verb processing in aphasia: A novel verb semantic battery

    Directory of Open Access Journals (Sweden)

    Reem S.W. Alyahya

    2018-01-01

    Full Text Available Typically, processing is more accurate and efficient for concrete than abstract concepts in both healthy adults and individuals with aphasia. While, concreteness effects have been thoroughly documented with respect to noun processing, other words classes have received little attention despite tending to be less concrete than nouns. The aim of the current study was to explore concrete-abstract differences in verbs and identify their neural correlates in post-stroke aphasia. Given the dearth of comprehension tests for verbs, a battery of neuropsychological tests was developed in this study to assess the comprehension of concrete and abstract verbs. Specifically, a sensitive verb synonym judgment test was generated that varied both the items' imageability and frequency, and a picture-to-word matching test with numerous concrete verbs. Normative data were then collected and the tests were administered to a cohort of 48 individuals with chronic post-stroke aphasia to explore the behavioural patterns and neural correlates of verb processing. The results revealed significantly better comprehension of concrete than abstract verbs, aligning with the existing aphasiological literature on noun processing. In addition, the patients performed better during verb comprehension than verb production. Lesion-symptom correlational analyses revealed common areas that support processing of concrete and abstract verbs, including the left anterior temporal lobe, posterior supramarginal gyrus and superior lateral occipital cortex. A direct contrast between them revealed additional regions with graded differences. Specifically, the left frontal regions were associated with processing abstract verbs; whereas, the left posterior temporal and occipital regions were associated with processing concrete verbs. Moreover, overlapping and distinct neural correlates were identified in association with the comprehension and production of concrete verbs. These patient findings

  2. The behavioural patterns and neural correlates of concrete and abstract verb processing in aphasia: A novel verb semantic battery.

    Science.gov (United States)

    Alyahya, Reem S W; Halai, Ajay D; Conroy, Paul; Lambon Ralph, Matthew A

    2018-01-01

    Typically, processing is more accurate and efficient for concrete than abstract concepts in both healthy adults and individuals with aphasia. While, concreteness effects have been thoroughly documented with respect to noun processing, other words classes have received little attention despite tending to be less concrete than nouns. The aim of the current study was to explore concrete-abstract differences in verbs and identify their neural correlates in post-stroke aphasia. Given the dearth of comprehension tests for verbs, a battery of neuropsychological tests was developed in this study to assess the comprehension of concrete and abstract verbs. Specifically, a sensitive verb synonym judgment test was generated that varied both the items' imageability and frequency, and a picture-to-word matching test with numerous concrete verbs. Normative data were then collected and the tests were administered to a cohort of 48 individuals with chronic post-stroke aphasia to explore the behavioural patterns and neural correlates of verb processing. The results revealed significantly better comprehension of concrete than abstract verbs, aligning with the existing aphasiological literature on noun processing. In addition, the patients performed better during verb comprehension than verb production. Lesion-symptom correlational analyses revealed common areas that support processing of concrete and abstract verbs, including the left anterior temporal lobe, posterior supramarginal gyrus and superior lateral occipital cortex. A direct contrast between them revealed additional regions with graded differences. Specifically, the left frontal regions were associated with processing abstract verbs; whereas, the left posterior temporal and occipital regions were associated with processing concrete verbs. Moreover, overlapping and distinct neural correlates were identified in association with the comprehension and production of concrete verbs. These patient findings align with data from

  3. Sex differences in the neural basis of emotional memories.

    Science.gov (United States)

    Canli, Turhan; Desmond, John E; Zhao, Zuo; Gabrieli, John D E

    2002-08-06

    Psychological studies have found better memory in women than men for emotional events, but the neural basis for this difference is unknown. We used event-related functional MRI to assess whether sex differences in memory for emotional stimuli is associated with activation of different neural systems in men and women. Brain activation in 12 men and 12 women was recorded while they rated their experience of emotional arousal in response to neutral and emotionally negative pictures. In a recognition memory test 3 weeks after scanning, highly emotional pictures were remembered best, and remembered better by women than by men. Men and women activated different neural circuits to encode stimuli effectively into memory even when the analysis was restricted to pictures rated equally arousing by both groups. Men activated significantly more structures than women in a network that included the right amygdala, whereas women activated significantly fewer structures in a network that included the left amygdala. Women had significantly more brain regions where activation correlated with both ongoing evaluation of emotional experience and with subsequent memory for the most emotionally arousing pictures. Greater overlap in brain regions sensitive to current emotion and contributing to subsequent memory may be a neural mechanism for emotions to enhance memory more powerfully in women than in men.

  4. Shared beliefs enhance shared feelings: religious/irreligious identifications modulate empathic neural responses.

    Science.gov (United States)

    Huang, Siyuan; Han, Shihui

    2014-01-01

    Recent neuroimaging research has revealed stronger empathic neural responses to same-race compared to other-race individuals. Is the in-group favouritism in empathic neural responses specific to race identification or a more general effect of social identification-including those based on religious/irreligious beliefs? The present study investigated whether and how intergroup relationships based on religious/irreligious identifications modulate empathic neural responses to others' pain expressions. We recorded event-related brain potentials from Chinese Christian and atheist participants while they perceived pain or neutral expressions of Chinese faces that were marked as being Christians or atheists. We found that both Christian and atheist participants showed stronger neural activity to pain (versus neutral) expressions at 132-168 ms and 200-320 ms over the frontal region to those with the same (versus different) religious/irreligious beliefs. The in-group favouritism in empathic neural responses was also evident in a later time window (412-612 ms) over the central/parietal regions in Christian but not in atheist participants. Our results indicate that the intergroup relationship based on shared beliefs, either religious or irreligious, can lead to in-group favouritism in empathy for others' suffering.

  5. Statistical downscaling of precipitation using long short-term memory recurrent neural networks

    Science.gov (United States)

    Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

    2017-11-01

    Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

  6. [Neural mechanism underlying autistic savant and acquired savant syndrome].

    Science.gov (United States)

    Takahata, Keisuke; Kato, Motoichiro

    2008-07-01

    It is well known that the cases with savant syndrome, demonstrate outstanding mental capability despite coexisting severe mental disabilities. In many cases, savant skills are characterized by its domain-specificity, enhanced memory capability, and excessive focus on low-level perceptual processing. In addition, impaired integrative cognitive processing such as social cognition or executive function, restricted interest, and compulsive repetition of the same act are observed in savant individuals. All these are significantly relevant to the behavioral characteristics observed in individuals with autistic spectrum disorders (ASD). A neurocognitive model of savant syndrome should explain these cognitive features and the juxtaposition of outstanding talents with cognitive disabilities. In recent neuropsychological studies, Miller (1998) reported clinical cases of "acquired savant," i.e., patients who improved or newly acquired an artistic savant-like skill in the early stage of frontotemporal dementia (FTD). Although the relationship between an autistic savant and acquired savant remains to be elucidated, the advent of neuroimaging study of ASD and the clarification of FTD patients with savant-like skills may clarify the shared neural mechanisms of both types of talent. In this review, we classified current cognitive models of savant syndrome into the following 3 categories. (1) A hypermnesic model that suggests that savant skills develop from existing or dormant cognitive functions such as memory. However, recent findings obtained through neuropsychological examinations imply that savant individuals solve problems using a strategy that is fairly different from a non-autistic one. (2) A paradoxical functional facilitation model (Kapur, 1996) that offers possible explanations about how pathological states in the brain lead to development of prodigious skills. This model emphasizes the role of reciprocal inhibitory interaction among adjacent or distant cortical regions

  7. Behavioral and Neural Sustained Attention Deficits in Disruptive Mood Dysregulation Disorder and Attention-Deficit/Hyperactivity Disorder.

    Science.gov (United States)

    Pagliaccio, David; Wiggins, Jillian Lee; Adleman, Nancy E; Curhan, Alexa; Zhang, Susan; Towbin, Kenneth E; Brotman, Melissa A; Pine, Daniel S; Leibenluft, Ellen

    2017-05-01

    Disruptive mood dysregulation disorder (DMDD), characterized by severe irritability, and attention-deficit/hyperactivity disorder (ADHD) are highly comorbid. This is the first study to characterize neural and behavioral similarities and differences in attentional functioning across these disorders. Twenty-seven healthy volunteers, 31 patients with DMDD, and 25 patients with ADHD (8 to 18 years old) completed a functional magnetic resonance imaging attention task. Group differences in intra-subject variability in reaction time (RT) were examined. The present functional magnetic resonance imaging analytic approach precisely quantified trial-wise associations between RT and brain activity. Group differences manifested in the relation between RT and brain activity (all regions: p  2.54, partial eta-squared [η p 2 ] > 0.06). Patients with DMDD showed specific alterations in the right paracentral lobule, superior parietal lobule, fusiform gyrus, and cerebellar culmen. In contrast, patients with DMDD and those with ADHD exhibited blunted compensatory increases in activity on long RT trials. In addition, youth with DMDD exhibited increased activity in the postcentral gyrus, medial frontal gyrus, and cerebellar tonsil and declive (all regions: p  2.46, η p 2 > 0.06). Groups in the imaging sample did not differ significantly in intra-subject variability in RT (F 2,79  = 2.664, p = .076, η p 2  = 0.063), although intra-subject variability in RT was significantly increased in youth with DMDD and ADHD when including those not meeting strict motion and accuracy criteria for imaging analysis (F 2,96  = 4.283, p = .017, η p 2  = 0.083). Patients with DMDD exhibited specific alterations in the relation between pre-stimulus brain activity and RT. Patients with DMDD and those with ADHD exhibited similar blunting of compensatory neural activity in frontal, parietal, and other regions. In addition, patients with DMDD showed increased RT variability compared with healthy

  8. Concurrent OCT imaging of stimulus evoked retinal neural activation and hemodynamic responses

    Science.gov (United States)

    Son, Taeyoon; Wang, Benquan; Lu, Yiming; Chen, Yanjun; Cao, Dingcai; Yao, Xincheng

    2017-02-01

    It is well established that major retinal diseases involve distortions of the retinal neural physiology and blood vascular structures. However, the details of distortions in retinal neurovascular coupling associated with major eye diseases are not well understood. In this study, a multi-modal optical coherence tomography (OCT) imaging system was developed to enable concurrent imaging of retinal neural activity and vascular hemodynamics. Flicker light stimulation was applied to mouse retinas to evoke retinal neural responses and hemodynamic changes. The OCT images were acquired continuously during the pre-stimulation, light-stimulation, and post-stimulation phases. Stimulus-evoked intrinsic optical signals (IOSs) and hemodynamic changes were observed over time in blood-free and blood regions, respectively. Rapid IOSs change occurred almost immediately after stimulation. Both positive and negative signals were observed in adjacent retinal areas. The hemodynamic changes showed time delays after stimulation. The signal magnitudes induced by light stimulation were observed in blood regions and did not show significant changes in blood-free regions. These differences may arise from different mechanisms in blood vessels and neural tissues in response to light stimulation. These characteristics agreed well with our previous observations in mouse retinas. Further development of the multimodal OCT may provide a new imaging method for studying how retinal structures and metabolic and neural functions are affected by age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and other diseases, which promises novel noninvasive biomarkers for early disease detection and reliable treatment evaluations of eye diseases.

  9. Altered Synchronizations among Neural Networks in Geriatric Depression.

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2014-08-01

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

  11. An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation

    DEFF Research Database (Denmark)

    Lyksborg, Mark; Puonti, Oula; Agn, Mikael

    2015-01-01

    Accurate tumor segmentation plays an important role in radiosurgery planning and the assessment of radiotherapy treatment efficacy. In this paper we propose a method combining an ensemble of 2D convolutional neural networks for doing a volumetric segmentation of magnetic resonance images....... The segmentation is done in three steps; first the full tumor region, is segmented from the background by a voxel-wise merging of the decisions of three networks learned from three orthogonal planes, next the segmentation is refined using a cellular automaton-based seed growing method known as growcut. Finally......, within-tumor sub-regions are segmented using an additional ensemble of networks trained for the task. We demonstrate the method on the MICCAI Brain Tumor Segmentation Challenge dataset of 2014, and show improved segmentation accuracy compared to an axially trained 2D network and an ensemble segmentation...

  12. Automated bony region identification using artificial neural networks: reliability and validation measurements

    International Nuclear Information System (INIS)

    Gassman, Esther E.; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H.; Powell, Stephanie M.; Magnotta, Vincent A.; Ramme, Austin J.; Adams, Brian D.; Grosland, Nicole M.

    2008-01-01

    The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen. Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand. The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater. The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality. (orig.)

  13. Automated bony region identification using artificial neural networks: reliability and validation measurements

    Energy Technology Data Exchange (ETDEWEB)

    Gassman, Esther E.; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States); Powell, Stephanie M. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Magnotta, Vincent A. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Ramme, Austin J. [University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Adams, Brian D. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Orthopaedics and Rehabilitation, Iowa City, IA (United States); Grosland, Nicole M. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Orthopaedics and Rehabilitation, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States)

    2008-04-15

    The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen. Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand. The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater. The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality. (orig.)

  14. Hidden Neural Networks: A Framework for HMM/NN Hybrids

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric; Krogh, Anders Stærmose

    1997-01-01

    This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is nor...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task...

  15. Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

    Science.gov (United States)

    Mitani, S; Okamoto, H

    1991-05-01

    Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

  16. The fiber-optic imaging and manipulation of neural activity during animal behavior.

    Science.gov (United States)

    Miyamoto, Daisuke; Murayama, Masanori

    2016-02-01

    Recent progress with optogenetic probes for imaging and manipulating neural activity has further increased the relevance of fiber-optic systems for neural circuitry research. Optical fibers, which bi-directionally transmit light between separate sites (even at a distance of several meters), can be used for either optical imaging or manipulating neural activity relevant to behavioral circuitry mechanisms. The method's flexibility and the specifications of the light structure are well suited for following the behavior of freely moving animals. Furthermore, thin optical fibers allow researchers to monitor neural activity from not only the cortical surface but also deep brain regions, including the hippocampus and amygdala. Such regions are difficult to target with two-photon microscopes. Optogenetic manipulation of neural activity with an optical fiber has the advantage of being selective for both cell-types and projections as compared to conventional electrophysiological brain tissue stimulation. It is difficult to extract any data regarding changes in neural activity solely from a fiber-optic manipulation device; however, the readout of data is made possible by combining manipulation with electrophysiological recording, or the simultaneous application of optical imaging and manipulation using a bundle-fiber. The present review introduces recent progress in fiber-optic imaging and manipulation methods, while also discussing fiber-optic system designs that are suitable for a given experimental protocol. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

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

    Science.gov (United States)

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

    2007-01-26

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

  18. Beneficial role of noise in artificial neural networks

    International Nuclear Information System (INIS)

    Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

    2008-01-01

    We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated

  19. Neural responses to macronutrients: hedonic and homeostatic mechanisms.

    Science.gov (United States)

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

    2015-05-01

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

  20. Review of the Neural Oscillations Underlying Meditation

    Directory of Open Access Journals (Sweden)

    Darrin J. Lee

    2018-03-01

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

  1. Young adult smokers' neural response to graphic cigarette warning labels

    Directory of Open Access Journals (Sweden)

    Adam E. Green

    2016-06-01

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

  2. The neural basis of the bystander effect--the influence of group size on neural activity when witnessing an emergency.

    Science.gov (United States)

    Hortensius, Ruud; de Gelder, Beatrice

    2014-06-01

    Naturalistic observation and experimental studies in humans and other primates show that observing an individual in need automatically triggers helping behavior. The aim of the present study is to clarify the neurofunctional basis of social influences on individual helping behavior. We investigate whether when participants witness an emergency, while performing an unrelated color-naming task in an fMRI scanner, the number of bystanders present at the emergency influences neural activity in regions related to action preparation. The results show a decrease in activity with the increase in group size in the left pre- and postcentral gyri and left medial frontal gyrus. In contrast, regions related to visual perception and attention show an increase in activity. These results demonstrate the neural mechanisms of social influence on automatic action preparation that is at the core of helping behavior when witnessing an emergency. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Neural plasticity and its initiating conditions in tinnitus.

    Science.gov (United States)

    Roberts, L E

    2018-03-01

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

  4. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

    Directory of Open Access Journals (Sweden)

    Seung Seog Han

    Full Text Available Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively, 125 images from Hallym University (C dataset, and 939 images from Seoul National University (D dataset. The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98, (82.7 / 96.7 / 0.95, (92.3 / 79.3 / 0.93, (87.7 / 69.3 / 0.82 for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01 higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

  5. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

    Science.gov (United States)

    Han, Seung Seog; Park, Gyeong Hun; Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo; Chang, Sung Eun

    2018-01-01

    Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

  6. The mouse that roared: neural mechanisms of social hierarchy.

    Science.gov (United States)

    Wang, Fei; Kessels, Helmut W; Hu, Hailan

    2014-11-01

    Hierarchical social status greatly influences behavior and health. Human and animal studies have begun to identify the brain regions that are activated during the formation of social hierarchies. They point towards the prefrontal cortex (PFC) as a central regulator, with brain areas upstream of the PFC conveying information about social status, and downstream brain regions executing dominance behavior. This review summarizes our current knowledge on the neural circuits that control social status. We discuss how the neural mechanisms for various types of dominance behavior can be studied in laboratory rodents by selective manipulation of neuronal activity or synaptic plasticity. These studies may help in finding the cause of social stress-related mental and physical health problems. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Regulation of Msx genes by a Bmp gradient is essential for neural crest specification.

    Science.gov (United States)

    Tribulo, Celeste; Aybar, Manuel J; Nguyen, Vu H; Mullins, Mary C; Mayor, Roberto

    2003-12-01

    There is evidence in Xenopus and zebrafish embryos that the neural crest/neural folds are specified at the border of the neural plate by a precise threshold concentration of a Bmp gradient. In order to understand the molecular mechanism by which a gradient of Bmp is able to specify the neural crest, we analyzed how the expression of Bmp targets, the Msx genes, is regulated and the role that Msx genes has in neural crest specification. As Msx genes are directly downstream of Bmp, we analyzed Msx gene expression after experimental modification in the level of Bmp activity by grafting a bead soaked with noggin into Xenopus embryos, by expressing in the ectoderm a dominant-negative Bmp4 or Bmp receptor in Xenopus and zebrafish embryos, and also through Bmp pathway component mutants in the zebrafish. All the results show that a reduction in the level of Bmp activity leads to an increase in the expression of Msx genes in the neural plate border. Interestingly, by reaching different levels of Bmp activity in animal cap ectoderm, we show that a specific concentration of Bmp induces msx1 expression to a level similar to that required to induce neural crest. Our results indicate that an intermediate level of Bmp activity specifies the expression of Msx genes in the neural fold region. In addition, we have analyzed the role that msx1 plays on neural crest specification. As msx1 has a role in dorsoventral pattering, we have carried out conditional gain- and loss-of-function experiments using different msx1 constructs fused to a glucocorticoid receptor element to avoid an early effect of this factor. We show that msx1 expression is able to induce all other early neural crest markers tested (snail, slug, foxd3) at the time of neural crest specification. Furthermore, the expression of a dominant negative of Msx genes leads to the inhibition of all the neural crest markers analyzed. It has been previously shown that snail is one of the earliest genes acting in the neural crest

  8. Psychosocial versus physiological stress – meta-analyses on deactivations and activations of the neural correlates of stress reactions

    Science.gov (United States)

    Kogler, Lydia; Mueller, Veronika I.; Chang, Amy; Eickhoff, Simon B.; Fox, Peter T.; Gur, Ruben C.; Derntl, Birgit

    2015-01-01

    Stress is present in everyday life in various forms and situations. Two stressors frequently investigated are physiological and psychosocial stress. Besides similar subjective and hormonal responses, it has been suggested that they also share common neural substrates. The current study used activation-likelihood-estimation meta-analysis to test this assumption by integrating results of previous neuroimaging studies on stress processing. Reported results are cluster-level FWE corrected. The inferior frontal gyrus (IFG) and the anterior insula (AI) were the only regions that demonstrated overlapping activation for both stressors. Analysis of physiological stress showed consistent activation of cognitive and affective components of pain processing such as the insula, striatum, or the middle cingulate cortex. Contrarily, analysis across psychosocial stress revealed consistent activation of the right superior temporal gyrus and deactivation of the striatum. Notably, parts of the striatum appeared to be functionally specified: the dorsal striatum was activated in physiological stress, whereas the ventral striatum was deactivated in psychosocial stress. Additional functional connectivity and decoding analyses further characterized this functional heterogeneity and revealed higher associations of the dorsal striatum with motor regions and of the ventral striatum with reward processing. Based on our meta-analytic approach, activation of the IFG and the AI seems to indicate a global neural stress reaction. While physiological stress activates a motoric fight-or-flight reaction, during psychosocial stress attention is shifted towards emotion regulation and goal-directed behavior, and reward processing is reduced. Our results show the significance of differentiating physiological and psychosocial stress in neural engagement. Furthermore, the assessment of deactivations in addition to activations in stress research is highly recommended. PMID:26123376

  9. Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

    Directory of Open Access Journals (Sweden)

    Zhonghua Wu

    2017-01-01

    Full Text Available This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

  10. A comparative study of two neural networks for document retrieval

    International Nuclear Information System (INIS)

    Hui, S.C.; Goh, A.

    1997-01-01

    In recent years there has been specific interest in adopting advanced computer techniques in the field of document retrieval. This interest is generated by the fact that classical methods such as the Boolean search, the vector space model or even probabilistic retrieval cannot handle the increasing demands of end-users in satisfying their needs. The most recent attempt is the application of the neural network paradigm as a means of providing end-users with a more powerful retrieval mechanism. Neural networks are not only good pattern matchers but also highly versatile and adaptable. In this paper, we demonstrate how to apply two neural networks, namely Adaptive Resonance Theory and Fuzzy Kohonen Neural Network, for document retrieval. In addition, a comparison of these two neural networks based on performance is also given

  11. Differentiation state determines neural effects on microvascular endothelial cells

    International Nuclear Information System (INIS)

    Muffley, Lara A.; Pan, Shin-Chen; Smith, Andria N.; Ga, Maricar; Hocking, Anne M.; Gibran, Nicole S.

