Direct process estimation from tomographic data using artificial neural systems

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Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

2001-07-01

The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

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

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

2017-08-01

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

Neural dynamics of event segmentation in music: converging evidence for dissociable ventral and dorsal networks.

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Sridharan, Devarajan; Levitin, Daniel J; Chafe, Chris H; Berger, Jonathan; Menon, Vinod

2007-08-02

The real world presents our sensory systems with a continuous stream of undifferentiated information. Segmentation of this stream at event boundaries is necessary for object identification and feature extraction. Here, we investigate the neural dynamics of event segmentation in entire musical symphonies under natural listening conditions. We isolated time-dependent sequences of brain responses in a 10 s window surrounding transitions between movements of symphonic works. A strikingly right-lateralized network of brain regions showed peak response during the movement transitions when, paradoxically, there was no physical stimulus. Model-dependent and model-free analysis techniques provided converging evidence for activity in two distinct functional networks at the movement transition: a ventral fronto-temporal network associated with detecting salient events, followed in time by a dorsal fronto-parietal network associated with maintaining attention and updating working memory. Our study provides direct experimental evidence for dissociable and causally linked ventral and dorsal networks during event segmentation of ecologically valid auditory stimuli.

Are individuals with higher psychopathic traits better learners at lying? Behavioural and neural evidence.

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Shao, R; Lee, T M C

2017-07-25

High psychopathy is characterized by untruthfulness and manipulativeness. However, existing evidence on higher propensity or capacity to lie among non-incarcerated high-psychopathic individuals is equivocal. Of particular importance, no research has investigated whether greater psychopathic tendency is associated with better 'trainability' of lying. An understanding of whether the neurobehavioral processes of lying are modifiable through practice offers significant theoretical and practical implications. By employing a longitudinal design involving university students with varying degrees of psychopathic traits, we successfully demonstrate that the performance speed of lying about face familiarity significantly improved following two sessions of practice, which occurred only among those with higher, but not lower, levels of psychopathic traits. Furthermore, this behavioural improvement associated with higher psychopathic tendency was predicted by a reduction in lying-related neural signals and by functional connectivity changes in the frontoparietal and cerebellum networks. Our findings provide novel and pivotal evidence suggesting that psychopathic traits are the key modulating factors of the plasticity of both behavioural and neural processes underpinning lying. These findings broadly support conceptualization of high-functioning individuals with higher psychopathic traits as having preserved, or arguably superior, functioning in neural networks implicated in cognitive executive processing, but deficiencies in affective neural processes, from a neuroplasticity perspective.

Gene array analysis of neural crest cells identifies transcription factors necessary for direct conversion of embryonic fibroblasts into neural crest cells

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Tsutomu Motohashi

2016-03-01

Full Text Available Neural crest cells (NC cells are multipotent cells that emerge from the edge of the neural folds and migrate throughout the developing embryo. Although the gene regulatory network for generation of NC cells has been elucidated in detail, it has not been revealed which of the factors in the network are pivotal to directing NC identity. In this study we analyzed the gene expression profile of a pure NC subpopulation isolated from Sox10-IRES-Venus mice and investigated whether these genes played a key role in the direct conversion of Sox10-IRES-Venus mouse embryonic fibroblasts (MEFs into NC cells. The comparative molecular profiles of NC cells and neural tube cells in 9.5-day embryos revealed genes including transcription factors selectively expressed in developing trunk NC cells. Among 25 NC cell-specific transcription factor genes tested, SOX10 and SOX9 were capable of converting MEFs into SOX10-positive (SOX10+ cells. The SOX10+ cells were then shown to differentiate into neurons, glial cells, smooth muscle cells, adipocytes and osteoblasts. These SOX10+ cells also showed limited self-renewal ability, suggesting that SOX10 and SOX9 directly converted MEFs into NC cells. Conversely, the remaining transcription factors, including well-known NC cell specifiers, were unable to convert MEFs into SOX10+ NC cells. These results suggest that SOX10 and SOX9 are the key factors necessary for the direct conversion of MEFs into NC cells.

Adult ADHD and working memory: neural evidence of impaired encoding.

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Kim, Soyeon; Liu, Zhongxu; Glizer, Daniel; Tannock, Rosemary; Woltering, Steven

2014-08-01

To investigate neural and behavioural correlates of visual encoding during a working memory (WM) task in young adults with and without Attention-Deficit/Hyperactivity Disorder (ADHD). A sample of 30 college students currently meeting a diagnosis of ADHD and 25 typically developing students, matched on age and gender, performed a delayed match-to-sample task with low and high memory load conditions. Dense-array electroencephalography was recorded. Specifically, the P3, an event related potential (ERP) associated with WM, was examined because of its relation with attentional allocation during WM. Task performance (accuracy, reaction time) as well as performance on other neuropsychological tasks of WM was analyzed. Neural differences were found between the groups. Specifically, the P3 amplitude was smaller in the ADHD group compared to the comparison group for both load conditions at parietal-occipital sites. Lower scores on behavioural working memory tasks were suggestive of impaired behavioural WM performance in the ADHD group. Findings from this study provide the first evidence of neural differences in the encoding stage of WM in young adults with ADHD, suggesting ineffective allocation of attentional resources involved in encoding of information in WM. These findings, reflecting alternate neural functioning of WM, may explain some of the difficulties related to WM functioning that college students with ADHD report in their every day cognitive functioning. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Direction-of-change forecasting using a volatility-based recurrent neural network

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Bekiros, S.D.; Georgoutsos, D.A.

2008-01-01

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub-period 8

Structural reliability calculation method based on the dual neural network and direct integration method.

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Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

Understanding the role of speech production in reading: Evidence for a print-to-speech neural network using graphical analysis.

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Cummine, Jacqueline; Cribben, Ivor; Luu, Connie; Kim, Esther; Bahktiari, Reyhaneh; Georgiou, George; Boliek, Carol A

2016-05-01

The neural circuitry associated with language processing is complex and dynamic. Graphical models are useful for studying complex neural networks as this method provides information about unique connectivity between regions within the context of the entire network of interest. Here, the authors explored the neural networks during covert reading to determine the role of feedforward and feedback loops in covert speech production. Brain activity of skilled adult readers was assessed in real word and pseudoword reading tasks with functional MRI (fMRI). The authors provide evidence for activity coherence in the feedforward system (inferior frontal gyrus-supplementary motor area) during real word reading and in the feedback system (supramarginal gyrus-precentral gyrus) during pseudoword reading. Graphical models provided evidence of an extensive, highly connected, neural network when individuals read real words that relied on coordination of the feedforward system. In contrast, when individuals read pseudowords the authors found a limited/restricted network that relied on coordination of the feedback system. Together, these results underscore the importance of considering multiple pathways and articulatory loops during language tasks and provide evidence for a print-to-speech neural network. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A review of evidence of health benefit from artificial neural networks in medical intervention.

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Lisboa, P J G

2002-01-01

The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The rĵle of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.

Direct Neural Conversion from Human Fibroblasts Using Self-Regulating and Nonintegrating Viral Vectors

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Shong Lau

2014-12-01

Full Text Available Summary: Recent findings show that human fibroblasts can be directly programmed into functional neurons without passing via a proliferative stem cell intermediate. These findings open up the possibility of generating subtype-specific neurons of human origin for therapeutic use from fetal cell, from patients themselves, or from matched donors. In this study, we present an improved system for direct neural conversion of human fibroblasts. The neural reprogramming genes are regulated by the neuron-specific microRNA, miR-124, such that each cell turns off expression of the reprogramming genes once the cell has reached a stable neuronal fate. The regulated system can be combined with integrase-deficient vectors, providing a nonintegrative and self-regulated conversion system that rids problems associated with the integration of viral transgenes into the host genome. These modifications make the system suitable for clinical use and therefore represent a major step forward in the development of induced neurons for cell therapy. : Lau et al. now use miRNA targeting to build a self-regulating neural conversion system. Combined with nonintegrating vectors, this system can efficiently drive conversion of human fibroblasts into functional induced neurons (iNs suitable for clinical applications.

The porous boundaries between explicit and implicit memory: behavioral and neural evidence.

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Dew, Ilana T Z; Cabeza, Roberto

2011-04-01

Explicit memory refers to the conscious retrieval of past information or experiences, whereas implicit memory refers to an unintentional or nonconscious form of retrieval. Much of the literature in cognitive psychology and cognitive neuroscience has focused on differences between explicit and implicit memory, and the traditional view is that they rely on distinct brain systems. However, the potential interplay between implicit and explicit memory is not always clear. This review draws from behavioral and functional neuroimaging evidence to evaluate three areas in which implicit and explicit memory may be interrelated. First, we discuss views of familiarity-based recognition in terms of its relationship with implicit memory. Second, we review the challenges of distinguishing between implicit memory and involuntary aware memory, at both behavioral and neural levels. Finally, we examine evidence indicating that implicit and explicit retrieval of relational information may rely on a common neural mechanism. Taken together, these areas indicate that, under certain circumstances, there may be an important and influential relationship between conscious and nonconscious expressions of memory. © 2011 New York Academy of Sciences.

Planning music-based amelioration and training in infancy and childhood based on neural evidence.

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Huotilainen, Minna; Tervaniemi, Mari

2018-05-04

Music-based amelioration and training of the developing auditory system has a long tradition, and recent neuroscientific evidence supports using music in this manner. Here, we present the available evidence showing that various music-related activities result in positive changes in brain structure and function, becoming helpful for auditory cognitive processes in everyday life situations for individuals with typical neural development and especially for individuals with hearing, learning, attention, or other deficits that may compromise auditory processing. We also compare different types of music-based training and show how their effects have been investigated with neural methods. Finally, we take a critical position on the multitude of error sources found in amelioration and training studies and on publication bias in the field. We discuss some future improvements of these issues in the field of music-based training and their potential results at the neural and behavioral levels in infants and children for the advancement of the field and for a more complete understanding of the possibilities and significance of the training. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Adult subependymal neural precursors, but not differentiated cells, undergo rapid cathodal migration in the presence of direct current electric fields.

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Robart Babona-Pilipos

Full Text Available BACKGROUND: The existence of neural stem and progenitor cells (together termed neural precursor cells in the adult mammalian brain has sparked great interest in utilizing these cells for regenerative medicine strategies. Endogenous neural precursors within the adult forebrain subependyma can be activated following injury, resulting in their proliferation and migration toward lesion sites where they differentiate into neural cells. The administration of growth factors and immunomodulatory agents following injury augments this activation and has been shown to result in behavioural functional recovery following stroke. METHODS AND FINDINGS: With the goal of enhancing neural precursor migration to facilitate the repair process we report that externally applied direct current electric fields induce rapid and directed cathodal migration of pure populations of undifferentiated adult subependyma-derived neural precursors. Using time-lapse imaging microscopy in vitro we performed an extensive single-cell kinematic analysis demonstrating that this galvanotactic phenomenon is a feature of undifferentiated precursors, and not differentiated phenotypes. Moreover, we have shown that the migratory response of the neural precursors is a direct effect of the electric field and not due to chemotactic gradients. We also identified that epidermal growth factor receptor (EGFR signaling plays a role in the galvanotactic response as blocking EGFR significantly attenuates the migratory behaviour. CONCLUSIONS: These findings suggest direct current electric fields may be implemented in endogenous repair paradigms to promote migration and tissue repair following neurotrauma.

Directive Nanophysical Cues for Regenerative Neural Cell Systems

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Ayres, Virginia; Tiryaki, Volkan Mujdat; Ahmed, Ijaz; Shreiber, David

Until recently, implantables such as stents, probes, wafers and scaffolds have been viewed as passive vehicles for the delivery of physical, pharmacological and cellular interventions. Recent research, however, indicates that the physical environments that implantables present supply directive cues in their own right that work in conjunction with biochemical cues and produce a jointly-directed outcome. We will present our research in CNS repairs using advanced scanning probe microscopy, electron microscopies and contact angle measurements to quantitatively describe the nanoscale elasticity, surface roughness, work of adhesion and surface polarity for investigation of scaffold environments. We will also present our research using super-resolution immunocytochemistry and atomic force microscopy to evaluate neural cell morphological responses with associated micro filament, microtubule and intermediate filament expressions, along with results on how and which integrin-family receptors are possibly involved. Finally, we will present our novel application of k-means cluster analysis applied across multiple experimental modalities for quantification of synergistic scaffold properties and cell responses.

Neural Plasticity following Abacus Training in Humans: A Review and Future Directions

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Yongxin Li

2016-01-01

Full Text Available The human brain has an enormous capacity to adapt to a broad variety of environmental demands. Previous studies in the field of abacus training have shown that this training can induce specific changes in the brain. However, the neural mechanism underlying these changes remains elusive. Here, we reviewed the behavioral and imaging findings of comparisons between abacus experts and average control subjects and focused on changes in activation patterns and changes in brain structure. Finally, we noted the limitations and the future directions of this field. We concluded that although current studies have provided us with information about the mechanisms of abacus training, more research on abacus training is needed to understand its neural impact.

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

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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.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

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Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

2017-07-28

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

Routes to the past: neural substrates of direct and generative autobiographical memory retrieval.

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Addis, Donna Rose; Knapp, Katie; Roberts, Reece P; Schacter, Daniel L

2012-02-01

Models of autobiographical memory propose two routes to retrieval depending on cue specificity. When available cues are specific and personally-relevant, a memory can be directly accessed. However, when available cues are generic, one must engage a generative retrieval process to produce more specific cues to successfully access a relevant memory. The current study sought to characterize the neural bases of these retrieval processes. During functional magnetic resonance imaging (fMRI), participants were shown personally-relevant cues to elicit direct retrieval, or generic cues (nouns) to elicit generative retrieval. We used spatiotemporal partial least squares to characterize the spatial and temporal characteristics of the networks associated with direct and generative retrieval. Both retrieval tasks engaged regions comprising the autobiographical retrieval network, including hippocampus, and medial prefrontal and parietal cortices. However, some key neural differences emerged. Generative retrieval differentially recruited lateral prefrontal and temporal regions early on during the retrieval process, likely supporting the strategic search operations and initial recovery of generic autobiographical information. However, many regions were activated more strongly during direct versus generative retrieval, even when we time-locked the analysis to the successful recovery of events in both conditions. This result suggests that there may be fundamental differences between memories that are accessed directly and those that are recovered via the iterative search and retrieval process that characterizes generative retrieval. Copyright © 2011 Elsevier Inc. All rights reserved.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing.

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Delis, Ioannis; Dmochowski, Jacek P; Sajda, Paul; Wang, Qi

2018-03-23

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making. Copyright © 2018 Elsevier Inc. All rights reserved.

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

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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.

Using deep recurrent neural network for direct beam solar irradiance cloud screening

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Chen, Maosi; Davis, John M.; Liu, Chaoshun; Sun, Zhibin; Zempila, Melina Maria; Gao, Wei

2017-09-01

Cloud screening is an essential procedure for in-situ calibration and atmospheric properties retrieval on (UV-)MultiFilter Rotating Shadowband Radiometer [(UV-)MFRSR]. Previous study has explored a cloud screening algorithm for direct-beam (UV-)MFRSR voltage measurements based on the stability assumption on a long time period (typically a half day or a whole day). To design such an algorithm requires in-depth understanding of radiative transfer and delicate data manipulation. Recent rapid developments on deep neural network and computation hardware have opened a window for modeling complicated End-to-End systems with a standardized strategy. In this study, a multi-layer dynamic bidirectional recurrent neural network is built for determining the cloudiness on each time point with a 17-year training dataset and tested with another 1-year dataset. The dataset is the daily 3-minute cosine corrected voltages, airmasses, and the corresponding cloud/clear-sky labels at two stations of the USDA UV-B Monitoring and Research Program. The results show that the optimized neural network model (3-layer, 250 hidden units, and 80 epochs of training) has an overall test accuracy of 97.87% (97.56% for the Oklahoma site and 98.16% for the Hawaii site). Generally, the neural network model grasps the key concept of the original model to use data in the entire day rather than short nearby measurements to perform cloud screening. A scrutiny of the logits layer suggests that the neural network model automatically learns a way to calculate a quantity similar to total optical depth and finds an appropriate threshold for cloud screening.

Direct imaging of neural currents using ultra-low field magnetic resonance techniques

Science.gov (United States)

Volegov, Petr L [Los Alamos, NM; Matlashov, Andrei N [Los Alamos, NM; Mosher, John C [Los Alamos, NM; Espy, Michelle A [Los Alamos, NM; Kraus, Jr., Robert H.

2009-08-11

Using resonant interactions to directly and tomographically image neural activity in the human brain using magnetic resonance imaging (MRI) techniques at ultra-low field (ULF), the present inventors have established an approach that is sensitive to magnetic field distributions local to the spin population in cortex at the Larmor frequency of the measurement field. Because the Larmor frequency can be readily manipulated (through varying B.sub.m), one can also envision using ULF-DNI to image the frequency distribution of the local fields in cortex. Such information, taken together with simultaneous acquisition of MEG and ULF-NMR signals, enables non-invasive exploration of the correlation between local fields induced by neural activity in cortex and more `distant` measures of brain activity such as MEG and EEG.

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

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Decker, Arthur J.; Krasowski, Michael J.; Weiland, Kenneth E.

1993-01-01

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Detection of directional eye movements based on the electrooculogram signals through an artificial neural network

International Nuclear Information System (INIS)

Erkaymaz, Hande; Ozer, Mahmut; Orak, İlhami Muharrem

2015-01-01

The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately

Neural Progenitors Adopt Specific Identities by Directly Repressing All Alternative Progenitor Transcriptional Programs.

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Kutejova, Eva; Sasai, Noriaki; Shah, Ankita; Gouti, Mina; Briscoe, James

2016-03-21

In the vertebrate neural tube, a morphogen-induced transcriptional network produces multiple molecularly distinct progenitor domains, each generating different neuronal subtypes. Using an in vitro differentiation system, we defined gene expression signatures of distinct progenitor populations and identified direct gene-regulatory inputs corresponding to locations of specific transcription factor binding. Combined with targeted perturbations of the network, this revealed a mechanism in which a progenitor identity is installed by active repression of the entire transcriptional programs of other neural progenitor fates. In the ventral neural tube, sonic hedgehog (Shh) signaling, together with broadly expressed transcriptional activators, concurrently activates the gene expression programs of several domains. The specific outcome is selected by repressive input provided by Shh-induced transcription factors that act as the key nodes in the network, enabling progenitors to adopt a single definitive identity from several initially permitted options. Together, the data suggest design principles relevant to many developing tissues. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

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

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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.

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

Science.gov (United States)

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

2018-04-01

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

Behavioral and Neural Representations of Spatial Directions across Words, Schemas, and Images.

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Weisberg, Steven M; Marchette, Steven A; Chatterjee, Anjan

2018-05-23

Modern spatial navigation requires fluency with multiple representational formats, including visual scenes, signs, and words. These formats convey different information. Visual scenes are rich and specific but contain extraneous details. Arrows, as an example of signs, are schematic representations in which the extraneous details are eliminated, but analog spatial properties are preserved. Words eliminate all spatial information and convey spatial directions in a purely abstract form. How does the human brain compute spatial directions within and across these formats? To investigate this question, we conducted two experiments on men and women: a behavioral study that was preregistered and a neuroimaging study using multivoxel pattern analysis of fMRI data to uncover similarities and differences among representational formats. Participants in the behavioral study viewed spatial directions presented as images, schemas, or words (e.g., "left"), and responded to each trial, indicating whether the spatial direction was the same or different as the one viewed previously. They responded more quickly to schemas and words than images, despite the visual complexity of stimuli being matched. Participants in the fMRI study performed the same task but responded only to occasional catch trials. Spatial directions in images were decodable in the intraparietal sulcus bilaterally but were not in schemas and words. Spatial directions were also decodable between all three formats. These results suggest that intraparietal sulcus plays a role in calculating spatial directions in visual scenes, but this neural circuitry may be bypassed when the spatial directions are presented as schemas or words. SIGNIFICANCE STATEMENT Human navigators encounter spatial directions in various formats: words ("turn left"), schematic signs (an arrow showing a left turn), and visual scenes (a road turning left). The brain must transform these spatial directions into a plan for action. Here, we investigate

Do neural nets learn statistical laws behind natural language?

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Shuntaro Takahashi

Full Text Available The performance of deep learning in natural language processing has been spectacular, but the reasons for this success remain unclear because of the inherent complexity of deep learning. This paper provides empirical evidence of its effectiveness and of a limitation of neural networks for language engineering. Precisely, we demonstrate that a neural language model based on long short-term memory (LSTM effectively reproduces Zipf's law and Heaps' law, two representative statistical properties underlying natural language. We discuss the quality of reproducibility and the emergence of Zipf's law and Heaps' law as training progresses. We also point out that the neural language model has a limitation in reproducing long-range correlation, another statistical property of natural language. This understanding could provide a direction for improving the architectures of neural networks.

RESEARCH ON THE DIRECT CARBON EMISSION FORECAST OF CHINA'S PROVINCIAL RESIDENTS BASED ON NEURAL NETWORK

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T. Zhang

2018-04-01

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

Characterization of the central neural projections to brown, white, and beige adipose tissue.

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Wiedmann, Nicole M; Stefanidis, Aneta; Oldfield, Brian J

2017-11-01

The functional recruitment of classic brown adipose tissue (BAT) and inducible brown-like or beige fat is, to a large extent, dependent on intact sympathetic neural input. Whereas the central neural circuits directed specifically to BAT or white adipose tissue (WAT) are well established, there is only a developing insight into the nature of neural inputs common to both fat types. Moreover, there is no clear view of the specific central and peripheral innervation of the browned component of WAT: beige fat. The objective of the present study is to examine the neural input to both BAT and WAT in the same animal and, by exposing different cohorts of rats to either thermoneutral or cold conditions, define changes in central neural organization that will ensure that beige fat is appropriately recruited and modulated after browning of inguinal WAT (iWAT). At thermoneutrality, injection of the neurotropic (pseudorabies) viruses into BAT and WAT demonstrates that there are dedicated axonal projections, as well as collateral axonal branches of command neurons projecting to both types of fat. After cold exposure, central neural circuits directed to iWAT showed evidence of reorganization with a greater representation of command neurons projecting to both brown and beiged WAT in hypothalamic (paraventricular nucleus and lateral hypothalamus) and brainstem (raphe pallidus and locus coeruleus) sites. This shift was driven by a greater number of supraspinal neurons projecting to iWAT under cold conditions. These data provide evidence for a reorganization of the nervous system at the level of neural connectivity following browning of WAT.-Wiedmann, N. M., Stefanidis, A., Oldfield, B. J. Characterization of the central neural projections to brown, white, and beige adipose tissue. © FASEB.

Neural overlap in processing music and speech

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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

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.

A Nonlinear Autoregressive Exogenous (NARX Neural Network Model for the Prediction of the Daily Direct Solar Radiation

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Zina Boussaada

2018-03-01

Full Text Available The solar photovoltaic (PV energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

Patterns of cortical oscillations organize neural activity into whole-brain functional networks evident in the fMRI BOLD signal

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Jennifer C Whitman

2013-03-01

Full Text Available Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG / MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks.

DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

OpenAIRE

Yin, Zi; Chang, Keng-hao; Zhang, Ruofei

2017-01-01

Information extraction and user intention identification are central topics in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes...

Routes to the past: Neural substrates of direct and generative autobiographical memory retrieval

OpenAIRE

Addis, Donna Rose; Knapp, Katie; Roberts, Reece P.; Schacter, Daniel L.