    2012-01-01

    Growing evidence indicates that nerves and capillaries interact paracrinely in uninjured skin and cutaneous wounds. Although mature neurons are the predominant neural cell in the skin, neural progenitor cells have also been detected in uninjured adult skin. The aim of this study was to characterize differential paracrine effects of neural progenitor cells and mature sensory neurons on dermal microvascular endothelial cells. Our results suggest that neural progenitor cells and mature sensory neurons have unique secretory profiles and distinct effects on dermal microvascular endothelial cell proliferation, migration, and nitric oxide production. Neural progenitor cells and dorsal root ganglion neurons secrete different proteins related to angiogenesis. Specific to neural progenitor cells were dipeptidyl peptidase-4, IGFBP-2, pentraxin-3, serpin f1, TIMP-1, TIMP-4 and VEGF. In contrast, endostatin, FGF-1, MCP-1 and thrombospondin-2 were specific to dorsal root ganglion neurons. Microvascular endothelial cell proliferation was inhibited by dorsal root ganglion neurons but unaffected by neural progenitor cells. In contrast, microvascular endothelial cell migration in a scratch wound assay was inhibited by neural progenitor cells and unaffected by dorsal root ganglion neurons. In addition, nitric oxide production by microvascular endothelial cells was increased by dorsal root ganglion neurons but unaffected by neural progenitor cells. -- Highlights: ► Dorsal root ganglion neurons, not neural progenitor cells, regulate microvascular endothelial cell proliferation. ► Neural progenitor cells, not dorsal root ganglion neurons, regulate microvascular endothelial cell migration. ► Neural progenitor cells and dorsal root ganglion neurons do not effect microvascular endothelial tube formation. ► Dorsal root ganglion neurons, not neural progenitor cells, regulate microvascular endothelial cell production of nitric oxide. ► Neural progenitor cells and dorsal root

  12. Neural Correlates of Social Influence Among Cannabis Users.

    Science.gov (United States)

    Gilman, Jodi M

    2017-06-01

    Although peer influence is an important factor in the initiation and maintenance of cannabis use, few studies have investigated the neural correlates of peer influence among cannabis users. The current review summarizes research on the neuroscience of social influence in cannabis users, with the goal of highlighting gaps in the literature and the need for future research. Brain regions underlying peer influence may function differently in cannabis users. Compared to non-using controls, regions of the brain underlying reward, such as the striatum, show greater connectivity with frontal regions, and also show hyperactivity when participants are presented with peer information. Other subcortical regions, such as the insula, show hypoactivation during social exclusion in cannabis users, indicating that neural responses to peer interactions may be altered in cannabis users. Although neuroscience is increasingly being used to study social behavior, few studies have specifically focused on cannabis use, and therefore it is difficult to draw conclusions about social mechanisms that may differentiate cannabis users and controls. This area of research may be a promising avenue in which to explore a critical factor underlying cannabis use and addiction.

  13. Neural predictors of sensorimotor adaptation rate and savings.

    Science.gov (United States)

    Cassady, Kaitlin; Ruitenberg, Marit; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Riascos Castenada, Roy; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael

    2018-04-01

    In this study, we investigate whether individual variability in the rate of visuomotor adaptation and multiday savings is associated with differences in regional gray matter volume and resting-state functional connectivity. Thirty-four participants performed a manual adaptation task during two separate test sessions, on average 9 days apart. Functional connectivity strength between sensorimotor, dorsal cingulate, and temporoparietal regions of the brain was found to predict the rate of learning during the early phase of the adaptation task. In contrast, default mode network connectivity strength was found to predict both the rate of learning during the late adaptation phase and savings. As for structural predictors, greater gray matter volume in temporoparietal and occipital regions predicted faster early learning, whereas greater gray matter volume in superior posterior regions of the cerebellum predicted faster late learning. These findings suggest that the offline neural predictors of early adaptation may facilitate the cognitive aspects of sensorimotor adaptation, supported by the involvement of temporoparietal and cingulate networks. The offline neural predictors of late adaptation and savings, including the default mode network and the cerebellum, likely support the storage and modification of newly acquired sensorimotor representations. © 2017 Wiley Periodicals, Inc.

  14. Forecasting Flare Activity Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Hernandez, T.

    2017-12-01

    Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

  15. A wirelessly powered microspectrometer for neural probe-pin device

    Science.gov (United States)

    Choi, Sang H.; Kim, Min H.; Song, Kyo D.; Yoon, Hargsoon; Lee, Uhn

    2015-12-01

    Treatment of neurological anomalies, whether done invasively or not, places stringent demands on device functionality and size. We have developed a micro-spectrometer for use as an implantable neural probe to monitor neuro-chemistry in synapses. The micro-spectrometer, based on a NASA-invented miniature Fresnel grating, is capable of differentiating the emission spectra from various brain tissues. The micro-spectrometer meets the size requirements, and is able to probe the neuro-chemistry and suppression voltage typically associated with a neural anomaly. This neural probe-pin device (PPD) is equipped with wireless power technology (WPT) to enable operation in a continuous manner without requiring an implanted battery. The implanted neural PPD, together with a neural electronics interface and WPT, enable real-time measurement and control/feedback for remediation of neural anomalies. The design and performance of the combined PPD/WPT device for monitoring dopamine in a rat brain will be presented to demonstrate the current level of development. Future work on this device will involve the addition of an embedded expert system capable of performing semi-autonomous management of neural functions through a routine of sensing, processing, and control.

  16. Adrenergic innervation of the developing chick heart: neural crest ablations to produce sympathetically aneural hearts

    International Nuclear Information System (INIS)

    Kirby, M.; Stewart, D.

    1984-01-01

    Ablation of various regions of premigratory trunk neural crest which gives rise to the sympathetic trunks was used to remove sympathetic cardiac innervation. Neuronal uptake of [ 3 H]-norepinephrine was used as an index of neuronal development in the chick atrium. Following ablation of neural crest over somites 10-15 or 15-20, uptake was significantly decreased in the atrium at 16 and 17 days of development. Ablation of neural crest over somites 5-10 and 20-25 caused no decrease in [ 3 H]-norepinephrine uptake. Removal of neural crest over somites 5-25 or 10-20 caused approximately equal depletions of [ 3 H]-norepinephrine uptake in the atrium. Cardiac norepinephrine concentration was significantly depressed following ablation of neural crest over somites 5-25 but not over somites 10-20. Light-microscopic and histofluorescent preparations confirmed the absence of sympathetic trunks in the region of the normal origin of the sympathetic cardiac nerves following neural crest ablation over somites 10-20. The neural tube and dorsal root ganglia were damaged in the area of the neural-crest ablation; however, all of these structures were normal cranial and caudal to the lesioned area. Development of most of the embryos as well as the morphology of all of the hearts was normal following the lesion. These results indicate that it is possible to produce sympathetically aneural hearts by neural-crest ablation; however, sympathetic cardiac nerves account for an insignificant amount of cardiac norepinephrine

  17. Neural network based multiscale image restoration approach

    Science.gov (United States)

    de Castro, Ana Paula A.; da Silva, José D. S.

    2007-02-01

    This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

  18. Adjusting neural additional stabilizers for damping interarea oscillations; Ajuste de estabilizadores suplementares neurais para amortecimento de oscilacoes interareas

    Energy Technology Data Exchange (ETDEWEB)

    Furini, M.A.; Araujo, P.B. de; Pereira, A.L.S. [Universidade Estadual Paulista (FEIS/UNESP), Ilha Solteira, SP (Brazil). Fac. de Engenharia. Dept. Engenharia Eletrica], Emails: mafurini@aluno.feis.unesp.br, percival@dee.feis.unesp.br, andspa@gmail.com

    2009-07-01

    This paper aims at analyzing the main operation and design of operationally robust controllers in order to damp the electromechanics oscillations type inter area. For this we used an intelligent control technique based on artificial neural networks, where a multilayer perceptron it was implemented. We used a symmetrical test system of four generators, ten bars and nine transmission lines to verify the performance of the power system stabilizers and power oscillation damping (POD) for the FACTS devices, unified power flow controller (UPFC), designed for neural networks. The results show the superiority in the operation and control of oscillations in power systems using UPFC equipped with the POD.

  19. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy

    Directory of Open Access Journals (Sweden)

    Nouri S.

    2017-03-01

    Full Text Available Background: The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. Objective: This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO estimating tumor positions in real-time radiotherapy. Method: One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. Results: The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. Conclusion: The internal target volume (ITV should be determined based on the applied neural network algorithm on training steps.

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

    Science.gov (United States)

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

    2013-01-09

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

  1. Medical image segmentation by means of constraint satisfaction neural network

    International Nuclear Information System (INIS)

    Chen, C.T.; Tsao, C.K.; Lin, W.C.

    1990-01-01

    This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency

  2. The neural basis of unconditional love.

    Science.gov (United States)

    Beauregard, Mario; Courtemanche, Jérôme; Paquette, Vincent; St-Pierre, Evelyne Landry

    2009-05-15

    Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.

  3. Brain noise is task dependent and region specific.

    Science.gov (United States)

    Misić, Bratislav; Mills, Travis; Taylor, Margot J; McIntosh, Anthony R

    2010-11-01

    The emerging organization of anatomical and functional connections during human brain development is thought to facilitate global integration of information. Recent empirical and computational studies have shown that this enhanced capacity for information processing enables a diversified dynamic repertoire that manifests in neural activity as irregularity and noise. However, transient functional networks unfold over multiple time, scales and the embedding of a particular region depends not only on development, but also on the manner in which sensory and cognitive systems are engaged. Here we show that noise is a facet of neural activity that is also sensitive to the task context and is highly region specific. Children (6-16 yr) and adults (20-41 yr) performed a one-back face recognition task with inverted and upright faces. Neuromagnetic activity was estimated at several hundred sources in the brain by applying a beamforming technique to the magnetoencephalogram (MEG). During development, neural activity became more variable across the whole brain, with most robust increases in medial parietal regions, such as the precuneus and posterior cingulate cortex. For young children and adults, activity evoked by upright faces was more variable and noisy compared with inverted faces, and this effect was reliable only in the right fusiform gyrus. These results are consistent with the notion that upright faces engender a variety of integrative neural computations, such as the relations among facial features and their holistic constitution. This study shows that transient changes in functional integration modulated by task demand are evident in the variability of regional neural activity.

  4. A regional neural network model for predicting mean daily river water temperature

    Science.gov (United States)

    Wagner, Tyler; DeWeber, Jefferson Tyrell

    2014-01-01

    Water temperature is a fundamental property of river habitat and often a key aspect of river resource management, but measurements to characterize thermal regimes are not available for most streams and rivers. As such, we developed an artificial neural network (ANN) ensemble model to predict mean daily water temperature in 197,402 individual stream reaches during the warm season (May–October) throughout the native range of brook trout Salvelinus fontinalis in the eastern U.S. We compared four models with different groups of predictors to determine how well water temperature could be predicted by climatic, landform, and land cover attributes, and used the median prediction from an ensemble of 100 ANNs as our final prediction for each model. The final model included air temperature, landform attributes and forested land cover and predicted mean daily water temperatures with moderate accuracy as determined by root mean squared error (RMSE) at 886 training sites with data from 1980 to 2009 (RMSE = 1.91 °C). Based on validation at 96 sites (RMSE = 1.82) and separately for data from 2010 (RMSE = 1.93), a year with relatively warmer conditions, the model was able to generalize to new stream reaches and years. The most important predictors were mean daily air temperature, prior 7 day mean air temperature, and network catchment area according to sensitivity analyses. Forest land cover at both riparian and catchment extents had relatively weak but clear negative effects. Predicted daily water temperature averaged for the month of July matched expected spatial trends with cooler temperatures in headwaters and at higher elevations and latitudes. Our ANN ensemble is unique in predicting daily temperatures throughout a large region, while other regional efforts have predicted at relatively coarse time steps. The model may prove a useful tool for predicting water temperatures in sampled and unsampled rivers under current conditions and future projections of climate

  5. Empirical Investigation of Optimization Algorithms in Neural Machine Translation

    Directory of Open Access Journals (Sweden)

    Bahar Parnia

    2017-06-01

    Full Text Available Training neural networks is a non-convex and a high-dimensional optimization problem. In this paper, we provide a comparative study of the most popular stochastic optimization techniques used to train neural networks. We evaluate the methods in terms of convergence speed, translation quality, and training stability. In addition, we investigate combinations that seek to improve optimization in terms of these aspects. We train state-of-the-art attention-based models and apply them to perform neural machine translation. We demonstrate our results on two tasks: WMT 2016 En→Ro and WMT 2015 De→En.

  6. Typology of nonlinear activity waves in a layered neural continuum.

    Science.gov (United States)

    Koch, Paul; Leisman, Gerry

    2006-04-01

    Neural tissue, a medium containing electro-chemical energy, can amplify small increments in cellular activity. The growing disturbance, measured as the fraction of active cells, manifests as propagating waves. In a layered geometry with a time delay in synaptic signals between the layers, the delay is instrumental in determining the amplified wavelengths. The growth of the waves is limited by the finite number of neural cells in a given region of the continuum. As wave growth saturates, the resulting activity patterns in space and time show a variety of forms, ranging from regular monochromatic waves to highly irregular mixtures of different spatial frequencies. The type of wave configuration is determined by a number of parameters, including alertness and synaptic conditioning as well as delay. For all cases studied, using numerical solution of the nonlinear Wilson-Cowan (1973) equations, there is an interval in delay in which the wave mixing occurs. As delay increases through this interval, during a series of consecutive waves propagating through a continuum region, the activity within that region changes from a single-frequency to a multiple-frequency pattern and back again. The diverse spatio-temporal patterns give a more concrete form to several metaphors advanced over the years to attempt an explanation of cognitive phenomena: Activity waves embody the "holographic memory" (Pribram, 1991); wave mixing provides a plausible cause of the competition called "neural Darwinism" (Edelman, 1988); finally the consecutive generation of growing neural waves can explain the discontinuousness of "psychological time" (Stroud, 1955).

  7. A loop-based neural architecture for structured behavior encoding and decoding.

    Science.gov (United States)

    Gisiger, Thomas; Boukadoum, Mounir

    2018-02-01

    We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. How are things adding up? Neural differences between arithmetic operations are due to general problem solving strategies.

    Science.gov (United States)

    Tschentscher, Nadja; Hauk, Olaf

    2014-05-15

    A number of previous studies have interpreted differences in brain activation between arithmetic operation types (e.g. addition and multiplication) as evidence in favor of distinct cortical representations, processes or neural systems. It is still not clear how differences in general task complexity contribute to these neural differences. Here, we used a mental arithmetic paradigm to disentangle brain areas related to general problem solving from those involved in operation type specific processes (addition versus multiplication). We orthogonally varied operation type and complexity. Importantly, complexity was defined not only based on surface criteria (for example number size), but also on the basis of individual participants' strategy ratings, which were validated in a detailed behavioral analysis. We replicated previously reported operation type effects in our analyses based on surface criteria. However, these effects vanished when controlling for individual strategies. Instead, procedural strategies contrasted with memory retrieval reliably activated fronto-parietal and motor regions, while retrieval strategies activated parietal cortices. This challenges views that operation types rely on partially different neural systems, and suggests that previously reported differences between operation types may have emerged due to invalid measures of complexity. We conclude that mental arithmetic is a powerful paradigm to study brain networks of abstract problem solving, as long as individual participants' strategies are taken into account. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition.

    Science.gov (United States)

    Scherf, K Suzanne; Elbich, Daniel B; Motta-Mena, Natalie V

    2017-01-01

    There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women.

  10. Investigating the Influence of Biological Sex on the Behavioral and Neural Basis of Face Recognition

    Science.gov (United States)

    2017-01-01

    Abstract There is interest in understanding the influence of biological factors, like sex, on the organization of brain function. We investigated the influence of biological sex on the behavioral and neural basis of face recognition in healthy, young adults. In behavior, there were no sex differences on the male Cambridge Face Memory Test (CFMT)+ or the female CFMT+ (that we created) and no own-gender bias (OGB) in either group. We evaluated the functional topography of ventral stream organization by measuring the magnitude and functional neural size of 16 individually defined face-, two object-, and two place-related regions bilaterally. There were no sex differences in any of these measures of neural function in any of the regions of interest (ROIs) or in group level comparisons. These findings reveal that men and women have similar category-selective topographic organization in the ventral visual pathway. Next, in a separate task, we measured activation within the 16 face-processing ROIs specifically during recognition of target male and female faces. There were no sex differences in the magnitude of the neural responses in any face-processing region. Furthermore, there was no OGB in the neural responses of either the male or female participants. Our findings suggest that face recognition behavior, including the OGB, is not inherently sexually dimorphic. Face recognition is an essential skill for navigating human social interactions, which is reflected equally in the behavior and neural architecture of men and women. PMID:28497111

  11. A light and faster regional convolutional neural network for object detection in optical remote sensing images

    Science.gov (United States)

    Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

    2018-07-01

    Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

  12. Neural system for updating object working memory from different sources: sensory stimuli or long-term memory.

    Science.gov (United States)

    Roth, Jennifer K; Courtney, Susan M

    2007-11-15

    Working memory (WM) is the active maintenance of currently relevant information so that it is available for use. A crucial component of WM is the ability to update the contents when new information becomes more relevant than previously maintained information. New information can come from different sources, including from sensory stimuli (SS) or from long-term memory (LTM). Updating WM may involve a single neural system regardless of source, distinct systems for each source, or a common network with additional regions involved specifically in sensory or LTM processes. The current series of experiments indicates that a single fronto-parietal network (including supplementary motor area, parietal, left inferior frontal junction, middle frontal gyrus) is active in updating WM regardless of the source of information. Bilateral cuneus was more active during updating WM from LTM than updating from SS, but the activity in this region was attributable to recalling information from LTM regardless of whether that information was to be entered into WM for future use or not. No regions were found to be more active during updating from SS than updating from LTM. Functional connectivity analysis revealed that different regions within this common update network were differentially more correlated with visual processing regions when participants updated from SS, and more correlated with LTM processing regions when participants updated from the contents of LTM. These results suggest that a single neural mechanism is responsible for controlling the contents of WM regardless of whether that information originates from a sensory stimulus or from LTM. This network of regions involved in updating WM interacts with the rest of the brain differently depending on the source of newly relevant information.

  13. The neural basis of task switching changes with skill acquisition

    Directory of Open Access Journals (Sweden)

    Koji eJimura

    2014-05-01

    Full Text Available Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task versus a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading and a highly practiced one (plain word reading, allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by two weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror versus plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

  14. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

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

  15. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

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

  16. In vitro effects of Epidiferphane™ on adult human neural progenitor cells

    Science.gov (United States)

    Neural stem cells have the capacity to respond to their environment, migrate to the injury site and generate functional cell types, and thus they hold great promise for cell therapies. In addition to representing a source for central nervous system (CNS) repair, neural stem and progenitor cells als...

  17. Cultural influences on neural basis of intergroup empathy.

    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, Hyun Wook; Chiao, Joan Y

    2011-07-15

    Cultures vary in the extent to which people prefer social hierarchical or egalitarian relations between individuals and groups. Here we examined the effect of cultural variation in preference for social hierarchy on the neural basis of intergroup empathy. Using cross-cultural neuroimaging, we measured neural responses while Korean and American participants observed scenes of racial ingroup and outgroup members in emotional pain. Compared to Caucasian-American participants, Korean participants reported experiencing greater empathy and elicited stronger activity in the left temporo-parietal junction (L-TPJ), a region previously associated with mental state inference, for ingroup compared to outgroup members. Furthermore, preferential reactivity within this region to the pain of ingroup relative to outgroup members was associated with greater preference for social hierarchy and ingroup biases in empathy. Together, these results suggest that cultural variation in preference for social hierarchy leads to cultural variation in ingroup-preferences in empathy, due to increased engagement of brain regions associated with representing and inferring the mental states of others. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Age-Related Reversals in Neural Recruitment across Memory Retrieval Phases.

    Science.gov (United States)

    Ford, Jaclyn H; Kensinger, Elizabeth A

    2017-05-17

    Over the last several decades, neuroimaging research has identified age-related neural changes that occur during cognitive tasks. These changes are used to help researchers identify functional changes that contribute to age-related impairments in cognitive performance. One commonly reported example of such a change is an age-related decrease in the recruitment of posterior sensory regions coupled with an increased recruitment of prefrontal regions across multiple cognitive tasks. This shift is often described as a compensatory recruitment of prefrontal regions due to age-related sensory-processing deficits in posterior regions. However, age is not only associated with spatial shifts in recruitment, but also with temporal shifts, in which younger and older adults recruit the same neural region at different points in a task trial. The current study examines the possible contribution of temporal modifications in the often-reported posterior-anterior shift. Participants, ages 19-85, took part in a memory retrieval task with a protracted retrieval trial consisting of an initial memory search phase and a subsequent detail elaboration phase. Age-related neural patterns during search replicated prior reports of age-related decreases in posterior recruitment and increases in prefrontal recruitment. However, during the later elaboration phase, the same posterior regions were associated with age-related increases in activation. Further, ROI and functional connectivity results suggest that these posterior regions function similarly during search and elaboration. These results suggest that the often-reported posterior-anterior shift may not reflect the inability of older adults to engage in sensory processing, but rather a change in when they recruit this processing. SIGNIFICANCE STATEMENT The current study provides evidence that the often-reported posterior-anterior shift in aging may not reflect a global sensory-processing deficit, as has often been reported, but rather a

  19. Photon spectrometry utilizing neural networks

    International Nuclear Information System (INIS)

    Silveira, R.; Benevides, C.; Lima, F.; Vilela, E.

    2015-01-01

    Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)

  20. Regional difference of radiosensitivity of neural cells in the fetal brain of mice on day 13 of gestation

    International Nuclear Information System (INIS)

    Hoshino, Kiyoshi; Kameyama, Yoshiro

    1986-01-01

    Pregnant Slc: ICR mice were exposed to a single whole-body X-irradiation at a dose of 12.5 R or 25 R on day 13 of gestation. After irradiation, fetuses were obtained from mothers at 1- or 3-hour intervals and coronal histological sections were made. Pyknotic cells were counted in the ventricular zone of brain mantle, hippocampal anlage and olfactory bulb. In the 25 R group, peak incidences of pyknotic cells in brain mantle, hippocampal anlage and olfactory bulb were 13.2 %, 6.9 % and 2.2 %, respectively. In the 12.5 R group, these were 6.0 %, 3.2 % and 1.7 %, respectively. This result indicates that neural cells in the ventricular zone of brain mantle are the most radiosensitive among the cerebral regions examined in day-13 mouse fetuses. (author)

  1. Noise Analysis studies with neural networks

    International Nuclear Information System (INIS)

    Seker, S.; Ciftcioglu, O.