2011-01-01

Models of autobiographical memory propose two routes to retrieval depending on cue specificity. When available cues are specific and personally-relevant, a memory can be directly accessed. However, when available cues are generic, one must engage a generative retrieval process to produce more specific cues to successfully access a relevant memory. The current study sought to characterize the neural bases of these retrieval processes. During functional magnetic resonance imaging (fMRI), partic...

The Neural Representation of Goal-Directed Actions and Outcomes in the Ventral Striatum's Olfactory Tubercle

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Gadziola, Marie A.

2016-01-01

The ventral striatum is critical for evaluating reward information and the initiation of goal-directed behaviors. The many cellular, afferent, and efferent similarities between the ventral striatum's nucleus accumbens and olfactory tubercle (OT) suggests the distributed involvement of neurons within the ventral striatopallidal complex in motivated behaviors. Although the nucleus accumbens has an established role in representing goal-directed actions and their outcomes, it is not known whether this function is localized within the nucleus accumbens or distributed also within the OT. Answering such a fundamental question will expand our understanding of the neural mechanisms underlying motivated behaviors. Here we address whether the OT encodes natural reinforcers and serves as a substrate for motivational information processing. In recordings from mice engaged in a novel water-motivated instrumental task, we report that OT neurons modulate their firing rate during initiation and progression of the instrumental licking behavior, with some activity being internally generated and preceding the first lick. We further found that as motivational drive decreases throughout a session, the activity of OT neurons is enhanced earlier relative to the behavioral action. Additionally, OT neurons discriminate the types and magnitudes of fluid reinforcers. Together, these data suggest that the processing of reward information and the orchestration of goal-directed behaviors is a global principle of the ventral striatum and have important implications for understanding the neural systems subserving addiction and mood disorders. SIGNIFICANCE STATEMENT Goal-directed behaviors are widespread among animals and underlie complex behaviors ranging from food intake, social behavior, and even pathological conditions, such as gambling and drug addiction. The ventral striatum is a neural system critical for evaluating reward information and the initiation of goal-directed behaviors. Here we

Neural mechanisms underlying transcranial direct current stimulation in aphasia: A feasibility study.

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Lena eUlm

2015-10-01

Full Text Available Little is known about the neural mechanisms by which transcranial direct current stimulation (tDCS impacts on language processing in post-stroke aphasia. This was addressed in a proof-of-principle study that explored the effects of tDCS application in aphasia during simultaneous functional magnetic resonance imaging (fMRI. We employed a single subject, cross-over, sham-tDCS controlled design and the stimulation was administered to an individualized perilesional stimulation site that was identified by a baseline fMRI scan and a picture naming task. Peak activity during the baseline scan was located in the spared left inferior frontal gyrus (IFG and this area was stimulated during a subsequent cross-over phase. tDCS was successfully administered to the target region and anodal- vs. sham-tDCS resulted in selectively increased activity at the stimulation site. Our results thus demonstrate that it is feasible to precisely target an individualized stimulation site in aphasia patients during simultaneous fMRI which allows assessing the neural mechanisms underlying tDCS application. The functional imaging results of this case report highlight one possible mechanism that may have contributed to beneficial behavioural stimulation effects in previous clinical tDCS trials in aphasia. In the future, this approach will allow identifying distinct patterns of stimulation effects on neural processing in larger cohorts of patients. This may ultimately yield information about the variability of tDCS-effects on brain functions in aphasia.

A novel neural-net-based nonlinear adaptive control and application to the cross-direction deviations control of a polymer film spread line

International Nuclear Information System (INIS)

Chen Zengqiang; Li Xiang; Liu Zhongxin; Yuan Zhuzhi

2008-01-01

A novel neural adaptive controller is presented to effectively control multivariable nonlinear systems. The proposed neural controller has been successfully applied to the cross-direction deviation control system of a polymer film spread line, whose good performance has been verified with real-time running results

Cross-Linguistic Differences in the Neural Representation of Human Language: Evidence from Users of Signed Languages

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Corina, David P.; Lawyer, Laurel A.; Cates, Deborah

2013-01-01

Studies of deaf individuals who are users of signed languages have provided profound insight into the neural representation of human language. Case studies of deaf signers who have incurred left- and right-hemisphere damage have shown that left-hemisphere resources are a necessary component of sign language processing. These data suggest that, despite frank differences in the input and output modality of language, core left perisylvian regions universally serve linguistic function. Neuroimaging studies of deaf signers have generally provided support for this claim. However, more fine-tuned studies of linguistic processing in deaf signers are beginning to show evidence of important differences in the representation of signed and spoken languages. In this paper, we provide a critical review of this literature and present compelling evidence for language-specific cortical representations in deaf signers. These data lend support to the claim that the neural representation of language may show substantive cross-linguistic differences. We discuss the theoretical implications of these findings with respect to an emerging understanding of the neurobiology of language. PMID:23293624

High school music classes enhance the neural processing of speech.

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Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina

2013-01-01

Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that 2 years of group music classes in high school enhance the neural encoding of speech. To tease apart the relationships between music and neural function, we tested high school students participating in either music or fitness-based training. These groups were matched at the onset of training on neural timing, reading ability, and IQ. Auditory brainstem responses were collected to a synthesized speech sound presented in background noise. After 2 years of training, the neural responses of the music training group were earlier than at pre-training, while the neural timing of students in the fitness training group was unchanged. These results represent the strongest evidence to date that in-school music education can cause enhanced speech encoding. The neural benefits of musical training are, therefore, not limited to expensive private instruction early in childhood but can be elicited by cost-effective group instruction during adolescence.

Neural Architecture for Feature Binding in Visual Working Memory.

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Schneegans, Sebastian; Bays, Paul M

2017-04-05

Binding refers to the operation that groups different features together into objects. We propose a neural architecture for feature binding in visual working memory that employs populations of neurons with conjunction responses. We tested this model using cued recall tasks, in which subjects had to memorize object arrays composed of simple visual features (color, orientation, and location). After a brief delay, one feature of one item was given as a cue, and the observer had to report, on a continuous scale, one or two other features of the cued item. Binding failure in this task is associated with swap errors, in which observers report an item other than the one indicated by the cue. We observed that the probability of swapping two items strongly correlated with the items' similarity in the cue feature dimension, and found a strong correlation between swap errors occurring in spatial and nonspatial report. The neural model explains both swap errors and response variability as results of decoding noisy neural activity, and can account for the behavioral results in quantitative detail. We then used the model to compare alternative mechanisms for binding nonspatial features. We found the behavioral results fully consistent with a model in which nonspatial features are bound exclusively via their shared location, with no indication of direct binding between color and orientation. These results provide evidence for a special role of location in feature binding, and the model explains how this special role could be realized in the neural system. SIGNIFICANCE STATEMENT The problem of feature binding is of central importance in understanding the mechanisms of working memory. How do we remember not only that we saw a red and a round object, but that these features belong together to a single object rather than to different objects in our environment? Here we present evidence for a neural mechanism for feature binding in working memory, based on encoding of visual

Behavioural and neural evidence for the impact of fluency context on conscious memory.

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Gomes, Carlos Alexandre; Mecklinger, Axel; Zimmer, Hubert

2017-07-01

It has been recently suggested that fluency may impact recognition memory performance when the fluency context varies from trial-to-trial. Surprisingly, such an effect has proved difficult to detect in the masked priming paradigm, one of the most popular means to increase fluency-based memory judgements. We conducted a functional magnetic resonance imaging (fMRI) experiment in which participants encoded words at study and, at test, performed a recognition memory task within a masked priming procedure. In order to optimise the chances of finding priming effects on recognition memory performance, we used low-frequency words, which have been shown to increase hits relative to false alarms and enhance masked priming effects. Fluency context was manipulated by either mixing primed and unprimed trials [Random context (RC) experiment] or blocking primed and unprimed trials [Blocked context (BC) experiment]. Behaviourally, priming affected high-confidence memory performance only in the RC experiment. This behavioural effect correlated positively with neural priming in several recognition memory regions. Moreover, we observed a functional coupling between the left middle temporal gyrus and the left parietal and posterior cingulate cortices that was greater for primed relative to unprimed words. In contrast, in the BC experiment, despite similar activity in recognition-memory-related regions, we did not find any significant correlations between neural and behavioural priming. Finally, we observed striking differences in the neural correlates of masked priming between the RC and BC experiments not only in location but also in direction of the neural response. Possible implications of these findings are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Peripheral neuropathies associated with antibodies directed to intracellular neural antigens.

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Antoine, J-C

2014-10-01

Antibodies directed to intracellular neural antigens have been mainly described in paraneoplastic peripheral neuropathies and mostly includes anti-Hu and anti-CV2/CRMP5 antibodies. These antibodies occur with different patterns of neuropathy. With anti-Hu antibody, the most frequent manifestation is sensory neuronopathy with frequent autonomic involvement. With anti-CV2/CRMP5 the neuropathy is more frequently sensory and motor with an axonal or mixed demyelinating and axonal electrophysiological pattern. The clinical pattern of these neuropathies is in keeping with the cellular distribution of HuD and CRMP5 in the peripheral nervous system. Although present in high titer, these antibodies are probably not directly responsible for the neuropathy. Pathological and experimental studies indicate that cytotoxic T-cells are probably the main effectors of the immune response. These disorders contrast with those in which antibodies recognize a cell surface antigen and are probably responsible for the disease. The neuronal cell death and axonal degeneration which result from T-cell mediated immunity explains why treating these disorders remains challenging. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Using strategic movement to calibrate a neural compass: a spiking network for tracking head direction in rats and robots.

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Peter Stratton

Full Text Available The head direction (HD system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that 'grounding' of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology, and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Using strategic movement to calibrate a neural compass: a spiking network for tracking head direction in rats and robots.

Science.gov (United States)

Stratton, Peter; Milford, Michael; Wyeth, Gordon; Wiles, Janet

2011-01-01

The head direction (HD) system in mammals contains neurons that fire to represent the direction the animal is facing in its environment. The ability of these cells to reliably track head direction even after the removal of external sensory cues implies that the HD system is calibrated to function effectively using just internal (proprioceptive and vestibular) inputs. Rat pups and other infant mammals display stereotypical warm-up movements prior to locomotion in novel environments, and similar warm-up movements are seen in adult mammals with certain brain lesion-induced motor impairments. In this study we propose that synaptic learning mechanisms, in conjunction with appropriate movement strategies based on warm-up movements, can calibrate the HD system so that it functions effectively even in darkness. To examine the link between physical embodiment and neural control, and to determine that the system is robust to real-world phenomena, we implemented the synaptic mechanisms in a spiking neural network and tested it on a mobile robot platform. Results show that the combination of the synaptic learning mechanisms and warm-up movements are able to reliably calibrate the HD system so that it accurately tracks real-world head direction, and that calibration breaks down in systematic ways if certain movements are omitted. This work confirms that targeted, embodied behaviour can be used to calibrate neural systems, demonstrates that 'grounding' of modelled biological processes in the real world can reveal underlying functional principles (supporting the importance of robotics to biology), and proposes a functional role for stereotypical behaviours seen in infant mammals and those animals with certain motor deficits. We conjecture that these calibration principles may extend to the calibration of other neural systems involved in motion tracking and the representation of space, such as grid cells in entorhinal cortex.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

Science.gov (United States)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Leader Cells Define Directionality of Trunk, but Not Cranial, Neural Crest Cell Migration

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Jo Richardson

2016-05-01

Full Text Available Collective cell migration is fundamental for life and a hallmark of cancer. Neural crest (NC cells migrate collectively, but the mechanisms governing this process remain controversial. Previous analyses in Xenopus indicate that cranial NC (CNC cells are a homogeneous population relying on cell-cell interactions for directional migration, while chick embryo analyses suggest a heterogeneous population with leader cells instructing directionality. Our data in chick and zebrafish embryos show that CNC cells do not require leader cells for migration and all cells present similar migratory capacities. In contrast, laser ablation of trunk NC (TNC cells shows that leader cells direct movement and cell-cell contacts are required for migration. Moreover, leader and follower identities are acquired before the initiation of migration and remain fixed thereafter. Thus, two distinct mechanisms establish the directionality of CNC cells and TNC cells. This implies the existence of multiple molecular mechanisms for collective cell migration.

Neural Correlates of the False Consensus Effect: Evidence for Motivated Projection and Regulatory Restraint.

Science.gov (United States)

Welborn, B Locke; Gunter, Benjamin C; Vezich, I Stephanie; Lieberman, Matthew D

2017-04-01

The false consensus effect (FCE), the tendency to project our attitudes and opinions on to others, is a pervasive bias in social reasoning with a range of ramifications for individuals and society. Research in social psychology has suggested that numerous factors (anchoring and adjustment, accessibility, motivated projection, etc.) may contribute to the FCE. In this study, we examine the neural correlates of the FCE and provide evidence that motivated projection plays a significant role. Activity in reward regions (ventromedial pFC and bilateral nucleus accumbens) during consensus estimation was positively associated with bias, whereas activity in right ventrolateral pFC (implicated in emotion regulation) was inversely associated with bias. Activity in reward and regulatory regions accounted for half of the total variation in consensus bias across participants (R 2 = .503). This research complements models of the FCE in social psychology, providing a glimpse into the neural mechanisms underlying this important phenomenon.

Systemic Injection of Neural Stem/progenitor Cells in Mice With Chronic EAE

OpenAIRE

Donegà, Matteo; Giusto, Elena; Cossetti, Chiara; Schaeffer, Julia; Pluchino, Stefano

2014-01-01

Neural stem/precursor cells (NPCs) are a promising stem cell source for transplantation approaches aiming at brain repair or restoration in regenerative neurology. This directive has arisen from the extensive evidence that brain repair is achieved after focal or systemic NPC transplantation in several pre clinical models of neurological diseases.

Sleep Deprivation Promotes Habitual Control over Goal-Directed Control: Behavioral and Neuroimaging Evidence.

Science.gov (United States)

Chen, Jie; Liang, Jie; Lin, Xiao; Zhang, Yang; Zhang, Yan; Lu, Lin; Shi, Jie

2017-12-06

Sleep is one of the most fundamental processes of life, playing an important role in the regulation of brain function. The long-term lack of sleep can cause memory impairments, declines in learning ability, and executive dysfunction. In the present study, we evaluated the effects of sleep deprivation on instrumental learning behavior, particularly goal-directed and habitual actions in humans, and investigated the underlying neural mechanisms. Healthy college students of either gender were enrolled and randomly divided into sleep deprivation group and sleep control group. fMRI data were collected. We found that one night of sleep deprivation led to greater responsiveness to stimuli that were associated with devalued outcomes in the slips-of-action test, indicating a deficit in the formation of goal-directed control and an overreliance on habits. Furthermore, sleep deprivation had no effect on the expression of acquired goal-directed action. The level of goal-directed action after sleep deprivation was positively correlated with baseline working memory capacity. The neuroimaging data indicated that goal-directed learning mainly recruited the ventromedial PFC (vmPFC), the activation of which was less pronounced during goal-directed learning after sleep deprivation. Activation of the vmPFC during goal-directed learning during training was positively correlated with the level of goal-directed action performance. The present study suggests that people rely predominantly on habits at the expense of goal-directed control after sleep deprivation, and this process involves the vmPFC. These results contribute to a better understanding of the effects of sleep loss on decision-making. SIGNIFICANCE STATEMENT Understanding the cognitive consequences of sleep deprivation has become extremely important over the past half century, given the continued decline in sleep duration in industrialized societies. Our results provide novel evidence that goal-directed action may be

Neural congruence between intertemporal and interpersonal self-control: Evidence from delay and social discounting.

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Hill, Paul F; Yi, Richard; Spreng, R Nathan; Diana, Rachel A

2017-11-15

Behavioral studies using delay and social discounting as indices of self-control and altruism, respectively, have revealed functional similarities between farsighted and social decisions. However, neural evidence for this functional link is lacking. Twenty-five young adults completed a delay and social discounting task during fMRI scanning. A spatiotemporal partial least squares analysis revealed that both forms of discounting were well characterized by a pattern of brain activity in areas comprising frontoparietal control, default, and mesolimbic reward networks. Both forms of discounting appear to draw on common neurocognitive mechanisms, regardless of whether choices involve intertemporal or interpersonal outcomes. We also observed neural profiles differentiating between high and low discounters. High discounters were well characterized by increased medial temporal lobe and limbic activity. In contrast, low discount rates were associated with activity in the medial prefrontal cortex and right temporoparietal junction. This pattern may reflect biological mechanisms underlying behavioral heterogeneity in discount rates. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural networks for aircraft control

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Linse, Dennis

1990-01-01

Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

Science.gov (United States)

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

2018-06-01

This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Chitosan derived co-spheroids of neural stem cells and mesenchymal stem cells for neural regeneration.

Science.gov (United States)

Han, Hao-Wei; Hsu, Shan-Hui

2017-10-01

Chitosan has been considered as candidate biomaterials for neural applications. The effective treatment of neurodegeneration or injury to the central nervous system (CNS) is still in lack nowadays. Adult neural stem cells (NSCs) represents a promising cell source to treat the CNS diseases but they are limited in number. Here, we developed the core-shell spheroids of NSCs (shell) and mesenchymal stem cells (MSCs, core) by co-culturing cells on the chitosan surface. The NSCs in chitosan derived co-spheroids displayed a higher survival rate than those in NSC homo-spheroids. The direct interaction of NSCs with MSCs in the co-spheroids increased the Notch activity and differentiation tendency of NSCs. Meanwhile, the differentiation potential of MSCs in chitosan derived co-spheroids was significantly enhanced toward neural lineages. Furthermore, NSC homo-spheroids and NSC/MSC co-spheroids derived on chitosan were evaluated for their in vivo efficacy by the embryonic and adult zebrafish brain injury models. The locomotion activity of zebrafish receiving chitosan derived NSC homo-spheroids or NSC/MSC co-spheroids was partially rescued in both models. Meanwhile, the higher survival rate was observed in the group of adult zebrafish implanted with chitosan derived NSC/MSC co-spheroids as compared to NSC homo-spheroids. These evidences indicate that chitosan may provide an extracellular matrix-like environment to drive the interaction and the morphological assembly between NSCs and MSCs and promote their neural differentiation capacities, which can be used for neural regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Electronic bypass of spinal lesions: activation of lower motor neurons directly driven by cortical neural signals.

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Li, Yan; Alam, Monzurul; Guo, Shanshan; Ting, K H; He, Jufang

2014-07-03

Lower motor neurons in the spinal cord lose supraspinal inputs after complete spinal cord injury, leading to a loss of volitional control below the injury site. Extensive locomotor training with spinal cord stimulation can restore locomotion function after spinal cord injury in humans and animals. However, this locomotion is non-voluntary, meaning that subjects cannot control stimulation via their natural "intent". A recent study demonstrated an advanced system that triggers a stimulator using forelimb stepping electromyographic patterns to restore quadrupedal walking in rats with spinal cord transection. However, this indirect source of "intent" may mean that other non-stepping forelimb activities may false-trigger the spinal stimulator and thus produce unwanted hindlimb movements. We hypothesized that there are distinguishable neural activities in the primary motor cortex during treadmill walking, even after low-thoracic spinal transection in adult guinea pigs. We developed an electronic spinal bridge, called "Motolink", which detects these neural patterns and triggers a "spinal" stimulator for hindlimb movement. This hardware can be head-mounted or carried in a backpack. Neural data were processed in real-time and transmitted to a computer for analysis by an embedded processor. Off-line neural spike analysis was conducted to calculate and preset the spike threshold for "Motolink" hardware. We identified correlated activities of primary motor cortex neurons during treadmill walking of guinea pigs with spinal cord transection. These neural activities were used to predict the kinematic states of the animals. The appropriate selection of spike threshold value enabled the "Motolink" system to detect the neural "intent" of walking, which triggered electrical stimulation of the spinal cord and induced stepping-like hindlimb movements. We present a direct cortical "intent"-driven electronic spinal bridge to restore hindlimb locomotion after complete spinal cord injury.

Does functional MRI detect activation in white matter? A review of emerging evidence, issues, and future directions

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Gawryluk, Jodie R.; Mazerolle, Erin L.; D'Arcy, Ryan C. N.

2014-01-01

Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows for visualization of activated brain regions. Until recently, fMRI studies have focused on gray matter. There are two main reasons white matter fMRI remains controversial: (1) the blood oxygen level dependent (BOLD) fMRI signal depends on cerebral blood flow and volume, which are lower in white matter than gray matter and (2) fMRI signal has been associated with post-synaptic potentials (mainly localized in gray matter) as opposed to action potentials (the primary type of neural activity in white matter). Despite these observations, there is no direct evidence against measuring fMRI activation in white matter and reports of fMRI activation in white matter continue to increase. The questions underlying white matter fMRI activation are important. White matter fMRI activation has the potential to greatly expand the breadth of brain connectivity research, as well as improve the assessment and diagnosis of white matter and connectivity disorders. The current review provides an overview of the motivation to investigate white matter fMRI activation, as well as the published evidence of this phenomenon. We speculate on possible neurophysiologic bases of white matter fMRI signals, and discuss potential explanations for why reports of white matter fMRI activation are relatively scarce. We end with a discussion of future basic and clinical research directions in the study of white matter fMRI. PMID:25152709

Neural evidence for description dependent reward processing in the framing effect.

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Yu, Rongjun; Zhang, Ping

2014-01-01

Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the "worse than expected" negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to "better than expected" positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect.

Neural evidence for description dependent reward processing in the framing effect

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Rongjun eYu

2014-03-01

Full Text Available Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN, which indexes the worse than expected negative prediction error in the anterior cingulate cortex, was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to better than expected positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect.

Evidence of directional and stabilizing selection in contemporary humans.

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Sanjak, Jaleal S; Sidorenko, Julia; Robinson, Matthew R; Thornton, Kevin R; Visscher, Peter M

2018-01-02

Modern molecular genetic datasets, primarily collected to study the biology of human health and disease, can be used to directly measure the action of natural selection and reveal important features of contemporary human evolution. Here we leverage the UK Biobank data to test for the presence of linear and nonlinear natural selection in a contemporary population of the United Kingdom. We obtain phenotypic and genetic evidence consistent with the action of linear/directional selection. Phenotypic evidence suggests that stabilizing selection, which acts to reduce variance in the population without necessarily modifying the population mean, is widespread and relatively weak in comparison with estimates from other species.

Designing neural networks that process mean values of random variables

International Nuclear Information System (INIS)

Barber, Michael J.; Clark, John W.

2014-01-01

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence

Designing neural networks that process mean values of random variables

Energy Technology Data Exchange (ETDEWEB)

Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

2014-06-13

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

Neural evidence for description dependent reward processing in the framing effect

Science.gov (United States)

Yu, Rongjun; Zhang, Ping

2014-01-01

Human decision making can be influenced by emotionally valenced contexts, known as the framing effect. We used event-related brain potentials to investigate how framing influences the encoding of reward. We found that the feedback related negativity (FRN), which indexes the “worse than expected” negative prediction error in the anterior cingulate cortex (ACC), was more negative for the negative frame than for the positive frame in the win domain. Consistent with previous findings that the FRN is not sensitive to “better than expected” positive prediction error, the FRN did not differentiate the positive and negative frame in the loss domain. Our results provide neural evidence that the description invariance principle which states that reward representation and decision making are not influenced by how options are presented is violated in the framing effect. PMID:24733998

Covalent growth factor tethering to direct neural stem cell differentiation and self-organization.

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Ham, Trevor R; Farrag, Mahmoud; Leipzig, Nic D

2017-04-15

Tethered growth factors offer exciting new possibilities for guiding stem cell behavior. However, many of the current methods present substantial drawbacks which can limit their application and confound results. In this work, we developed a new method for the site-specific covalent immobilization of azide-tagged growth factors and investigated its utility in a model system for guiding neural stem cell (NSC) behavior. An engineered interferon-γ (IFN-γ) fusion protein was tagged with an N-terminal azide group, and immobilized to two different dibenzocyclooctyne-functionalized biomimetic polysaccharides (chitosan and hyaluronan). We successfully immobilized azide-tagged IFN-γ under a wide variety of reaction conditions, both in solution and to bulk hydrogels. To understand the interplay between surface chemistry and protein immobilization, we cultured primary rat NSCs on both materials and showed pronounced biological effects. Expectedly, immobilized IFN-γ increased neuronal differentiation on both materials. Expression of other lineage markers varied depending on the material, suggesting that the interplay of surface chemistry and protein immobilization plays a large role in nuanced cell behavior. We also investigated the bioactivity of immobilized IFN-γ in a 3D environment in vivo and found that it sparked the robust formation of neural tube-like structures from encapsulated NSCs. These findings support a wide range of potential uses for this approach and provide further evidence that adult NSCs are capable of self-organization when exposed to the proper microenvironment. For stem cells to be used effectively in regenerative medicine applications, they must be provided with the appropriate cues and microenvironment so that they integrate with existing tissue. This study explores a new method for guiding stem cell behavior: covalent growth factor tethering. We found that adding an N-terminal azide-tag to interferon-γ enabled stable and robust Cu-free 'click

SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

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Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

2013-04-30

The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials.

Science.gov (United States)

Jin, Jia; Yu, Liping; Ma, Qingguo

2015-01-01

Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW) task and a boring watch-stop task (WS) to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 amplitude than that of the WS task. Furthermore, in the outcome feedback stage, the outcome of the SW task induced smaller FRN amplitude and larger P300 amplitude than that of the WS task. These results suggested that human intrinsic motivation did exist and that it can be detected at the neural level. Furthermore, intrinsic motivation could be quantitatively indexed by the amplitude of ERP components, such as N2, FRN, and P300, in the cue priming stage or feedback stage. Quantitative measurements would also be convenient for intrinsic motivation to be added as a candidate social factor in the construction of a machine learning model.

Analysis of wave directional spreading using neural networks

Digital Repository Service at National Institute of Oceanography (India)

Deo, M.C.; Gondane, D.S.; SanilKumar, V.

describes how a representative spreading parameter could be arrived at from easily available wave parameters such as significant wave height and average zero-cross wave period, using the technique of neural networks. It is shown that training of the network...

Neural Basis of Action Understanding: Evidence from Sign Language Aphasia.

Science.gov (United States)

Rogalsky, Corianne; Raphel, Kristin; Tomkovicz, Vivian; O'Grady, Lucinda; Damasio, Hanna; Bellugi, Ursula; Hickok, Gregory

2013-01-01

The neural basis of action understanding is a hotly debated issue. The mirror neuron account holds that motor simulation in fronto-parietal circuits is critical to action understanding including speech comprehension, while others emphasize the ventral stream in the temporal lobe. Evidence from speech strongly supports the ventral stream account, but on the other hand, evidence from manual gesture comprehension (e.g., in limb apraxia) has led to contradictory findings. Here we present a lesion analysis of sign language comprehension. Sign language is an excellent model for studying mirror system function in that it bridges the gap between the visual-manual system in which mirror neurons are best characterized and language systems which have represented a theoretical target of mirror neuron research. Twenty-one life long deaf signers with focal cortical lesions performed two tasks: one involving the comprehension of individual signs and the other involving comprehension of signed sentences (commands). Participants' lesions, as indicated on MRI or CT scans, were mapped onto a template brain to explore the relationship between lesion location and sign comprehension measures. Single sign comprehension was not significantly affected by left hemisphere damage. Sentence sign comprehension impairments were associated with left temporal-parietal damage. We found that damage to mirror system related regions in the left frontal lobe were not associated with deficits on either of these comprehension tasks. We conclude that the mirror system is not critically involved in action understanding.

Resolution of conflict between goal-directed actions: outcome encoding and neural control processes.

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de Wit, Sanne; Ostlund, Sean B; Balleine, Bernard W; Dickinson, Anthony

2009-07-01

According to O-R theory of instrumental learning, incongruent biconditional discriminations should be impossible to solve in a goal-directed manner because the event acting as the outcome of one response also acts as a discriminative stimulus for an opposite response. Each event should therefore be associated with two competing responses. However, Dickinson and de Wit (2003) have presented evidence that rats can learn incongruent discriminations. The present study investigated whether rats were able to engage additional processes to solve incongruent discriminations in a goal-directed manner. Experiment 1 provides evidence that rats resolve the response conflict that arises in the incongruent discrimination by differentially encoding events in their roles as discriminative stimulus and as outcome. Furthermore, Experiment 2 shows that once goal-directed control has been established the dorsomedial prefrontal cortex is not directly involved in its maintenance but rather plays a central role in conflict resolution processes.

High school music classes enhance the neural processing of speech

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Adam eTierney

2013-12-01

Full Text Available Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that two years of group music classes in high school enhance the subcortical encoding of speech. To tease apart the relationships between music and neural function, we tested high school students participating in either music or fitness-based training. These groups were matched at the onset of training on neural timing, reading ability, and IQ. Auditory brainstem responses were collected to a synthesized speech sound presented in background noise. After 2 years of training, the subcortical responses of the music training group were earlier than at pretraining, while the neural timing of students in the fitness training group was unchanged. These results represent the strongest evidence to date that in-school music education can cause enhanced speech encoding. The neural benefits of musical training are, therefore, not limited to expensive private instruction early in childhood but can be elicited by cost-effective group instruction during adolescence.

Synaptic E-I Balance Underlies Efficient Neural Coding.

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Zhou, Shanglin; Yu, Yuguo

2018-01-01

Both theoretical and experimental evidence indicate that synaptic excitation and inhibition in the cerebral cortex are well-balanced during the resting state and sensory processing. Here, we briefly summarize the evidence for how neural circuits are adjusted to achieve this balance. Then, we discuss how such excitatory and inhibitory balance shapes stimulus representation and information propagation, two basic functions of neural coding. We also point out the benefit of adopting such a balance during neural coding. We conclude that excitatory and inhibitory balance may be a fundamental mechanism underlying efficient coding.

Improved head direction command classification using an optimised Bayesian neural network.

Science.gov (United States)

Nguyen, Son T; Nguyen, Hung T; Taylor, Philip B; Middleton, James

2006-01-01

Assistive technologies have recently emerged to improve the quality of life of severely disabled people by enhancing their independence in daily activities. Since many of those individuals have limited or non-existing control from the neck downward, alternative hands-free input modalities have become very important for these people to access assistive devices. In hands-free control, head movement has been proved to be a very effective user interface as it can provide a comfortable, reliable and natural way to access the device. Recently, neural networks have been shown to be useful not only for real-time pattern recognition but also for creating user-adaptive models. Since multi-layer perceptron neural networks trained using standard back-propagation may cause poor generalisation, the Bayesian technique has been proposed to improve the generalisation and robustness of these networks. This paper describes the use of Bayesian neural networks in developing a hands-free wheelchair control system. The experimental results show that with the optimised architecture, classification Bayesian neural networks can detect head commands of wheelchair users accurately irrespective to their levels of injuries.

Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG Device

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Agus Siswoyo

2017-07-01

Full Text Available The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1. There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.     Keywords: Navigation, Neural network, Real-time training, Arduino

The alcoholic brain: neural bases of impaired reward-based decision-making in alcohol use disorders.

Science.gov (United States)

Galandra, Caterina; Basso, Gianpaolo; Cappa, Stefano; Canessa, Nicola

2018-03-01

Neuroeconomics is providing insights into the neural bases of decision-making in normal and pathological conditions. In the neuropsychiatric domain, this discipline investigates how abnormal functioning of neural systems associated with reward processing and cognitive control promotes different disorders, and whether such evidence may inform treatments. This endeavor is crucial when studying different types of addiction, which share a core promoting mechanism in the imbalance between impulsive subcortical neural signals associated with immediate pleasurable outcomes and inhibitory signals mediated by a prefrontal reflective system. The resulting impairment in behavioral control represents a hallmark of alcohol use disorders (AUDs), a chronic relapsing disorder characterized by excessive alcohol consumption despite devastating consequences. This review aims to summarize available magnetic resonance imaging (MRI) evidence on reward-related decision-making alterations in AUDs, and to envision possible future research directions. We review functional MRI (fMRI) studies using tasks involving monetary rewards, as well as MRI studies relating decision-making parameters to neurostructural gray- or white-matter metrics. The available data suggest that excessive alcohol exposure affects neural signaling within brain networks underlying adaptive behavioral learning via the implementation of prediction errors. Namely, weaker ventromedial prefrontal cortex activity and altered connectivity between ventral striatum and dorsolateral prefrontal cortex likely underpin a shift from goal-directed to habitual actions which, in turn, might underpin compulsive alcohol consumption and relapsing episodes despite adverse consequences. Overall, these data highlight abnormal fronto-striatal connectivity as a candidate neurobiological marker of impaired choice in AUDs. Further studies are needed, however, to unveil its implications in the multiple facets of decision-making.

Fuzzy Evidence in Identification, Forecasting and Diagnosis

CERN Document Server

Rotshtein, Alexander P

2012-01-01

The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems in order to identify nonlinear objects; that is, to build input-output models using expert and experimental information. The results of these identifications are used for direct and inverse fuzzy evidence in forecasting and diagnosis problem solving. The book is organised as follows: Chapter 1 presents the basic knowledge about fuzzy sets, genetic algorithms and neural nets necessary for a clear understanding of the rest of this book. Chapter 2 analyzes direct fuzzy inference based on fuzzy if-then rules. Chapter 3 is devoted to the tuning of fuzzy rules for direct inference using genetic algorithms and neural nets. Chapter 4 presents models and algorithms for extracting fuzzy rules from experimental data. Chapter 5 describes a method for solving fuzzy logic equations necessary for the inverse fuzzy inference in diagnostic systems. Chapters 6 and 7 are devoted to inverse fuzzy inference based on fu...

Innervation of Extrahepatic Biliary Tract, With Special Reference to the Direct Bidirectional Neural Connections of the Gall Bladder, Sphincter of Oddi and Duodenum in Suncus murinus, in Whole-Mount Immunohistochemical Study.

Science.gov (United States)

Yi, S-Q; Ren, K; Kinoshita, M; Takano, N; Itoh, M; Ozaki, N

2016-06-01

Sphincter of Oddi dysfunction is one of the most important symptoms in post-cholecystectomy syndrome. Using either electrical or mechanical stimulation and retrogradely transported neuronal dyes, it has been demonstrated that there are direct neural pathways connecting gall bladder and the sphincter of Oddi in the Australian opossum and the golden hamster. In the present study, we employed whole-mount immunohistochemistry staining to observe and verify that there are two different plexuses of the extrahepatic biliary tract in Suncus murinus. One, named Pathway One, showed a fine, irregular but dense network plexus that ran adhesively and resided on/in the extrahepatic biliary tract wall, and the plexus extended into the intrahepatic area. On the other hand, named Pathway Two, exhibiting simple, thicker and straight neural bundles, ran parallel to the surface of the extrahepatic biliary tract and passed between the gall bladder and duodenum, but did not give off any branches to the liver. Pathway Two was considered to involve direct bidirectional neural connections between the duodenum and the biliary tract system. For the first time, morphologically, we demonstrated direct neural connections between gall bladder and duodenum in S. murinus. Malfunction of the sphincter of Oddi may be caused by injury of the direct neural pathways between gall bladder and duodenum by cholecystectomy. From the viewpoint of preserving the function of the major duodenal papilla and common bile duct, we emphasize the importance of avoiding kocherization of the common bile duct so as to preserve the direct neural connections between gall bladder and sphincter of Oddi. © 2015 Blackwell Verlag GmbH.

Rituals decrease the neural response to performance failure

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Nicholas M. Hobson

2017-05-01

Full Text Available Rituals are found in all types of performance domains, from high-stakes athletics and military to the daily morning preparations of the working family. Yet despite their ubiquity and widespread importance for humans, we know very little of ritual’s causal basis and how (if at all they facilitate goal-directed performance. Here, in a fully pre-registered pre/post experimental design, we examine a candidate proximal mechanism, the error-related negativity (ERN, in testing the prediction that ritual modulates neural performance-monitoring. Participants completed an arbitrary ritual—novel actions repeated at home over one week—followed by an executive function task in the lab during electroencephalographic (EEG recording. Results revealed that relative to pre rounds, participants showed a reduced ERN in the post rounds, after completing the ritual in the lab. Despite a muted ERN, there was no evidence that the reduction in neural monitoring led to performance deficit (nor a performance improvement. Generally, the findings are consistent with the longstanding view that ritual buffers against uncertainty and anxiety. Our results indicate that ritual guides goal-directed performance by regulating the brain’s response to personal failure.

Computational modeling of neural plasticity for self-organization of neural networks.

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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.

Neural dynamics of motion perception: direction fields, apertures, and resonant grouping.

Science.gov (United States)

Grossberg, S; Mingolla, E

1993-03-01

A neural network model of global motion segmentation by visual cortex is described. Called the motion boundary contour system (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively reorganized into a coherent global motion signal. Unlike many previous researchers, we analyze how a coherent motion signal is imparted to all regions of a moving figure, not only to regions at which unambiguous motion signals exist. The model hereby suggests a solution to the global aperture problem. The motion BCS describes how preprocessing of motion signals by a motion oriented contrast (MOC) filter is joined to long-range cooperative grouping mechanisms in a motion cooperative-competitive (MOCC) loop to control phenomena such as motion capture. The motion BCS is computed in parallel with the static BCS of Grossberg and Mingolla (1985a, 1985b, 1987). Homologous properties of the motion BCS and the static BCS, specialized to process motion directions and static orientations, respectively, support a unified explanation of many data about static form perception and motion form perception that have heretofore been unexplained or treated separately. Predictions about microscopic computational differences of the parallel cortical streams V1-->MT and V1-->V2-->MT are made--notably, the magnocellular thick stripe and parvocellular interstripe streams. It is shown how the motion BCS can compute motion directions that may be synthesized from multiple orientations with opposite directions of contrast. Interactions of model simple cells, complex cells, hyper-complex cells, and bipole cells are described, with special emphasis given to new functional roles in direction disambiguation for endstopping at multiple processing stages and to the dynamic interplay of spatially short-range and long-range interactions.

Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

Directory of Open Access Journals (Sweden)

Jair Minoro Abe

Full Text Available Abstract EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

Emerging Directions in Emotional Episodic Memory

Science.gov (United States)

Dolcos, Florin; Katsumi, Yuta; Weymar, Mathias; Moore, Matthew; Tsukiura, Takashi; Dolcos, Sanda

2017-01-01

Building upon the existing literature on emotional memory, the present review examines emerging evidence from brain imaging investigations regarding four research directions: (1) Social Emotional Memory, (2) The Role of Emotion Regulation in the Impact of Emotion on Memory, (3) The Impact of Emotion on Associative or Relational Memory, and (4) The Role of Individual Differences in Emotional Memory. Across these four domains, available evidence demonstrates that emotion- and memory-related medial temporal lobe brain regions (amygdala and hippocampus, respectively), together with prefrontal cortical regions, play a pivotal role during both encoding and retrieval of emotional episodic memories. This evidence sheds light on the neural mechanisms of emotional memories in healthy functioning, and has important implications for understanding clinical conditions that are associated with negative affective biases in encoding and retrieving emotional memories. PMID:29255432

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.

Serotonin, neural markers and memory

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Alfredo eMeneses

2015-07-01

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

Neural substrates of decision-making.

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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.

Evidence for Neural Computations of Temporal Coherence in an Auditory Scene and Their Enhancement during Active Listening.

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O'Sullivan, James A; Shamma, Shihab A; Lalor, Edmund C

2015-05-06

The human brain has evolved to operate effectively in highly complex acoustic environments, segregating multiple sound sources into perceptually distinct auditory objects. A recent theory seeks to explain this ability by arguing that stream segregation occurs primarily due to the temporal coherence of the neural populations that encode the various features of an individual acoustic source. This theory has received support from both psychoacoustic and functional magnetic resonance imaging (fMRI) studies that use stimuli which model complex acoustic environments. Termed stochastic figure-ground (SFG) stimuli, they are composed of a "figure" and background that overlap in spectrotemporal space, such that the only way to segregate the figure is by computing the coherence of its frequency components over time. Here, we extend these psychoacoustic and fMRI findings by using the greater temporal resolution of electroencephalography to investigate the neural computation of temporal coherence. We present subjects with modified SFG stimuli wherein the temporal coherence of the figure is modulated stochastically over time, which allows us to use linear regression methods to extract a signature of the neural processing of this temporal coherence. We do this under both active and passive listening conditions. Our findings show an early effect of coherence during passive listening, lasting from ∼115 to 185 ms post-stimulus. When subjects are actively listening to the stimuli, these responses are larger and last longer, up to ∼265 ms. These findings provide evidence for early and preattentive neural computations of temporal coherence that are enhanced by active analysis of an auditory scene. Copyright © 2015 the authors 0270-6474/15/357256-08$15.00/0.

Neck muscle biomechanics and neural control.

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Fice, Jason Bradley; Siegmund, Gunter P; Blouin, Jean-Sebastien

2018-04-18

The mechanics, morphometry, and geometry of our joints, segments and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate if the biomechanical actions of individual neck muscles predicts their neural control. Specifically, we compared the moment direction & variability produced by electrical stimulation of a neck muscle (biomechanics) to their preferred activation direction & variability (neural control). Subjects sat upright with their head fixed to a 6-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2-19mA) was passed through each muscle's electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from RMS EMG when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 3D directions. The spatial tuning curves at 15% MVC were well-defined (unimodal, pbiomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles.

Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models.

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Singh, Inder; Tiganj, Zoran; Howard, Marc W

2018-04-23

A growing body of evidence suggests that short-term memory does not only store the identity of recently experienced stimuli, but also information about when they were presented. This representation of 'what' happened 'when' constitutes a neural timeline of recent past. Behavioral results suggest that people can sequentially access memories for the recent past, as if they were stored along a timeline to which attention is sequentially directed. In the short-term judgment of recency (JOR) task, the time to choose between two probe items depends on the recency of the more recent probe but not on the recency of the more remote probe. This pattern of results suggests a backward self-terminating search model. We review recent neural evidence from the macaque lateral prefrontal cortex (lPFC) (Tiganj, Cromer, Roy, Miller, & Howard, in press) and behavioral evidence from human JOR task (Singh & Howard, 2017) bearing on this question. Notably, both lines of evidence suggest that the timeline is logarithmically compressed as predicted by Weber-Fechner scaling. Taken together, these findings provide an integrative perspective on temporal organization and neural underpinnings of short-term memory. Copyright © 2018 Elsevier Inc. All rights reserved.

Microglia modulate hippocampal neural precursor activity in response to exercise and aging.

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Vukovic, Jana; Colditz, Michael J; Blackmore, Daniel G; Ruitenberg, Marc J; Bartlett, Perry F

2012-05-09

Exercise has been shown to positively augment adult hippocampal neurogenesis; however, the cellular and molecular pathways mediating this effect remain largely unknown. Previous studies have suggested that microglia may have the ability to differentially instruct neurogenesis in the adult brain. Here, we used transgenic Csf1r-GFP mice to investigate whether hippocampal microglia directly influence the activation of neural precursor cells. Our results revealed that an exercise-induced increase in neural precursor cell activity was mediated via endogenous microglia and abolished when these cells were selectively removed from hippocampal cultures. Conversely, microglia from the hippocampi of animals that had exercised were able to activate latent neural precursor cells when added to neurosphere preparations from sedentary mice. We also investigated the role of CX(3)CL1, a chemokine that is known to provide a more neuroprotective microglial phenotype. Intraparenchymal infusion of a blocking antibody against the CX(3)CL1 receptor, CX(3)CR1, but not control IgG, dramatically reduced the neurosphere formation frequency in mice that had exercised. While an increase in soluble CX(3)CL1 was observed following running, reduced levels of this chemokine were found in the aged brain. Lower levels of CX(3)CL1 with advancing age correlated with the natural decline in neural precursor cell activity, a state that could be partially alleviated through removal of microglia. These findings provide the first direct evidence that endogenous microglia can exert a dual and opposing influence on neural precursor cell activity within the hippocampus, and that signaling through the CX(3)CL1-CX(3)CR1 axis critically contributes toward this process.

Directing the public to evidence-based online content.

Science.gov (United States)

Cooper, Crystale Purvis; Gelb, Cynthia A; Vaughn, Alexandra N; Smuland, Jenny; Hughes, Alexandra G; Hawkins, Nikki A

2015-04-01

To direct online users searching for gynecologic cancer information to accurate content, the Centers for Disease Control and Prevention's (CDC) 'Inside Knowledge: Get the Facts About Gynecologic Cancer' campaign sponsored search engine advertisements in English and Spanish. From June 2012 to August 2013, advertisements appeared when US Google users entered search terms related to gynecologic cancer. Users who clicked on the advertisements were directed to relevant content on the CDC website. Compared with the 3 months before the initiative (March-May 2012), visits to the CDC web pages linked to the advertisements were 26 times higher after the initiative began (June-August 2012) (padvertisements were supplemented with promotion on television and additional websites (September 2012-August 2013) (padvertisements can direct users to evidence-based content at a highly teachable moment--when they are seeking relevant information. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Modelling and evaluating customer loyalty using neural networks: Evidence from startup insurance companies

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Azarnoush Ansari

2016-06-01

Full Text Available The purpose of this study is to investigate the customer–service provider relationship in the insurance industry using artificial neural networks and linear regression. Using a sample of 389 customers from 10 different startup insurance companies, it was found that artificial neural networks are an efficient way to evaluate the factors affecting customer loyalty. The results indicated that customer satisfaction and perceived value are significant predictors of customer loyalty. Additionally, it was found that trust, perceived quality, and empathy have a significant impact on both customer satisfaction and perceived value. The results also showed that customer commitment to service provider is positively associated with customer satisfaction and loyalty. After comparing the performance of linear regression models with artificial neural networks, it was found that the use of neural networks is a better approach for analyzing the customer loyalty, satisfaction, and perceived value. The use of new techniques such as artificial neural networks for analyzing the customer behavior can be particularly beneficial for startup companies who aspire to gain competitive advantage over their strong and well-established rivals.

How awareness changes the relative weights of evidence during human decision-making.

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de Lange, Floris P; van Gaal, Simon; Lamme, Victor A F; Dehaene, Stanislas

2011-11-01

Human decisions are based on accumulating evidence over time for different options. Here we ask a simple question: How is the accumulation of evidence affected by the level of awareness of the information? We examined the influence of awareness on decision-making using combined behavioral methods and magneto-encephalography (MEG). Participants were required to make decisions by accumulating evidence over a series of visually presented arrow stimuli whose visibility was modulated by masking. Behavioral results showed that participants could accumulate evidence under both high and low visibility. However, a top-down strategic modulation of the flow of incoming evidence was only present for stimuli with high visibility: once enough evidence had been accrued, participants strategically reduced the impact of new incoming stimuli. Also, decision-making speed and confidence were strongly modulated by the strength of the evidence for high-visible but not low-visible evidence, even though direct priming effects were identical for both types of stimuli. Neural recordings revealed that, while initial perceptual processing was independent of visibility, there was stronger top-down amplification for stimuli with high visibility than low visibility. Furthermore, neural markers of evidence accumulation over occipito-parietal cortex showed a strategic bias only for highly visible sensory information, speeding up processing and reducing neural computations related to the decision process. Our results indicate that the level of awareness of information changes decision-making: while accumulation of evidence already exists under low visibility conditions, high visibility allows evidence to be accumulated up to a higher level, leading to important strategical top-down changes in decision-making. Our results therefore suggest a potential role of awareness in deploying flexible strategies for biasing information acquisition in line with one's expectations and goals.