    1996-01-01

    Noise analysis studies with neural network are aimed. Stochastic signals at the input of the network are used to obtain an algorithmic multivariate stochastic signal modeling. To this end, lattice modeling of a stochastic signal is performed to obtain backward residual noise sources which are uncorrelated among themselves. There are applied together with an additional input to the network to obtain an algorithmic model which is used for signal detection for early failure in plant monitoring. The additional input provides the information to the network to minimize the difference between the signal and the network's one-step-ahead prediction. A stochastic algorithm is used for training where the errors reflecting the measurement error during the training are also modelled so that fast and consistent convergence of network's weights is obtained. The lattice structure coupled to neural network investigated with measured signals from an actual power plant. (authors)

  2. The neural correlates of maternal and romantic love.

    Science.gov (United States)

    Bartels, Andreas; Zeki, Semir

    2004-03-01

    Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and 'mentalizing', that is, the assessment of other people's intentions and emotions. We conclude that human attachment employs a push-pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.

  3. An efficient automated parameter tuning framework for spiking neural networks.

    Science.gov (United States)

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  4. Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

    Science.gov (United States)

    Xia, Kewei; Huo, Wei

    2016-05-01

    This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  5. Roles of neural stem cells in the repair of peripheral nerve injury.

    Science.gov (United States)

    Wang, Chong; Lu, Chang-Feng; Peng, Jiang; Hu, Cheng-Dong; Wang, Yu

    2017-12-01

    Currently, researchers are using neural stem cell transplantation to promote regeneration after peripheral nerve injury, as neural stem cells play an important role in peripheral nerve injury repair. This article reviews recent research progress of the role of neural stem cells in the repair of peripheral nerve injury. Neural stem cells can not only differentiate into neurons, astrocytes and oligodendrocytes, but can also differentiate into Schwann-like cells, which promote neurite outgrowth around the injury. Transplanted neural stem cells can differentiate into motor neurons that innervate muscles and promote the recovery of neurological function. To promote the repair of peripheral nerve injury, neural stem cells secrete various neurotrophic factors, including brain-derived neurotrophic factor, fibroblast growth factor, nerve growth factor, insulin-like growth factor and hepatocyte growth factor. In addition, neural stem cells also promote regeneration of the axonal myelin sheath, angiogenesis, and immune regulation. It can be concluded that neural stem cells promote the repair of peripheral nerve injury through a variety of ways.

  6. Functional overlap of top-down emotion regulation and generation: an fMRI study identifying common neural substrates between cognitive reappraisal and cognitively generated emotions.

    Science.gov (United States)

    Otto, Benjamin; Misra, Supriya; Prasad, Aditya; McRae, Kateri

    2014-09-01

    One factor that influences the success of emotion regulation is the manner in which the regulated emotion was generated. Recent research has suggested that reappraisal, a top-down emotion regulation strategy, is more effective in decreasing self-reported negative affect when emotions were generated from the top-down, versus the bottom-up. On the basis of a process overlap framework, we hypothesized that the neural regions active during reappraisal would overlap more with emotions that were generated from the top-down, rather than from the bottom-up. In addition, we hypothesized that increased neural overlap between reappraisal and the history effects of top-down emotion generation would be associated with increased reappraisal success. The results of several analyses suggested that reappraisal and emotions that were generated from the top-down share a core network of prefrontal, temporal, and cingulate regions. This overlap is specific; no such overlap was observed between reappraisal and emotions that were generated in a bottom-up fashion. This network consists of regions previously implicated in linguistic processing, cognitive control, and self-relevant appraisals, which are processes thought to be crucial to both reappraisal and top-down emotion generation. Furthermore, individuals with high reappraisal success demonstrated greater neural overlap between reappraisal and the history of top-down emotion generation than did those with low reappraisal success. The overlap of these key regions, reflecting overlapping processes, provides an initial insight into the mechanism by which generation history may facilitate emotion regulation.

  7. Novel paths towards neural cellular products for neurological disorders.

    Science.gov (United States)

    Daadi, Marcel M

    2011-11-01

    The prospect of using neural cells derived from stem cells or from reprogrammed adult somatic cells provides a unique opportunity in cell therapy and drug discovery for developing novel strategies for brain repair. Cell-based therapeutic approaches for treating CNS afflictions caused by disease or injury aim to promote structural repair of the injured or diseased neural tissue, an outcome currently not achieved by drug therapy. Preclinical research in animal models of various diseases or injuries report that grafts of neural cells enhance endogenous repair, provide neurotrophic support to neurons undergoing degeneration and replace lost neural cells. In recent years, the sources of neural cells for treating neurological disorders have been rapidly expanding and in addition to offering therapeutic potential, neural cell products hold promise for disease modeling and drug discovery use. Specific neural cell types have been derived from adult or fetal brain, from human embryonic stem cells, from induced pluripotent stem cells and directly transdifferentiated from adult somatic cells, such as skin cells. It is yet to be determined if the latter approach will evolve into a paradigm shift in the fields of stem cell research and regenerative medicine. These multiple sources of neural cells cover a wide spectrum of safety that needs to be balanced with efficacy to determine the viability of the cellular product. In this article, we will review novel sources of neural cells and discuss current obstacles to developing them into viable cellular products for treating neurological disorders.

  8. Neural activity predicts attitude change in cognitive dissonance.

    Science.gov (United States)

    van Veen, Vincent; Krug, Marie K; Schooler, Jonathan W; Carter, Cameron S

    2009-11-01

    When our actions conflict with our prior attitudes, we often change our attitudes to be more consistent with our actions. This phenomenon, known as cognitive dissonance, is considered to be one of the most influential theories in psychology. However, the neural basis of this phenomenon is unknown. Using a Solomon four-group design, we scanned participants with functional MRI while they argued that the uncomfortable scanner environment was nevertheless a pleasant experience. We found that cognitive dissonance engaged the dorsal anterior cingulate cortex and anterior insula; furthermore, we found that the activation of these regions tightly predicted participants' subsequent attitude change. These effects were not observed in a control group. Our findings elucidate the neural representation of cognitive dissonance, and support the role of the anterior cingulate cortex in detecting cognitive conflict and the neural prediction of attitude change.

  9. Neural circuitry of emotional and cognitive conflict revealed through facial expressions.

    Science.gov (United States)

    Chiew, Kimberly S; Braver, Todd S

    2011-03-09

    Neural systems underlying conflict processing have been well studied in the cognitive realm, but the extent to which these overlap with those underlying emotional conflict processing remains unclear. A novel adaptation of the AX Continuous Performance Task (AX-CPT), a stimulus-response incompatibility paradigm, was examined that permits close comparison of emotional and cognitive conflict conditions, through the use of affectively-valenced facial expressions as the response modality. Brain activity was monitored with functional magnetic resonance imaging (fMRI) during performance of the emotional AX-CPT. Emotional conflict was manipulated on a trial-by-trial basis, by requiring contextually pre-cued facial expressions to emotional probe stimuli (IAPS images) that were either affectively compatible (low-conflict) or incompatible (high-conflict). The emotion condition was contrasted against a matched cognitive condition that was identical in all respects, except that probe stimuli were emotionally neutral. Components of the brain cognitive control network, including dorsal anterior cingulate cortex (ACC) and lateral prefrontal cortex (PFC), showed conflict-related activation increases in both conditions, but with higher activity during emotion conditions. In contrast, emotion conflict effects were not found in regions associated with affective processing, such as rostral ACC. These activation patterns provide evidence for a domain-general neural system that is active for both emotional and cognitive conflict processing. In line with previous behavioural evidence, greatest activity in these brain regions occurred when both emotional and cognitive influences additively combined to produce increased interference.

  10. Attention training improves aberrant neural dynamics during working memory processing in veterans with PTSD.

    Science.gov (United States)

    McDermott, Timothy J; Badura-Brack, Amy S; Becker, Katherine M; Ryan, Tara J; Bar-Haim, Yair; Pine, Daniel S; Khanna, Maya M; Heinrichs-Graham, Elizabeth; Wilson, Tony W

    2016-12-01

    Posttraumatic stress disorder (PTSD) is associated with executive functioning deficits, including disruptions in working memory (WM). Recent studies suggest that attention training reduces PTSD symptomatology, but the underlying neural mechanisms are unknown. We used high-density magnetoencephalography (MEG) to evaluate whether attention training modulates brain regions serving WM processing in PTSD. Fourteen veterans with PTSD completed a WM task during a 306-sensor MEG recording before and after 8 sessions of attention training treatment. A matched comparison sample of 12 combat-exposed veterans without PTSD completed the same WM task during a single MEG session. To identify the spatiotemporal dynamics, each group's data were transformed into the time-frequency domain, and significant oscillatory brain responses were imaged using a beamforming approach. All participants exhibited activity in left hemispheric language areas consistent with a verbal WM task. Additionally, veterans with PTSD and combat-exposed healthy controls each exhibited oscillatory responses in right hemispheric homologue regions (e.g., right Broca's area); however, these responses were in opposite directions. Group differences in oscillatory activity emerged in the theta band (4-8 Hz) during encoding and in the alpha band (9-12 Hz) during maintenance and were significant in right prefrontal and right supramarginal and inferior parietal regions. Importantly, following attention training, these significant group differences were reduced or eliminated. This study provides initial evidence that attention training improves aberrant neural activity in brain networks serving WM processing.

  11. A dynamic neural field model of temporal order judgments.

    Science.gov (United States)

    Hecht, Lauren N; Spencer, John P; Vecera, Shaun P

    2015-12-01

    Temporal ordering of events is biased, or influenced, by perceptual organization-figure-ground organization-and by spatial attention. For example, within a region assigned figural status or at an attended location, onset events are processed earlier (Lester, Hecht, & Vecera, 2009; Shore, Spence, & Klein, 2001), and offset events are processed for longer durations (Hecht & Vecera, 2011; Rolke, Ulrich, & Bausenhart, 2006). Here, we present an extension of a dynamic field model of change detection (Johnson, Spencer, Luck, & Schöner, 2009; Johnson, Spencer, & Schöner, 2009) that accounts for both the onset and offset performance for figural and attended regions. The model posits that neural populations processing the figure are more active, resulting in a peak of activation that quickly builds toward a detection threshold when the onset of a target is presented. This same enhanced activation for some neural populations is maintained when a present target is removed, creating delays in the perception of the target's offset. We discuss the broader implications of this model, including insights regarding how neural activation can be generated in response to the disappearance of information. (c) 2015 APA, all rights reserved).

  12. Adaptive enhanced sampling by force-biasing using neural networks

    Science.gov (United States)

    Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

    2018-04-01

    A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.

  13. Decreased neural precursor cell pool in NADPH oxidase 2-deficiency: From mouse brain to neural differentiation of patient derived iPSC

    Directory of Open Access Journals (Sweden)

    Zeynab Nayernia

    2017-10-01

    Full Text Available There is emerging evidence for the involvement of reactive oxygen species (ROS in the regulation of stem cells and cellular differentiation. Absence of the ROS-generating NADPH oxidase NOX2 in chronic granulomatous disease (CGD patients, predominantly manifests as immune deficiency, but has also been associated with decreased cognition. Here, we investigate the role of NOX enzymes in neuronal homeostasis in adult mouse brain and in neural cells derived from human induced pluripotent stem cells (iPSC. High levels of NOX2 were found in mouse adult neurogenic regions. In NOX2-deficient mice, neurogenic regions showed diminished redox modifications, as well as decrease in neuroprecursor numbers and in expression of genes involved in neural differentiation including NES, BDNF and OTX2. iPSC from healthy subjects and patients with CGD were used to study the role of NOX2 in human in vitro neuronal development. Expression of NOX2 was low in undifferentiated iPSC, upregulated upon neural induction, and disappeared during neuronal differentiation. In human neurospheres, NOX2 protein and ROS generation were polarized within the inner cell layer of rosette structures. NOX2 deficiency in CGD-iPSCs resulted in an abnormal neural induction in vitro, as revealed by a reduced expression of neuroprogenitor markers (NES, BDNF, OTX2, NRSF/REST, and a decreased generation of mature neurons. Vector-mediated NOX2 expression in NOX2-deficient iPSCs rescued neurogenesis. Taken together, our study provides novel evidence for a regulatory role of NOX2 during early stages of neurogenesis in mouse and human.

  14. Structural neural correlates of multitasking: A voxel-based morphometry study.

    Science.gov (United States)

    Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

    2016-12-01

    Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  15. The neural coding of creative idea generation across adolescence and early adulthood

    Directory of Open Access Journals (Sweden)

    Sietske eKleibeuker

    2013-12-01

    Full Text Available Creativity is considered key to human prosperity, yet the neurocognitive principles underlying creative performance, and their development, are still poorly understood. To fill this void, we examined the neural correlates of divergent thinking in adults (25-30 yrs and adolescents (15-17 yrs. Participants generated alternative uses (AU or ordinary characteristics (OC for common objects while brain activity was assessed using fMRI. Adults outperformed adolescents on the number of solutions for AU and OC trials. Contrasting neural activity for AU with OC trials revealed increased recruitment of left angular gyrus, left supramarginal gyrus, and bilateral middle temporal gyrus in both adults and adolescents. When only trials with multiple alternative uses were included in the analysis, participants showed additional left inferior frontal gyrus (IFG/middle frontal gyrus (MFG activation for AU compared to OC trials. Correspondingly, individual difference analyses showed a positive correlation between activations for AU relative to OC trials in left IFG/MFG and divergent thinking performance and activations were more pronounced in adults than in adolescents. Taken together, the results of this study demonstrated that creative idea generation involves recruitment of mainly left lateralized parietal and temporal brain regions. Generating multiple creative ideas, a hallmark of divergent thinking, shows additional lateral PFC activation that is not yet optimized in adolescence.

  16. Neural pathway in the right hemisphere underlies verbal insight problem solving.

    Science.gov (United States)

    Zhao, Q; Zhou, Z; Xu, H; Fan, W; Han, L

    2014-01-03

    Verbal insight problem solving means to break mental sets, to select the novel semantic information and to form novel, task-related associations. Although previous studies have identified the brain regions associated with these key processes, the interaction among these regions during insight is still unclear. In the present study, we explored the functional connectivity between the key regions during solving Chinese 'chengyu' riddles by using event-related functional magnetic resonance imaging. Results showed that both insight and noninsight solutions activated the bilateral inferior frontal gyri, middle temporal gyri and hippocampi, and these regions constituted a frontal to temporal to hippocampal neural pathway. Compared with noninsight solution, insight solution had a stronger functional connectivity between the inferior frontal gyrus and middle temporal gyrus in the right hemisphere. Our study reveals the neural pathway of information processing during verbal insight problem solving, and supports the right-hemisphere advantage theory of insight. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  17. The Wnt Co-Receptor Lrp5 Is Required for Cranial Neural Crest Cell Migration in Zebrafish.

    Directory of Open Access Journals (Sweden)

    Bernd Willems

    Full Text Available During vertebrate neurulation, cranial neural crest cells (CNCCs undergo epithelial to mesenchymal transition (EMT, delaminate from the neural plate border, and migrate as separate streams into different cranial regions. There, they differentiate into distinct parts of the craniofacial skeleton. Canonical Wnt signaling has been shown to be essential for this process at different levels but the involved receptors remained unclear. Here we show that the frizzled co-receptor low-density-lipoprotein (LDL receptor-related protein 5 (Lrp5 plays a crucial role in CNCC migration and morphogenesis of the cranial skeleton. Early during induction and migration of CNCCs, lrp5 is expressed ubiquitously but later gets restricted to CNCC derivatives in the ventral head region besides different regions in the CNS. A knock-down of lrp5 does not interfere with induction of CNCCs but leads to reduced proliferation of premigratory CNCCs. In addition, cell migration is disrupted as CNCCs are found in clusters at ectopic positions in the dorsomedial neuroepithelium after lrp5 knock-down and transient CRISPR/Cas9 gene editing. These migratory defects consequently result in malformations of the craniofacial skeleton. To date, Lrp5 has mainly been associated with bone homeostasis in mammals. Here we show that in zebrafish, lrp5 also controls cell migration during early morphogenetic processes and contributes to shaping the craniofacial skeleton.

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

    International Nuclear Information System (INIS)

    Yoon, Eun Jin; Cho, Sang Soo; Kim, Hee Jung; Bang, Seong Ae; Park, Hyun Soo; Kim, Yu Kyeong; Kim, Sang Eun

    2007-01-01

    Although it is well known that explicit memory is affected by the deleterious changes in brain with aging, but effect of aging in implicit memory such as probabilistic category learning (PCL) is not clear. To identify the effect of aging on the neural interaction for successful PCL, we investigated the neural substrates of PCL and the age-related changes of the neural network between these brain regions. 23 young (age, 252 y; 11 males) and 14 elderly (673 y; 7 males) healthy subjects underwent FDG PET during a resting state and 150-trial weather prediction (WP) task. Correlations between the WP hit rates and regional glucose metabolism were assessed using SPM2 (P diff (37) = 142.47, P<0.005), Systematic comparisons of each path revealed that frontal crosscallosal and the frontal to parahippocampal connection were most responsible for the model differences (P<0.05). For the successful PCL, the elderly recruits the basal ganglia implicit memory system but MTL recruitment differs from the young. The inadequate MTL correlation pattern in the elderly is may be caused by the changes of the neural pathway related with explicit memory. These neural changes can explain the decreased performance of PCL in elderly subjects

  19. Interaction of lithium ferrite with low-melting additives in the region of their low concentrations

    International Nuclear Information System (INIS)

    Mozhaev, A.P.; Olejnikov, N.N.; Shumilkin, N.S.; Fadeeva, V.I.

    1977-01-01

    The state diagrams of the Lisub(0.5)Fesub(2.5)O 4 - V 2 O 5 system has been studied in the region of small concentrations of V 2 O 5 (up to 3 wt.%). The existance regions of V 2 O 5 solid solutions in lithium ferrite have been determined. The possibility of using the state diagram for choosing optimum conditions of sintering has been shown. Addition of V 2 O 5 (up to 3 wt.%) intensifies the process of magnetic ceramics sintering

  20. The breaking of a delayed ring neural network contributes to stability: The rule and exceptions.

    Science.gov (United States)

    Khokhlova, T N; Kipnis, M M

    2013-12-01

    We prove that in our mathematical model the breaking of a delayed ring neural network extends the stability region in the parameters space, if the number of the neurons is sufficiently large. If the number of neurons is small, then a "paradoxical" region exists in the parameters space, wherein the ring neural configuration is stable, while the linear one is unstable. We study the conditions under which the paradoxical region is nonempty. We discuss how our mathematical modeling reflects neurosurgical operations with the severing of particular connections in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Neural Correlates of Attitude Change Following Positive and Negative Advertisements

    Science.gov (United States)

    Kato, Junko; Ide, Hiroko; Kabashima, Ikuo; Kadota, Hiroshi; Takano, Kouji; Kansaku, Kenji

    2009-01-01

    Understanding changes in attitudes towards others is critical to understanding human behaviour. Neuropolitical studies have found that the activation of emotion-related areas in the brain is linked to resilient political preferences, and neuroeconomic research has analysed the neural correlates of social preferences that favour or oppose consideration of intrinsic rewards. This study aims to identify the neural correlates in the prefrontal cortices of changes in political attitudes toward others that are linked to social cognition. Functional magnetic resonance imaging (fMRI) experiments have presented videos from previous electoral campaigns and television commercials for major cola brands and then used the subjects' self-rated affinity toward political candidates as behavioural indicators. After viewing negative campaign videos, subjects showing stronger fMRI activation in the dorsolateral prefrontal cortex lowered their ratings of the candidate they originally supported more than did those with smaller fMRI signal changes in the same region. Subjects showing stronger activation in the medial prefrontal cortex tended to increase their ratings more than did those with less activation. The same regions were not activated by viewing negative advertisements for cola. Correlations between the self-rated values and the neural signal changes underscore the metric representation of observed decisions (i.e., whether to support or not) in the brain. This indicates that neurometric analysis may contribute to the exploration of the neural correlates of daily social behaviour. PMID:19503749

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

    Science.gov (United States)

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

    2016-04-01

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

  3. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  4. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

    Science.gov (United States)

    Cui, Shaoguo; Mao, Lei; Jiang, Jingfeng; Liu, Chang; Xiong, Shuyu

    2018-01-01

    Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.

  5. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

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

  6. Neural Stem Cells: Implications for the Conventional Radiotherapy of Central Nervous System Malignancies

    International Nuclear Information System (INIS)

    Barani, Igor J.; Benedict, Stanley H.; Lin, Peck-Sun

    2007-01-01

    Advances in basic neuroscience related to neural stem cells and their malignant counterparts are challenging traditional models of central nervous system tumorigenesis and intrinsic brain repair. Neurogenesis persists into adulthood predominantly in two neurogenic centers: subventricular zone and subgranular zone. Subventricular zone is situated adjacent to lateral ventricles and subgranular zone is confined to the dentate gyrus of the hippocampus. Neural stem cells not only self-renew and differentiate along multiple lineages in these regions, but also contribute to intrinsic brain plasticity and repair. Ionizing radiation can depopulate these exquisitely sensitive regions directly or impair in situ neurogenesis by indirect, dose-dependent and inflammation-mediated mechanisms, even at doses <2 Gy. This review discusses the fundamental neural stem cell concepts within the framework of cumulative clinical experience with the treatment of central nervous system malignancies using conventional radiotherapy

  7. Hierarchical modular granular neural networks with fuzzy aggregation

    CERN Document Server

    Sanchez, Daniela

    2016-01-01

    In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

  8. Detection of neural activity in the brains of Japanese honeybee workers during the formation of a "hot defensive bee ball".