How awareness changes the relative weights of evidence during human decision-making.

Directory of Open Access Journals (Sweden)

Floris P de Lange

2011-11-01

Full Text Available Human decisions are based on accumulating evidence over time for different options. Here we ask a simple question: How is the accumulation of evidence affected by the level of awareness of the information? We examined the influence of awareness on decision-making using combined behavioral methods and magneto-encephalography (MEG. Participants were required to make decisions by accumulating evidence over a series of visually presented arrow stimuli whose visibility was modulated by masking. Behavioral results showed that participants could accumulate evidence under both high and low visibility. However, a top-down strategic modulation of the flow of incoming evidence was only present for stimuli with high visibility: once enough evidence had been accrued, participants strategically reduced the impact of new incoming stimuli. Also, decision-making speed and confidence were strongly modulated by the strength of the evidence for high-visible but not low-visible evidence, even though direct priming effects were identical for both types of stimuli. Neural recordings revealed that, while initial perceptual processing was independent of visibility, there was stronger top-down amplification for stimuli with high visibility than low visibility. Furthermore, neural markers of evidence accumulation over occipito-parietal cortex showed a strategic bias only for highly visible sensory information, speeding up processing and reducing neural computations related to the decision process. Our results indicate that the level of awareness of information changes decision-making: while accumulation of evidence already exists under low visibility conditions, high visibility allows evidence to be accumulated up to a higher level, leading to important strategical top-down changes in decision-making. Our results therefore suggest a potential role of awareness in deploying flexible strategies for biasing information acquisition in line with one's expectations and goals.

Neural markers of errors as endophenotypes in neuropsychiatric disorders

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Dara S Manoach

2013-07-01

Full Text Available Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a promising approach.

Neural markers of errors as endophenotypes in neuropsychiatric disorders.

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Manoach, Dara S; Agam, Yigal

2013-01-01

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

Delay-dependent exponential stability analysis of bi-directional associative memory neural networks with time delay: an LMI approach

International Nuclear Information System (INIS)

Li Chuandong; Liao Xiaofeng; Zhang Rong

2005-01-01

For bi-directional associative memory (BAM) neural networks (NNs) with different constant or time-varying delays, the problems of determining the exponential stability and estimating the exponential convergence rate are investigated in this paper. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problems, which provide bounds on the interconnection matrix and the activation functions, so as to guarantee the system's exponential stability. Some criteria for the exponential stability, which give information on the delay-dependent property, are derived. The results obtained in this paper provide one more set of easily verified guidelines for determining the exponential stability of delayed BAM (DBAM) neural networks, which are less conservative and less restrictive than the ones reported so far in the literature. Some typical examples are presented to show the application of the criteria obtained in this paper

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

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Mingyue Qiu

Full Text Available In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA. We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

Science.gov (United States)

Qiu, Mingyue; Song, Yu

2016-01-01

In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

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Tosun Erdi

2017-01-01

Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

Lifelong bilingualism maintains neural efficiency for cognitive control in aging.

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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.

Neural processing of emotional-intensity predicts emotion regulation choice.

Science.gov (United States)

Shafir, Roni; Thiruchselvam, Ravi; Suri, Gaurav; Gross, James J; Sheppes, Gal

2016-12-01

Emotional-intensity is a core characteristic of affective events that strongly determines how individuals choose to regulate their emotions. Our conceptual framework suggests that in high emotional-intensity situations, individuals prefer to disengage attention using distraction, which can more effectively block highly potent emotional information, as compared with engagement reappraisal, which is preferred in low emotional-intensity. However, existing supporting evidence remains indirect because prior intensity categorization of emotional stimuli was based on subjective measures that are potentially biased and only represent the endpoint of emotional-intensity processing. Accordingly, this study provides the first direct evidence for the role of online emotional-intensity processing in predicting behavioral regulatory-choices. Utilizing the high temporal resolution of event-related potentials, we evaluated online neural processing of stimuli's emotional-intensity (late positive potential, LPP) prior to regulatory-choices between distraction and reappraisal. Results showed that enhanced neural processing of intensity (enhanced LPP amplitudes) uniquely predicted (above subjective measures of intensity) increased tendency to subsequently choose distraction over reappraisal. Additionally, regulatory-choices led to adaptive consequences, demonstrated in finding that actual implementation of distraction relative to reappraisal-choice resulted in stronger attenuation of LPPs and self-reported arousal. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Safety of Transcranial Direct Current Stimulation: Evidence Based Update 2016.

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Bikson, Marom; Grossman, Pnina; Thomas, Chris; Zannou, Adantchede Louis; Jiang, Jimmy; Adnan, Tatheer; Mourdoukoutas, Antonios P; Kronberg, Greg; Truong, Dennis; Boggio, Paulo; Brunoni, André R; Charvet, Leigh; Fregni, Felipe; Fritsch, Brita; Gillick, Bernadette; Hamilton, Roy H; Hampstead, Benjamin M; Jankord, Ryan; Kirton, Adam; Knotkova, Helena; Liebetanz, David; Liu, Anli; Loo, Colleen; Nitsche, Michael A; Reis, Janine; Richardson, Jessica D; Rotenberg, Alexander; Turkeltaub, Peter E; Woods, Adam J

2016-01-01

This review updates and consolidates evidence on the safety of transcranial Direct Current Stimulation (tDCS). Safety is here operationally defined by, and limited to, the absence of evidence for a Serious Adverse Effect, the criteria for which are rigorously defined. This review adopts an evidence-based approach, based on an aggregation of experience from human trials, taking care not to confuse speculation on potential hazards or lack of data to refute such speculation with evidence for risk. Safety data from animal tests for tissue damage are reviewed with systematic consideration of translation to humans. Arbitrary safety considerations are avoided. Computational models are used to relate dose to brain exposure in humans and animals. We review relevant dose-response curves and dose metrics (e.g. current, duration, current density, charge, charge density) for meaningful safety standards. Special consideration is given to theoretically vulnerable populations including children and the elderly, subjects with mood disorders, epilepsy, stroke, implants, and home users. Evidence from relevant animal models indicates that brain injury by Direct Current Stimulation (DCS) occurs at predicted brain current densities (6.3-13 A/m(2)) that are over an order of magnitude above those produced by conventional tDCS. To date, the use of conventional tDCS protocols in human trials (≤40 min, ≤4 milliamperes, ≤7.2 Coulombs) has not produced any reports of a Serious Adverse Effect or irreversible injury across over 33,200 sessions and 1000 subjects with repeated sessions. This includes a wide variety of subjects, including persons from potentially vulnerable populations. Copyright © 2016 Elsevier Inc. All rights reserved.

Google matrix analysis of C.elegans neural network

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Kandiah, V., E-mail: kandiah@irsamc.ups-tlse.fr; Shepelyansky, D.L., E-mail: dima@irsamc.ups-tlse.fr

2014-05-01

We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

Google matrix analysis of C.elegans neural network

International Nuclear Information System (INIS)

Kandiah, V.; Shepelyansky, D.L.

2014-01-01

We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

Therapeutically engineered induced neural stem cells are tumour-homing and inhibit progression of glioblastoma

OpenAIRE

Bag?, Juli R.; Alfonso-Pecchio, Adolfo; Okolie, Onyi; Dumitru, Raluca; Rinkenbaugh, Amanda; Baldwin, Albert S.; Miller, C. Ryan; Magness, Scott T.; Hingtgen, Shawn D.

2016-01-01

Transdifferentiation (TD) is a recent advancement in somatic cell reprogramming. The direct conversion of TD eliminates the pluripotent intermediate state to create cells that are ideal for personalized cell therapy. Here we provide evidence that TD-derived induced neural stem cells (iNSCs) are an efficacious therapeutic strategy for brain cancer. We find that iNSCs genetically engineered with optical reporters and tumouricidal gene products retain the capacity to differentiate and induced ap...

Direct Evidence on Learning by Exporting

DEFF Research Database (Denmark)

Grimpe, Christoph; Belderbos, Rene

2014-01-01

learning and export learning – an issue that has been ignored in prior studies. Learning from foreign customers is more prevalent than learning from foreign competitors. Firms’ underlying innovation strategy appears as an important moderator of the propensity to learn and of the relationship between......We examine first direct evidence on the occurrence of learning by exporting, using unique survey data for German innovating firms on the role of (foreign) customers and competitors as sources of ideas and impetus for innovation. Export intensive firms frequently benefit simultaneously from domestic...... learning and innovativeness. Firms adopting a strategy of technology leadership more often exhibit effective learning from diverse foreign customers and this learning is strongly associated with innovative sales – but mostly so if combined with domestic customer learning. In contrast, firms that have...

Direct evidence of milk consumption from ancient human dental calculus.

Science.gov (United States)

Warinner, C; Hendy, J; Speller, C; Cappellini, E; Fischer, R; Trachsel, C; Arneborg, J; Lynnerup, N; Craig, O E; Swallow, D M; Fotakis, A; Christensen, R J; Olsen, J V; Liebert, A; Montalva, N; Fiddyment, S; Charlton, S; Mackie, M; Canci, A; Bouwman, A; Rühli, F; Gilbert, M T P; Collins, M J

2014-11-27

Milk is a major food of global economic importance, and its consumption is regarded as a classic example of gene-culture evolution. Humans have exploited animal milk as a food resource for at least 8500 years, but the origins, spread, and scale of dairying remain poorly understood. Indirect lines of evidence, such as lipid isotopic ratios of pottery residues, faunal mortality profiles, and lactase persistence allele frequencies, provide a partial picture of this process; however, in order to understand how, where, and when humans consumed milk products, it is necessary to link evidence of consumption directly to individuals and their dairy livestock. Here we report the first direct evidence of milk consumption, the whey protein β-lactoglobulin (BLG), preserved in human dental calculus from the Bronze Age (ca. 3000 BCE) to the present day. Using protein tandem mass spectrometry, we demonstrate that BLG is a species-specific biomarker of dairy consumption, and we identify individuals consuming cattle, sheep, and goat milk products in the archaeological record. We then apply this method to human dental calculus from Greenland's medieval Norse colonies, and report a decline of this biomarker leading up to the abandonment of the Norse Greenland colonies in the 15(th) century CE.

Radioactive fallout and neural tube defects

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Nejat Akar

2015-10-01

Full Text Available Possible link between radioactivity and the occurrence of neural tube defects is a long lasting debate since the Chernobyl nuclear fallout in 1986. A recent report on the incidence of neural defects in the west coast of USA, following Fukushima disaster, brought another evidence for effect of radioactive fallout on the occurrence of NTD’s. Here a literature review was performed focusing on this special subject.

Goal-directed behaviour and instrumental devaluation: a neural system-level computational model

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Francesco Mannella

2016-10-01

Full Text Available Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviours guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers activate the representation of rewards (or `action-outcomes', e.g. foods while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods. The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b the three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and integrates the results of different devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behaviour.

Direct evidence of milk consumption from ancient human dental calculus

DEFF Research Database (Denmark)

Warinner, C.; Hendy, J.; Speller, C.

2014-01-01

directly to individuals and their dairy livestock. Here we report the first direct evidence of milk consumption, the whey protein β-lactoglobulin (BLG), preserved in human dental calculus from the Bronze Age (ca. 3000 BCE) to the present day. Using protein tandem mass spectrometry, we demonstrate that BLG...... is a species-specific biomarker of dairy consumption, and we identify individuals consuming cattle, sheep, and goat milk products in the archaeological record. We then apply this method to human dental calculus from Greenland's medieval Norse colonies, and report a decline of this biomarker leading up...

Modeling and control of magnetorheological fluid dampers using neural networks

Science.gov (United States)

Wang, D. H.; Liao, W. H.

2005-02-01

Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

Performance analysis and comparison of a minimum interconnections direct storage model with traditional neural bidirectional memories.

Science.gov (United States)

Bhatti, A Aziz

2009-12-01

This study proposes an efficient and improved model of a direct storage bidirectional memory, improved bidirectional associative memory (IBAM), and emphasises the use of nanotechnology for efficient implementation of such large-scale neural network structures at a considerable lower cost reduced complexity, and less area required for implementation. This memory model directly stores the X and Y associated sets of M bipolar binary vectors in the form of (MxN(x)) and (MxN(y)) memory matrices, requires O(N) or about 30% of interconnections with weight strength ranging between +/-1, and is computationally very efficient as compared to sequential, intraconnected and other bidirectional associative memory (BAM) models of outer-product type that require O(N(2)) complex interconnections with weight strength ranging between +/-M. It is shown that it is functionally equivalent to and possesses all attributes of a BAM of outer-product type, and yet it is simple and robust in structure, very large scale integration (VLSI), optical and nanotechnology realisable, modular and expandable neural network bidirectional associative memory model in which the addition or deletion of a pair of vectors does not require changes in the strength of interconnections of the entire memory matrix. The analysis of retrieval process, signal-to-noise ratio, storage capacity and stability of the proposed model as well as of the traditional BAM has been carried out. Constraints on and characteristics of unipolar and bipolar binaries for improved storage and retrieval are discussed. The simulation results show that it has log(e) N times higher storage capacity, superior performance, faster convergence and retrieval time, when compared to traditional sequential and intraconnected bidirectional memories.

Directed forgetting of visual symbols: evidence for nonverbal selective rehearsal.

Science.gov (United States)

Hourihan, Kathleen L; Ozubko, Jason D; MacLeod, Colin M

2009-12-01

Is selective rehearsal possible for nonverbal information? Two experiments addressed this question using the item method directed forgetting paradigm, where the advantage of remember items over forget items is ascribed to selective rehearsal favoring the remember items. In both experiments, difficult-to-name abstract symbols were presented for study, followed by a recognition test. Directed forgetting effects were evident for these symbols, regardless of whether they were or were not spontaneously named. Critically, a directed forgetting effect was observed for unnamed symbols even when the symbols were studied under verbal suppression to prevent verbal rehearsal. This pattern indicates that a form of nonverbal rehearsal can be used strategically (i.e., selectively) to enhance memory, even when verbal rehearsal is not possible.

Preventing neural tube defects in Europe : A missed opportunity

NARCIS (Netherlands)

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

2005-01-01

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

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.

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

Spatially Compact Neural Clusters in the Dorsal Striatum Encode Locomotion Relevant Information.

Science.gov (United States)

Barbera, Giovanni; Liang, Bo; Zhang, Lifeng; Gerfen, Charles R; Culurciello, Eugenio; Chen, Rong; Li, Yun; Lin, Da-Ting

2016-10-05

An influential striatal model postulates that neural activities in the striatal direct and indirect pathways promote and inhibit movement, respectively. Normal behavior requires coordinated activity in the direct pathway to facilitate intended locomotion and indirect pathway to inhibit unwanted locomotion. In this striatal model, neuronal population activity is assumed to encode locomotion relevant information. Here, we propose a novel encoding mechanism for the dorsal striatum. We identified spatially compact neural clusters in both the direct and indirect pathways. Detailed characterization revealed similar cluster organization between the direct and indirect pathways, and cluster activities from both pathways were correlated with mouse locomotion velocities. Using machine-learning algorithms, cluster activities could be used to decode locomotion relevant behavioral states and locomotion velocity. We propose that neural clusters in the dorsal striatum encode locomotion relevant information and that coordinated activities of direct and indirect pathway neural clusters are required for normal striatal controlled behavior. VIDEO ABSTRACT. Published by Elsevier Inc.

Two stages of directed forgetting: Electrophysiological evidence from a short-term memory task.

Science.gov (United States)

Gao, Heming; Cao, Bihua; Qi, Mingming; Wang, Jing; Zhang, Qi; Li, Fuhong

2016-06-01

In this study, a short-term memory test was used to investigate the temporal course and neural mechanism of directed forgetting under different memory loads. Within each trial, two memory items with high or low load were presented sequentially, followed by a cue indicating whether the presented items should be remembered. After an interval, subjects were asked to respond to the probe stimuli. The ERPs locked to the cues showed that (a) the effect of cue type was initially observed during the P2 (160-240 ms) time window, with more positive ERPs for remembering relative to forgetting cues; (b) load effects were observed during the N2-P3 (250-500 ms) time window, with more positive ERPs for the high-load than low-load condition; (c) the cue effect was also observed during the N2-P3 time window, with more negative ERPs for forgetting versus remembering cues. These results demonstrated that directed forgetting involves two stages: task-relevance identification and information discarding. The cue effects during the N2 epoch supported the view that directed forgetting is an active process. © 2016 Society for Psychophysiological Research.

Neural Networks in Control Applications

DEFF Research Database (Denmark)

Sørensen, O.

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

Analysis of neural activity in human motor cortex -- Towards brain machine interface system

Science.gov (United States)

Secundo, Lavi

, the correlation of ECoG activity to kinematic parameters of arm movement is context-dependent, an important constraint to consider in future development of BMI systems. The third chapter delves into a fundamental organizational principle of the primate motor system---cortical control of contralateral limb movements. However, ipsilateral motor areas also appear to play a role in the control of ipsilateral limb movements. Several studies in monkeys have shown that individual neurons in ipsilateral primary motor cortex (M1) may represent, on average, the direction of movements of the ipsilateral arm. Given the increasing body of evidence demonstrating that neural ensembles can reliably represent information with a high temporal resolution, here we characterize the distributed neural representation of ipsilateral upper limb kinematics in both monkey and man. In two macaque monkeys trained to perform center-out reaching movements, we found that the ensemble spiking activity in M1 could continuously represent ipsilateral limb position. We also recorded cortical field potentials from three human subjects and also consistently found evidence of a neural representation for ipsilateral movement parameters. Together, our results demonstrate the presence of a high-fidelity neural representation for ipsilateral movement and illustrates that it can be successfully incorporated into a brain-machine interface.

A voltage-sensitive dye-based assay for the identification of differentiated neurons derived from embryonic neural stem cell cultures.

Directory of Open Access Journals (Sweden)

Richardson N Leão

Full Text Available BACKGROUND: Pluripotent and multipotent stem cells hold great therapeutical promise for the replacement of degenerated tissue in neurological diseases. To fulfill that promise we have to understand the mechanisms underlying the differentiation of multipotent cells into specific types of neurons. Embryonic stem cell (ESC and embryonic neural stem cell (NSC cultures provide a valuable tool to study the processes of neural differentiation, which can be assessed using immunohistochemistry, gene expression, Ca(2+-imaging or electrophysiology. However, indirect methods such as protein and gene analysis cannot provide direct evidence of neuronal functionality. In contrast, direct methods such as electrophysiological techniques are well suited to produce direct evidence of neural functionality but are limited to the study of a few cells on a culture plate. METHODOLOGY/PRINCIPAL FINDINGS: In this study we describe a novel method for the detection of action potential-capable neurons differentiated from embryonic NSC cultures using fast voltage-sensitive dyes (VSD. We found that the use of extracellularly applied VSD resulted in a more detailed labeling of cellular processes compared to calcium indicators. In addition, VSD changes in fluorescence translated precisely to action potential kinetics as assessed by the injection of simulated slow and fast sodium currents using the dynamic clamp technique. We further demonstrate the use of a finite element model of the NSC culture cover slip for optimizing electrical stimulation parameters. CONCLUSIONS/SIGNIFICANCE: Our method allows for a repeatable fast and accurate stimulation of neurons derived from stem cell cultures to assess their differentiation state, which is capable of monitoring large amounts of cells without harming the overall culture.

Embryonic cell-cell adhesion: a key player in collective neural crest migration.

Science.gov (United States)

Barriga, Elias H; Mayor, Roberto

2015-01-01

Cell migration is essential for morphogenesis, adult tissue remodeling, wound healing, and cancer cell migration. Cells can migrate as individuals or groups. When cells migrate in groups, cell-cell interactions are crucial in order to promote the coordinated behavior, essential for collective migration. Interestingly, recent evidence has shown that cell-cell interactions are also important for establishing and maintaining the directionality of these migratory events. We focus on neural crest cells, as they possess extraordinary migratory capabilities that allow them to migrate and colonize tissues all over the embryo. Neural crest cells undergo an epithelial-to-mesenchymal transition at the same time than perform directional collective migration. Cell-cell adhesion has been shown to be an important source of planar cell polarity and cell coordination during collective movement. We also review molecular mechanisms underlying cadherin turnover, showing how the modulation and dynamics of cell-cell adhesions are crucial in order to maintain tissue integrity and collective migration in vivo. We conclude that cell-cell adhesion during embryo development cannot be considered as simple passive resistance to force, but rather participates in signaling events that determine important cell behaviors required for cell migration. © 2015 Elsevier Inc. All rights reserved.

Media Multitasking and Cognitive, Psychological, Neural, and Learning Differences.

Science.gov (United States)

Uncapher, Melina R; Lin, Lin; Rosen, Larry D; Kirkorian, Heather L; Baron, Naomi S; Bailey, Kira; Cantor, Joanne; Strayer, David L; Parsons, Thomas D; Wagner, Anthony D

2017-11-01

American youth spend more time with media than any other waking activity: an average of 7.5 hours per day, every day. On average, 29% of that time is spent juggling multiple media streams simultaneously (ie, media multitasking). This phenomenon is not limited to American youth but is paralleled across the globe. Given that a large number of media multitaskers (MMTs) are children and young adults whose brains are still developing, there is great urgency to understand the neurocognitive profiles of MMTs. It is critical to understand the relation between the relevant cognitive domains and underlying neural structure and function. Of equal importance is understanding the types of information processing that are necessary in 21st century learning environments. The present review surveys the growing body of evidence demonstrating that heavy MMTs show differences in cognition (eg, poorer memory), psychosocial behavior (eg, increased impulsivity), and neural structure (eg, reduced volume in anterior cingulate cortex). Furthermore, research indicates that multitasking with media during learning (in class or at home) can negatively affect academic outcomes. Until the direction of causality is understood (whether media multitasking causes such behavioral and neural differences or whether individuals with such differences tend to multitask with media more often), the data suggest that engagement with concurrent media streams should be thoughtfully considered. Findings from such research promise to inform policy and practice on an increasingly urgent societal issue while significantly advancing our understanding of the intersections between cognitive, psychosocial, neural, and academic factors. Copyright © 2017 by the American Academy of Pediatrics.

Decentralized neural control application to robotics

CERN Document Server

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

2017-01-01

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

Radioactive fallout and neural tube defects | Akar | Egyptian Journal ...

African Journals Online (AJOL)

Possible link between radioactivity and the occurrence of neural tube defects is a long lasting debate since the Chernobyl nuclear fallout in 1986. A recent report on the incidence of neural defects in the west coast of USA, following Fukushima disaster, brought another evidence for effect of radioactive fallout on the ...