    Directory of Open Access Journals (Sweden)

    Atsushi Ugajin

    Full Text Available Anti-predator behaviors are essential to survival for most animals. The neural bases of such behaviors, however, remain largely unknown. Although honeybees commonly use their stingers to counterattack predators, the Japanese honeybee (Apis cerana japonica uses a different strategy to fight against the giant hornet (Vespa mandarinia japonica. Instead of stinging the hornet, Japanese honeybees form a "hot defensive bee ball" by surrounding the hornet en masse, killing it with heat. The European honeybee (A. mellifera ligustica, on the other hand, does not exhibit this behavior, and their colonies are often destroyed by a hornet attack. In the present study, we attempted to analyze the neural basis of this behavior by mapping the active brain regions of Japanese honeybee workers during the formation of a hot defensive bee ball. First, we identified an A. cerana homolog (Acks = Apis cerana kakusei of kakusei, an immediate early gene that we previously identified from A. mellifera, and showed that Acks has characteristics similar to kakusei and can be used to visualize active brain regions in A. cerana. Using Acks as a neural activity marker, we demonstrated that neural activity in the mushroom bodies, especially in Class II Kenyon cells, one subtype of mushroom body intrinsic neurons, and a restricted area between the dorsal lobes and the optic lobes was increased in the brains of Japanese honeybee workers involved in the formation of a hot defensive bee ball. In addition, workers exposed to 46°C heat also exhibited Acks expression patterns similar to those observed in the brains of workers involved in the formation of a hot defensive bee ball, suggesting that the neural activity observed in the brains of workers involved in the hot defensive bee ball mainly reflects thermal stimuli processing.

  9. The influence of motherhood on neural systems for reward processing in low income, minority, young women.

    Science.gov (United States)

    Moses-Kolko, Eydie L; Forbes, Erika E; Stepp, Stephanie; Fraser, David; Keenan, Kate E; Guyer, Amanda E; Chase, Henry W; Phillips, Mary L; Zevallos, Carlos R; Guo, Chaohui; Hipwell, Alison E

    2016-04-01

    Given the association between maternal caregiving behavior and heightened neural reward activity in experimental animal studies, the present study examined whether motherhood in humans positively modulates reward-processing neural circuits, even among mothers exposed to various life stressors and depression. Subjects were 77 first-time mothers and 126 nulliparous young women from the Pittsburgh Girls Study, a longitudinal study beginning in childhood. Subjects underwent a monetary reward task during functional magnetic resonance imaging in addition to assessment of current depressive symptoms. Life stress was measured by averaging data collected between ages 8-15 years. Using a region-of-interest approach, we conducted hierarchical regression to examine the relationship of psychosocial factors (life stress and current depression) and motherhood with extracted ventral striatal (VST) response to reward anticipation. Whole-brain regression analyses were performed post-hoc to explore non-striatal regions associated with reward anticipation in mothers vs nulliparous women. Anticipation of monetary reward was associated with increased neural activity in expected regions including caudate, orbitofrontal, occipital, superior and middle frontal cortices. There was no main effect of motherhood nor motherhood-by-psychosocial factor interaction effect on VST response during reward anticipation. Depressive symptoms were associated with increased VST activity across the entire sample. In exploratory whole brain analysis, motherhood was associated with increased somatosensory cortex activity to reward (FWE cluster forming threshold preward anticipation-related VST activity nor does motherhood modulate the impact of depression or life stress on VST activity. Future studies are needed to evaluate whether earlier postpartum assessment of reward function, inclusion of mothers with more severe depressive symptoms, and use of reward tasks specific for social reward might reveal an

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1997-05-27

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

  11. Dissociating the neural correlates of intra-item and inter-item working-memory binding.

    Directory of Open Access Journals (Sweden)

    Carinne Piekema

    Full Text Available BACKGROUND: Integration of information streams into a unitary representation is an important task of our cognitive system. Within working memory, the medial temporal lobe (MTL has been conceptually linked to the maintenance of bound representations. In a previous fMRI study, we have shown that the MTL is indeed more active during working-memory maintenance of spatial associations as compared to non-spatial associations or single items. There are two explanations for this result, the mere presence of the spatial component activates the MTL, or the MTL is recruited to bind associations between neurally non-overlapping representations. METHODOLOGY/PRINCIPAL FINDINGS: The current fMRI study investigates this issue further by directly comparing intrinsic intra-item binding (object/colour, extrinsic intra-item binding (object/location, and inter-item binding (object/object. The three binding conditions resulted in differential activation of brain regions. Specifically, we show that the MTL is important for establishing extrinsic intra-item associations and inter-item associations, in line with the notion that binding of information processed in different brain regions depends on the MTL. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that different forms of working-memory binding rely on specific neural structures. In addition, these results extend previous reports indicating that the MTL is implicated in working-memory maintenance, challenging the classic distinction between short-term and long-term memory systems.

  12. Doctor, Teacher, and Stethoscope: Neural Representation of Different Types of Semantic Relations.

    Science.gov (United States)

    Xu, Yangwen; Wang, Xiaosha; Wang, Xiaoying; Men, Weiwei; Gao, Jia-Hong; Bi, Yanchao

    2018-03-28

    Concepts can be related in many ways. They can belong to the same taxonomic category (e.g., "doctor" and "teacher," both in the category of people) or be associated with the same event context (e.g., "doctor" and "stethoscope," both associated with medical scenarios). How are these two major types of semantic relations coded in the brain? We constructed stimuli from three taxonomic categories (people, manmade objects, and locations) and three thematic categories (school, medicine, and sports) and investigated the neural representations of these two dimensions using representational similarity analyses in human participants (10 men and nine women). In specific regions of interest, the left anterior temporal lobe (ATL) and the left temporoparietal junction (TPJ), we found that, whereas both areas had significant effects of taxonomic information, the taxonomic relations had stronger effects in the ATL than in the TPJ ("doctor" and "teacher" closer in ATL neural activity), with the reverse being true for thematic relations ("doctor" and "stethoscope" closer in TPJ neural activity). A whole-brain searchlight analysis revealed that widely distributed regions, mainly in the left hemisphere, represented the taxonomic dimension. Interestingly, the significant effects of the thematic relations were only observed after the taxonomic differences were controlled for in the left TPJ, the right superior lateral occipital cortex, and other frontal, temporal, and parietal regions. In summary, taxonomic grouping is a primary organizational dimension across distributed brain regions, with thematic grouping further embedded within such taxonomic structures. SIGNIFICANCE STATEMENT How are concepts organized in the brain? It is well established that concepts belonging to the same taxonomic categories (e.g., "doctor" and "teacher") share neural representations in specific brain regions. How concepts are associated in other manners (e.g., "doctor" and "stethoscope," which are thematically

  13. Neural correlates of proactive and reactive aggression in adolescent twins.

    Science.gov (United States)

    Yang, Yaling; Joshi, Shantanu H; Jahanshad, Neda; Thompson, Paul M; Baker, Laura A

    2017-05-01

    Verbal and physical aggression begin early in life and steadily decline thereafter in normal development. As a result, elevated aggressive behavior in adolescence may signal atypical development and greater vulnerability for negative mental and health outcomes. Converging evidence suggests that brain disturbances in regions involved in impulse control, emotional regulation, and sensation seeking may contribute to heightened aggression. However, little is known regarding the neural mechanisms underlying subtypes of aggression (i.e., proactive and reactive aggression) and whether they differ between males and females. Using a sample of 106 14-year-old adolescent twins, this study found that striatal enlargement was associated with both proactive and reactive aggression. We also found that volumetric alterations in several frontal regions including smaller middle frontal and larger orbitofrontal cortex were correlated with higher levels of aggression in adolescent twins. In addition, cortical thickness analysis showed that thickness alterations in many overlapping regions including middle frontal, superior frontal, and anterior cingulate cortex and temporal regions were associated with aggression in adolescent twins. Results support the involvement of fronto-limbic-striatal circuit in the etiology of aggression during adolescence. Aggr. Behav. 43:230-240, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  14. The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

    Science.gov (United States)

    Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

    2014-01-01

    Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

  15. Sex Differences in Neural Activation to Facial Expressions Denoting Contempt and Disgust

    Science.gov (United States)

    Aleman, André; Swart, Marte

    2008-01-01

    The facial expression of contempt has been regarded to communicate feelings of moral superiority. Contempt is an emotion that is closely related to disgust, but in contrast to disgust, contempt is inherently interpersonal and hierarchical. The aim of this study was twofold. First, to investigate the hypothesis of preferential amygdala responses to contempt expressions versus disgust. Second, to investigate whether, at a neural level, men would respond stronger to biological signals of interpersonal superiority (e.g., contempt) than women. We performed an experiment using functional magnetic resonance imaging (fMRI), in which participants watched facial expressions of contempt and disgust in addition to neutral expressions. The faces were presented as distractors in an oddball task in which participants had to react to one target face. Facial expressions of contempt and disgust activated a network of brain regions, including prefrontal areas (superior, middle and medial prefrontal gyrus), anterior cingulate, insula, amygdala, parietal cortex, fusiform gyrus, occipital cortex, putamen and thalamus. Contemptuous faces did not elicit stronger amygdala activation than did disgusted expressions. To limit the number of statistical comparisons, we confined our analyses of sex differences to the frontal and temporal lobes. Men displayed stronger brain activation than women to facial expressions of contempt in the medial frontal gyrus, inferior frontal gyrus, and superior temporal gyrus. Conversely, women showed stronger neural responses than men to facial expressions of disgust. In addition, the effect of stimulus sex differed for men versus women. Specifically, women showed stronger responses to male contemptuous faces (as compared to female expressions), in the insula and middle frontal gyrus. Contempt has been conceptualized as signaling perceived moral violations of social hierarchy, whereas disgust would signal violations of physical purity. Thus, our results suggest a

  16. Sex differences in neural activation to facial expressions denoting contempt and disgust.

    Science.gov (United States)

    Aleman, André; Swart, Marte

    2008-01-01

    The facial expression of contempt has been regarded to communicate feelings of moral superiority. Contempt is an emotion that is closely related to disgust, but in contrast to disgust, contempt is inherently interpersonal and hierarchical. The aim of this study was twofold. First, to investigate the hypothesis of preferential amygdala responses to contempt expressions versus disgust. Second, to investigate whether, at a neural level, men would respond stronger to biological signals of interpersonal superiority (e.g., contempt) than women. We performed an experiment using functional magnetic resonance imaging (fMRI), in which participants watched facial expressions of contempt and disgust in addition to neutral expressions. The faces were presented as distractors in an oddball task in which participants had to react to one target face. Facial expressions of contempt and disgust activated a network of brain regions, including prefrontal areas (superior, middle and medial prefrontal gyrus), anterior cingulate, insula, amygdala, parietal cortex, fusiform gyrus, occipital cortex, putamen and thalamus. Contemptuous faces did not elicit stronger amygdala activation than did disgusted expressions. To limit the number of statistical comparisons, we confined our analyses of sex differences to the frontal and temporal lobes. Men displayed stronger brain activation than women to facial expressions of contempt in the medial frontal gyrus, inferior frontal gyrus, and superior temporal gyrus. Conversely, women showed stronger neural responses than men to facial expressions of disgust. In addition, the effect of stimulus sex differed for men versus women. Specifically, women showed stronger responses to male contemptuous faces (as compared to female expressions), in the insula and middle frontal gyrus. Contempt has been conceptualized as signaling perceived moral violations of social hierarchy, whereas disgust would signal violations of physical purity. Thus, our results suggest a

  17. Sex differences in neural activation to facial expressions denoting contempt and disgust.

    Directory of Open Access Journals (Sweden)

    André Aleman

    Full Text Available The facial expression of contempt has been regarded to communicate feelings of moral superiority. Contempt is an emotion that is closely related to disgust, but in contrast to disgust, contempt is inherently interpersonal and hierarchical. The aim of this study was twofold. First, to investigate the hypothesis of preferential amygdala responses to contempt expressions versus disgust. Second, to investigate whether, at a neural level, men would respond stronger to biological signals of interpersonal superiority (e.g., contempt than women. We performed an experiment using functional magnetic resonance imaging (fMRI, in which participants watched facial expressions of contempt and disgust in addition to neutral expressions. The faces were presented as distractors in an oddball task in which participants had to react to one target face. Facial expressions of contempt and disgust activated a network of brain regions, including prefrontal areas (superior, middle and medial prefrontal gyrus, anterior cingulate, insula, amygdala, parietal cortex, fusiform gyrus, occipital cortex, putamen and thalamus. Contemptuous faces did not elicit stronger amygdala activation than did disgusted expressions. To limit the number of statistical comparisons, we confined our analyses of sex differences to the frontal and temporal lobes. Men displayed stronger brain activation than women to facial expressions of contempt in the medial frontal gyrus, inferior frontal gyrus, and superior temporal gyrus. Conversely, women showed stronger neural responses than men to facial expressions of disgust. In addition, the effect of stimulus sex differed for men versus women. Specifically, women showed stronger responses to male contemptuous faces (as compared to female expressions, in the insula and middle frontal gyrus. Contempt has been conceptualized as signaling perceived moral violations of social hierarchy, whereas disgust would signal violations of physical purity. Thus, our

  18. Neural stem cells induce bone-marrow-derived mesenchymal stem cells to generate neural stem-like cells via juxtacrine and paracrine interactions

    International Nuclear Information System (INIS)

    Alexanian, Arshak R.

    2005-01-01

    Several recent reports suggest that there is far more plasticity that previously believed in the developmental potential of bone-marrow-derived cells (BMCs) that can be induced by extracellular developmental signals of other lineages whose nature is still largely unknown. In this study, we demonstrate that bone-marrow-derived mesenchymal stem cells (MSCs) co-cultured with mouse proliferating or fixed (by paraformaldehyde or methanol) neural stem cells (NSCs) generate neural stem cell-like cells with a higher expression of Sox-2 and nestin when grown in NS-A medium supplemented with N2, NSC conditioned medium (NSCcm) and bFGF. These neurally induced MSCs eventually differentiate into β-III-tubulin and GFAP expressing cells with neuronal and glial morphology when grown an additional week in Neurobasal/B27 without bFGF. We conclude that juxtacrine interaction between NSCs and MSCs combined with soluble factors released from NSCs are important for generation of neural-like cells from bone-marrow-derived adherent MSCs

  19. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, J.; Schutz, J.; Chirayath, V.; Li, A.

    2017-12-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Net's convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign.Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users' input against pre-classified coral imagery to gauge their accuracy and utilizes in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  20. A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

    Science.gov (United States)

    van den Bergh, Jarrett; Schutz, Joey; Li, Alan; Chirayath, Ved

    2017-01-01

    NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

  1. The neural correlates of risk propensity in males and females using resting-state fMRI

    Directory of Open Access Journals (Sweden)

    Yuan eZhou

    2014-01-01

    Full Text Available Men are more risk prone than women, but the underlying basis remains unclear. To investigate this question, we developed a trait-like measure of risk propensity which we correlated with resting-state functional connectivity to identify sex differences. Specifically, we used short- and long-range functional connectivity densities to identify associated brain regions and examined their functional connectivities in resting-state functional magnetic resonance imaging (fMRI data collected from a large sample of healthy young volunteers. We found that men had a higher level of general risk propensity (GRP than women. At the neural level, although they shared a common neural correlate of GRP in a network centered at the right inferior frontal gyrus, men and women differed in a network centered at the right secondary somatosensory cortex, which included the bilateral dorsal anterior/middle insular cortices and the dorsal anterior cingulate cortex. In addition, men and women differed in a local network centered at the left inferior orbitofrontal cortex. Most of the regions identified by this resting-state fMRI study have been previously implicated in risk processing when people make risky decisions. This study provides a new perspective on the brain-behavioral relationships in risky decision making and contributes to our understanding of sex differences in risk propensity.

  2. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Directory of Open Access Journals (Sweden)

    Karuna Subramaniam

    2015-01-01

    Full Text Available Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life.

  3. Neural signal during immediate reward anticipation in schizophrenia: Relationship to real-world motivation and function

    Science.gov (United States)

    Subramaniam, Karuna; Hooker, Christine I.; Biagianti, Bruno; Fisher, Melissa; Nagarajan, Srikantan; Vinogradov, Sophia

    2015-01-01

    Amotivation in schizophrenia is a central predictor of poor functioning, and is thought to occur due to deficits in anticipating future rewards, suggesting that impairments in anticipating pleasure can contribute to functional disability in schizophrenia. In healthy comparison (HC) participants, reward anticipation is associated with activity in frontal–striatal networks. By contrast, schizophrenia (SZ) participants show hypoactivation within these frontal–striatal networks during this motivated anticipatory brain state. Here, we examined neural activation in SZ and HC participants during the anticipatory phase of stimuli that predicted immediate upcoming reward and punishment, and during the feedback/outcome phase, in relation to trait measures of hedonic pleasure and real-world functional capacity. SZ patients showed hypoactivation in ventral striatum during reward anticipation. Additionally, we found distinct differences between HC and SZ groups in their association between reward-related immediate anticipatory neural activity and their reported experience of pleasure. HC participants recruited reward-related regions in striatum that significantly correlated with subjective consummatory pleasure, while SZ patients revealed activation in attention-related regions, such as the IPL, which correlated with consummatory pleasure and functional capacity. These findings may suggest that SZ patients activate compensatory attention processes during anticipation of immediate upcoming rewards, which likely contribute to their functional capacity in daily life. PMID:26413478

  4. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

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

  5. Fine mapping in the MHC region accounts for 18% additional genetic risk for celiac disease

    NARCIS (Netherlands)

    Gutierrez-Achury, Javier; Zhernakova, Alexandra; Pulit, Sara L.; Trynka, Gosia; Hunt, Karen A.; Romanos, Jihane; Raychaudhuri, Soumya; van Heel, David A.; Wijmenga, Cisca; de Balcker, Paul I. W.

    Although dietary gluten is the trigger for celiac disease, risk is strongly influenced by genetic variation in the major histocompatibility complex (MHC) region. We fine mapped the MHC association signal to identify additional risk factors independent of the HLA-DQA1 and HLA-DQB1 alleles and

  6. 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

  7. Developmental phonagnosia: Linking neural mechanisms with the behavioural phenotype.

    Science.gov (United States)

    Roswandowitz, Claudia; Schelinski, Stefanie; von Kriegstein, Katharina

    2017-07-15

    Human voice recognition is critical for many aspects of social communication. Recently, a rare disorder, developmental phonagnosia, which describes the inability to recognise a speaker's voice, has been discovered. The underlying neural mechanisms are unknown. Here, we used two functional magnetic resonance imaging experiments to investigate brain function in two behaviourally well characterised phonagnosia cases, both 32 years old: AS has apperceptive and SP associative phonagnosia. We found distinct malfunctioned brain mechanisms in AS and SP matching their behavioural profiles. In apperceptive phonagnosia, right-hemispheric auditory voice-sensitive regions (i.e., Heschl's gyrus, planum temporale, superior temporal gyrus) showed lower responses than in matched controls (n AS =16) for vocal versus non-vocal sounds and for speaker versus speech recognition. In associative phonagnosia, the connectivity between voice-sensitive (i.e. right posterior middle/inferior temporal gyrus) and supramodal (i.e. amygdala) regions was reduced in comparison to matched controls (n SP =16) during speaker versus speech recognition. Additionally, both cases recruited distinct potential compensatory mechanisms. Our results support a central assumption of current two-system models of voice-identity processing: They provide the first evidence that dysfunction of voice-sensitive regions and impaired connectivity between voice-sensitive and supramodal person recognition regions can selectively contribute to deficits in person recognition by voice. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Altered topology of neural circuits in congenital prosopagnosia.

    Science.gov (United States)

    Rosenthal, Gideon; Tanzer, Michal; Simony, Erez; Hasson, Uri; Behrmann, Marlene; Avidan, Galia

    2017-08-21

    Using a novel, fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with an impairment in face recognition, congenital prosopagnosia (CP), and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent of this hyper-connectivity was correlated with the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.

  9. Ground Motion Prediction Model Using Artificial Neural Network

    Science.gov (United States)

    Dhanya, J.; Raghukanth, S. T. G.

    2018-03-01

    This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg-Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude ( M w), closest distance to rupture plane ( R rup), shear wave velocity in the region ( V s30) and focal mechanism ( F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

  10. Airplane detection in remote sensing images using convolutional neural networks

    Science.gov (United States)

    Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

    2018-03-01

    Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

  11. The neural organization of perception in chess experts.

    Science.gov (United States)

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

    2011-07-20

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

  12. Radial basis function neural network for power system load-flow

    International Nuclear Information System (INIS)

    Karami, A.; Mohammadi, M.S.

    2008-01-01

    This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)

  13. hmmr mediates anterior neural tube closure and morphogenesis in the frog Xenopus.

    Science.gov (United States)

    Prager, Angela; Hagenlocher, Cathrin; Ott, Tim; Schambony, Alexandra; Feistel, Kerstin

    2017-10-01

    Development of the central nervous system requires orchestration of morphogenetic processes which drive elevation and apposition of the neural folds and their fusion into a neural tube. The newly formed tube gives rise to the brain in anterior regions and continues to develop into the spinal cord posteriorly. Conspicuous differences between the anterior and posterior neural tube become visible already during neural tube closure (NTC). Planar cell polarity (PCP)-mediated convergent extension (CE) movements are restricted to the posterior neural plate, i.e. hindbrain and spinal cord, where they propagate neural fold apposition. The lack of CE in the anterior neural plate correlates with a much slower mode of neural fold apposition anteriorly. The morphogenetic processes driving anterior NTC have not been addressed in detail. Here, we report a novel role for the breast cancer susceptibility gene and microtubule (MT) binding protein Hmmr (Hyaluronan-mediated motility receptor, RHAMM) in anterior neurulation and forebrain development in Xenopus laevis. Loss of hmmr function resulted in a lack of telencephalic hemisphere separation, arising from defective roof plate formation, which in turn was caused by impaired neural tissue narrowing. hmmr regulated polarization of neural cells, a function which was dependent on the MT binding domains. hmmr cooperated with the core PCP component vangl2 in regulating cell polarity and neural morphogenesis. Disrupted cell polarization and elongation in hmmr and vangl2 morphants prevented radial intercalation (RI), a cell behavior essential for neural morphogenesis. Our results pinpoint a novel role of hmmr in anterior neural development and support the notion that RI is a major driving force for anterior neurulation and forebrain morphogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Decomposition of Rotor Hopfield Neural Networks Using Complex Numbers.