Neural evidence for cultural differences in the valuation of positive facial expressions

Science.gov (United States)

Park, BoKyung; Chim, Louise; Blevins, Elizabeth; Knutson, Brian

2016-01-01

European Americans value excitement more and calm less than Chinese. Within cultures, European Americans value excited and calm states similarly, whereas Chinese value calm more than excited states. To examine how these cultural differences influence people’s immediate responses to excited vs calm facial expressions, we combined a facial rating task with functional magnetic resonance imaging. During scanning, European American (n = 19) and Chinese (n = 19) females viewed and rated faces that varied by expression (excited, calm), ethnicity (White, Asian) and gender (male, female). As predicted, European Americans showed greater activity in circuits associated with affect and reward (bilateral ventral striatum, left caudate) while viewing excited vs calm expressions than did Chinese. Within cultures, European Americans responded to excited vs calm expressions similarly, whereas Chinese showed greater activity in these circuits in response to calm vs excited expressions regardless of targets’ ethnicity or gender. Across cultural groups, greater ventral striatal activity while viewing excited vs. calm expressions predicted greater preference for excited vs calm expressions months later. These findings provide neural evidence that people find viewing the specific positive facial expressions valued by their cultures to be rewarding and relevant. PMID:26342220

Factor structure of suggestibility revisited: new evidence for direct and indirect suggestibility

OpenAIRE

Romuald Polczyk

2016-01-01

Background Yielding to suggestions can be viewed as a relatively stable individual trait, called suggestibility. It has been long proposed that there are two kinds of suggestible influence, and two kinds of suggestibility corresponding to them: direct and indirect. Direct suggestion involves overt unhidden influence, while indirect suggestion concerns influence that is hidden, and the participant does not know that the suggestibility is being measured. So far however, empirical evidence ...

Direct-to-consumer marketing of evidence-based psychological interventions: introduction.

Science.gov (United States)

Santucci, Lauren C; McHugh, R Kathryn; Barlow, David H

2012-06-01

The dissemination and implementation of evidence-based psychological interventions (EBPIs) to service provision settings has been a major challenge. Most efforts to disseminate and implement EBPIs have focused on clinicians and clinical systems as the consumers of these treatments and thus have targeted efforts to these groups. An alternative, complementary approach to achieve more widespread utilization of EBPIs is to disseminate directly to patients themselves. The aim of this special section is to explore several direct-to-consumer (i.e., patient) dissemination and education efforts currently underway. This manuscript highlights the rationale for direct-to-patient dissemination strategies as well as the application of marketing science to dissemination efforts. Achieving greater access to EBPIs will require the use of multiple approaches to overcome the many and varied barriers to successful dissemination and implementation. Copyright © 2011. Published by Elsevier Ltd.

Modelling and evaluating customer loyalty using neural networks: Evidence from startup insurance companies

OpenAIRE

Azarnoush Ansari; Arash Riasi

2016-01-01

The purpose of this study is to investigate the customer–service provider relationship in the insurance industry using artificial neural networks and linear regression. Using a sample of 389 customers from 10 different startup insurance companies, it was found that artificial neural networks are an efficient way to evaluate the factors affecting customer loyalty. The results indicated that customer satisfaction and perceived value are significant predictors of customer loyalty. Additionally, ...

Modulation of neural activity in the temporoparietal junction with transcranial direct current stimulation changes the role of beliefs in moral judgment

Directory of Open Access Journals (Sweden)

Hang eYe

2015-12-01

Full Text Available Judgments about whether an action is morally right or wrong typically depend on our capacity to infer the actor’s beliefs and the outcomes of the action. Prior neuroimaging studies have found that mental state (e.g., beliefs, intentions attribution for moral judgment involves a complex neural network that includes the temporoparietal junction (TPJ. However, neuroimaging studies cannot demonstrate a direct causal relationship between the activity of this brain region and mental state attribution for moral judgment. In the current study, we used transcranial direct current stimulation (tDCS to transiently alter neural activity in the TPJ. The participants were randomly assigned to one of three stimulation treatments (right anodal/left cathodal tDCS, left anodal/right cathodal tDCS, or sham stimulation. Each participant was required to complete two similar tasks of moral judgment before receiving tDCS and after receiving tDCS. We studied whether tDCS to the TPJ altered mental state attribution for moral judgment. The results indicated that restraining the activity of the right temporoparietal junction (RTPJ or the left the temporoparietal junction (LTPJ decreased the role of beliefs in moral judgments and led to an increase in the dependence of the participants’ moral judgments on the action’s consequences. We also found that the participants exhibited reduced reaction times both in the cases of intentional harms and attempted harms after receiving right cathodal/left anodal tDCS to the TPJ. These findings inform and extend the current neural models of moral judgment and moral development in typically developing people and in individuals with neurodevelopmental disorders such as autism.

Evidence-Based Systematic Review: Effects of Neuromuscular Electrical Stimulation on Swallowing and Neural Activation

Science.gov (United States)

Clark, Heather; Lazarus, Cathy; Arvedson, Joan; Schooling, Tracy; Frymark, Tobi

2009-01-01

Purpose: To systematically review the literature examining the effects of neuromuscular electrical stimulation (NMES) on swallowing and neural activation. The review was conducted as part of a series examining the effects of oral motor exercises (OMEs) on speech, swallowing, and neural activation. Method: A systematic search was conducted to…

Evidence for increased SOX3 dosage as a risk factor for X-linked hypopituitarism and neural tube defects.

Science.gov (United States)

Bauters, Marijke; Frints, Suzanna G; Van Esch, Hilde; Spruijt, Liesbeth; Baldewijns, Marcella M; de Die-Smulders, Christine E M; Fryns, Jean-Pierre; Marynen, Peter; Froyen, Guy

2014-08-01

Genomic duplications of varying lengths at Xq26-q27 involving SOX3 have been described in families with X-linked hypopituitarism. Using array-CGH we detected a 1.1 Mb microduplication at Xq27 in a large family with three males suffering from X-linked hypopituitarism. The duplication was mapped from 138.7 to 139.8 Mb, harboring only two annotated genes, SOX3 and ATP11C, and was shown to be a direct tandem copy number gain. Unexpectedly, the microduplication did not fully segregate with the disease in this family suggesting that SOX3 duplications have variable penetrance for X-linked hypopituitarism. In the same family, a female fetus presenting with a neural tube defect was also shown to carry the SOX3 copy number gain. Since we also demonstrated increased SOX3 mRNA levels in amnion cells derived from an unrelated t(X;22)(q27;q11) female fetus with spina bifida, we propose that increased levels of SOX3 could be a risk factor for neural tube defects. © 2014 Wiley Periodicals, Inc.

Neural evidence that utterance-processing entails mentalizing: the case of irony.

Science.gov (United States)

Spotorno, Nicola; Koun, Eric; Prado, Jérôme; Van Der Henst, Jean-Baptiste; Noveck, Ira A

2012-10-15

It is now well established that communicators interpret others' mental states through what has been called "Theory of Mind" (ToM). From a linguistic-pragmatics perspective, this mentalizing ability is considered critical because it is assumed that the linguistic code in all utterances underdetermines the speaker's meaning, leaving a vital role for ToM to fill the gap. From a neuroscience perspective, understanding others' intentions has been shown to activate a neural ToM network that includes the right and left temporal parietal junction (rTPJ, lTPJ), the medial prefrontal cortex (MPFC) and the precuneus (PC). Surprisingly, however, there are no studies - to our knowledge - that aim to uncover a direct, on-line link between language processing and ToM through neuroimaging. This is why we focus on verbal irony, an obviously pragmatic phenomenon that compels a listener to detect the speaker's (dissociated, mocking) attitude (Wilson, 2009). In the present fMRI investigation, we compare participants' comprehension of 18 target sentences as contexts make them either ironic or literal. Consider an opera singer who tells her interlocutor: "Tonight we gave a superb performance!" when the performance in question was clearly awful (making the statement ironic) or very good (making the statement literal). We demonstrate that the ToM network becomes active while a participant is understanding verbal irony. Moreover, we demonstrate - through Psychophysiological Interactions (PPI) analyses - that ToM activity is directly linked with language comprehension processes. The paradigm, its predictions, and the reported results contrast dramatically with those from seven prior fMRI studies on irony. Copyright © 2012 Elsevier Inc. All rights reserved.

Goal-Directed Decision Making with Spiking Neurons.

Science.gov (United States)

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules

Neural entrainment to the rhythmic structure of music.

Science.gov (United States)

Tierney, Adam; Kraus, Nina

2015-02-01

The neural resonance theory of musical meter explains musical beat tracking as the result of entrainment of neural oscillations to the beat frequency and its higher harmonics. This theory has gained empirical support from experiments using simple, abstract stimuli. However, to date there has been no empirical evidence for a role of neural entrainment in the perception of the beat of ecologically valid music. Here we presented participants with a single pop song with a superimposed bassoon sound. This stimulus was either lined up with the beat of the music or shifted away from the beat by 25% of the average interbeat interval. Both conditions elicited a neural response at the beat frequency. However, although the on-the-beat condition elicited a clear response at the first harmonic of the beat, this frequency was absent in the neural response to the off-the-beat condition. These results support a role for neural entrainment in tracking the metrical structure of real music and show that neural meter tracking can be disrupted by the presentation of contradictory rhythmic cues.

Neural computation and the computational theory of cognition.

Science.gov (United States)

Piccinini, Gualtiero; Bahar, Sonya

2013-04-01

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism-neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. Copyright © 2012 Cognitive Science Society, Inc.

Small numbers are sensed directly, high numbers constructed from size and density.

Science.gov (United States)

Zimmermann, Eckart

2018-04-01

Two theories compete to explain how we estimate the numerosity of visual object sets. The first suggests that the apparent numerosity is derived from an analysis of more low-level features like size and density of the set. The second theory suggests that numbers are sensed directly. Consistent with the latter claim is the existence of neurons in parietal cortex which are specialized for processing the numerosity of elements in the visual scene. However, recent evidence suggests that only low numbers can be sensed directly whereas the perception of high numbers is supported by the analysis of low-level features. Processing of low and high numbers, being located at different levels of the neural hierarchy should involve different receptive field sizes. Here, I tested this idea with visual adaptation. I measured the spatial spread of number adaptation for low and high numerosities. A focused adaptation spread of high numerosities suggested the involvement of early neural levels where receptive fields are comparably small and the broad spread for low numerosities was consistent with processing of number neurons which have larger receptive fields. These results provide evidence for the claim that different mechanism exist generating the perception of visual numerosity. Whereas low numbers are sensed directly as a primary visual attribute, the estimation of high numbers however likely depends on the area size over which the objects are spread. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of teeth in chick embryos after mouse neural crest transplantations.

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Mitsiadis, Thimios A; Chéraud, Yvonnick; Sharpe, Paul; Fontaine-Pérus, Josiane

2003-05-27

Teeth were lost in birds 70-80 million years ago. Current thinking holds that it is the avian cranial neural crest-derived mesenchyme that has lost odontogenic capacity, whereas the oral epithelium retains the signaling properties required to induce odontogenesis. To investigate the odontogenic capacity of ectomesenchyme, we have used neural tube transplantations from mice to chick embryos to replace the chick neural crest cell populations with mouse neural crest cells. The mouse/chick chimeras obtained show evidence of tooth formation showing that avian oral epithelium is able to induce a nonavian developmental program in mouse neural crest-derived mesenchymal cells.

NMDA Receptor Signaling Is Important for Neural Tube Formation and for Preventing Antiepileptic Drug-Induced Neural Tube Defects.

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Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N

2018-05-16

Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.

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

Memory Consolidation and Neural Substrate of Reward

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Redolar-Ripoll, Diego

2012-08-01

Full Text Available The aim of this report is to analyze the relationships between reward and learning and memory processes. Different studies have described how information about rewards influences behavior and how the brain uses this reward information to control learning and memory processes. Reward nature seems to be processed in different ways by neurons in different brain structures, ranging from the detection and perception of rewards to the use of information about predicted rewards for the control of goal-directed behavior. The neural substrate underling this processing of reward information is a reliable way of improving learning and memory processes. Evidence from several studies indicates that this neural system can facilitate memory consolidation in a wide variety of learning tasks. From a molecular perspective, certain cardinal features of reward have been described as forms of memory. Studies of human addicts and studies in animal models of addiction show that chronic drug exposure produces stable changes in the brain at the cellular and molecular levels that underlie the long-lasting behavioral plasticity associated with addiction. These molecular and cellular adaptations involved in addiction are also implicated in learning and memory processes. Dopamine seems to be a critical common signal to activate different genetic mechanisms that ultimately remodel synapses and circuits. Despite memory is an active and complex process mediated by different brain areas, the neural substrate of reward is able to improve memory consolidation in a several paradigms. We believe that there are many equivalent traits between reward and learning and memory processes.

Meditation reduces pain-related neural activity in the anterior cingulate cortex, insula, secondary somatosensory cortex, and thalamus

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Nakata, Hiroki; Sakamoto, Kiwako; Kakigi, Ryusuke

2014-01-01

Recent studies have shown that meditation inhibits or relieves pain perception. To clarify the underlying mechanisms for this phenomenon, neuroimaging methods, such as functional magnetic resonance imaging, and neurophysiological methods, such as magnetoencephalography and electroencephalography, have been used. However, it has been difficult to interpret the results, because there is some paradoxical evidence. For example, some studies reported increased neural responses to pain stimulation during meditation in the anterior cingulate cortex (ACC) and insula, whereas others showed a decrease in these regions. There have been inconsistent findings to date. Moreover, in general, since the activities of the ACC and insula are correlated with pain perception, the increase in neural activities during meditation would be related to the enhancement of pain perception rather than its reduction. These contradictions might directly contribute to the ‘mystery of meditation.’ In this review, we presented previous findings for brain regions during meditation and the anatomical changes that occurred in the brain with long-term meditation training. We then discussed the findings of previous studies that examined pain-related neural activity during meditation. We also described the brain mechanisms responsible for pain relief during meditation, and possible reasons for paradoxical evidence among previous studies. By thoroughly overviewing previous findings, we hypothesized that meditation reduces pain-related neural activity in the ACC, insula, secondary somatosensory cortex, and thalamus. We suggest that the characteristics of the modulation of this activity may depend on the kind of meditation and/or number of years of experience of meditation, which were associated with paradoxical findings among previous studies that investigated pain-related neural activities during meditation. PMID:25566158

Neural crest contributions to the lamprey head

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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.

FORECASTING KUALA LUMPUR COMPOSITE INDEX: EVIDENCE OF THE ARTIFICIAL NEURAL NETWORK AND ARIMA

OpenAIRE

Sukmana, Raditya; Solihin, Mahmud Iwan

2007-01-01

The aim of this paper is to use, compare, and analyze two forecasting technique: namelyAuto Regressive Integrated Moving Average(ARIMA) and Artificial NeuralNetwork(ANN) using Kuala Lumpur Composite Index(KLCI) in Malaysia. ArtificialNeural Network is used because of its popularity of capturing the volatility patterns innonlinear time series while ARIMA used since it is a standard method in the forecastingtool. Daily data of Kuala Lumpur Composite Index from 4 January 1999 to 26 September2005...

Forecasting Kuala Lumpur Composite Index: Evidence of the Artificial Neural Network and Arima

OpenAIRE

Mahmud Iwan, Raditya Sukmana,

2007-01-01

The aim of this paper is to use, compare, and analyze two forecasting technique: namely Auto Regressive Integrated Moving Average(ARIMA) and Artificial Neural Network(ANN) using Kuala Lumpur Composite Index(KLCI) in Malaysia. Artificial Neural Network is used because of its popularity of capturing the volatility patterns in nonlinear time series while ARIMA used since it is a standard method in the forecasting tool. Daily data of Kuala Lumpur Composite Index from 4 January 1999 to 26 Septembe...

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

Wittgenstein running: neural mechanisms of collective intentionality and we-mode.

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Becchio, Cristina; Bertone, Cesare

2004-03-01

In this paper we discuss the problem of the neural conditions of shared attitudes and intentions: which neural mechanisms underlie "we-mode" processes or serve as precursors to such processes? Neurophysiological and neuropsychological evidence suggests that in different areas of the brain neural representations are shared by several individuals. This situation, on the one hand, creates a potential problem for correct attribution. On the other hand, it may provide the conditions for shared attitudes and intentions.

Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

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Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

2018-02-01

This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

Non-Viral Generation of Neural Precursor-like Cells from Adult Human Fibroblasts

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Maucksch C

2012-01-01

Full Text Available Recent studies have reported direct reprogramming of human fibroblasts to mature neurons by the introduction of defined neural genes. This technology has potential use in the areas of neurological disease modeling and drug development. However, use of induced neurons for large-scale drug screening and cell-based replacement strategies is limited due to their inability to expand once reprogrammed. We propose it would be more desirable to induce expandable neural precursor cells directly from human fibroblasts. To date several pluripotent and neural transcription factors have been shown to be capable of converting mouse fibroblasts to neural stem/precursor-like cells when delivered by viral vectors. Here we extend these findings and demonstrate that transient ectopic insertion of the transcription factors SOX2 and PAX6 to adult human fibroblasts through use of non-viral plasmid transfection or protein transduction allows the generation of induced neural precursor (iNP colonies expressing a range of neural stem and pro-neural genes. Upon differentiation, iNP cells give rise to neurons exhibiting typical neuronal morphologies and expressing multiple neuronal markers including tyrosine hydroxylase and GAD65/67. Importantly, iNP-derived neurons demonstrate electrophysiological properties of functionally mature neurons with the capacity to generate action potentials. In addition, iNP cells are capable of differentiating into glial fibrillary acidic protein (GFAP-expressing astrocytes. This study represents a novel virus-free approach for direct reprogramming of human fibroblasts to a neural precursor fate.

Neural Networks in Mobile Robot Motion

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Danica Janglová

2004-03-01

Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

Direct Evidence for Vision-based Control of Flight Speed in Budgerigars.

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Schiffner, Ingo; Srinivasan, Mandyam V

2015-06-05

We have investigated whether, and, if so, how birds use vision to regulate the speed of their flight. Budgerigars, Melopsittacus undulatus, were filmed in 3-D using high-speed video cameras as they flew along a 25 m tunnel in which stationary or moving vertically oriented black and white stripes were projected on the side walls. We found that the birds increased their flight speed when the stripes were moved in the birds' flight direction, but decreased it only marginally when the stripes were moved in the opposite direction. The results provide the first direct evidence that Budgerigars use cues based on optic flow, to regulate their flight speed. However, unlike the situation in flying insects, it appears that the control of flight speed in Budgerigars is direction-specific. It does not rely solely on cues derived from optic flow, but may also be determined by energy constraints.

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

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Lisa Jane Burklund

2014-03-01

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

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

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Wang, W.; Wang, D.; Peng, Z. H.

2017-09-01

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.

The Laplacian spectrum of neural networks

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

2014-01-01

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

A neural network approach to burst detection.

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Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

2002-01-01

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

Capnography during cardiopulmonary resuscitation: Current evidence and future directions

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Bhavani Shankar Kodali

2014-01-01

Full Text Available Capnography continues to be an important tool in measuring expired carbon dioxide (CO 2 . Most recent Advanced Cardiac Life Support (ACLS guidelines now recommend using capnography to ascertain the effectiveness of chest compressions and duration of cardiopulmonary resuscitation (CPR. Based on an extensive review of available published literature, we selected all available peer-reviewed research investigations and case reports. Available evidence suggests that there is significant correlation between partial pressure of end-tidal CO 2 (PETCO 2 and cardiac output that can indicate the return of spontaneous circulation (ROSC. Additional evidence favoring the use of capnography during CPR includes definitive proof of correct placement of the endotracheal tube and possible prediction of patient survival following cardiac arrest, although the latter will require further investigations. There is emerging evidence that PETCO 2 values can guide the initiation of extracorporeal life support (ECLS in refractory cardiac arrest (RCA. There is also increasing recognition of the value of capnography in intensive care settings in intubated patients. Future directions include determining the outcomes based on capnography waveforms PETCO 2 values and determining a reasonable duration of CPR. In the future, given increasing use of capnography during CPR large databases can be analyzed to predict outcomes.

An RNA-binding protein, Qki5, regulates embryonic neural stem cells through pre-mRNA processing in cell adhesion signaling.

Science.gov (United States)

Hayakawa-Yano, Yoshika; Suyama, Satoshi; Nogami, Masahiro; Yugami, Masato; Koya, Ikuko; Furukawa, Takako; Zhou, Li; Abe, Manabu; Sakimura, Kenji; Takebayashi, Hirohide; Nakanishi, Atsushi; Okano, Hideyuki; Yano, Masato

2017-09-15

Cell type-specific transcriptomes are enabled by the action of multiple regulators, which are frequently expressed within restricted tissue regions. In the present study, we identify one such regulator, Quaking 5 (Qki5), as an RNA-binding protein (RNABP) that is expressed in early embryonic neural stem cells and subsequently down-regulated during neurogenesis. mRNA sequencing analysis in neural stem cell culture indicates that Qki proteins play supporting roles in the neural stem cell transcriptome and various forms of mRNA processing that may result from regionally restricted expression and subcellular localization. Also, our in utero electroporation gain-of-function study suggests that the nuclear-type Qki isoform Qki5 supports the neural stem cell state. We next performed in vivo transcriptome-wide protein-RNA interaction mapping to search for direct targets of Qki5 and elucidate how Qki5 regulates neural stem cell function. Combined with our transcriptome analysis, this mapping analysis yielded a bona fide map of Qki5-RNA interaction at single-nucleotide resolution, the identification of 892 Qki5 direct target genes, and an accurate Qki5-dependent alternative splicing rule in the developing brain. Last, our target gene list provides the first compelling evidence that Qki5 is associated with specific biological events; namely, cell-cell adhesion. This prediction was confirmed by histological analysis of mice in which Qki proteins were genetically ablated, which revealed disruption of the apical surface of the lateral wall in the developing brain. These data collectively indicate that Qki5 regulates communication between neural stem cells by mediating numerous RNA processing events and suggest new links between splicing regulation and neural stem cell states. © 2017 Hayakawa-Yano et al.; Published by Cold Spring Harbor Laboratory Press.

Neural neworks in a management information systems

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Jana Weinlichová

2009-01-01

Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

Neural evidence for cultural differences in the valuation of positive facial expressions.

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Park, BoKyung; Tsai, Jeanne L; Chim, Louise; Blevins, Elizabeth; Knutson, Brian

2016-02-01

European Americans value excitement more and calm less than Chinese. Within cultures, European Americans value excited and calm states similarly, whereas Chinese value calm more than excited states. To examine how these cultural differences influence people's immediate responses to excited vs calm facial expressions, we combined a facial rating task with functional magnetic resonance imaging. During scanning, European American (n = 19) and Chinese (n = 19) females viewed and rated faces that varied by expression (excited, calm), ethnicity (White, Asian) and gender (male, female). As predicted, European Americans showed greater activity in circuits associated with affect and reward (bilateral ventral striatum, left caudate) while viewing excited vs calm expressions than did Chinese. Within cultures, European Americans responded to excited vs calm expressions similarly, whereas Chinese showed greater activity in these circuits in response to calm vs excited expressions regardless of targets' ethnicity or gender. Across cultural groups, greater ventral striatal activity while viewing excited vs. calm expressions predicted greater preference for excited vs calm expressions months later. These findings provide neural evidence that people find viewing the specific positive facial expressions valued by their cultures to be rewarding and relevant. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Photopolymerized materials and patterning for improved performance of neural prosthetics

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Tuft, Bradley William

Neural prosthetics are used to replace or substantially augment remaining motor and sensory functions of neural pathways that were lost or damaged due to physical trauma, disease, or genetics. However, due to poor spatial signal resolution, neural prostheses fail to recapitulate the intimate, precise interactions inherent to neural networks. Designing materials and interfaces that direct de novo nerve growth to spatially specific stimulating elements is, therefore, a promising method to enhance signal specificity and performance of prostheses such as the successful cochlear implant (CI) and the developing retinal implant. In this work, the spatial and temporal reaction control inherent to photopolymerization was used to develop methods to generate micro and nanopatterned materials that direct neurite growth from prosthesis relevant neurons. In particular, neurite growth and directionality has been investigated in response to physical, mechanical, and chemical cues on photopolymerized surfaces. Spiral ganglion neurons (SGNs) serve as the primary neuronal model as they are the principal target for CI stimulation. The objective of the research is to rationally design materials that spatially direct neurite growth and to translate fundamental understanding of nerve cell-material interactions into methods of nerve regeneration that improve neural prosthetic performance. A rapid, single-step photopolymerization method was developed to fabricate micro and nanopatterned physical cues on methacrylate surfaces by selectively blocking light with photomasks. Feature height is readily tuned by modulating parameters of the photopolymerizaiton including initiator concentration and species, light intensity, separation distance from the photomask, and radiation exposure time. Alignment of neural elements increases significantly with increasing feature amplitude and constant periodicity, as well as with decreasing periodicity and constant amplitude. SGN neurite alignment strongly

Action Planning and the Timescale of Evidence Accumulation.