    Science.gov (United States)

    Kobayashi, Masaki

    2018-04-01

    A complex-valued Hopfield neural network (CHNN) is a multistate model of a Hopfield neural network. It has the disadvantage of low noise tolerance. Meanwhile, a symmetric CHNN (SCHNN) is a modification of a CHNN that improves noise tolerance. Furthermore, a rotor Hopfield neural network (RHNN) is an extension of a CHNN. It has twice the storage capacity of CHNNs and SCHNNs, and much better noise tolerance than CHNNs, although it requires twice many connection parameters. In this brief, we investigate the relations between CHNN, SCHNN, and RHNN; an RHNN is uniquely decomposed into a CHNN and SCHNN. In addition, the Hebbian learning rule for RHNNs is decomposed into those for CHNNs and SCHNNs.

  15. Probing neural cell behaviors through micro-/nano-patterned chitosan substrates

    International Nuclear Information System (INIS)

    Sung, Chun-Yen; Yang, Chung-Yao; Yeh, J Andrew; Chen, Wen-Shiang; Wang, Yang-Kao; Cheng, Chao-Min

    2015-01-01

    In this study, we describe the development of surface-modified chitosan substrates to examine topographically related Neuro-2a cell behaviors. Different functional groups can be modified on chitosan surfaces to probe Neuro-2a cell morphology. To prepare chitosan substrates with micro/nano-scaled features, we demonstrated an easy-to-handle method that combined photolithography, inductively coupled plasma reactive ion etching, Ag nanoparticle-assisted etching, and solution casting. The results show that Neuro-2a cells preferred to adhere to a flat chitosan surface rather than a nanotextured chitosan surface as evidenced by greater immobilization and differentiation, suggesting that surface topography is crucial for neural patterning. In addition, we developed chitosan substrates with different geometric patterns and flat region depth; this allowed us to re-arrange or re-pattern Neuro-2a cell colonies at desired locations. We found that a polarity-induced micropattern provided the most suitable surface pattern for promoting neural network formation on a chitosan substrate. The cellular polarity of single Neuro-2a cell spreading correlated to a diamond-like geometry and neurite outgrowth was induced from the corners toward the grooves of the structures. This study provide greater insight into neurobiology, including neurotransmitter screening, electrophysiological stimulation platforms, and biomedical engineering. (paper)

  16. The response of early neural genes to FGF signaling or inhibition of BMP indicate the absence of a conserved neural induction module

    Directory of Open Access Journals (Sweden)

    Rogers Crystal D

    2011-12-01

    Full Text Available Abstract Background The molecular mechanism that initiates the formation of the vertebrate central nervous system has long been debated. Studies in Xenopus and mouse demonstrate that inhibition of BMP signaling is sufficient to induce neural tissue in explants or ES cells respectively, whereas studies in chick argue that instructive FGF signaling is also required for the expression of neural genes. Although additional signals may be involved in neural induction and patterning, here we focus on the roles of BMP inhibition and FGF8a. Results To address the question of necessity and sufficiency of BMP inhibition and FGF signaling, we compared the temporal expression of the five earliest genes expressed in the neuroectoderm and determined their requirements for induction at the onset of neural plate formation in Xenopus. Our results demonstrate that the onset and peak of expression of the genes vary and that they have different regulatory requirements and are therefore unlikely to share a conserved neural induction regulatory module. Even though all require inhibition of BMP for expression, some also require FGF signaling; expression of the early-onset pan-neural genes sox2 and foxd5α requires FGF signaling while other early genes, sox3, geminin and zicr1 are induced by BMP inhibition alone. Conclusions We demonstrate that BMP inhibition and FGF signaling induce neural genes independently of each other. Together our data indicate that although the spatiotemporal expression patterns of early neural genes are similar, the mechanisms involved in their expression are distinct and there are different signaling requirements for the expression of each gene.

  17. Neural net generated seismic facies map and attribute facies map

    International Nuclear Information System (INIS)

    Addy, S.K.; Neri, P.

    1998-01-01

    The usefulness of 'seismic facies maps' in the analysis of an Upper Wilcox channel system in a 3-D survey shot by CGG in 1995 in Lavaca county in south Texas was discussed. A neural net-generated seismic facies map is a quick hydrocarbon exploration tool that can be applied regionally as well as on a prospect scale. The new technology is used to classify a constant interval parallel to a horizon in a 3-D seismic volume based on the shape of the wiggle traces using a neural network technology. The tool makes it possible to interpret sedimentary features of a petroleum deposit. The same technology can be used in regional mapping by making 'attribute facies maps' in which various forms of amplitude attributes, phase attributes or frequency attributes can be used

  18. Differential neural network configuration during human path integration

    Science.gov (United States)

    Arnold, Aiden E. G. F; Burles, Ford; Bray, Signe; Levy, Richard M.; Iaria, Giuseppe

    2014-01-01

    Path integration is a fundamental skill for navigation in both humans and animals. Despite recent advances in unraveling the neural basis of path integration in animal models, relatively little is known about how path integration operates at a neural level in humans. Previous attempts to characterize the neural mechanisms used by humans to visually path integrate have suggested a central role of the hippocampus in allowing accurate performance, broadly resembling results from animal data. However, in recent years both the central role of the hippocampus and the perspective that animals and humans share similar neural mechanisms for path integration has come into question. The present study uses a data driven analysis to investigate the neural systems engaged during visual path integration in humans, allowing for an unbiased estimate of neural activity across the entire brain. Our results suggest that humans employ common task control, attention and spatial working memory systems across a frontoparietal network during path integration. However, individuals differed in how these systems are configured into functional networks. High performing individuals were found to more broadly express spatial working memory systems in prefrontal cortex, while low performing individuals engaged an allocentric memory system based primarily in the medial occipito-temporal region. These findings suggest that visual path integration in humans over short distances can operate through a spatial working memory system engaging primarily the prefrontal cortex and that the differential configuration of memory systems recruited by task control networks may help explain individual biases in spatial learning strategies. PMID:24808849

  19. Neural Mechanisms of Selective Visual Attention.

    Science.gov (United States)

    Moore, Tirin; Zirnsak, Marc

    2017-01-03

    Selective visual attention describes the tendency of visual processing to be confined largely to stimuli that are relevant to behavior. It is among the most fundamental of cognitive functions, particularly in humans and other primates for whom vision is the dominant sense. We review recent progress in identifying the neural mechanisms of selective visual attention. We discuss evidence from studies of different varieties of selective attention and examine how these varieties alter the processing of stimuli by neurons within the visual system, current knowledge of their causal basis, and methods for assessing attentional dysfunctions. In addition, we identify some key questions that remain in identifying the neural mechanisms that give rise to the selective processing of visual information.

  20. A decomposition approach to analysis of competitive-cooperative neural networks with delay

    International Nuclear Information System (INIS)

    Chu Tianguang; Zhang Zongda; Wang Zhaolin

    2003-01-01

    Competitive-cooperative or inhibitory-excitatory configurations abound in neural networks. It is demonstrated here how such a configuration may be exploited to give a detailed characterization of the fixed point dynamics in general neural networks with time delay. The idea is to divide the connection weights into inhibitory and excitatory types and thereby to embed a competitive-cooperative delay neural network into an augmented cooperative delay system through a symmetric transformation. This allows for the use of the powerful monotone properties of cooperative systems. By the method, we derive several simple necessary and sufficient conditions on guaranteed trapping regions and guaranteed componentwise (exponential) convergence of the neural networks. The results relate specific decay rate and trajectory bounds to system parameters and are therefore of practical significance in designing a network with desired performance

  1. Neural decoding of collective wisdom with multi-brain computing.

    Science.gov (United States)

    Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

    2012-01-02

    Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally

  2. Down image recognition based on deep convolutional neural network

    Directory of Open Access Journals (Sweden)

    Wenzhu Yang

    2018-06-01

    Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition

  3. An amphioxus Msx gene expressed predominantly in the dorsal neural tube.

    Science.gov (United States)

    Sharman, A C; Shimeld, S M; Holland, P W

    1999-04-01

    Genomic and cDNA clones of an Msx class homeobox gene were isolated from amphioxus (Branchiostoma floridae). The gene, AmphiMsx, is expressed in the neural plate from late gastrulation; in later embryos it is expressed in dorsal cells of the neural tube, excluding anterior and posterior regions, in an irregular reiterated pattern. There is transient expression in dorsal cells within somites, reminiscent of migrating neural crest cells of vertebrates. In larvae, mRNA is detected in two patches of anterior ectoderm proposed to be placodes. Evolutionary analyses show there is little phylogenetic information in Msx protein sequences; however, it is likely that duplication of Msx genes occurred in the vertebrate lineage.

  4. Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada

    Directory of Open Access Journals (Sweden)

    Rasim Latifovic

    2018-02-01

    Full Text Available Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other, accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found

  5. Neural signatures of social conformity: A coordinate-based activation likelihood estimation meta-analysis of functional brain imaging studies.

    Science.gov (United States)

    Wu, Haiyan; Luo, Yi; Feng, Chunliang

    2016-12-01

    People often align their behaviors with group opinions, known as social conformity. Many neuroscience studies have explored the neuropsychological mechanisms underlying social conformity. Here we employed a coordinate-based meta-analysis on neuroimaging studies of social conformity with the purpose to reveal the convergence of the underlying neural architecture. We identified a convergence of reported activation foci in regions associated with normative decision-making, including ventral striatum (VS), dorsal posterior medial frontal cortex (dorsal pMFC), and anterior insula (AI). Specifically, consistent deactivation of VS and activation of dorsal pMFC and AI are identified when people's responses deviate from group opinions. In addition, the deviation-related responses in dorsal pMFC predict people's conforming behavioral adjustments. These are consistent with current models that disagreement with others might evoke "error" signals, cognitive imbalance, and/or aversive feelings, which are plausibly detected in these brain regions as control signals to facilitate subsequent conforming behaviors. Finally, group opinions result in altered neural correlates of valuation, manifested as stronger responses of VS to stimuli endorsed than disliked by others. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. A Neural Basis for the Acquired Capability for Suicide

    Directory of Open Access Journals (Sweden)

    Gopikrishna Deshpande

    2016-08-01

    Full Text Available The high rate of fatal suicidal behavior in men is an urgent issue as highlighted in the public eye via news sources and media outlets. In this study, we have attempted to address this issue and understand the neural substrates underlying the gender differences in the rate of fatal suicidal behavior. The Interpersonal-Psychological Theory of Suicide (IPTS has proposed an explanation for the seemingly paradoxical relationship between gender and suicidal behavior, i.e. greater non-fatal suicide attempts by women but higher number of deaths by suicide in men. This theory states that possessing suicidal desire (due to conditions such as depression alone is not sufficient for a lethal suicide attempt. It is imperative for an individual to have acquired the capability for suicide (ACS along with suicidal desire in order to die by suicide. Therefore, higher levels of ACS in men may explain why men are more likely to die by suicide than women, despite being less likely to experience suicidal ideation or depression. In this study, we used activation likelihood estimation meta-analysis to investigate a potential ACS network that involves neural substrates underlying emotional stoicism, sensation seeking, pain tolerance, and fearlessness of death along with a potential depression network that involves neural substrates that underlie clinical depression. Brain regions commonly found in ACS and depression networks for males and females were further used as seeds to obtain regions functionally and structurally connected to them. We found that the male-specific networks were more widespread and diverse than the female-specific ones. Also, while the former involved motor regions such as the premotor cortex and cerebellum, the latter was dominated by limbic regions. This may support the fact that suicidal desire generally leads to fatal/decisive action in males while in females, it manifests as depression, ideation and generally non-fatal actions. The proposed

  7. Synchronized stability in a reaction–diffusion neural network model

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Ling; Zhao, Hongyong, E-mail: hongyongz@126.com

    2014-11-14

    The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability.

  8. Synchronized stability in a reaction–diffusion neural network model

    International Nuclear Information System (INIS)

    Wang, Ling; Zhao, Hongyong

    2014-01-01

    The reaction–diffusion neural network consisting of a pair of identical tri-neuron loops is considered. We present detailed discussions about the synchronized stability and Hopf bifurcation, deducing the non-trivial role that delay plays in different locations. The corresponding numerical simulations are used to illustrate the effectiveness of the obtained results. In addition, the numerical results about the effects of diffusion reveal that diffusion may speed up the tendency to synchronization and induce the synchronized equilibrium point to be stable. Furthermore, if the parameters are located in appropriate regions, multiple unstability and bistability or unstability and bistability may coexist. - Highlights: • Point to non-trivial role that τ plays in different positions. • Diffusion speeds up the tendency to synchronization. • Diffusion induces the synchronized equilibrium point to be stable. • The coexistence of multiple unstability and bistability or unstability and bistability

  9. Robo signaling regulates the production of cranial neural crest cells.

    Science.gov (United States)

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

    2017-12-01

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

  10. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  11. Application of neural network technology to setpoint control of a simulated reactor experiment loop

    International Nuclear Information System (INIS)

    Cordes, G.A.; Bryan, S.R.; Powell, R.H.; Chick, D.R.

    1991-01-01

    This paper describes the design, implementation, and application of artificial neural networks to achieve temperature and flow rate control for a simulation of a typical experiment loop in the Advanced Test Reactor (ATR) located at the Idaho National Engineering Laboratory (INEL). The goal of the project was to research multivariate, nonlinear control using neural networks. A loop simulation code was adapted for the project and used to create a training set and test the neural network controller for comparison with the existing loop controllers. The results for the best neural network design are documented and compared with existing loop controller action. The neural network was shown to be as accurate at loop control as the classical controllers in the operating region represented by the training set. 5 refs., 8 figs., 3 tabs

  12. Neural correlates of a single-session massage treatment.

    Science.gov (United States)

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

    2012-03-01

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

  13. Neural Blockade for Persistent Pain After Breast Cancer Surgery

    DEFF Research Database (Denmark)

    Wijayasinghe, Nelun; Andersen, Kenneth Geving; Kehlet, Henrik

    2014-01-01

    involved in neuropathic pain syndromes or to be used as a treatment in its own right. The purpose of this review was to examine the evidence for neural blockade as a potential diagnostic tool or treatment for persistent pain after breast cancer surgery. In this systematic review, we found only 7 studies (n......Persistent pain after breast cancer surgery is predominantly a neuropathic pain syndrome affecting 25% to 60% of patients and related to injury of the intercostobrachial nerve, intercostal nerves, and other nerves in the region. Neural blockade can be useful for the identification of nerves...

  14. Neural correlates of attitude change following positive and negative advertisements

    Directory of Open Access Journals (Sweden)

    Junko Kato

    2009-05-01

    Full Text Available Understanding changes in attitudes towards others is critical to understanding human behaviour. Neuropolitical studies have found that the activation of emotion-related areas in the brain is linked to resilient political preferences, and neuroeconomic research has analysed the neural correlates of social preferences that favour or oppose consideration of intrinsic rewards. This study aims to identify the neural correlates in the prefrontal cortices of changes in political attitudes toward others that are linked to social cognition. Functional magnetic resonance imaging (fMRI experiments have presented videos from previous electoral campaigns and television commercials for major cola brands and then used the subjects’ self-rated affinity toward political candidates as behavioural indicators. After viewing negative campaign videos, subjects showing stronger fMRI activation in the dorsolateral prefrontal cortex lowered their ratings of the candidate they originally supported more than did those with smaller fMRI signal changes in the same region. Subjects showing stronger activation in the medial prefrontal cortex tended to increase their ratings more than did those with less activation. The same regions were not activated by viewing negative advertisements for cola. Correlations between the self-rated values and the neural signal changes underscore the metric representation of observed decisions (i.e., whether to support or not in the brain. This indicates that neurometric analysis may contribute to the exploration of the neural correlates of daily social behaviour.

  15. "Neural overlap of L1 and L2 semantic representations across visual and auditory modalities: a decoding approach".

    Science.gov (United States)

    Van de Putte, Eowyn; De Baene, Wouter; Price, Cathy J; Duyck, Wouter

    2018-05-01

    This study investigated whether brain activity in Dutch-French bilinguals during semantic access to concepts from one language could be used to predict neural activation during access to the same concepts from another language, in different language modalities/tasks. This was tested using multi-voxel pattern analysis (MVPA), within and across language comprehension (word listening and word reading) and production (picture naming). It was possible to identify the picture or word named, read or heard in one language (e.g. maan, meaning moon) based on the brain activity in a distributed bilateral brain network while, respectively, naming, reading or listening to the picture or word in the other language (e.g. lune). The brain regions identified differed across tasks. During picture naming, brain activation in the occipital and temporal regions allowed concepts to be predicted across languages. During word listening and word reading, across-language predictions were observed in the rolandic operculum and several motor-related areas (pre- and postcentral, the cerebellum). In addition, across-language predictions during reading were identified in regions typically associated with semantic processing (left inferior frontal, middle temporal cortex, right cerebellum and precuneus) and visual processing (inferior and middle occipital regions and calcarine sulcus). Furthermore, across modalities and languages, the left lingual gyrus showed semantic overlap across production and word reading. These findings support the idea of at least partially language- and modality-independent semantic neural representations. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

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

  17. MATLAB Simulation of Gradient-Based Neural Network for Online Matrix Inversion

    Science.gov (United States)

    Zhang, Yunong; Chen, Ke; Ma, Weimu; Li, Xiao-Dong

    This paper investigates the simulation of a gradient-based recurrent neural network for online solution of the matrix-inverse problem. Several important techniques are employed as follows to simulate such a neural system. 1) Kronecker product of matrices is introduced to transform a matrix-differential-equation (MDE) to a vector-differential-equation (VDE); i.e., finally, a standard ordinary-differential-equation (ODE) is obtained. 2) MATLAB routine "ode45" is introduced to solve the transformed initial-value ODE problem. 3) In addition to various implementation errors, different kinds of activation functions are simulated to show the characteristics of such a neural network. Simulation results substantiate the theoretical analysis and efficacy of the gradient-based neural network for online constant matrix inversion.

  18. Assessing Rainfall Erosivity with Artificial Neural Networks for the Ribeira Valley, Brazil

    Directory of Open Access Journals (Sweden)

    Reginald B. Silva

    2010-01-01

    Full Text Available Soil loss is one of the main causes of pauperization and alteration of agricultural soil properties. Various empirical models (e.g., USLE are used to predict soil losses from climate variables which in general have to be derived from spatial interpolation of point measurements. Alternatively, Artificial Neural Networks may be used as a powerful option to obtain site-specific climate data from independent factors. This study aimed to develop an artificial neural network to estimate rainfall erosivity in the Ribeira Valley and Coastal region of the State of São Paulo. In the development of the Artificial Neural Networks the input variables were latitude, longitude, and annual rainfall and a mathematical equation of the activation function for use in the study area as the output variable. It was found among other things that the Artificial Neural Networks can be used in the interpolation of rainfall erosivity values for the Ribeira Valley and Coastal region of the State of São Paulo to a satisfactory degree of precision in the estimation of erosion. The equation performance has been demonstrated by comparison with the mathematical equation of the activation function adjusted to the specific conditions of the study area.

  19. Noun and verb processing in aphasia: Behavioural profiles and neural correlates

    Directory of Open Access Journals (Sweden)

    Reem S.W. Alyahya

    Full Text Available The behavioural and neural processes underpinning different word classes, particularly nouns and verbs, have been a long-standing area of interest in psycholinguistic, neuropsychology and aphasiology research. This topic has theoretical implications concerning the organisation of the language system, as well as clinical consequences related to the management of patients with language deficits. Research findings, however, have diverged widely, which might, in part, reflect methodological differences, particularly related to controlling the psycholinguistic variations between nouns and verbs. The first aim of this study, therefore, was to develop a set of neuropsychological tests that assessed single-word production and comprehension with a matched set of nouns and verbs. Secondly, the behavioural profiles and neural correlates of noun and verb processing were explored, based on these novel tests, in a relatively large cohort of 48 patients with chronic post-stroke aphasia. A data-driven approach, principal component analysis (PCA, was also used to determine how noun and verb production and comprehension were related to the patients' underlying fundamental language domains. The results revealed no performance differences between noun and verb production and comprehension once matched on multiple psycholinguistic features including, most critically, imageability. Interestingly, the noun-verb differences found in previous studies were replicated in this study once un-matched materials were used. Lesion-symptom mapping revealed overlapping neural correlates of noun and verb processing along left temporal and parietal regions. These findings support the view that the neural representation of noun and verb processing at single-word level are jointly-supported by distributed cortical regions. The PCA generated five fundamental language and cognitive components of aphasia: phonological production, phonological recognition, semantics, fluency, and

  20. The neural network z-vertex trigger for the Belle II detector

    Energy Technology Data Exchange (ETDEWEB)

    Skambraks, Sebastian; Neuhaus, Sara [Technische Universitaet Muenchen (Germany); Chen, Yang; Kiesling, Christian [Max-Planck-Institut fuer Physik, Muenchen (Germany); Collaboration: Belle II-Collaboration

    2016-07-01

    We present a neural network based first level track trigger for the upcoming Belle II detector at the high luminosity SuperKEKB flavor factory. Using hit and drift time information from the Central Drift Chamber (CDC), neural networks estimate the z-coordinates of single track vertex positions. Especially beam induced background, with vertices outside of the interaction region, can clearly be rejected. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity. In the CDC trigger pipeline, the preceding 2D pattern recognition enables a unique per track input representation and a sectorization of the track parameter phase space. The precise z-vertices are then estimated by an ensemble of sector-specific local expert neural networks. After an introduction to the neural trigger system, the benefits of an improved 3D pattern recognition are discussed.