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Konstantinos Tsetsos

Full Text Available Perceptual decisions are based on the temporal integration of sensory evidence for different states of the outside world. The timescale of this integration process varies widely across behavioral contexts and individuals, and it is diagnostic for the underlying neural mechanisms. In many situations, the decision-maker knows the required mapping between perceptual evidence and motor response (henceforth termed "sensory-motor contingency" before decision formation. Here, the integrated evidence can be directly translated into a motor plan and, indeed, neural signatures of the integration process are evident as build-up activity in premotor brain regions. In other situations, however, the sensory-motor contingencies are unknown at the time of decision formation. We used behavioral psychophysics and computational modeling to test if knowledge about sensory-motor contingencies affects the timescale of perceptual evidence integration. We asked human observers to perform the same motion discrimination task, with or without trial-to-trial variations of the mapping between perceptual choice and motor response. When the mapping varied, it was either instructed before or after the stimulus presentation. We quantified the timescale of evidence integration under these different sensory-motor mapping conditions by means of two approaches. First, we analyzed subjects' discrimination threshold as a function of stimulus duration. Second, we fitted a dynamical decision-making model to subjects' choice behavior. The results from both approaches indicated that observers (i integrated motion information for several hundred ms, (ii used a shorter than optimal integration timescale, and (iii used the same integration timescale under all sensory-motor mappings. We conclude that the mechanisms limiting the timescale of perceptual decisions are largely independent from long-term learning (under fixed mapping or rapid acquisition (under variable mapping of sensory

Neural synchronization during face-to-face communication.

Science.gov (United States)

Jiang, Jing; Dai, Bohan; Peng, Danling; Zhu, Chaozhe; Liu, Li; Lu, Chunming

2012-11-07

Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., telephone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face communication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-face dialog between partners but none during a back-to-back dialog, a face-to-face monologue, or a back-to-back monologue. Moreover, the neural synchronization between partners during the face-to-face dialog resulted primarily from the direct interactions between the partners, including multimodal sensory information integration and turn-taking behavior. The communicating behavior during the face-to-face dialog could be predicted accurately based on the neural synchronization level. These results suggest that face-to-face communication, particularly dialog, has special neural features that other types of communication do not have and that the neural synchronization between partners may underlie successful face-to-face communication.

Learning text representation using recurrent convolutional neural network with highway layers

OpenAIRE

Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

2016-01-01

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

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Sergey A. Sannikov

2017-03-01

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

Non-invasive neural stimulation

Science.gov (United States)

Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

2017-05-01

Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

Aging differentially affects male and female neural stem cell neurogenic properties

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Jay Waldron

2010-09-01

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

Runoff Calculation by Neural Networks Using Radar Rainfall Data

OpenAIRE

岡田, 晋作; 四俵, 正俊

1997-01-01

Neural networks, are used to calculate runoff from weather radar data and ground rain gauge data. Compared to usual runoff models, it is easier to use radar data in neural network runoff calculation. Basically you can use the radar data directly, or without transforming them into rainfall, as the input of the neural network. A situation with the difficulty of ground measurement is supposed. To cover the area lacking ground rain gauge, radar data are used. In case that the distribution of grou...

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.

Neural systems supporting and affecting economically relevant behavior

Directory of Open Access Journals (Sweden)

Braeutigam S

2012-05-01

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

Specific Intensity Direct Current (DC) Electric Field Improves Neural Stem Cell Migration and Enhances Differentiation towards βIII-Tubulin+ Neurons

Science.gov (United States)

Zhao, Huiping; Steiger, Amanda; Nohner, Mitch; Ye, Hui

2015-01-01

Control of stem cell migration and differentiation is vital for efficient stem cell therapy. Literature reporting electric field–guided migration and differentiation is emerging. However, it is unknown if a field that causes cell migration is also capable of guiding cell differentiation—and the mechanisms for these processes remain unclear. Here, we report that a 115 V/m direct current (DC) electric field can induce directional migration of neural precursor cells (NPCs). Whole cell patching revealed that the cell membrane depolarized in the electric field, and buffering of extracellular calcium via EGTA prevented cell migration under these conditions. Immunocytochemical staining indicated that the same electric intensity could also be used to enhance differentiation and increase the percentage of cell differentiation into neurons, but not astrocytes and oligodendrocytes. The results indicate that DC electric field of this specific intensity is capable of promoting cell directional migration and orchestrating functional differentiation, suggestively mediated by calcium influx during DC field exposure. PMID:26068466

Accumulation of evidence during sequential decision making: the importance of top-down factors.

Science.gov (United States)

de Lange, Floris P; Jensen, Ole; Dehaene, Stanislas

2010-01-13

In the last decade, great progress has been made in characterizing the accumulation of neural information during simple unitary perceptual decisions. However, much less is known about how sequentially presented evidence is integrated over time for successful decision making. The aim of this study was to study the mechanisms of sequential decision making in humans. In a magnetoencephalography (MEG) study, we presented healthy volunteers with sequences of centrally presented arrows. Sequence length varied between one and five arrows, and the accumulated directions of the arrows informed the subject about which hand to use for a button press at the end of the sequence (e.g., LRLRR should result in a right-hand press). Mathematical modeling suggested that nonlinear accumulation was the rational strategy for performing this task in the presence of no or little noise, whereas quasilinear accumulation was optimal in the presence of substantial noise. MEG recordings showed a correlate of evidence integration over parietal and central cortex that was inversely related to the amount of accumulated evidence (i.e., when more evidence was accumulated, neural activity for new stimuli was attenuated). This modulation of activity likely reflects a top-down influence on sensory processing, effectively constraining the influence of sensory information on the decision variable over time. The results indicate that, when making decisions on the basis of sequential information, the human nervous system integrates evidence in a nonlinear manner, using the amount of previously accumulated information to constrain the accumulation of additional evidence.

Neural adaptations to electrical stimulation strength training

NARCIS (Netherlands)

Hortobagyi, Tibor; Maffiuletti, Nicola A.

2011-01-01

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

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...

Evidence accumulation as a model for lexical selection.

Science.gov (United States)

Anders, R; Riès, S; van Maanen, L; Alario, F X

2015-11-01

We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.

Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls

Science.gov (United States)

Kallenbach, Cynthia M.; Frey, Serita D.; Grandy, A. Stuart

2016-11-01

Soil organic matter (SOM) and the carbon and nutrients therein drive fundamental submicron- to global-scale biogeochemical processes and influence carbon-climate feedbacks. Consensus is emerging that microbial materials are an important constituent of stable SOM, and new conceptual and quantitative SOM models are rapidly incorporating this view. However, direct evidence demonstrating that microbial residues account for the chemistry, stability and abundance of SOM is still lacking. Further, emerging models emphasize the stabilization of microbial-derived SOM by abiotic mechanisms, while the effects of microbial physiology on microbial residue production remain unclear. Here we provide the first direct evidence that soil microbes produce chemically diverse, stable SOM. We show that SOM accumulation is driven by distinct microbial communities more so than clay mineralogy, where microbial-derived SOM accumulation is greatest in soils with higher fungal abundances and more efficient microbial biomass production.

Neural Crossroads in the Hematopoietic Stem Cell Niche.

Science.gov (United States)

Agarwala, Sobhika; Tamplin, Owen J

2018-05-29

The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Neural Correlates Underlying Belief Reasoning for Self and for Others: Evidence from ERPs.

Science.gov (United States)

Jiang, Qin; Wang, Qi; Li, Peng; Li, Hong

2016-01-01

Belief reasoning is typical mental state reasoning in theory of mind (ToM). Although previous studies have explored the neural bases of belief reasoning, the neural correlates of belief reasoning for self and for others are rarely addressed. The decoupling mechanism of distinguishing the mental state of others from one's own is essential for ToM processing. To address the electrophysiological bases underlying the decoupling mechanism, the present event-related potential study compared the time course of neural activities associated with belief reasoning for self and for others when the belief belonging to self was consistent or inconsistent with others. Results showed that during a 450-600 ms period, belief reasoning for self elicited a larger late positive component (LPC) than for others when beliefs were inconsistent with each other. The LPC divergence is assumed to reflect the categorization of agencies in ToM processes.

Mirroring "meaningful" actions: sensorimotor learning modulates imitation of goal-directed actions.

Science.gov (United States)

Catmur, Caroline; Heyes, Cecilia

2017-06-19

Imitation is important in the development of social and technological skills throughout the lifespan. Experiments investigating the acquisition and modulation of imitation (and of its proposed neural substrate, the mirror neuron system) have produced evidence that the capacity for imitation depends on associative learning in which connections are formed between sensory and motor representations of actions. However, evidence that the development of imitation depends on associative learning has been found only for non-goal-directed actions. One reason for the lack of research on goal-directed actions is that imitation of such actions is commonly confounded with the tendency to respond in a spatially compatible manner. However, since the most prominent account of mirror neuron function, and hence of imitation, suggests that these cells encode goal-directed actions, it is important to establish whether sensorimotor learning can also modulate imitation of goal-directed actions. Experiment 1 demonstrated that imitation of goal-directed grasping can be measured while controlling for spatial compatibility, and Experiment 2 showed that this imitation effect can be modulated by sensorimotor training. Together these data support the hypothesis that the capacity for behavioural imitation, and the properties of the mirror neuron system, are constructed in the course of development through associative learning.

Prediction of properties of polymer concrete composite with tire rubber using neural networks

International Nuclear Information System (INIS)

Diaconescu, Rodica-Mariana; Barbuta, Marinela; Harja, Maria

2013-01-01

Highlights: ► Using waste a new composite material was obtained with specific characteristics. ► The objective was to maximize tire powder content with the minimum resin content. ► By direct modeling, the maximum compressive strength was obtained for 30% tire powder. ► Inverse neural modeling was used for obtaining maximum values of strengths. -- Abstract: The neural network method was used to investigate the influence of filler and resin content on the mechanical properties of polymer concrete with powdered tire waste. The mechanical strengths of 10 experimentally determined combinations using mixed epoxy resin, aggregates and tire powder as filler were optimized using direct neural modeling and inverse neural modeling, by imposing a minimum cost (content in resin). Direct neural modeling gave the optimum composition for obtaining maximum values for compressive strength, flexural strength and split tensile strength. Inverse neural modeling analyzed the possibility of obtaining maximum values of mechanical properties by variations in the dosages of the epoxy resin and tire powder. Neural network modeling generated the mixes with the lowest cost and maximum strength. The modeling method has shown that two mechanical properties can be simultaneously optimized in the investigation domain. From direct modeling, the maximum compressive strength was obtained for a composition with 0.215 (fraction weight) epoxy resin and 0.3 (fraction weight) tire powder. Maximum flexural strength was obtained for experimental values of 0.23 epoxy resin and 0.17 tire powder with a severe reduction noted for smaller resin dosages. The maximum split tensile strength was obtained for a resin dosage of 0.24 and tire powder dosage of 0.17

Estimation of wave directional spreading

Digital Repository Service at National Institute of Oceanography (India)

Deo, M.C.; Gondane, D.S.; SanilKumar, V.

One of the useful measures of waves directional spreading at a given location is the directional spreading parameter. This paper presents a new approach to arrive at its characteristic value using the computational technique of Artificial Neural...

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.

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

The neural subjective frame: from bodily signals to perceptual consciousness.

Science.gov (United States)

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

Human Episodic Memory Retrieval Is Accompanied by a Neural Contiguity Effect.

Science.gov (United States)

Folkerts, Sarah; Rutishauser, Ueli; Howard, Marc W

2018-04-25

Cognitive psychologists have long hypothesized that experiences are encoded in a temporal context that changes gradually over time. When an episodic memory is retrieved, the state of context is recovered-a jump back in time. We recorded from single units in the medial temporal lobe of epilepsy patients performing an item recognition task. The population vector changed gradually over minutes during presentation of the list. When a probe from the list was remembered with high confidence, the population vector reinstated the temporal context of the original presentation of that probe during study, a neural contiguity effect that provides a possible mechanism for behavioral contiguity effects. This pattern was only observed for well remembered probes; old probes that were not well remembered showed an anti-contiguity effect. These results constitute the first direct evidence that recovery of an episodic memory in humans is associated with retrieval of a gradually changing state of temporal context, a neural "jump back in time" that parallels the act of remembering. SIGNIFICANCE STATEMENT Episodic memory is the ability to relive a specific experience from one's life. For decades, researchers have hypothesized that, unlike other forms of memory that can be described as simple associations between stimuli, episodic memory depends on the recovery of a neural representation of spatiotemporal context. During study of a sequence of stimuli, the brain state of epilepsy patients changed slowly over at least a minute. When the participant remembered a particular event from the list, this gradually changing state was recovered. This provides direct confirmation of the prediction from computational models of episodic memory. The resolution of this point means that the study of episodic memory can focus on the mechanisms by which this representation of spatiotemporal context is maintained and sometimes recovered. Copyright © 2018 the authors 0270-6474/18/384200-12$15.00/0.

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

DEFF Research Database (Denmark)

Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

2014-01-01

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

Evaluating the evidence: direct-to-consumer screening tests advertised online.

Science.gov (United States)

Lovett, Kimberly M; Mackey, Timothy K; Liang, Bryan A

2012-09-01

Unsupervised online direct-to-consumer (DTC) access to medical services has rapidly expanded to medical screening tests, which have not been critically evaluated for their evidence basis. The objective of this study is to identify the scope of online-advertised DTC screening tests, outline the evidence for use of available DTC testing and suggest regulatory reform to address the relevant issues. An observational study of website advertisements, testing services and counselling/follow-up services for DTC testing was conducted. Data were collected from websites between 4 April and 1 June 2011. Each website was assessed for tests offered, advertised indications and availability of counselling/follow-up services. Advertised testing indications were compared with US Preventive Services Task Force recommendations and/or specialty guidelines and categorized as Supported, Against, Insufficient Evidence or No Guidance. Of 20 companies identified as offering DTC screening tests, 95% (19/20) do not clearly offer pretest counselling, post-test counselling and/or test follow-up. One hundred and twenty-seven different tests were identified. Only 19/127 (15%) could be Supported for screening in a target group selected for testing; 38/127 (30%) were given recommendations to avoid use in specific target group(s) selected for testing ('Against recommendations'); 29/127 (23%) had Insufficient Evidence of value, and for 64/127 (50%) No Guidance could be given. Only 4/127 (3%) tests were Supported for general screening use. Virtually all identified medical tests advertised and offered DTC are not recommended for use in screening by evidence-based guidelines. Limited oversight may lead to inaccurate self-diagnosis, treatment and wasted health resources.

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

Representation of neutron noise data using neural networks

International Nuclear Information System (INIS)

Korsah, K.; Damiano, B.; Wood, R.T.

1992-01-01

This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems

A common neural substrate for perceiving and knowing about color

Science.gov (United States)

Simmons, W. Kyle; Ramjee, Vimal; Beauchamp, Michael S.; McRae, Ken; Martin, Alex; Barsalou, Lawrence W.

2013-01-01

Functional neuroimaging research has demonstrated that retrieving information about object-associated colors activates the left fusiform gyrus in posterior temporal cortex. Although regions near the fusiform have previously been implicated in color perception, it remains unclear whether color knowledge retrieval actually activates the color perception system. Evidence to this effect would be particularly strong if color perception cortex was activated by color knowledge retrieval triggered strictly with linguistic stimuli. To address this question, subjects performed two tasks while undergoing fMRI. First, subjects performed a property verification task using only words to assess conceptual knowledge. On each trial, subjects verified whether a named color or motor property was true of a named object (e.g., TAXI-yellow, HAIR-combed). Next, subjects performed a color perception task. A region of the left fusiform gyrus that was highly responsive during color perception also showed greater activity for retrieving color than motor property knowledge. These data provide the first evidence for a direct overlap in the neural bases of color perception and stored information about object-associated color, and they significantly add to accumulating evidence that conceptual knowledge is grounded in the brain’s modality-specific systems. PMID:17575989

Direct and indirect evidence for earthquakes; an example from the Lake Tahoe Basin, California-Nevada

Science.gov (United States)

Maloney, J. M.; Noble, P. J.; Driscoll, N. W.; Kent, G.; Schmauder, G. C.

2012-12-01

High-resolution seismic CHIRP data can image direct evidence of earthquakes (i.e., offset strata) beneath lakes and the ocean. Nevertheless, direct evidence often is not imaged due to conditions such as gas in the sediments, or steep basement topography. In these cases, indirect evidence for earthquakes (i.e., debris flows) may provide insight into the paleoseismic record. The four sub-basins of the tectonically active Lake Tahoe Basin provide an ideal opportunity to image direct evidence for earthquake deformation and compare it to indirect earthquake proxies. We present results from high-resolution seismic CHIRP surveys in Emerald Bay, Fallen Leaf Lake, and Cascade Lake to constrain the recurrence interval on the West Tahoe Dollar Point Fault (WTDPF), which was previously identified as potentially the most hazardous fault in the Lake Tahoe Basin. Recently collected CHIRP profiles beneath Fallen Leaf Lake image slide deposits that appear synchronous with slides in other sub-basins. The temporal correlation of slides between multiple basins suggests triggering by events on the WTDPF. If correct, we postulate a recurrence interval for the WTDPF of ~3-4 k.y., indicating that the WTDPF is near its seismic recurrence cycle. In addition, CHIRP data beneath Cascade Lake image strands of the WTDPF that offset the lakefloor as much as ~7 m. The Cascade Lake data combined with onshore LiDAR allowed us to map the geometry of the WTDPF continuously across the southern Lake Tahoe Basin and yielded an improved geohazard assessment.

Feedforward Nonlinear Control Using Neural Gas Network

OpenAIRE

Machón-González, Iván; López-García, Hilario

2017-01-01

Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the ...

Differentiating neural systems mediating the acquisition versus expression of goal-directed and habitual behavioral control

Science.gov (United States)

Liljeholm, Mimi; Dunne, Simon; O'Doherty, John P.

2015-01-01

Considerable behavioral data indicates that operant actions can become habitual, as evidenced by insensitivity to changes in the action-outcome contingency and in subjective outcome values. Notably, although several studies have investigated the neural substrates of habits, none has clearly differentiated the areas of the human brain that support habit formation from those that implement habitual control. We scanned participants with fMRI as they learned and performed an operant task in which the conditional structure of the environment encouraged either goal-directed encoding of the consequences of actions, or a habit-like mapping of actions to antecedent cues. Participants were also scanned during a subsequent assessment of insensitivity to outcome devaluation. We identified dissociable roles of the cerebellum and ventral striatum, across learning and test performance, in behavioral insensitivity to outcome devaluation. We also show that the inferior parietal lobule – an area previously implicated in several aspects of goal-directed action selection, including the attribution of intent and awareness of agency – predicts sensitivity to outcome devaluation. Finally, we reveal a potential functional homology between the human subgenual cortex and rodent infralimbic cortex in the implementation of habitual control. In summary, our findings suggest a broad systems division, at the cortical and subcortical levels, between brain areas mediating the encoding and expression of action-outcome and stimulus-response associations. PMID:25892332

Working memory in schizophrenia: behavioral and neural evidence for reduced susceptibility to item-specific proactive interference.

Science.gov (United States)

Kaller, Christoph P; Loosli, Sandra V; Rahm, Benjamin; Gössel, Astrid; Schieting, Stephan; Hornig, Tobias; Hennig, Jürgen; Tebartz van Elst, Ludger; Weiller, Cornelius; Katzev, Michael

2014-09-15

Susceptibility to item-specific proactive interference (PI) contributes to interindividual differences in working memory (WM) capacity and complex cognition relying on WM. Although WM deficits are a well-recognized impairment in schizophrenia, the underlying pathophysiological effects on specific WM control functions, such as the ability to resist item-specific PI, remain unknown. Moreover, opposing hypotheses on increased versus reduced PI susceptibility in schizophrenia are both justifiable by the extant literature. To provide first insights into the behavioral and neural correlates of PI-related WM control in schizophrenia, a functional magnetic resonance imaging experiment was conducted in a sample of 20 patients and 20 well-matched control subjects. Demands on item-specific PI were experimentally manipulated in a recent-probes task (three runs, 64 trials each) requiring subjects to encode and maintain a set of four target items per trial. Compared with healthy control subjects, schizophrenia patients showed a significantly reduced PI susceptibility in both accuracy and latency measures. Notably, reduced PI susceptibility in schizophrenia was not associated with overall WM impairments and thus constituted an independent phenomenon. In addition, PI-related activations in inferior frontal gyrus and anterior insula, typically assumed to support PI resistance, were reduced in schizophrenia, thus ruling out increased neural efforts as a potential cause of the patients' reduced PI susceptibility. The present study provides first evidence for a diminished vulnerability of schizophrenia patients to item-specific PI, which is presumably a consequence of the patients' more efficient clearing of previously relevant WM traces and the accordingly reduced likelihood for item-specific PI to occur. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

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

Artificial neural networks for stiffness estimation in magnetic resonance elastography.

Science.gov (United States)

Murphy, Matthew C; Manduca, Armando; Trzasko, Joshua D; Glaser, Kevin J; Huston, John; Ehman, Richard L

2018-07-01

To investigate the feasibility of using artificial neural networks to estimate stiffness from MR elastography (MRE) data. Artificial neural networks were fit using model-based training patterns to estimate stiffness from images of displacement using a patch size of ∼1 cm in each dimension. These neural network inversions (NNIs) were then evaluated in a set of simulation experiments designed to investigate the effects of wave interference and noise on NNI accuracy. NNI was also tested in vivo, comparing NNI results against currently used methods. In 4 simulation experiments, NNI performed as well or better than direct inversion (DI) for predicting the known stiffness of the data. Summary NNI results were also shown to be significantly correlated with DI results in the liver (R 2  = 0.974) and in the brain (R 2  = 0.915), and also correlated with established biological effects including fibrosis stage in the liver and age in the brain. Finally, repeatability error was lower in the brain using NNI compared to DI, and voxel-wise modeling using NNI stiffness maps detected larger effects than using DI maps with similar levels of smoothing. Artificial neural networks represent a new approach to inversion of MRE data. Summary results from NNI and DI are highly correlated and both are capable of detecting biologically relevant signals. Preliminary evidence suggests that NNI stiffness estimates may be more resistant to noise than an algebraic DI approach. Taken together, these results merit future investigation into NNIs to improve the estimation of stiffness in small regions. Magn Reson Med 80:351-360, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Measures of Coupling between Neural Populations Based on Granger Causality Principle.