  1. Activity in part of the neural correlates of consciousness reflects integration.

    Science.gov (United States)

    Eriksson, Johan

    2017-10-01

    Integration is commonly viewed as a key process for generating conscious experiences. Accordingly, there should be increased activity within the neural correlates of consciousness when demands on integration increase. We used fMRI and "informational masking" to isolate the neural correlates of consciousness and measured how the associated brain activity changed as a function of required integration. Integration was manipulated by comparing the experience of hearing simple reoccurring tones to hearing harmonic tone triplets. The neural correlates of auditory consciousness included superior temporal gyrus, lateral and medial frontal regions, cerebellum, and also parietal cortex. Critically, only activity in left parietal cortex increased significantly as a function of increasing demands on integration. We conclude that integration can explain part of the neural activity associated with the generation conscious experiences, but that much of associated brain activity apparently reflects other processes. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Research Review: Neural response to threat in children, adolescents, and adults after child maltreatment - a quantitative meta-analysis.

    Science.gov (United States)

    Hein, Tyler C; Monk, Christopher S

    2017-03-01

    Child maltreatment is common and has long-term consequences for affective function. Investigations of neural consequences of maltreatment have focused on the amygdala. However, developmental neuroscience indicates that other brain regions are also likely to be affected by child maltreatment, particularly in the social information processing network (SIPN). We conducted a quantitative meta-analysis to: confirm that maltreatment is related to greater bilateral amygdala activation in a large sample that was pooled across studies; investigate other SIPN structures that are likely candidates for altered function; and conduct a data-driven examination to identify additional regions that show altered activation in maltreated children, teens, and adults. We conducted an activation likelihood estimation analysis with 1,733 participants across 20 studies of emotion processing in maltreated individuals. Maltreatment is associated with increased bilateral amygdala activation to emotional faces. One SIPN structure is altered: superior temporal gyrus, of the detection node, is hyperactive in maltreated individuals. The results of the whole-brain corrected analysis also show hyperactivation of the parahippocampal gyrus and insula in maltreated individuals. The meta-analysis confirms that maltreatment is related to increased bilateral amygdala reactivity and also shows that maltreatment affects multiple additional structures in the brain that have received little attention in the literature. Thus, although the majority of studies examining maltreatment and brain function have focused on the amygdala, these findings indicate that the neural consequences of child maltreatment involve a broader network of structures. © 2016 Association for Child and Adolescent Mental Health.

  3. Neural responses to exclusion predict susceptibility to social influence.

    Science.gov (United States)

    Falk, Emily B; Cascio, Christopher N; O'Donnell, Matthew Brook; Carp, Joshua; Tinney, Francis J; Bingham, C Raymond; Shope, Jean T; Ouimet, Marie Claude; Pradhan, Anuj K; Simons-Morton, Bruce G

    2014-05-01

    Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving. Copyright © 2014 Society for Adolescent Health and Medicine. All rights reserved.

  4. Recurrent Neural Network for Computing the Drazin Inverse.

    Science.gov (United States)

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

    2015-11-01

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

  5. Neural networks and its application in biomedical engineering

    International Nuclear Information System (INIS)

    Husnain, S.K.; Bhatti, M.I.

    2002-01-01

    Artificial network (ANNs) is an information processing system that has certain performance characteristics in common with biological neural networks. A neural network is characterized by connections between the neurons, method of determining the weights on the connections and its activation functions while a biological neuron has three types of components that are of particular interest in understanding an artificial neuron: its dendrites, soma, and axon. The actin of the chemical transmitter modifies the incoming signal. The study of neural networks is an extremely interdisciplinary field. Computer-based diagnosis is an increasingly used method that tries to improve the quality of health care. Systems on Neural Networks have been developed extensively in the last ten years with the hope that medical diagnosis and therefore medical care would improve dramatically. The addition of a symbolic processing layer enhances the ANNs in a number of ways. It is, for instance, possible to supplement a network that is purely diagnostic with a level that recommends or nodes in order to more closely simulate the nervous system. (author)

  6. The neural basis of learning to spell again: An fMRI study of spelling training in acquired dysgraphia.

    Directory of Open Access Journals (Sweden)

    Jeremy Purcell

    2015-05-01

    1 For all participants we identified brain areas associated with a normalized response for the TRAINING words at the post-training time point. 2 For all participants we identified an up-regulation of the TRAINING response (i.e., the TRAINING neural response was initially low and then increased post-training; whereas in only one participant did we also observe a down-regulation of the training response (i.e., the TRAINING neural response was initially high, but then decreased post-training. 3 Although the areas associated with the normalized TRAINING response were different in each individual, they all include areas typically associated with the spelling system (Purcell et al. 2011, including the right homologues of typically left hemisphere spelling regions. Across the participants, the following areas of normalization were observed: bilateral superior temporal gyrus, inferior frontal gyrus, and the bilateral inferior temporal/fusiform gyrus. Discussion: We found that the predominant BOLD response to training involved an up-regulation of the neural response to spelling the TRAINING items. In addition, we found individual differences in the neurotopography of the normalization response patterns although all were with within brain areas that form a part of the spelling network(Purcell et al. 2011. This work provides evidence regarding one aspect of the multiplicity of neural responses associated with recovery of spelling in individuals with acquired dysgraphia.

  7. A Neural Signature Encoding Decisions under Perceptual Ambiguity.

    Science.gov (United States)

    Sun, Sai; Yu, Rongjun; Wang, Shuo

    2017-01-01

    People often make perceptual decisions with ambiguous information, but it remains unclear whether the brain has a common neural substrate that encodes various forms of perceptual ambiguity. Here, we used three types of perceptually ambiguous stimuli as well as task instructions to examine the neural basis for both stimulus-driven and task-driven perceptual ambiguity. We identified a neural signature, the late positive potential (LPP), that encoded a general form of stimulus-driven perceptual ambiguity. In addition to stimulus-driven ambiguity, the LPP was also modulated by ambiguity in task instructions. To further specify the functional role of the LPP and elucidate the relationship between stimulus ambiguity, behavioral response, and the LPP, we employed regression models and found that the LPP was specifically associated with response latency and confidence rating, suggesting that the LPP encoded decisions under perceptual ambiguity. Finally, direct behavioral ratings of stimulus and task ambiguity confirmed our neurophysiological findings, which could not be attributed to differences in eye movements either. Together, our findings argue for a common neural signature that encodes decisions under perceptual ambiguity but is subject to the modulation of task ambiguity. Our results represent an essential first step toward a complete neural understanding of human perceptual decision making.

  8. Distinct Neural Substrates for Maintaining Locations and Spatial Relations in Working Memory

    Directory of Open Access Journals (Sweden)

    Kara J Blacker

    2016-11-01

    Full Text Available Previous work has demonstrated a distinction between maintenance of two types of spatial information in working memory (WM: spatial locations and spatial relations. While a body of work has investigated the neural mechanisms of sensory-based information like spatial locations, little is known about how spatial relations are maintained in WM. In two experiments, we used fMRI to investigate the involvement of early visual cortex in the maintenance of spatial relations in WM. In both experiments, we found less quadrant-specific BOLD activity in visual cortex when a single spatial relation, compared to a single spatial location, was held in WM. Also across both experiments, we found a consistent set of brain regions that were differentially activated during maintenance of locations versus relations. Maintaining a location, compared to a relation, was associated with greater activity in typical spatial WM regions like posterior parietal cortex and prefrontal regions. Whereas maintaining a relation, compared to a location, was associated with greater activity in the parahippocampal gyrus and precuneus/retrosplenial cortex. Further, in Experiment 2 we manipulated WM load and included trials where participants had to maintain three spatial locations or relations. Under this high load condition, the regions sensitive to locations versus relations were somewhat different than under low load. We also identified regions that were sensitive to load specifically for location or relation maintenance, as well as overlapping regions sensitive to load more generally. These results suggest that the neural substrates underlying WM maintenance of spatial locations and relations are distinct from one another and that the neural representations of these distinct types of spatial information change with load.

  9. A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints.

    Science.gov (United States)

    Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui

    2017-10-01

    Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.

  10. Morphological self-organizing feature map neural network with applications to automatic target recognition

    Science.gov (United States)

    Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

    2005-01-01

    The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

  11. Neural network tracking and extension of positive tracking periods

    Science.gov (United States)

    Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

    2004-04-01

    Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

  12. Application of two neural network paradigms to the study of voluntary employee turnover.

    Science.gov (United States)

    Somers, M J

    1999-04-01

    Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

  13. Getting the word out: neural correlates of enthusiastic message propagation.

    Science.gov (United States)

    Falk, Emily B; O'Donnell, Matthew Brook; Lieberman, Matthew D

    2012-01-01

    What happens in the mind of a person who first hears a potentially exciting idea?We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and

  14. Getting the word out: Neural correlates of enthusiastic message propagation

    Directory of Open Access Journals (Sweden)

    Emily eFalk

    2012-11-01

    Full Text Available What happens in the mind of a person who first hears a potentially exciting idea? We examined the neural precursors of spreading ideas with enthusiasm, and dissect enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing with data gathered using fMRI, to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants’ neural activity was recorded as they reviewed ideas for potential television show pilots. Participants’ language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis classifier, which returned ratings for evaluative language (evaluative vs. descriptive and valence (positive vs. negative. Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal parietal junction (TPJ. Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, MTL as well as in ventral striatum, inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. These data also demonstrate the novel use of machine learning tools to link natural language data to neuroimaging data.

  15. Getting the word out: neural correlates of enthusiastic message propagation

    Science.gov (United States)

    Falk, Emily B.; O'Donnell, Matthew Brook; Lieberman, Matthew D.

    2012-01-01

    What happens in the mind of a person who first hears a potentially exciting idea?We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and

  16. Dysfunction of Rapid Neural Adaptation in Dyslexia.

    Science.gov (United States)

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

    2016-12-21

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

  17. Conserved gene regulatory module specifies lateral neural borders across bilaterians.

    Science.gov (United States)

    Li, Yongbin; Zhao, Di; Horie, Takeo; Chen, Geng; Bao, Hongcun; Chen, Siyu; Liu, Weihong; Horie, Ryoko; Liang, Tao; Dong, Biyu; Feng, Qianqian; Tao, Qinghua; Liu, Xiao

    2017-08-01

    The lateral neural plate border (NPB), the neural part of the vertebrate neural border, is composed of central nervous system (CNS) progenitors and peripheral nervous system (PNS) progenitors. In invertebrates, PNS progenitors are also juxtaposed to the lateral boundary of the CNS. Whether there are conserved molecular mechanisms determining vertebrate and invertebrate lateral neural borders remains unclear. Using single-cell-resolution gene-expression profiling and genetic analysis, we present evidence that orthologs of the NPB specification module specify the invertebrate lateral neural border, which is composed of CNS and PNS progenitors. First, like in vertebrates, the conserved neuroectoderm lateral border specifier Msx/vab-15 specifies lateral neuroblasts in Caenorhabditis elegans Second, orthologs of the vertebrate NPB specification module ( Msx/vab-15 , Pax3/7/pax-3 , and Zic/ref-2 ) are significantly enriched in worm lateral neuroblasts. In addition, like in other bilaterians, the expression domain of Msx/vab-15 is more lateral than those of Pax3/7/pax-3 and Zic/ref- 2 in C. elegans Third, we show that Msx/vab-15 regulates the development of mechanosensory neurons derived from lateral neural progenitors in multiple invertebrate species, including C. elegans , Drosophila melanogaster , and Ciona intestinalis We also identify a novel lateral neural border specifier, ZNF703/tlp-1 , which functions synergistically with Msx/vab- 15 in both C. elegans and Xenopus laevis These data suggest a common origin of the molecular mechanism specifying lateral neural borders across bilaterians.

  18. A One-Layer Recurrent Neural Network for Real-Time Portfolio Optimization With Probability Criterion.

    Science.gov (United States)

    Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen

    2013-02-01

    This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.

  19. DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

    Science.gov (United States)

    Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

    2014-01-01

    Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

  20. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  1. Neural correlates of eating disorders: translational potential

    Directory of Open Access Journals (Sweden)

    McAdams CJ

    2015-09-01

    Full Text Available Carrie J McAdams,1,2 Whitney Smith1 1University of Texas at Southwestern Medical Center, 2Department of Psychiatry, Texas Health Presbyterian Hospital of Dallas, Dallas, TX, USA Abstract: Eating disorders are complex and serious psychiatric illnesses whose etiology includes psychological, biological, and social factors. Treatment of eating disorders is challenging as there are few evidence-based treatments and limited understanding of the mechanisms that result in sustained recovery. In the last 20 years, we have begun to identify neural pathways that are altered in eating disorders. Consideration of how these pathways may contribute to an eating disorder can provide an understanding of expected responses to treatments. Eating disorder behaviors include restrictive eating, compulsive overeating, and purging behaviors after eating. Eating disorders are associated with changes in many neural systems. In this targeted review, we focus on three cognitive processes associated with neurocircuitry differences in subjects with eating disorders such as reward, decision-making, and social behavior. We briefly examine how each of these systems function in healthy people, using Neurosynth meta-analysis to identify key regions commonly implicated in these circuits. We review the evidence for disruptions of these regions and systems in eating disorders. Finally, we describe psychiatric and psychological treatments that are likely to function by impacting these regions. Keywords: anorexia nervosa, bulimia nervosa, social cognition, reward processing, decision-making

  2. Neural 17β-estradiol facilitates long-term potentiation in the hippocampal CA1 region.

    Science.gov (United States)

    Grassi, S; Tozzi, A; Costa, C; Tantucci, M; Colcelli, E; Scarduzio, M; Calabresi, P; Pettorossi, V E

    2011-09-29

    In the hippocampal formation many neuromodulators are possibly implied in the synaptic plasticity such as the long-term potentiation (LTP) induced by high-frequency stimulation (HFS) of afferent fibers. We investigated the involvement of locally synthesized neural 17β-estradiol (nE(2)) in the induction of HFS-LTP in hippocampal slices from male rats by stimulating the Schaffer collateral fibers and recording the evoked field excitatory postsynaptic potential (fEPSP) in the CA1 region. We demonstrated that either the blockade of nE(2) synthesis by the aromatase inhibitor letrozole, or the antagonism of E(2) receptors (ERs) by ICI 182,780 did not prevent the induction of HFS-LTP, but reduced its amplitude by ∼60%, without influencing its maintenance. Moreover, letrozole and ICI 182,780 did not affect the first short-term post-tetanic component of LTP and the paired-pulse facilitation (PPF). These findings demonstrate that nE(2) plays an important role in the induction phase of HFS-dependent LTP. Since the basal responses were not affected by the blocking agents, we suggest that the synthesis of nE(2) is induced or enhanced by HFS through aromatase activation. In this context, the local production of nE(2) seems to be a very effective mechanism to modulate the amplitude of LTP. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

  3. Multimodal neural correlates of cognitive control in the Human Connectome Project.

    Science.gov (United States)

    Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas; Kandala, Sridhar; Calhoun, Vince D; Barch, Deanna M

    2017-12-01

    Cognitive control is a construct that refers to the set of functions that enable decision-making and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions

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

    Directory of Open Access Journals (Sweden)

    Thomas Gisiger

    2006-04-01

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

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

    Science.gov (United States)

    Gisiger, Thomas; Kerszberg, Michel

    2006-04-01

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

  6. Neural representations of social status hierarchy in human inferior parietal cortex.

    Science.gov (United States)

    Chiao, Joan Y; Harada, Tokiko; Oby, Emily R; Li, Zhang; Parrish, Todd; Bridge, Donna J

    2009-01-01

    Mental representations of social status hierarchy share properties with that of numbers. Previous neuroimaging studies have shown that the neural representation of numerical magnitude lies within a network of regions within inferior parietal cortex. However the neural basis of social status hierarchy remains unknown. Using fMRI, we studied subjects while they compared social status magnitude of people, objects and symbols, as well as numerical magnitude. Both social status and number comparisons recruited bilateral intraparietal sulci. We also observed a semantic distance effect whereby neural activity within bilateral intraparietal sulci increased for semantically close relative to far numerical and social status comparisons. These results demonstrate that social status and number comparisons recruit distinct and overlapping neuronal representations within human inferior parietal cortex.

  7. 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.

  8. Abnormal neural responses to social exclusion in schizophrenia.

    Directory of Open Access Journals (Sweden)

    Victoria B Gradin

    Full Text Available Social exclusion is an influential concept in politics, mental health and social psychology. Studies on healthy subjects have implicated the medial prefrontal cortex (mPFC, a region involved in emotional and social information processing, in neural responses to social exclusion. Impairments in social interactions are common in schizophrenia and are associated with reduced quality of life. Core symptoms such as delusions usually have a social content. However little is known about the neural underpinnings of social abnormalities. The aim of this study was to investigate the neural substrates of social exclusion in schizophrenia. Patients with schizophrenia and healthy controls underwent fMRI while participating in a popular social exclusion paradigm. This task involves passing a 'ball' between the participant and two cartoon representations of other subjects. The extent of social exclusion (ball not being passed to the participant was parametrically varied throughout the task. Replicating previous findings, increasing social exclusion activated the mPFC in controls. In contrast, patients with schizophrenia failed to modulate mPFC responses with increasing exclusion. Furthermore, the blunted response to exclusion correlated with increased severity of positive symptoms. These data support the hypothesis that the neural response to social exclusion differs in schizophrenia, highlighting the mPFC as a potential substrate of impaired social interactions.

  9. A common neural code for perceived and inferred emotion.

    Science.gov (United States)

    Skerry, Amy E; Saxe, Rebecca

    2014-11-26

    Although the emotions of other people can often be perceived from overt reactions (e.g., facial or vocal expressions), they can also be inferred from situational information in the absence of observable expressions. How does the human brain make use of these diverse forms of evidence to generate a common representation of a target's emotional state? In the present research, we identify neural patterns that correspond to emotions inferred from contextual information and find that these patterns generalize across different cues from which an emotion can be attributed. Specifically, we use functional neuroimaging to measure neural responses to dynamic facial expressions with positive and negative valence and to short animations in which the valence of a character's emotion could be identified only from the situation. Using multivoxel pattern analysis, we test for regions that contain information about the target's emotional state, identifying representations specific to a single stimulus type and representations that generalize across stimulus types. In regions of medial prefrontal cortex (MPFC), a classifier trained to discriminate emotional valence for one stimulus (e.g., animated situations) could successfully discriminate valence for the remaining stimulus (e.g., facial expressions), indicating a representation of valence that abstracts away from perceptual features and generalizes across different forms of evidence. Moreover, in a subregion of MPFC, this neural representation generalized to trials involving subjectively experienced emotional events, suggesting partial overlap in neural responses to attributed and experienced emotions. These data provide a step toward understanding how the brain transforms stimulus-bound inputs into abstract representations of emotion. Copyright © 2014 the authors 0270-6474/14/3315997-12$15.00/0.

  10. Neural Correlates of Sexual Orientation in Heterosexual, Bisexual, and Homosexual Men

    Science.gov (United States)

    Safron, Adam; Sylva, David; Klimaj, Victoria; Rosenthal, A. M.; Li, Meng; Walter, Martin; Bailey, J. Michael

    2017-01-01

    Studies of subjective and genital sexual arousal in monosexual (i.e. heterosexual and homosexual) men have repeatedly found that erotic stimuli depicting men’s preferred sex produce strong responses, whereas erotic stimuli depicting the other sex produce much weaker responses. Inconsistent results have previously been obtained in bisexual men, who have sometimes demonstrated distinctly bisexual responses, but other times demonstrated patterns more similar to those observed in monosexual men. We used fMRI to investigate neural correlates of responses to erotic pictures and videos in heterosexual, bisexual, and homosexual men, ages 25–50. Sixty participants were included in video analyses, and 62 were included in picture analyses. We focused on the ventral striatum (VS), due to its association with incentive motivation. Patterns were consistent with sexual orientation, with heterosexual and homosexual men showing female-favoring and male-favoring responses, respectively. Bisexual men tended to show less differentiation between male and female stimuli. Consistent patterns were observed in the whole brain, including the VS, and also in additional regions such as occipitotemporal, anterior cingulate, and orbitofrontal cortices. This study extends previous findings of gender-specific neural responses in monosexual men, and provides initial evidence for distinct brain activity patterns in bisexual men. PMID:28145518

  11. The neural basis of human social values: evidence from functional MRI.

    Science.gov (United States)

    Zahn, Roland; Moll, Jorge; Paiva, Mirella; Garrido, Griselda; Krueger, Frank; Huey, Edward D; Grafman, Jordan

    2009-02-01

    Social values are composed of social concepts (e.g., "generosity") and context-dependent moral sentiments (e.g., "pride"). The neural basis of this intricate cognitive architecture has not been investigated thus far. Here, we used functional magnetic resonance imaging while subjects imagined their own actions toward another person (self-agency) which either conformed or were counter to a social value and were associated with pride or guilt, respectively. Imagined actions of another person toward the subjects (other-agency) in accordance with or counter to a value were associated with gratitude or indignation/anger. As hypothesized, superior anterior temporal lobe (aTL) activity increased with conceptual detail in all conditions. During self-agency, activity in the anterior ventromedial prefrontal cortex correlated with pride and guilt, whereas activity in the subgenual cingulate solely correlated with guilt. In contrast, indignation/anger activated lateral orbitofrontal-insular cortices. Pride and gratitude additionally evoked mesolimbic and basal forebrain activations. Our results demonstrate that social values emerge from coactivation of stable abstract social conceptual representations in the superior aTL and context-dependent moral sentiments encoded in fronto-mesolimbic regions. This neural architecture may provide the basis of our ability to communicate about the meaning of social values across cultural contexts without limiting our flexibility to adapt their emotional interpretation.

  12. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    Zhang, Feng-zhe; Yang, Xia

    2018-04-01

    In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.

  13. Generation and properties of a new human ventral mesencephalic neural stem cell line

    Energy Technology Data Exchange (ETDEWEB)

    Villa, Ana; Liste, Isabel; Courtois, Elise T.; Seiz, Emma G.; Ramos, Milagros [Center of Molecular Biology ' Severo Ochoa' , Autonomous University of Madrid-C.S.I.C., Campus Cantoblanco 28049-Madrid (Spain); Meyer, Morten [Department of Anatomy and Neurobiology, Institute of Medical Biology, University of Southern Denmark, Winslowparken 21,st, DK-500, Odense C (Denmark); Juliusson, Bengt; Kusk, Philip [NsGene A/S, Ballerup (Denmark); Martinez-Serrano, Alberto, E-mail: amserrano@cbm.uam.es [Center of Molecular Biology ' Severo Ochoa' , Autonomous University of Madrid-C.S.I.C., Campus Cantoblanco 28049-Madrid (Spain)

    2009-07-01

    Neural stem cells (NSCs) are powerful research tools for the design and discovery of new approaches to cell therapy in neurodegenerative diseases like Parkinson's disease. Several epigenetic and genetic strategies have been tested for long-term maintenance and expansion of these cells in vitro. Here we report the generation of a new stable cell line of human neural stem cells derived from ventral mesencephalon (hVM1) based on v-myc immortalization. The cells expressed neural stem cell and radial glia markers like nestin, vimentin and 3CB2 under proliferation conditions. After withdrawal of growth factors, proliferation and expression of v-myc were dramatically reduced and the cells differentiated into astrocytes, oligodendrocytes and neurons. hVM1 cells yield a large number of dopaminergic neurons (about 12% of total cells are TH{sup +}) after differentiation, which also produce dopamine. In addition to proneural genes (NGN2, MASH1), differentiated cells show expression of several genuine mesencephalic dopaminergic markers such as: LMX1A, LMX1B, GIRK2, ADH2, NURR1, PITX3, VMAT2 and DAT, indicating that they retain their regional identity. Our data indicate that this cell line and its clonal derivatives may constitute good candidates for the study of development and physiology of human dopaminergic neurons in vitro, and to develop tools for Parkinson's disease cell replacement preclinical research and drug testing.

  14. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

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

  15. Neural correlate of human reciprocity in social interactions.

    Science.gov (United States)

    Sakaiya, Shiro; Shiraito, Yuki; Kato, Junko; Ide, Hiroko; Okada, Kensuke; Takano, Kouji; Kansaku, Kenji

    2013-01-01

    Reciprocity plays a key role maintaining cooperation in society. However, little is known about the neural process that underpins human reciprocity during social interactions. Our neuroimaging study manipulated partner identity (computer, human) and strategy (random, tit-for-tat) in repeated prisoner's dilemma games and investigated the neural correlate of reciprocal interaction with humans. Reciprocal cooperation with humans but exploitation of computers by defection was associated with activation in the left amygdala. Amygdala activation was also positively and negatively correlated with a preference change for human partners following tit-for-tat and random strategies, respectively. The correlated activation represented the intensity of positive feeling toward reciprocal and negative feeling toward non-reciprocal partners, and so reflected reciprocity in social interaction. Reciprocity in social interaction, however, might plausibly be misinterpreted and so we also examined the neural coding of insight into the reciprocity of partners. Those with and without insight revealed differential brain activation across the reward-related circuitry (i.e., the right middle dorsolateral prefrontal cortex and dorsal caudate) and theory of mind (ToM) regions [i.e., ventromedial prefrontal cortex (VMPFC) and precuneus]. Among differential activations, activation in the precuneus, which accompanied deactivation of the VMPFC, was specific to those without insight into human partners who were engaged in a tit-for-tat strategy. This asymmetric (de)activation might involve specific contributions of ToM regions to the human search for reciprocity. Consequently, the intensity of emotion attached to human reciprocity was represented in the amygdala, whereas insight into the reciprocity of others was reflected in activation across the reward-related and ToM regions. This suggests the critical role of mentalizing, which was not equated with reward expectation during social interactions.

  16. Neural correlate of human reciprocity in social interactions

    Directory of Open Access Journals (Sweden)

    Shiro eSakaiya

    2013-12-01

    Full Text Available Reciprocity plays a key role maintaining cooperation in society. However, little is known about the neural process that underpins human reciprocity during social interactions. Our neuroimaging study manipulated partner identity (computer, human and strategy (random, tit-for-tat in repeated prisoner’s dilemma games and investigated the neural correlate of reciprocal interaction with humans. Reciprocal cooperation with humans but exploitation of computers by defection was associated with activation in the left amygdala. Amygdala activation was also positively and negatively correlated with a preference change for human partners following tit-for-tat and random strategies, respectively. The correlated activation represented the intensity of positive feeling toward reciprocal and negative feeling toward non-reciprocal partners, and so reflected reciprocity in social interaction. Reciprocity in social interaction, however, might plausibly be misinterpreted and so we also examined the neural coding of insight into the reciprocity of partners. Those with and without insight revealed differential brain activation across the reward-related circuitry (i.e., the right middle dorsolateral prefrontal cortex and dorsal caudate and theory of mind (ToM regions (i.e., ventromedial prefrontal cortex [VMPFC] and precuneus. Among differential activations, activation in the precuneus, which accompanied deactivation of the VMPFC, was specific to those without insight into human partners who were engaged in a tit-for-tat strategy. This asymmetric (deactivation might involve specific contributions of ToM regions to the human search for reciprocity. Consequently, the intensity of emotion attached to human reciprocity was represented in the amygdala, whereas insight into the reciprocity of others was reflected in activation across the reward-related and ToM regions. This suggests the critical role of mentalizing, which was not equated with reward expectation during

  17. Neural activity when people solve verbal problems with insight.

    Directory of Open Access Journals (Sweden)

    Mark Jung-Beeman

    2004-04-01

    Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

  18. Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

    Science.gov (United States)

    Mills, Kyle; Tamblyn, Isaac

    2018-03-01

    We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

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

    Science.gov (United States)

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

    2013-10-02

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

  20. Intrusive images in psychological disorders: characteristics, neural mechanisms, and treatment implications.

    Science.gov (United States)

    Brewin, Chris R; Gregory, James D; Lipton, Michelle; Burgess, Neil

    2010-01-01

    Involuntary images and visual memories are prominent in many types of psychopathology. Patients with posttraumatic stress disorder, other anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions corresponding to a small number of real or imaginary events, usually extremely vivid, detailed, and with highly distressing content. Both memory and imagery appear to rely on common networks involving medial prefrontal regions, posterior regions in the medial and lateral parietal cortices, the lateral temporal cortex, and the medial temporal lobe. Evidence from cognitive psychology and neuroscience implies distinct neural bases to abstract, flexible, contextualized representations (C-reps) and to inflexible, sensory-bound representations (S-reps). We revise our previous dual representation theory of posttraumatic stress disorder to place it within a neural systems model of healthy memory and imagery. The revised model is used to explain how the different types of distressing visual intrusions associated with clinical disorders arise, in terms of the need for correct interaction between the neural systems supporting S-reps and C-reps via visuospatial working memory. Finally, we discuss the treatment implications of the new model and relate it to existing forms of psychological therapy.

  1. The neural correlates of cognitive reappraisal during emotional autobiographical memory recall.

    Science.gov (United States)

    Holland, Alisha C; Kensinger, Elizabeth A

    2013-01-01

    We used fMRI to investigate the neural processes engaged as individuals down- and up-regulated the emotions associated with negative autobiographical memories (AMs) using cognitive reappraisal strategies. Our analyses examined neural activity during three separate phases, as participants (a) viewed a reappraisal instruction (i.e., Decrease, Increase, Maintain), (b) searched for an AM referenced by a self-generated cue, and (c) elaborated upon the details of the AM being held in mind. Decreasing emotional intensity primarily engaged activity in regions previously implicated in cognitive control (e.g., dorsal and ventral lateral pFC), emotion generation and processing (e.g., amygdala, insula), and visual imagery (e.g., precuneus) as participants searched for and retrieved events. In contrast, increasing emotional intensity engaged similar regions during the instruction phase (i.e., before a memory cue was presented) and again as individuals later elaborated upon the details of the events they had recalled. These findings confirm that reappraisal can modulate neural activity during the recall of personally relevant events, although the time course of this modulation appears to depend on whether individuals are attempting to down- or up-regulate their emotions.

  2. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  3. Exploring the neural correlates of visual creativity

    Science.gov (United States)

    Liew, Sook-Lei; Dandekar, Francesco

    2013-01-01

    Although creativity has been called the most important of all human resources, its neural basis is still unclear. In the current study, we used fMRI to measure neural activity in participants solving a visuospatial creativity problem that involves divergent thinking and has been considered a canonical right hemisphere task. As hypothesized, both the visual creativity task and the control task as compared to rest activated a variety of areas including the posterior parietal cortex bilaterally and motor regions, which are known to be involved in visuospatial rotation of objects. However, directly comparing the two tasks indicated that the creative task more strongly activated left hemisphere regions including the posterior parietal cortex, the premotor cortex, dorsolateral prefrontal cortex (DLPFC) and the medial PFC. These results demonstrate that even in a task that is specialized to the right hemisphere, robust parallel activity in the left hemisphere supports creative processing. Furthermore, the results support the notion that higher motor planning may be a general component of creative improvisation and that such goal-directed planning of novel solutions may be organized top-down by the left DLPFC and by working memory processing in the medial prefrontal cortex. PMID:22349801

  4. Neural Tube Defects and Pregnancy

    Directory of Open Access Journals (Sweden)

    Emine Çoşar

    2009-09-01

    Full Text Available OBJECTIVE: Neural tube defects are congenital malformations those mostly causing life-long morbidities. They are prevented by the periconseptional folic acid usage and prenatal diagnostic methods. MATERIALS-METHODS: Pregnants from Afyonkarahisar and neighbourhood cities applied to our hospital and determined NTD, were investigated. RESULTS: In our obstetrics clinic 1403 delivery were made and 43 of them had fetus with NTD. Among these fetuses 41.3% had meningomyelocel, 17.4% had meningocel, 21.7% had encephalocel, 8.7% had unencephali and 4.3% had iniencephali. CONCLUSION: Incidence of NTD is high in our region and geographic region, nutrition and other socioeconomic factors may be related to the high incidence. Education of the mother and periconceptional folic acid usage may reduce teh incidence of NTD.

  5. Engaged listeners: shared neural processing of powerful political speeches.

    Science.gov (United States)

    Schmälzle, Ralf; Häcker, Frank E K; Honey, Christopher J; Hasson, Uri

    2015-08-01

    Powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners. What is happening in the brains of a captivated audience? Here, we assess audience-wide functional brain dynamics during listening to speeches of varying rhetorical quality. The speeches were given by German politicians and evaluated as rhetorically powerful or weak. Listening to each of the speeches induced similar neural response time courses, as measured by inter-subject correlation analysis, in widespread brain regions involved in spoken language processing. Crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex. Thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses. Weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses. These patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances. Overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  6. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  7. Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network

    Directory of Open Access Journals (Sweden)

    Miao Suzhen

    2016-01-01

    Full Text Available Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO were used as inputs and ST was used as output of the model.

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

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

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

  9. Programmed Cell Death and Caspase Functions During Neural Development.

    Science.gov (United States)

    Yamaguchi, Yoshifumi; Miura, Masayuki

    2015-01-01

    Programmed cell death (PCD) is a fundamental component of nervous system development. PCD serves as the mechanism for quantitative matching of the number of projecting neurons and their target cells through direct competition for neurotrophic factors in the vertebrate peripheral nervous system. In addition, PCD plays roles in regulating neural cell numbers, canceling developmental errors or noise, and tissue remodeling processes. These findings are mainly derived from genetic studies that prevent cells from dying by apoptosis, which is a major form of PCD and is executed by activation of evolutionarily conserved cysteine protease caspases. Recent studies suggest that caspase activation can be coordinated in time and space at multiple levels, which might underlie nonapoptotic roles of caspases in neural development in addition to apoptotic roles. © 2015 Elsevier Inc. All rights reserved.

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

    International Nuclear Information System (INIS)

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

    2010-01-01

    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.

  11. Dynamics in a delayed-neural network

    International Nuclear Information System (INIS)

    Yuan Yuan

    2007-01-01

    In this paper, we consider a neural network of four identical neurons with time-delayed connections. Some parameter regions are given for global, local stability and synchronization using the theory of functional differential equations. The root distributions in the corresponding characteristic transcendental equation are analyzed, Pitchfork bifurcation, Hopf and equivariant Hopf bifurcations are investigated by revealing the center manifolds and normal forms. Numerical simulations are shown the agreements with the theoretical results

  12. Neural mechanisms underlying morphine withdrawal in addicted patients: a review

    Directory of Open Access Journals (Sweden)

    Nima Babhadiashar

    2015-06-01

    Full Text Available Morphine is one of the most potent alkaloid in opium, which has substantial medical uses and needs and it is the first active principle purified from herbal source. Morphine has commonly been used for relief of moderate to severe pain as it acts directly on the central nervous system; nonetheless, its chronic abuse increases tolerance and physical dependence, which is commonly known as opiate addiction. Morphine withdrawal syndrome is physiological and behavioral symptoms that stem from prolonged exposure to morphine. A majority of brain regions are hypofunctional over prolonged abstinence and acute morphine withdrawal. Furthermore, several neural mechanisms are likely to contribute to morphine withdrawal. The present review summarizes the literature pertaining to neural mechanisms underlying morphine withdrawal. Despite the fact that morphine withdrawal is a complex process, it is suggested that neural mechanisms play key roles in morphine withdrawal.

  13. Understanding the Implications of Neural Population Activity on Behavior

    Science.gov (United States)

    Briguglio, John

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

  14. 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.

  15. End-to-End Neural Optical Music Recognition of Monophonic Scores

    Directory of Open Access Journals (Sweden)

    Jorge Calvo-Zaragoza

    2018-04-01

    Full Text Available Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Music Scores dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.

  16. Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Daoyu Lin

    2018-01-01

    Full Text Available Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.

  17. A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

    Science.gov (United States)

    Li, Lin; Brockmeier, Austin J.; Choi, John S.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2014-01-01

    Brain machine interfaces (BMIs) have attracted intense attention as a promising technology for directly interfacing computers or prostheses with the brain's motor and sensory areas, thereby bypassing the body. The availability of multiscale neural recordings including spike trains and local field potentials (LFPs) brings potential opportunities to enhance computational modeling by enriching the characterization of the neural system state. However, heterogeneity on data type (spike timing versus continuous amplitude signals) and spatiotemporal scale complicates the model integration of multiscale neural activity. In this paper, we propose a tensor-product-kernel-based framework to integrate the multiscale activity and exploit the complementary information available in multiscale neural activity. This provides a common mathematical framework for incorporating signals from different domains. The approach is applied to the problem of neural decoding and control. For neural decoding, the framework is able to identify the nonlinear functional relationship between the multiscale neural responses and the stimuli using general purpose kernel adaptive filtering. In a sensory stimulation experiment, the tensor-product-kernel decoder outperforms decoders that use only a single neural data type. In addition, an adaptive inverse controller for delivering electrical microstimulation patterns that utilizes the tensor-product kernel achieves promising results in emulating the responses to natural stimulation. PMID:24829569

  18. Sequential and parallel image restoration: neural network implementations.

    Science.gov (United States)

    Figueiredo, M T; Leitao, J N

    1994-01-01

    Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.

  19. Decoding the neural signatures of emotions expressed through sound.

    Science.gov (United States)

    Sachs, Matthew E; Habibi, Assal; Damasio, Antonio; Kaplan, Jonas T

    2018-03-01

    Effective social functioning relies in part on the ability to identify emotions from auditory stimuli and respond appropriately. Previous studies have uncovered brain regions engaged by the affective information conveyed by sound. But some of the acoustical properties of sounds that express certain emotions vary remarkably with the instrument used to produce them, for example the human voice or a violin. Do these brain regions respond in the same way to different emotions regardless of the sound source? To address this question, we had participants (N = 38, 20 females) listen to brief audio excerpts produced by the violin, clarinet, and human voice, each conveying one of three target emotions-happiness, sadness, and fear-while brain activity was measured with fMRI. We used multivoxel pattern analysis to test whether emotion-specific neural responses to the voice could predict emotion-specific neural responses to musical instruments and vice-versa. A whole-brain searchlight analysis revealed that patterns of activity within the primary and secondary auditory cortex, posterior insula, and parietal operculum were predictive of the affective content of sound both within and across instruments. Furthermore, classification accuracy within the anterior insula was correlated with behavioral measures of empathy. The findings suggest that these brain regions carry emotion-specific patterns that generalize across sounds with different acoustical properties. Also, individuals with greater empathic ability have more distinct neural patterns related to perceiving emotions. These results extend previous knowledge regarding how the human brain extracts emotional meaning from auditory stimuli and enables us to understand and connect with others effectively. Copyright © 2018 Elsevier Inc. All rights reserved.

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

    KAUST Repository

    Alfadly, Modar

    2018-01-01

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

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

    KAUST Repository

    Alfadly, Modar M.

    2018-04-12

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

  2. Gelatin methacrylamide hydrogel with graphene nanoplatelets for neural cell-laden 3D bioprinting.

    Science.gov (United States)

    Wei Zhu; Harris, Brent T; Zhang, Lijie Grace

    2016-08-01

    Nervous system is extremely complex which leads to rare regrowth of nerves once injury or disease occurs. Advanced 3D bioprinting strategy, which could simultaneously deposit biocompatible materials, cells and supporting components in a layer-by-layer manner, may be a promising solution to address neural damages. Here we presented a printable nano-bioink composed of gelatin methacrylamide (GelMA), neural stem cells, and bioactive graphene nanoplatelets to target nerve tissue regeneration in the assist of stereolithography based 3D bioprinting technique. We found the resultant GelMA hydrogel has a higher compressive modulus with an increase of GelMA concentration. The porous GelMA hydrogel can provide a biocompatible microenvironment for the survival and growth of neural stem cells. The cells encapsulated in the hydrogel presented good cell viability at the low GelMA concentration. Printed neural construct exhibited well-defined architecture and homogenous cell distribution. In addition, neural stem cells showed neuron differentiation and neurites elongation within the printed construct after two weeks of culture. These findings indicate the 3D bioprinted neural construct has great potential for neural tissue regeneration.

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

    Science.gov (United States)

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

    2016-06-22

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

  4. Decreased N-TAF1 expression in X-linked dystonia-parkinsonism patient-specific neural stem cells

    Directory of Open Access Journals (Sweden)

    Naoto Ito

    2016-04-01

    Full Text Available X-linked dystonia-parkinsonism (XDP is a hereditary neurodegenerative disorder involving a progressive loss of striatal medium spiny neurons. The mechanisms underlying neurodegeneration are not known, in part because there have been few cellular models available for studying the disease. The XDP haplotype consists of multiple sequence variations in a region of the X chromosome containing TAF1, a large gene with at least 38 exons, and a multiple transcript system (MTS composed of five unconventional exons. A previous study identified an XDP-specific insertion of a SINE-VNTR-Alu (SVA-type retrotransposon in intron 32 of TAF1, as well as a neural-specific TAF1 isoform, N-TAF1, which showed decreased expression in post-mortem XDP brain compared with control tissue. Here, we generated XDP patient and control fibroblasts and induced pluripotent stem cells (iPSCs in order to further probe cellular defects associated with this disease. As initial validation of the model, we compared expression of TAF1 and MTS transcripts in XDP versus control fibroblasts and iPSC-derived neural stem cells (NSCs. Compared with control cells, XDP fibroblasts exhibited decreased expression of TAF1 transcript fragments derived from exons 32-36, a region spanning the SVA insertion site. N-TAF1, which incorporates an alternative exon (exon 34′, was not expressed in fibroblasts, but was detectable in iPSC-differentiated NSCs at levels that were ∼threefold lower in XDP cells than in controls. These results support the previous findings that N-TAF1 expression is impaired in XDP, but additionally indicate that this aberrant transcription might occur in neural cells at relatively early stages of development that precede neurodegeneration.

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

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

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

  6. Bilingualism provides a neural reserve for aging populations.

    Science.gov (United States)

    Abutalebi, Jubin; Guidi, Lucia; Borsa, Virginia; Canini, Matteo; Della Rosa, Pasquale A; Parris, Ben A; Weekes, Brendan S

    2015-03-01

    It has been postulated that bilingualism may act as a cognitive reserve and recent behavioral evidence shows that bilinguals are diagnosed with dementia about 4-5 years later compared to monolinguals. In the present study, we investigated the neural basis of these putative protective effects in a group of aging bilinguals as compared to a matched monolingual control group. For this purpose, participants completed the Erikson Flanker task and their performance was correlated to gray matter (GM) volume in order to investigate if cognitive performance predicts GM volume specifically in areas affected by aging. We performed an ex-Gaussian analysis on the resulting RTs and report that aging bilinguals performed better than aging monolinguals on the Flanker task. Bilingualism was overall associated with increased GM in the ACC. Likewise, aging induced effects upon performance correlated only for monolinguals to decreased gray matter in the DLPFC. Taken together, these neural regions might underlie the benefits of bilingualism and act as a neural reserve that protects against the cognitive decline that occurs during aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2015-05-01

    This paper is concerned with the problem of coexistence and dynamical behaviors of multiple equilibrium points for neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays. The fixed point theorem and other analytical tools are used to develop certain sufficient conditions that ensure that the n-dimensional discontinuous neural networks with time-varying delays can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable. The importance of the derived results is that it reveals that the discontinuous neural networks can have greater storage capacity than the continuous ones. Moreover, different from the existing results on multistability of neural networks with discontinuous activation functions, the 3(n) locally stable equilibrium points obtained in this paper are located in not only saturated regions, but also unsaturated regions, due to the non-monotonic structure of discontinuous activation functions. A numerical simulation study is conducted to illustrate and support the derived theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Neural correlates of four broad temperament dimensions: testing predictions for a novel construct of personality.