Science.gov (United States)

Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

2016-01-01

This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

A fully implantable rodent neural stimulator

Science.gov (United States)

Perry, D. W. J.; Grayden, D. B.; Shepherd, R. K.; Fallon, J. B.

2012-02-01

The ability to electrically stimulate neural and other excitable tissues in behaving experimental animals is invaluable for both the development of neural prostheses and basic neurological research. We developed a fully implantable neural stimulator that is able to deliver two channels of intra-cochlear electrical stimulation in the rat. It is powered via a novel omni-directional inductive link and includes an on-board microcontroller with integrated radio link, programmable current sources and switching circuitry to generate charge-balanced biphasic stimulation. We tested the implant in vivo and were able to elicit both neural and behavioural responses. The implants continued to function for up to five months in vivo. While targeted to cochlear stimulation, with appropriate electrode arrays the stimulator is well suited to stimulating other neurons within the peripheral or central nervous systems. Moreover, it includes significant on-board data acquisition and processing capabilities, which could potentially make it a useful platform for telemetry applications, where there is a need to chronically monitor physiological variables in unrestrained animals.

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

Neural mechanisms of oculomotor abnormalities in the infantile strabismus syndrome.

Science.gov (United States)

Walton, Mark M G; Pallus, Adam; Fleuriet, Jérome; Mustari, Michael J; Tarczy-Hornoch, Kristina

2017-07-01

Infantile strabismus is characterized by numerous visual and oculomotor abnormalities. Recently nonhuman primate models of infantile strabismus have been established, with characteristics that closely match those observed in human patients. This has made it possible to study the neural basis for visual and oculomotor symptoms in infantile strabismus. In this review, we consider the available evidence for neural abnormalities in structures related to oculomotor pathways ranging from visual cortex to oculomotor nuclei. These studies provide compelling evidence that a disturbance of binocular vision during a sensitive period early in life, whatever the cause, results in a cascade of abnormalities through numerous brain areas involved in visual functions and eye movements. Copyright © 2017 the American Physiological Society.

Direct Conversion of Human Umbilical Cord Blood into Induced Neural Stem Cells with SOX2 and HMGA2.

Science.gov (United States)

Kim, Jae-Jun; Shin, Ji-Hee; Yu, Kyung-Rok; Lee, Byung-Chul; Kang, Insung; Lee, Jin Young; Kim, Da-Hyun; Seo, Yoojin; Kim, Hyung-Sik; Choi, Soon Won; Kang, Kyung-Sun

2017-11-30

Recent advances have shown the direct reprogramming of mouse and human fibroblasts into induced neural stem cells (iNSCs) without passing through an intermediate pluripotent state. Thus, direct reprogramming strategy possibly provides a safe and homogeneous cellular platform. However, the applications of iNSCs for regenerative medicine are limited by the restricted availability of cell sources. Human umbilical cord blood (hUCB) cells hold great potential in that immunotyped hUCB units can be immediately obtained from public banks. Moreover, hUCB samples do not require invasive procedures during collection or an extensive culture period prior to reprogramming. We recently reported that somatic cells can be directly converted into iNSCs with high efficiency and a short turnaround time. Here, we describe the detailed method for the generation of iNSCs derived from hUCB (hUCB iNSCs) using the lineage-specific transcription factors SOX2 and HMGA2. The protocol for deriving iNSC-like colonies takes 1∼2 weeks and establishment of homogenous hUCB iNSCs takes additional 2 weeks. Established hUCB iNSCs are clonally expandable and multipotent producing neurons and glia. Our study provides an accessible method for generating hUCB iNSCs, contributing development of in vitro neuropathological model systems.

Efficiency turns the table on neural encoding, decoding and noise.

Science.gov (United States)

Deneve, Sophie; Chalk, Matthew

2016-04-01

Sensory neurons are usually described with an encoding model, for example, a function that predicts their response from the sensory stimulus using a receptive field (RF) or a tuning curve. However, central to theories of sensory processing is the notion of 'efficient coding'. We argue here that efficient coding implies a completely different neural coding strategy. Instead of a fixed encoding model, neural populations would be described by a fixed decoding model (i.e. a model reconstructing the stimulus from the neural responses). Because the population solves a global optimization problem, individual neurons are variable, but not noisy, and have no truly invariant tuning curve or receptive field. We review recent experimental evidence and implications for neural noise correlations, robustness and adaptation. Copyright © 2016. Published by Elsevier Ltd.

Direct geoelectrical evidence of mass transfer at the laboratory scale

Science.gov (United States)

Swanson, Ryan D.; Singha, Kamini; Day-Lewis, Frederick D.; Binley, Andrew; Keating, Kristina; Haggerty, Roy

2012-10-01

Previous field-scale experimental data and numerical modeling suggest that the dual-domain mass transfer (DDMT) of electrolytic tracers has an observable geoelectrical signature. Here we present controlled laboratory experiments confirming the electrical signature of DDMT and demonstrate the use of time-lapse electrical measurements in conjunction with concentration measurements to estimate the parameters controlling DDMT, i.e., the mobile and immobile porosity and rate at which solute exchanges between mobile and immobile domains. We conducted column tracer tests on unconsolidated quartz sand and a material with a high secondary porosity: the zeolite clinoptilolite. During NaCl tracer tests we collected nearly colocated bulk direct-current electrical conductivity (σb) and fluid conductivity (σf) measurements. Our results for the zeolite show (1) extensive tailing and (2) a hysteretic relation between σf and σb, thus providing evidence of mass transfer not observed within the quartz sand. To identify best-fit parameters and evaluate parameter sensitivity, we performed over 2700 simulations of σf, varying the immobile and mobile domain and mass transfer rate. We emphasized the fit to late-time tailing by minimizing the Box-Cox power transformed root-mean square error between the observed and simulated σf. Low-field proton nuclear magnetic resonance (NMR) measurements provide an independent quantification of the volumes of the mobile and immobile domains. The best-fit parameters based on σf match the NMR measurements of the immobile and mobile domain porosities and provide the first direct electrical evidence for DDMT. Our results underscore the potential of using electrical measurements for DDMT parameter inference.

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.

Morphogenetic movements during cranial neural tube closure in the chick embryo and the effect of homocysteine

OpenAIRE

Brouns, M.R.; Afman, L.A.; VanHauten, B.A.M.; Hekking, J.W.M.; Kohler, E.S.; Straaten, van, H.W.M.

2005-01-01

In order to unravel morphogenetic mechanisms involved in neural tube closure, critical cell movements that are fundamental to remodelling of the cranial neural tube in the chick embryo were studied in vitro by quantitative time-lapse video microscopy. Two main directions of movements were observed. The earliest was directed medially; these cells invaginated into a median groove and were the main contributors to the initial neural tube closure. Once the median groove was completed, cells chang...

Recent Advances in Neural Recording Microsystems

Directory of Open Access Journals (Sweden)

Benoit Gosselin

2011-04-01

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

Music enrichment programs improve the neural encoding of speech in at-risk children.

Science.gov (United States)

Kraus, Nina; Slater, Jessica; Thompson, Elaine C; Hornickel, Jane; Strait, Dana L; Nicol, Trent; White-Schwoch, Travis

2014-09-03

Musicians are often reported to have enhanced neurophysiological functions, especially in the auditory system. Musical training is thought to improve nervous system function by focusing attention on meaningful acoustic cues, and these improvements in auditory processing cascade to language and cognitive skills. Correlational studies have reported musician enhancements in a variety of populations across the life span. In light of these reports, educators are considering the potential for co-curricular music programs to provide auditory-cognitive enrichment to children during critical developmental years. To date, however, no studies have evaluated biological changes following participation in existing, successful music education programs. We used a randomized control design to investigate whether community music participation induces a tangible change in auditory processing. The community music training was a longstanding and successful program that provides free music instruction to children from underserved backgrounds who stand at high risk for learning and social problems. Children who completed 2 years of music training had a stronger neurophysiological distinction of stop consonants, a neural mechanism linked to reading and language skills. One year of training was insufficient to elicit changes in nervous system function; beyond 1 year, however, greater amounts of instrumental music training were associated with larger gains in neural processing. We therefore provide the first direct evidence that community music programs enhance the neural processing of speech in at-risk children, suggesting that active and repeated engagement with sound changes neural function. Copyright © 2014 the authors 0270-6474/14/3411913-06$15.00/0.

No Evidence That Gratitude Enhances Neural Performance Monitoring or Conflict-Driven Control.

Science.gov (United States)

Saunders, Blair; He, Frank F H; Inzlicht, Michael

2015-01-01

It has recently been suggested that gratitude can benefit self-regulation by reducing impulsivity during economic decision making. We tested if comparable benefits of gratitude are observed for neural performance monitoring and conflict-driven self-control. In a pre-post design, 61 participants were randomly assigned to either a gratitude or happiness condition, and then performed a pre-induction flanker task. Subsequently, participants recalled an autobiographical event where they had felt grateful or happy, followed by a post-induction flanker task. Despite closely following existing protocols, participants in the gratitude condition did not report elevated gratefulness compared to the happy group. In regard to self-control, we found no association between gratitude--operationalized by experimental condition or as a continuous predictor--and any control metric, including flanker interference, post-error adjustments, or neural monitoring (the error-related negativity, ERN). Thus, while gratitude might increase economic patience, such benefits may not generalize to conflict-driven control processes.

Social power and approach-related neural activity.

Science.gov (United States)

Boksem, Maarten A S; Smolders, Ruud; De Cremer, David

2012-06-01

It has been argued that power activates a general tendency to approach whereas powerlessness activates a tendency to inhibit. The assumption is that elevated power involves reward-rich environments, freedom and, as a consequence, triggers an approach-related motivational orientation and attention to rewards. In contrast, reduced power is associated with increased threat, punishment and social constraint and thereby activates inhibition-related motivation. Moreover, approach motivation has been found to be associated with increased relative left-sided frontal brain activity, while withdrawal motivation has been associated with increased right sided activations. We measured EEG activity while subjects engaged in a task priming either high or low social power. Results show that high social power is indeed associated with greater left-frontal brain activity compared to low social power, providing the first neural evidence for the theory that high power is associated with approach-related motivation. We propose a framework accounting for differences in both approach motivation and goal-directed behaviour associated with different levels of power.

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.

A cell junction pathology of neural stem cells leads to abnormal neurogenesis and hydrocephalus

NARCIS (Netherlands)

Rodríguez, Esteban M; Guerra, María M; Vío, Karin; González, César; Ortloff, Alexander; Bátiz, Luis F; Rodríguez, Sara; Jara, María C; Muñoz, Rosa I; Ortega, Eduardo; Jaque, Jaime; Guerra, Francisco; Sival, Deborah A; den Dunnen, Wilfred F A; Jiménez, Antonio J; Domínguez-Pinos, María D; Pérez-Fígares, José M; McAllister, James P; Johanson, Conrad

2012-01-01

Most cells of the developing mammalian brain derive from the ventricular (VZ) and the subventricular (SVZ) zones. The VZ is formed by the multipotent radial glia/neural stem cells (NSCs) while the SVZ harbors the rapidly proliferative neural precursor cells (NPCs). Evidence from human and animal

Molecular Ultrasound Imaging for the Detection of Neural Inflammation

Science.gov (United States)

Volz, Kevin R.

Molecular imaging is a form of nanotechnology that enables the noninvasive examination of biological processes in vivo. Radiopharmaceutical agents are used to selectively target biochemical markers, which permits their detection and evaluation. Early visualization of molecular variations indicative of pathophysiological processes can aid in patient diagnoses and management decisions. Molecular imaging is performed by introducing molecular probes into the body. Molecular probes are often contrast agents that have been nanoengineered to selectively target and tether to molecules, enabling their radiologic identification. Ultrasound contrast agents have been demonstrated as an effective method of detecting perfusion at the tissue level. Through a nanoengineering process, ultrasound contrast agents can be targeted to specific molecules, thereby extending ultrasound's capabilities from the tissue to molecular level. Molecular ultrasound, or targeted contrast enhanced ultrasound (TCEUS), has recently emerged as a popular molecular imaging technique due to its ability to provide real-time anatomical and functional information in the absence of ionizing radiation. However, molecular ultrasound represents a novel form of molecular imaging, and consequently remains largely preclinical. A review of the TCEUS literature revealed multiple preclinical studies demonstrating its success in detecting inflammation in a variety of tissues. Although, a gap was identified in the existing evidence, as TCEUS effectiveness for detection of neural inflammation in the spinal cord was unable to be uncovered. This gap in knowledge, coupled with the profound impacts that this TCEUS application could have clinically, provided rationale for its exploration, and use as contributory evidence for the molecular ultrasound body of literature. An animal model that underwent a contusive spinal cord injury was used to establish preclinical evidence of TCEUS to detect neural inflammation. Imaging was

Neural controller for adaptive movements with unforeseen payloads.

Science.gov (United States)

Kuperstein, M; Wang, J

1990-01-01

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

Neural patterning of human induced pluripotent stem cells in 3-D cultures for studying biomolecule-directed differential cellular responses.

Science.gov (United States)

Yan, Yuanwei; Bejoy, Julie; Xia, Junfei; Guan, Jingjiao; Zhou, Yi; Li, Yan

2016-09-15

Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells/tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capacity of signaling factors that regulate 3-D neural tissue patterning in vitro and differential responses of the resulting neural populations to various biomolecules have not yet been fully understood. By tuning neural patterning of hiPSCs with small molecules targeting sonic hedgehog (SHH) signaling, this study generated different 3-D neuronal cultures that were mainly comprised of either cortical glutamatergic neurons or motor neurons. Abundant glutamatergic neurons were observed following the treatment with an antagonist of SHH signaling, cyclopamine, while Islet-1 and HB9-expressing motor neurons were enriched by an SHH agonist, purmorphamine. In neurons derived with different neural patterning factors, whole-cell patch clamp recordings showed similar voltage-gated Na(+)/K(+) currents, depolarization-evoked action potentials and spontaneous excitatory post-synaptic currents. Moreover, these different neuronal populations exhibited differential responses to three classes of biomolecules, including (1) matrix metalloproteinase inhibitors that affect extracellular matrix remodeling; (2) N-methyl-d-aspartate that induces general neurotoxicity; and (3) amyloid β (1-42) oligomers that cause neuronal subtype-specific neurotoxicity. This study should advance our understanding of hiPSC self-organization and neural tissue development and provide a transformative approach to establish 3-D models for neurological disease modeling and drug discovery. Appropriate neural patterning of human induced pluripotent stem cells (hiPSCs) is critical to generate specific neural cells, tissues and even mini-brains that are physiologically relevant to model neurological diseases. However, the capability of sonic hedgehog-related small molecules to tune

Neural evidence for reduced apprehensiveness of familiarized stimuli in a mere exposure paradigm.

Science.gov (United States)

Zebrowitz, Leslie A; Zhang, Yi

2012-07-01

Mere familiarization with a stimulus increases liking for it or similar stimuli ("mere exposure" effects) as well as perceptual fluency, indexed by the speed and accuracy of categorizing it or similar stimuli ("priming" effects). Candidate mechanisms proposed to explain mere exposure effects include both increased positive affect associated with greater perceptual fluency, and reduced negative affect associated with diminished apprehensiveness of novel stimuli. Although these two mechanisms are not mutually exclusive, it is difficult for behavioral measures to disentangle them, since increased liking or other indices of greater positive affect toward exposed stimuli could result from increases in positive feelings or decreases in negative feelings or both. The present study sought to clarify this issue by building on research showing a dissociation at the neural level in which the lateral orbitofrontal cortex (LOFC) is activated more by negatively valenced than by neutral or positively valenced stimuli, with the reverse effect for medial orbitofrontal cortex (MOFC). Supporting the reduced apprehensiveness hypothesis, we found lower LOFC activation to familiarized faces and objects (repetition suppression). We did not find evidence to support the positive affect hypothesis in increased activation to familiarized stimuli in MOFC or in other parts of the reward circuit that respond more to positively valenced stimuli (repetition enhancement), although enhancement effects were shown in some regions.

Unjoined primary and secondary neural tubes: junctional neural tube defect, a new form of spinal dysraphism caused by disturbance of junctional neurulation.

Science.gov (United States)

Eibach, Sebastian; Moes, Greg; Hou, Yong Jin; Zovickian, John; Pang, Dachling

2017-10-01

Primary and secondary neurulation are the two known processes that form the central neuraxis of vertebrates. Human phenotypes of neural tube defects (NTDs) mostly fall into two corresponding categories consistent with the two types of developmental sequence: primary NTD features an open skin defect, an exposed, unclosed neural plate (hence an open neural tube defect, or ONTD), and an unformed or poorly formed secondary neural tube, and secondary NTD with no skin abnormality (hence a closed NTD) and a malformed conus caudal to a well-developed primary neural tube. We encountered three cases of a previously unrecorded form of spinal dysraphism in which the primary and secondary neural tubes are individually formed but are physically separated far apart and functionally disconnected from each other. One patient was operated on, in whom both the lumbosacral spinal cord from primary neurulation and the conus from secondary neurulation are each anatomically complete and endowed with functioning segmental motor roots tested by intraoperative triggered electromyography and direct spinal cord stimulation. The remarkable feature is that the two neural tubes are unjoined except by a functionally inert, probably non-neural band. The developmental error of this peculiar malformation probably occurs during the critical transition between the end of primary and the beginning of secondary neurulation, in a stage aptly called junctional neurulation. We describe the current knowledge concerning junctional neurulation and speculate on the embryogenesis of this new class of spinal dysraphism, which we call junctional neural tube defect.

Neural correlates of mirth and laughter: a direct electrical cortical stimulation study.

Science.gov (United States)

Yamao, Yukihiro; Matsumoto, Riki; Kunieda, Takeharu; Shibata, Sumiya; Shimotake, Akihiro; Kikuchi, Takayuki; Satow, Takeshi; Mikuni, Nobuhiro; Fukuyama, Hidenao; Ikeda, Akio; Miyamoto, Susumu

2015-05-01

Laughter consists of both motor and emotional aspects. The emotional component, known as mirth, is usually associated with the motor component, namely, bilateral facial movements. Previous electrical cortical stimulation (ES) studies revealed that mirth was associated with the basal temporal cortex, inferior frontal cortex, and medial frontal cortex. Functional neuroimaging implicated a role for the left inferior frontal and bilateral temporal cortices in humor processing. However, the neural origins and pathways linking mirth with facial movements are still unclear. We hereby report two cases with temporal lobe epilepsy undergoing subdural electrode implantation in whom ES of the left basal temporal cortex elicited both mirth and laughter-related facial muscle movements. In one case with normal hippocampus, high-frequency ES consistently caused contralateral facial movement, followed by bilateral facial movements with mirth. In contrast, in another case with hippocampal sclerosis (HS), ES elicited only mirth at low intensity and short duration, and eventually laughter at higher intensity and longer duration. In both cases, the basal temporal language area (BTLA) was located within or adjacent to the cortex where ES produced mirth. In conclusion, the present direct ES study demonstrated that 1) mirth had a close relationship with language function, 2) intact mesial temporal structures were actively engaged in the beginning of facial movements associated with mirth, and 3) these emotion-related facial movements had contralateral dominance. Copyright © 2014 Elsevier Ltd. All rights reserved.

cables1 Is Required for Embryonic Neural Development: Molecular, Cellular, and Behavioral Evidence From the Zebrafish

Science.gov (United States)

GROENEWEG, JOLIJN W.; WHITE, YVONNE A.R.; KOKEL, DAVID; PETERSON, RANDALL T.; ZUKERBERG, LAWRENCE R.; BERIN, INNA; RUEDA, BO R.; WOOD, ANTONY W.

2014-01-01

SUMMARY In vitro studies have suggested that the Cables1 gene regulates epithelial cell proliferation, whereas other studies suggest a role in promoting neural differentiation. In efforts to clarify the functions of Cables1 in vivo, we conducted gain- and loss-of-function studies targeting its ortholog (cables1) in the zebrafish embryo. Similar to rodents, zebrafish cables1 mRNA expression is detected most robustly in embryonic neural tissues. Antisense knockdown of cables1 leads to increased numbers of apoptotic cells, particularly in brain tissue, in addition to a distinct behavioral phenotype, characterized by hyperactivity in response to stimulation. Apoptosis and the behavioral abnormality could be rescued by co-expression of a morpholino-resistant cables1 construct. Suppression of p53 expression in cables1 morphants partially rescued both apoptosis and the behavioral phenotype, suggesting that the phenotype of cables1 morphants is due in part to p53-dependent apoptosis. Alterations in the expression patterns of several neural transcription factors were observed in cables1 morphants during early neurulation, suggesting that cables1 is required for early neural differentiation. Ectopic overexpression of cables1 strongly disrupted embryonic morphogenesis, while overexpression of a cables1 mutant lacking the C-terminal cyclin box had little effect, suggesting functional importance of the cyclin box. Lastly, marked reductions in p35, but not Cdk5, were observed in cables1 morphants. Collectively, these data suggest that cables1 is important for neural differentiation during embryogenesis, in a mechanism that likely involves interactions with the Cdk5/p35 kinase pathway. PMID:21268180

Resolution enhancement in neural networks with dynamical synapses

Directory of Open Access Journals (Sweden)

C. C. Alan Fung

2013-06-01

Full Text Available Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

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.

Directed differentiation of porcine epiblast-derived neural progenitor cells into neurons and glia

DEFF Research Database (Denmark)

Rasmussen, Mikkel Aabech; Hall, Vanessa Jane; Carter, T.F.

2011-01-01

Neural progenitor cells (NPCs) are promising candidates for cell-based therapy of neurodegenerative diseases; however, safety concerns must be addressed through transplantation studies in large animal models, such as the pig. The aim of this study was to derive NPCs from porcine blastocysts...

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.

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.

Measures of coupling between neural populations based on Granger causality principle

Directory of Open Access Journals (Sweden)

Maciej Kaminski

2016-10-01

Full Text Available This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a weak node. Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

Neural Alterations in Acquired Age-Related Hearing Loss

Directory of Open Access Journals (Sweden)

Raksha Anand Mudar

2016-06-01

Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.

Connexin 43-mediated modulation of polarized cell movement and the directional migration of cardiac neural crest cells.

Science.gov (United States)

Xu, Xin; Francis, Richard; Wei, Chih Jen; Linask, Kaari L; Lo, Cecilia W

2006-09-01

Connexin 43 knockout (Cx43alpha1KO) mice have conotruncal heart defects that are associated with a reduction in the abundance of cardiac neural crest cells (CNCs) targeted to the heart. In this study, we show CNCs can respond to changing fibronectin matrix density by adjusting their migratory behavior, with directionality increasing and speed decreasing with increasing fibronectin density. However, compared with wild-type CNCs, Cx43alpha1KO CNCs show reduced directionality and speed, while CNCs overexpressing Cx43alpha1 from the CMV43 transgenic mice show increased directionality and speed. Altered integrin signaling was indicated by changes in the distribution of vinculin containing focal contacts, and altered temporal response of Cx43alpha1KO and CMV43 CNCs to beta1 integrin function blocking antibody treatment. High resolution motion analysis showed Cx43alpha1KO CNCs have increased cell protrusive activity accompanied by the loss of polarized cell movement. They exhibited an unusual polygonal arrangement of actin stress fibers that indicated a profound change in cytoskeletal organization. Semaphorin 3A, a chemorepellent known to inhibit integrin activation, was found to inhibit CNC motility, but in the Cx43alpha1KO and CMV43 CNCs, cell processes failed to retract with semaphorin 3A treatment. Immunohistochemical and biochemical analyses suggested close interactions between Cx43alpha1, vinculin and other actin-binding proteins. However, dye coupling analysis showed no correlation between gap junction communication level and fibronectin plating density. Overall, these findings indicate Cx43alpha1 may have a novel function in mediating crosstalk with cell signaling pathways that regulate polarized cell movement essential for the directional migration of CNCs.