    Directory of Open Access Journals (Sweden)

    Lucy L Brown

    Full Text Available Four suites of behavioral traits have been associated with four broad neural systems: the 1 dopamine and related norepinephrine system; 2 serotonin; 3 testosterone; 4 and estrogen and oxytocin system. A 56-item questionnaire, the Fisher Temperament Inventory (FTI, was developed to define four temperament dimensions associated with these behavioral traits and neural systems. The questionnaire has been used to suggest romantic partner compatibility. The dimensions were named: Curious/Energetic; Cautious/Social Norm Compliant; Analytical/Tough-minded; and Prosocial/Empathetic. For the present study, the FTI was administered to participants in two functional magnetic resonance imaging studies that elicited feelings of love and attachment, near-universal human experiences. Scores for the Curious/Energetic dimension co-varied with activation in a region of the substantia nigra, consistent with the prediction that this dimension reflects activity in the dopamine system. Scores for the Cautious/Social Norm Compliant dimension correlated with activation in the ventrolateral prefrontal cortex in regions associated with social norm compliance, a trait linked with the serotonin system. Scores on the Analytical/Tough-minded scale co-varied with activity in regions of the occipital and parietal cortices associated with visual acuity and mathematical thinking, traits linked with testosterone. Also, testosterone contributes to brain architecture in these areas. Scores on the Prosocial/Empathetic scale correlated with activity in regions of the inferior frontal gyrus, anterior insula and fusiform gyrus. These are regions associated with mirror neurons or empathy, a trait linked with the estrogen/oxytocin system, and where estrogen contributes to brain architecture. These findings, replicated across two studies, suggest that the FTI measures influences of four broad neural systems, and that these temperament dimensions and neural systems could constitute

  9. Neural correlates of four broad temperament dimensions: testing predictions for a novel construct of personality.

    Science.gov (United States)

    Brown, Lucy L; Acevedo, Bianca; Fisher, Helen E

    2013-01-01

    Four suites of behavioral traits have been associated with four broad neural systems: the 1) dopamine and related norepinephrine system; 2) serotonin; 3) testosterone; 4) and estrogen and oxytocin system. A 56-item questionnaire, the Fisher Temperament Inventory (FTI), was developed to define four temperament dimensions associated with these behavioral traits and neural systems. The questionnaire has been used to suggest romantic partner compatibility. The dimensions were named: Curious/Energetic; Cautious/Social Norm Compliant; Analytical/Tough-minded; and Prosocial/Empathetic. For the present study, the FTI was administered to participants in two functional magnetic resonance imaging studies that elicited feelings of love and attachment, near-universal human experiences. Scores for the Curious/Energetic dimension co-varied with activation in a region of the substantia nigra, consistent with the prediction that this dimension reflects activity in the dopamine system. Scores for the Cautious/Social Norm Compliant dimension correlated with activation in the ventrolateral prefrontal cortex in regions associated with social norm compliance, a trait linked with the serotonin system. Scores on the Analytical/Tough-minded scale co-varied with activity in regions of the occipital and parietal cortices associated with visual acuity and mathematical thinking, traits linked with testosterone. Also, testosterone contributes to brain architecture in these areas. Scores on the Prosocial/Empathetic scale correlated with activity in regions of the inferior frontal gyrus, anterior insula and fusiform gyrus. These are regions associated with mirror neurons or empathy, a trait linked with the estrogen/oxytocin system, and where estrogen contributes to brain architecture. These findings, replicated across two studies, suggest that the FTI measures influences of four broad neural systems, and that these temperament dimensions and neural systems could constitute foundational mechanisms

  10. Design of Robust Neural Network Classifiers

    DEFF Research Database (Denmark)

    Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads

    1998-01-01

    This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...

  11. A novel neural-wavelet approach for process diagnostics and complex system modeling

    Science.gov (United States)

    Gao, Rong

    Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

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

    International Nuclear Information System (INIS)

    Nikityuk, N.M.; )

    2001-01-01

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

  13. Heparan Sulfate Proteoglycans as Drivers of Neural Progenitors Derived From Human Mesenchymal Stem Cells.

    Science.gov (United States)

    Okolicsanyi, Rachel K; Oikari, Lotta E; Yu, Chieh; Griffiths, Lyn R; Haupt, Larisa M

    2018-01-01

    Background: Due to their relative ease of isolation and their high ex vivo and in vitro expansive potential, human mesenchymal stem cells (hMSCs) are an attractive candidate for therapeutic applications in the treatment of brain injury and neurological diseases. Heparan sulfate proteoglycans (HSPGs) are a family of ubiquitous proteins involved in a number of vital cellular processes including proliferation and stem cell lineage differentiation. Methods: Following the determination that hMSCs maintain neural potential throughout extended in vitro expansion, we examined the role of HSPGs in mediating the neural potential of hMSCs. hMSCs cultured in basal conditions (undifferentiated monolayer cultures) were found to co-express neural markers and HSPGs throughout expansion with modulation of the in vitro niche through the addition of exogenous HS influencing cellular HSPG and neural marker expression. Results: Conversion of hMSCs into hMSC Induced Neurospheres (hMSC IN) identified distinctly localized HSPG staining within the spheres along with altered gene expression of HSPG core protein and biosynthetic enzymes when compared to undifferentiated hMSCs. Conclusion: Comparison of markers of pluripotency, neural self-renewal and neural lineage specification between hMSC IN, hMSC and human neural stem cell (hNSC H9) cultures suggest that in vitro generated hMSC IN may represent an intermediary neurogenic cell type, similar to a common neural progenitor cell. In addition, this data demonstrates HSPGs and their biosynthesis machinery, are associated with hMSC IN formation. The identification of specific HSPGs driving hMSC lineage-specification will likely provide new markers to allow better use of hMSCs in therapeutic applications and improve our understanding of human neurogenesis.

  14. Relation of obesity to neural activation in response to food commercials.

    Science.gov (United States)

    Gearhardt, Ashley N; Yokum, Sonja; Stice, Eric; Harris, Jennifer L; Brownell, Kelly D

    2014-07-01

    Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean to obese in response to food and non-food commercials imbedded in a television show. Adolescents exhibited greater activation in regions implicated in visual processing (e.g. occipital gyrus), attention (e.g. parietal lobes), cognition (e.g. temporal gyrus and posterior cerebellar lobe), movement (e.g. anterior cerebellar cortex), somatosensory response (e.g. postcentral gyrus) and reward [e.g. orbitofrontal cortex and anterior cingulate cortex (ACC)] during food commercials. Obese participants exhibited less activation during food relative to non-food commercials in neural regions implicated in visual processing (e.g. cuneus), attention (e.g. posterior cerebellar lobe), reward (e.g. ventromedial prefrontal cortex and ACC) and salience detection (e.g. precuneus). Obese participants did exhibit greater activation in a region implicated in semantic control (e.g. medial temporal gyrus). These findings may inform current policy debates regarding the impact of food advertising to minors. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  15. Motivational orientation modulates the neural response to reward.

    Science.gov (United States)

    Linke, Julia; Kirsch, Peter; King, Andrea V; Gass, Achim; Hennerici, Michael G; Bongers, André; Wessa, Michèle

    2010-02-01

    Motivational orientation defines the source of motivation for an individual to perform a particular action and can either originate from internal desires (e.g., interest) or external compensation (e.g., money). To this end, motivational orientation should influence the way positive or negative feedback is processed during learning situations and this might in turn have an impact on the learning process. In the present study, we thus investigated whether motivational orientation, i.e., extrinsic and intrinsic motivation modulates the neural response to reward and punishment as well as learning from reward and punishment in 33 healthy individuals. To assess neural responses to reward, punishment and learning of reward contingencies we employed a probabilistic reversal learning task during functional magnetic resonance imaging. Extrinsic and intrinsic motivation were assessed with a self-report questionnaire. Rewarding trials fostered activation in the medial orbitofrontal cortex and anterior cingulate gyrus (ACC) as well as the amygdala and nucleus accumbens, whereas for punishment an increased neural response was observed in the medial and inferior prefrontal cortex, the superior parietal cortex and the insula. High extrinsic motivation was positively correlated to increased neural responses to reward in the ACC, amygdala and putamen, whereas a negative relationship between intrinsic motivation and brain activation in these brain regions was observed. These findings show that motivational orientation indeed modulates the responsiveness to reward delivery in major components of the human reward system and therefore extends previous results showing a significant influence of individual differences in reward-related personality traits on the neural processing of reward. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  16. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  17. Artificial neural network detects human uncertainty

    Science.gov (United States)

    Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

    2018-03-01

    Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

  18. Appearance Matters: Neural Correlates of Food Choice and Packaging Aesthetics

    NARCIS (Netherlands)

    Laan, van der L.N.; Ridder, de D.T.D.; Viergever, M.A.; Smeets, P.A.M.

    2012-01-01

    Neuro-imaging holds great potential for predicting choice behavior from brain responses. In this study we used both traditional mass-univariate and state-of-the-art multivariate pattern analysis to establish which brain regions respond to preferred packages and to what extent neural activation

  19. DWI-Based Neural Fingerprinting Technology: A Preliminary Study on Stroke Analysis

    Directory of Open Access Journals (Sweden)

    Chenfei Ye

    2014-01-01

    Full Text Available Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI and diffusion tensor imaging (DTI. Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

  20. High Frequency Deep Brain Stimulation and Neural Rhythms in Parkinson's Disease.

    Science.gov (United States)

    Blumenfeld, Zack; Brontë-Stewart, Helen

    2015-12-01

    High frequency (HF) deep brain stimulation (DBS) is an established therapy for the treatment of Parkinson's disease (PD). It effectively treats the cardinal motor signs of PD, including tremor, bradykinesia, and rigidity. The most common neural target is the subthalamic nucleus, located within the basal ganglia, the region most acutely affected by PD pathology. Using chronically-implanted DBS electrodes, researchers have been able to record underlying neural rhythms from several nodes in the PD network as well as perturb it using DBS to measure the ensuing neural and behavioral effects, both acutely and over time. In this review, we provide an overview of the PD neural network, focusing on the pathophysiological signals that have been recorded from PD patients as well as the mechanisms underlying the therapeutic benefits of HF DBS. We then discuss evidence for the relationship between specific neural oscillations and symptoms of PD, including the aberrant relationships potentially underlying functional connectivity in PD as well as the use of different frequencies of stimulation to more specifically target certain symptoms. Finally, we briefly describe several current areas of investigation and how the ability to record neural data in ecologically-valid settings may allow researchers to explore the relationship between brain and behavior in an unprecedented manner, culminating in the future automation of neurostimulation therapy for the treatment of a variety of neuropsychiatric diseases.

  1. Neural networks, nativism, and the plausibility of constructivism.

    Science.gov (United States)

    Quartz, S R

    1993-09-01

    Recent interest in PDP (parallel distributed processing) models is due in part to the widely held belief that they challenge many of the assumptions of classical cognitive science. In the domain of language acquisition, for example, there has been much interest in the claim that PDP models might undermine nativism. Related arguments based on PDP learning have also been given against Fodor's anti-constructivist position--a position that has contributed to the widespread dismissal of constructivism. A limitation of many of the claims regarding PDP learning, however, is that the principles underlying this learning have not been rigorously characterized. In this paper, I examine PDP models from within the framework of Valiant's PAC (probably approximately correct) model of learning, now the dominant model in machine learning, and which applies naturally to neural network learning. From this perspective, I evaluate the implications of PDP models for nativism and Fodor's influential anti-constructivist position. In particular, I demonstrate that, contrary to a number of claims, PDP models are nativist in a robust sense. I also demonstrate that PDP models actually serve as a good illustration of Fodor's anti-constructivist position. While these results may at first suggest that neural network models in general are incapable of the sort of concept acquisition that is required to refute Fodor's anti-constructivist position, I suggest that there is an alternative form of neural network learning that demonstrates the plausibility of constructivism. This alternative form of learning is a natural interpretation of the constructivist position in terms of neural network learning, as it employs learning algorithms that incorporate the addition of structure in addition to weight modification schemes. By demonstrating that there is a natural and plausible interpretation of constructivism in terms of neural network learning, the position that nativism is the only plausible model of

  2. Application of neural network and pattern recognition software to the automated analysis of continuous nuclear monitoring of on-load reactors

    Energy Technology Data Exchange (ETDEWEB)

    Howell, J.A.; Eccleston, G.W.; Halbig, J.K.; Klosterbuer, S.F. [Los Alamos National Lab., NM (United States); Larson, T.W. [California Polytechnic State Univ., San Luis Obispo, CA (US)

    1993-08-01

    Automated analysis using pattern recognition and neural network software can help interpret data, call attention to potential anomalies, and improve safeguards effectiveness. Automated software analysis, based on pattern recognition and neural networks, was applied to data collected from a radiation core discharge monitor system located adjacent to an on-load reactor core. Unattended radiation sensors continuously collect data to monitor on-line refueling operations in the reactor. The huge volume of data collected from a number of radiation channels makes it difficult for a safeguards inspector to review it all, check for consistency among the measurement channels, and find anomalies. Pattern recognition and neural network software can analyze large volumes of data from continuous, unattended measurements, thereby improving and automating the detection of anomalies. The authors developed a prototype pattern recognition program that determines the reactor power level and identifies the times when fuel bundles are pushed through the core during on-line refueling. Neural network models were also developed to predict fuel bundle burnup to calculate the region on the on-load reactor face from which fuel bundles were discharged based on the radiation signals. In the preliminary data set, which was limited and consisted of four distinct burnup regions, the neural network model correctly predicted the burnup region with an accuracy of 92%.

  3. Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Chunmei Wu

    2015-01-01

    Full Text Available We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.

  4. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    Directory of Open Access Journals (Sweden)

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

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

    Science.gov (United States)

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

    1999-02-01

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

  6. Neural correlates of heat-evoked pain memory in humans.

    Science.gov (United States)

    Wang, Liping; Gui, Peng; Li, Lei; Ku, Yixuan; Bodner, Mark; Fan, Gaojie; Zhou, Yong-Di; Dong, Xiao-Wei

    2016-03-01

    The neural processes underlying pain memory are not well understood. To explore these processes, contact heat-evoked potentials (CHEPs) were recorded in humans with electroencephalography (EEG) technique during a delayed matching-to-sample task, a working memory task involving presentations of two successive painful heat stimuli (S-1 and S-2) with different intensities separated by a 2-s interval (the memorization period). At the end of the task, the subject was required to discriminate the stimuli by indicating which (S-1 or S-2) induced more pain. A control task was used, in which no active discrimination was required between stimuli. All event-related potential (ERP) analysis was aligned to the onset of S-1. EEG activity exhibited two successive CHEPs: an N2-P2 complex (∼400 ms after onset of S-1) and an ultralate component (ULC, ∼900 ms). The amplitude of the N2-P2 at vertex, but not the ULC, was significantly correlated with stimulus intensity in these two tasks, suggesting that the N2-P2 represents neural coding of pain intensity. A late negative component (LNC) in the frontal recording region was observed only in the memory task during a 500-ms period before onset of S-2. LNC amplitude differed between stimulus intensities and exhibited significant correlations with the N2-P2 complex. These indicate that the frontal LNC is involved in maintenance of intensity of pain in working memory. Furthermore, alpha-band oscillations observed in parietal recording regions during the late delay displayed significant power differences between tasks. This study provides in the temporal domain previously unidentified neural evidence showing the neural processes involved in working memory of painful stimuli. Copyright © 2016 the American Physiological Society.

  7. Two distinct neural mechanisms underlying indirect reciprocity.

    Science.gov (United States)

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

    2014-03-18

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

  8. Neural Tube Defects, Folic Acid and Methylation

    Science.gov (United States)

    Imbard, Apolline; Benoist, Jean-François; Blom, Henk J.

    2013-01-01

    Neural tube defects (NTDs) are common complex congenital malformations resulting from failure of the neural tube closure during embryogenesis. It is established that folic acid supplementation decreases the prevalence of NTDs, which has led to national public health policies regarding folic acid. To date, animal studies have not provided sufficient information to establish the metabolic and/or genomic mechanism(s) underlying human folic acid responsiveness in NTDs. However, several lines of evidence suggest that not only folates but also choline, B12 and methylation metabolisms are involved in NTDs. Decreased B12 vitamin and increased total choline or homocysteine in maternal blood have been shown to be associated with increased NTDs risk. Several polymorphisms of genes involved in these pathways have also been implicated in risk of development of NTDs. This raises the question whether supplementation with B12 vitamin, betaine or other methylation donors in addition to folic acid periconceptional supplementation will further reduce NTD risk. The objective of this article is to review the role of methylation metabolism in the onset of neural tube defects. PMID:24048206

  9. A systematic review of the neural bases of psychotherapy for anxiety and related disorders.

    Science.gov (United States)

    Brooks, Samantha J; Stein, Dan J

    2015-09-01

    Brain imaging studies over two decades have delineated the neural circuitry of anxiety and related disorders, particularly regions involved in fear processing and in obsessive-compulsive symptoms. The neural circuitry of fear processing involves the amygdala, anterior cingulate, and insular cortex, while cortico-striatal-thalamic circuitry plays a key role in obsessive-compulsive disorder. More recently, neuroimaging studies have examined how psychotherapy for anxiety and related disorders impacts on these neural circuits. Here we conduct a systematic review of the findings of such work, which yielded 19 functional magnetic resonance imaging studies examining the neural bases of cognitive-behavioral therapy (CBT) in 509 patients with anxiety and related disorders. We conclude that, although each of these related disorders is mediated by somewhat different neural circuitry, CBT may act in a similar way to increase prefrontal control of subcortical structures. These findings are consistent with an emphasis in cognitive-affective neuroscience on the potential therapeutic value of enhancing emotional regulation in various psychiatric conditions.

  10. 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.

  11. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  12. Impact of load-related neural processes on feature binding in visuospatial working memory.

    Directory of Open Access Journals (Sweden)

    Nicole A Kochan

    Full Text Available BACKGROUND: The capacity of visual working memory (WM is substantially limited and only a fraction of what we see is maintained as a temporary trace. The process of binding visual features has been proposed as an adaptive means of minimising information demands on WM. However the neural mechanisms underlying this process, and its modulation by task and load effects, are not well understood. OBJECTIVE: To investigate the neural correlates of feature binding and its modulation by WM load during the sequential phases of encoding, maintenance and retrieval. METHODS AND FINDINGS: 18 young healthy participants performed a visuospatial WM task with independent factors of load and feature conjunction (object identity and position in an event-related functional MRI study. During stimulus encoding, load-invariant conjunction-related activity was observed in left prefrontal cortex and left hippocampus. During maintenance, greater activity for task demands of feature conjunction versus single features, and for increased load was observed in left-sided regions of the superior occipital cortex, precuneus and superior frontal cortex. Where these effects were expressed in overlapping cortical regions, their combined effect was additive. During retrieval, however, an interaction of load and feature conjunction was observed. This modulation of feature conjunction activity under increased load was expressed through greater deactivation in medial structures identified as part of the default mode network. CONCLUSIONS AND SIGNIFICANCE: The relationship between memory load and feature binding qualitatively differed through each phase of the WM task. Of particular interest was the interaction of these factors observed within regions of the default mode network during retrieval which we interpret as suggesting that at low loads, binding processes may be 'automatic' but at higher loads it becomes a resource-intensive process leading to disengagement of activity in this

  13. Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection.

    Science.gov (United States)

    Sarikaya, Duygu; Corso, Jason J; Guru, Khurshid A

    2017-07-01

    Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To the best of our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a region proposal network (RPN) and a multimodal two stream convolutional network for object detection to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an average precision of 91% and a mean computation time of 0.1 s per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new data set, ATLAS Dione, for RAS video understanding. Our data set provides video data of ten surgeons from Roswell Park Cancer Institute, Buffalo, NY, USA, performing six different surgical tasks on the daVinci Surgical System (dVSS) with annotations of robotic tools per frame.

  14. The effects of gratitude expression on neural activity.

    Science.gov (United States)

    Kini, Prathik; Wong, Joel; McInnis, Sydney; Gabana, Nicole; Brown, Joshua W

    2016-03-01

    Gratitude is a common aspect of social interaction, yet relatively little is known about the neural bases of gratitude expression, nor how gratitude expression may lead to longer-term effects on brain activity. To address these twin issues, we recruited subjects who coincidentally were entering psychotherapy for depression and/or anxiety. One group participated in a gratitude writing intervention, which required them to write letters expressing gratitude. The therapy-as-usual control group did not perform a writing intervention. After three months, subjects performed a "Pay It Forward" task in the fMRI scanner. In the task, subjects were repeatedly endowed with a monetary gift and then asked to pass it on to a charitable cause to the extent they felt grateful for the gift. Operationalizing gratitude as monetary gifts allowed us to engage the subjects and quantify the gratitude expression for subsequent analyses. We measured brain activity and found regions where activity correlated with self-reported gratitude experience during the task, even including related constructs such as guilt motivation and desire to help as statistical controls. These were mostly distinct from brain regions activated by empathy or theory of mind. Also, our between groups cross-sectional study found that a simple gratitude writing intervention was associated with significantly greater and lasting neural sensitivity to gratitude - subjects who participated in gratitude letter writing showed both behavioral increases in gratitude and significantly greater neural modulation by gratitude in the medial prefrontal cortex three months later. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. 1/f neural noise and electrophysiological indices of contextual prediction in aging.

    Science.gov (United States)

    Dave, S; Brothers, T A; Swaab, T Y

    2018-07-15

    Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations. Copyright © 2018 Elsevier B.V. All rights reserved.

  16. 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.

  17. Image recovery using diffusion equation embedded neural network

    International Nuclear Information System (INIS)

    Torkamani-Azar, F.

    2001-01-01

    Artificial neural networks with their inherent parallelism have been shown to perform well in many processing applications. In this paper, a new self-organizing approach for the recovery of gray level images degraded by additive noise based on embedding the diffusion equation in a neural network (without using a priori knowledge about the image point spread function, noise or original image) is described which enhances and restores gray levels of degraded images and is for application in low-level processing. Two learning features have been proposed which would be effective in the practical implementation of such a network. The recovery procedure needs some parameter estimation such as different error goals. While the required computation is not excessive, the procedure dose not require too many iterations and convergence is very fast. In addition, through the simulation the new network showed that it has superior ability to give a better quality result with a minimum of the sum of the squared error

  18. Neural stem cells in the ischemic and injured brain: endogenous and transplanted.

    Science.gov (United States)

    Dong, Jing; Liu, Baohua; Song, Lei; Lu, Lei; Xu, Haitao; Gu, Yue

    2012-12-01

    Neural stem cells functions as the pool of new neurons in adult brain, and plays important roles in normal brain function. Additionally, this pool reacts to brain ischemia, hemorrhage, trauma and many kinds of diseases, serving as endogenous repair mechanisms. The present manuscript discussed the responses of adult neurogenesis to brain ischemia and other insults, then the potential of neural stem cell transplantation therapy to treat such brain injury conditions.

  19. The role of the mesenchyme in cranial neural fold elevation

    International Nuclear Information System (INIS)

    Morris-Wiman, J.A.

    1988-01-01

    It has been previously postulated that the expansion of an hyaluronate-rich extracellular matrix in the fold m