Livermore Big Artificial Neural Network Toolkit

Energy Technology Data Exchange (ETDEWEB)

2016-07-01

LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

EEG source reconstruction evidence for the noun-verb neural dissociation along semantic dimensions.

Science.gov (United States)

Zhao, Bin; Dang, Jianwu; Zhang, Gaoyan

2017-09-17

One of the long-standing issues in neurolinguistic research is about the neural basis of word representation, concerning whether grammatical classification or semantic difference causes the neural dissociation of brain activity patterns when processing different word categories, especially nouns and verbs. To disentangle this puzzle, four orthogonalized word categories in Chinese: unambiguous nouns (UN), unambiguous verbs (UV), ambiguous words with noun-biased semantics (AN), and ambiguous words with verb-biased semantics (AV) were adopted in an auditory task for recording electroencephalographic (EEG) signals from 128 electrodes on the scalps of twenty-two subjects. With the advanced current density reconstruction (CDR) algorithm and the constraint of standardized low-resolution electromagnetic tomography, the spatiotemporal brain dynamics of word processing were explored with the results that in multiple time periods including P1 (60-90ms), N1 (100-140ms), P200 (150-250ms) and N400 (350-450ms), noun-verb dissociation over the parietal-occipital and frontal-central cortices appeared not only between the UN-UV grammatical classes but also between the grammatically identical but semantically different AN-AV pairs. The apparent semantic dissociation within one grammatical class strongly suggests that the semantic difference rather than grammatical classification could be interpreted as the origin of the noun-verb neural dissociation. Our results also revealed that semantic dissociation occurs from an early stage and repeats in multiple phases, thus supporting a functionally hierarchical word processing mechanism. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

The Athlete’s Brain: Cross-Sectional Evidence for Neural Efficiency during Cycling Exercise

Directory of Open Access Journals (Sweden)

Sebastian Ludyga

2016-01-01

Full Text Available The “neural efficiency” hypothesis suggests that experts are characterized by a more efficient cortical function in cognitive tests. Although this hypothesis has been extended to a variety of movement-related tasks within the last years, it is unclear whether or not neural efficiency is present in cyclists performing endurance exercise. Therefore, this study examined brain cortical activity at rest and during exercise between cyclists of higher (HIGH; n=14; 55.6 ± 2.8 mL/min/kg and lower (LOW; n=15; 46.4 ± 4.1 mL/min/kg maximal oxygen consumption (VO2MAX. Male and female participants performed a graded exercise test with spirometry to assess VO2MAX. After 3 to 5 days, EEG was recorded at rest with eyes closed and during cycling at the individual anaerobic threshold over a 30 min period. Possible differences in alpha/beta ratio as well as alpha and beta power were investigated at frontal, central, and parietal sites. The statistical analysis revealed significant differences between groups (F=12.04; p=0.002, as the alpha/beta ratio was increased in HIGH compared to LOW in both the resting state (p≤0.018 and the exercise condition (p≤0.025. The present results indicate enhanced neural efficiency in subjects with high VO2MAX, possibly due to the inhibition of task-irrelevant cognitive processes.

Novel perspectives of neural stem cell differentiation: from neurotransmitters to therapeutics.

Science.gov (United States)

Trujillo, Cleber A; Schwindt, Telma T; Martins, Antonio H; Alves, Janaína M; Mello, Luiz Eugênio; Ulrich, Henning

2009-01-01

In the past years, many reports have described the existence of neural progenitor and stem cells in the adult central nervous system capable of generating new neurons, astrocytes, and oligodendrocytes. This discovery has overturned the central assumption in the neuroscience field, of no new neurons being originated in the brain after birth and provided the fundaments to understand the molecular basis of neural differentiation and to develop new therapies for neural tissue repair. Although the mechanisms underlying cell fate during neural development are not yet understood, the importance of intrinsic and extrinsic factors and of an appropriate microenvironment is well known. In this context, emerging evidence strongly suggests that glial cells play a key role in controlling multiple steps of neurogenesis. Those cells, of particular radial glia, are important for migration, cell specification, and integration of neurons into a functional neural network. This review aims to present an update in the neurogenesis area and highlight the modulation of neural stem cell differentiation by neurotransmitters, growth factors, and their receptors, with possible applications for cell therapy strategies of neurological disorders.

Deformable image registration using convolutional neural networks

NARCIS (Netherlands)

Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.

2018-01-01

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between

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.

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.

Feedforward Nonlinear Control Using Neural Gas Network

Directory of Open Access Journals (Sweden)

Iván Machón-González

2017-01-01

Full Text Available Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.

System Identification, Prediction, Simulation and Control with Neural Networks

DEFF Research Database (Denmark)

Sørensen, O.

1997-01-01

a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

Culture-sensitive neural substrates of human cognition: a transcultural neuroimaging approach.

Science.gov (United States)

Han, Shihui; Northoff, Georg

2008-08-01

Our brains and minds are shaped by our experiences, which mainly occur in the context of the culture in which we develop and live. Although psychologists have provided abundant evidence for diversity of human cognition and behaviour across cultures, the question of whether the neural correlates of human cognition are also culture-dependent is often not considered by neuroscientists. However, recent transcultural neuroimaging studies have demonstrated that one's cultural background can influence the neural activity that underlies both high- and low-level cognitive functions. The findings provide a novel approach by which to distinguish culture-sensitive from culture-invariant neural mechanisms of human cognition.

Nano-topography Enhances Communication in Neural Cells Networks

KAUST Repository

Onesto, V.

2017-08-23

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

Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition.

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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

Coordinated vigilance provides evidence for direct reciprocity in coral reef fishes.

Science.gov (United States)

Brandl, Simon J; Bellwood, David R

2015-09-25

Reciprocity is frequently assumed to require complex cognitive abilities. Therefore, it has been argued that reciprocity may be restricted to animals that can meet these demands. Here, we provide evidence for the potential presence of direct reciprocity in teleost fishes. We demonstrate that in pairs of coral reef rabbitfishes (f. Siganidae), one fish frequently assumes an upright vigilance position in the water column, while the partner forages in small crevices in the reef substratum. Both behaviours are strongly coordinated and partners regularly alternate their positions, resulting in a balanced distribution of foraging activity. Compared to solitary individuals, fishes in pairs exhibit longer vigilance bouts, suggesting that the help provided to the partner is costly. In turn, fishes in pairs take more consecutive bites and penetrate deeper into crevices than solitary individuals, suggesting that the safety provided by a vigilant partner may outweigh initial costs by increasing foraging efficiency. Thus, the described system appears to meet all of the requirements for direct reciprocity. We argue that the nature of rabbitfish pairs provides favourable conditions for the establishment of direct reciprocity, as continuous interaction with the same partner, simultaneous needs, interdependence, and communication relax the cognitive demands of reciprocal cooperation.

A direct comparison of appetitive and aversive anticipation: Overlapping and distinct neural activation.

Science.gov (United States)

Sege, Christopher T; Bradley, Margaret M; Weymar, Mathias; Lang, Peter J

2017-05-30

fMRI studies of reward find increased neural activity in ventral striatum and medial prefrontal cortex (mPFC), whereas other regions, including the dorsolateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC), and anterior insula, are activated when anticipating aversive exposure. Although these data suggest differential activation during anticipation of pleasant or of unpleasant exposure, they also arise in the context of different paradigms (e.g., preparation for reward vs. threat of shock) and participants. To determine overlapping and unique regions active during emotional anticipation, we compared neural activity during anticipation of pleasant or unpleasant exposure in the same participants. Cues signalled the upcoming presentation of erotic/romantic, violent, or everyday pictures while BOLD activity during the 9-s anticipatory period was measured using fMRI. Ventral striatum and a ventral mPFC subregion were activated when anticipating pleasant, but not unpleasant or neutral, pictures, whereas activation in other regions was enhanced when anticipating appetitive or aversive scenes. Copyright © 2017 Elsevier B.V. All rights reserved.

The neural correlates of agrammatism: Evidence from aphasic and healthy speakers performing an overt picture description task

Directory of Open Access Journals (Sweden)

Eva eSchoenberger

2014-03-01

Full Text Available Functional brain imaging studies have improved our knowledge of the neural localization of language functions and the functional recovery after a lesion. However, the neural correlates of agrammatic symptoms in aphasia remain largely unknown. The present fMRI study examined the neural correlates of morpho-syntactic encoding and agrammatic errors in continuous language production by combining three approaches. First, the neural mechanisms underlying natural morpho-syntactic processing in a picture description task were analyzed in 15 healthy speakers. Second, agrammatic-like speech behavior was induced in the same group of healthy speakers to study the underlying functional processes by limiting the utterance length. In a third approach, five agrammatic participants performed the picture description task to gain insights in the neural correlates of agrammatism and the functional reorganization of language processing after stroke. In all approaches, utterances were analyzed for syntactic completeness, complexity and morphology. Event-related data analysis was conducted by defining every clause-like unit (CLU as an event with its onset-time and duration. Agrammatic and correct CLUs were contrasted. Due to the small sample size as well as heterogeneous lesion sizes and sites with lesion foci in the insula lobe, inferior frontal, superior temporal and inferior parietal areas the activation patterns in the agrammatic speakers were analyzed on a single subject level. In the group of healthy speakers, posterior temporal and inferior parietal areas were associated with greater morpho-syntactic demands in complete and complex CLUs. The intentional manipulation of morpho-syntactic structures and the omission of function words were associated with additional inferior frontal activation. Overall, the results revealed that the investigation of the neural correlates of agrammatic language production can be reasonably conducted with an overt language production

The neural correlates of agrammatism: Evidence from aphasic and healthy speakers performing an overt picture description task.

Science.gov (United States)

Schönberger, Eva; Heim, Stefan; Meffert, Elisabeth; Pieperhoff, Peter; da Costa Avelar, Patricia; Huber, Walter; Binkofski, Ferdinand; Grande, Marion

2014-01-01

Functional brain imaging studies have improved our knowledge of the neural localization of language functions and the functional reorganization after a lesion. However, the neural correlates of agrammatic symptoms in aphasia remain largely unknown. The present fMRI study examined the neural correlates of morpho-syntactic encoding and agrammatic errors in continuous language production by combining three approaches. First, the neural mechanisms underlying natural morpho-syntactic processing in a picture description task were analyzed in 15 healthy speakers. Second, agrammatic-like speech behavior was induced in the same group of healthy speakers to study the underlying functional processes by limiting the utterance length. In a third approach, five agrammatic participants performed the picture description task to gain insights in the neural correlates of agrammatism and the functional reorganization of language processing after stroke. In all approaches, utterances were analyzed for syntactic completeness, complexity, and morphology. Event-related data analysis was conducted by defining every clause-like unit (CLU) as an event with its onset-time and duration. Agrammatic and correct CLUs were contrasted. Due to the small sample size as well as heterogeneous lesion sizes and sites with lesion foci in the insula lobe, inferior frontal, superior temporal and inferior parietal areas the activation patterns in the agrammatic speakers were analyzed on a single subject level. In the group of healthy speakers, posterior temporal and inferior parietal areas were associated with greater morpho-syntactic demands in complete and complex CLUs. The intentional manipulation of morpho-syntactic structures and the omission of function words were associated with additional inferior frontal activation. Overall, the results revealed that the investigation of the neural correlates of agrammatic language production can be reasonably conducted with an overt language production paradigm.

Markers of neural degeneration and regeneration in Down ...

African Journals Online (AJOL)

Iman Ehsan Abdel-Meguid

2012-11-02

Nov 2, 2012 ... The Egyptian Journal of Medical Human Genetics www.ejmhg.eg.net ... stem cells (TCSCs), including neural TCSCs and endothelial. TCSCs [10,11]. ..... directs human embryonic stem cell proliferation and differentia- tion into.

Neural network approach for the calculation of potential coefficients in quantum mechanics

Science.gov (United States)

Ossandón, Sebastián; Reyes, Camilo; Cumsille, Patricio; Reyes, Carlos M.

2017-05-01

A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct problem for different parametrizations of the chosen potential function. Then, using the attainable eigenvalues as a training set of the direct radial basis neural network a map of new eigenvalues was obtained. This relationship was later inverted and refined by training an inverse radial basis neural network, allowing the calculation of the unknown parameters and therefore estimating the potential function. Three numerical examples are presented in order to prove the effectiveness of the method. The results show that the method proposed has the advantage to use less computational resources without a significant accuracy loss.

Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process.

Science.gov (United States)

Hamamé, Carlos M; Cosmelli, Diego; Henriquez, Rodrigo; Aboitiz, Francisco

2011-04-26

Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed. We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30-60 Hz) and alpha (8-14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing. We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.

Embedding recurrent neural networks into predator-prey models.

Science.gov (United States)

Moreau, Yves; Louiès, Stephane; Vandewalle, Joos; Brenig, Leon

1999-03-01

We study changes of coordinates that allow the embedding of ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models-also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form (Brenig, L. (1988). Complete factorization and analytic solutions of generalized Lotka-Volterra equations. Physics Letters A, 133(7-8), 378-382), where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoid. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network. We expect that this transformation will permit the application of existing techniques for the analysis of Lotka-Volterra systems to recurrent neural networks. Furthermore, our results show that Lotka-Volterra systems are universal approximators of dynamical systems, just as are continuous-time neural networks.

An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Manoonpong, Poramate

2016-01-01

expensive algorithms. We present a novel bioinspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural...

Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks

Directory of Open Access Journals (Sweden)

Minoru Sasaki

2012-01-01

Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.

review of current evidence for folate in the prevention of neural tube ...

African Journals Online (AJOL)

The incidence of neural tube defects (NTD) among black South. Africans living in urban areas is low compared with reports of _". I\\TTD incidence in rural areasY A NTD incidence of 0.95 per -;:-. 1000 live births was reported in Cape Town,l while an incidence of 0.99 per 1 000 live births was reported in a study performed at ...

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.

Cytoplasmic-genetic male sterility gene provides direct evidence for some hybrid rice recently evolving into weedy rice

Science.gov (United States)

Zhang, Jingxu; Lu, Zuomei; Dai, Weimin; Song, Xiaoling; Peng, Yufa; Valverde, Bernal E.; Qiang, Sheng

2015-01-01

Weedy rice infests paddy fields worldwide at an alarmingly increasing rate. There is substantial evidence indicating that many weedy rice forms originated from or are closely related to cultivated rice. There is suspicion that the outbreak of weedy rice in China may be related to widely grown hybrid rice due to its heterosis and the diversity of its progeny, but this notion remains unsupported by direct evidence. We screened weedy rice accessions by both genetic and molecular marker tests for the cytoplasmic male sterility (CMS) genes (Wild abortive, WA, and Boro type, BT) most widely used in the production of indica and japonica three-line hybrid rice as a diagnostic trait of direct parenthood. Sixteen weedy rice accessions of the 358 tested (4.5%) contained the CMS-WA gene; none contained the CMS-BT gene. These 16 accessions represent weedy rices recently evolved from maternal hybrid rice derivatives, given the primarily maternal inheritance of this trait. Our results provide key direct evidence that hybrid rice can be involved in the evolution of some weedy rice accessions, but is not a primary factor in the recent outbreak of weedy rice in China. PMID:26012494

Cytoplasmic-genetic male sterility gene provides direct evidence for some hybrid rice recently evolving into weedy rice.

Science.gov (United States)

Zhang, Jingxu; Lu, Zuomei; Dai, Weimin; Song, Xiaoling; Peng, Yufa; Valverde, Bernal E; Qiang, Sheng

2015-05-27

Weedy rice infests paddy fields worldwide at an alarmingly increasing rate. There is substantial evidence indicating that many weedy rice forms originated from or are closely related to cultivated rice. There is suspicion that the outbreak of weedy rice in China may be related to widely grown hybrid rice due to its heterosis and the diversity of its progeny, but this notion remains unsupported by direct evidence. We screened weedy rice accessions by both genetic and molecular marker tests for the cytoplasmic male sterility (CMS) genes (Wild abortive, WA, and Boro type, BT) most widely used in the production of indica and japonica three-line hybrid rice as a diagnostic trait of direct parenthood. Sixteen weedy rice accessions of the 358 tested (4.5%) contained the CMS-WA gene; none contained the CMS-BT gene. These 16 accessions represent weedy rices recently evolved from maternal hybrid rice derivatives, given the primarily maternal inheritance of this trait. Our results provide key direct evidence that hybrid rice can be involved in the evolution of some weedy rice accessions, but is not a primary factor in the recent outbreak of weedy rice in China.

Expert music performance: cognitive, neural, and developmental bases.

Science.gov (United States)

Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

2015-01-01

In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. © 2015 Elsevier B.V. All rights reserved.

Accelerated DNA Methylation Age: Associations with PTSD and Neural Integrity

Science.gov (United States)

Wolf, Erika J.; Logue, Mark W.; Hayes, Jasmeet P.; Sadeh, Naomi; Schichman, Steven A.; Stone, Annjanette; Salat, David H.; Milberg, William; McGlinchey, Regina; Miller, Mark W.

2015-01-01

Background Accumulating evidence suggests that post traumatic stress disorder (PTSD) may accelerate cellular aging and lead to premature morbidity and neurocognitive decline. Methods This study evaluated associations between PTSD and DNA methylation (DNAm) age using recently developed algorithms of cellular age by Horvath (2013) and Hannum et al. (2013). These estimates reflect accelerated aging when they exceed chronological age. We also examined if accelerated cellular age manifested in degraded neural integrity, indexed via diffusion tensor imaging. Results Among 281 male and female veterans of the conflicts in Iraq and Afghanistan, DNAm age was strongly related to chronological age (rs ~.88). Lifetime PTSD severity was associated with Hannum DNAm age estimates residualized for chronological age (β = .13, p= .032). Advanced DNAm age was associated with reduced integrity in the genu of the corpus callosum (β = −.17, p= .009) and indirectly linked to poorer working memory performance via this region (indirect β = − .05, p= .029). Horvath DNAm age estimates were not associated with PTSD or neural integrity. Conclusions Results provide novel support for PTSD-related accelerated aging in DNAm and extend the evidence base of known DNAm age correlates to the domains of neural integrity and cognition. PMID:26447678

Transcranial direct current stimulation for motor recovery of upper limb function after stroke.

Science.gov (United States)

Lüdemann-Podubecká, Jitka; Bösl, Kathrin; Rothhardt, Sandra; Verheyden, Geert; Nowak, Dennis Alexander

2014-11-01

Changes in neural processing after stroke have been postulated to impede recovery from stroke. Transcranial direct current stimulation has the potential to alter cortico-spinal excitability and thereby might be beneficial in stroke recovery. We review the pertinent literature prior to 30/09/2013 on transcranial direct current stimulation in promoting motor recovery of the affected upper limb after stroke. We found overall 23 trials (they included 523 participants). All stimulation protocols pride on interhemispheric imbalance model. In a comparative approach, methodology and effectiveness of (a) facilitation of the affected hemisphere, (b) inhibition of the unaffected hemisphere and (c) combined application of transcranial direct current stimulation over the affected and unaffected hemispheres to treat impaired hand function after stroke are presented. Transcranial direct current stimulation is associated with improvement of the affected upper limb after stroke, but current evidence does not support its routine use. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Representation of neural networks as Lotka-Volterra systems

International Nuclear Information System (INIS)

Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon

1999-01-01

We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

Science.gov (United States)

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Controlling the dynamics of multi-state neural networks

International Nuclear Information System (INIS)

Jin, Tao; Zhao, Hong

2008-01-01

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

Inward foreign direct investment and industrial restructuring: micro evidence – the Slovenian firms’ growth model

Directory of Open Access Journals (Sweden)

Katja Zajc Kejžar

2006-12-01

Full Text Available We examine the impact of inward foreign direct investment (FDI on the growth of local firms in terms of employment and total factor productivity (TFP for the Slovenian manufacturing sector in the 1994-2003 period. The theoretically predicted channels through which inward FDI affects the firm dynamics in a host country prove to be in general significant. First, there is evidence of the direct impact offoreign firms through so-called direct technology transfer as foreign-owned firms have higher growth of TFP compared to domestically-owned firms after controlling for other determinants. Secondly, the entry of foreign firms stimulates the reshuffling of the resources from less to more efficient local firms. The firm selection process is, namely, characterised by the least efficient firms experiencing a drop in their employment growth upon a foreign firm’s entry. Thirdly, regarding the productivity spillover effects from foreign to local firms we provide indirect evidence that they mostly operate through vertical linkages rather than within the same industry.In general, it seems that not all firms are equally able to benefit from foreign firms’ presence and that absorptive capacity plays an important role.

A neurally inspired musical instrument classification system based upon the sound onset.

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Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

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

DEFF Research Database (Denmark)

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

2001-01-01

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

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

DEFF Research Database (Denmark)

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

2001-01-01

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

Go with the Flow: Cerebrospinal Fluid Flow Regulates Neural Stem Cell Proliferation.

Science.gov (United States)

Kaneko, Naoko; Sawamoto, Kazunobu

2018-06-01

Adult neural stem cells in the wall of brain ventricles make direct contact with cerebrospinal fluid. In this issue of Cell Stem Cell, Petrik et al. (2018) demonstrate that these neural stem cells sense the flow of cerebrospinal fluid through a transmembrane sodium channel, ENaC, which regulates their proliferation. Copyright © 2018 Elsevier Inc. All rights reserved.

Generation of artificial accelerograms using neural networks for data of Iran

International Nuclear Information System (INIS)

Bargi, Kh.; Loux, C.; Rohani, H.

2002-01-01

A new method for generation of artificial earthquake accelerograms from response spectra is proposed by Ghaboussi and Lin in 1997 using neural networks. In this paper the methodology has been extended and enhanced for data of Iran. For this purpose, first 40 records of Iran acceleration is chosen, then an RBF neural network which called generalized regression neural network learn the inverse mapping directly from the response spectrum to the Discrete Cosine Transform of accelerograms. Discrete Cosine Transform has been used as an assisting device to extract the content of frequency domain. Learning of network is reasonable and a generalized regression neural network learns it in a few second. Outputs are presented to demonstrate the performance of this method and show its capabilities

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.

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.

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...

Continual Learning through Evolvable Neural Turing Machines

DEFF Research Database (Denmark)

Lüders, Benno; Schläger, Mikkel; Risi, Sebastian

2016-01-01

Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM...

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

Science.gov (United States)

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity.

Science.gov (United States)

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities-0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking.

Processing of chromatic information in a deep convolutional neural network.

Science.gov (United States)

Flachot, Alban; Gegenfurtner, Karl R

2018-04-01

Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

Neural and Behavioral Evidence for an Online Resetting Process in Visual Working Memory.

Science.gov (United States)

Balaban, Halely; Luria, Roy

2017-02-01

Visual working memory (VWM) guides behavior by holding a set of active representations and modifying them according to changes in the environment. This updating process relies on a unique mapping between each VWM representation and an actual object in the environment. Here, we destroyed this mapping by either presenting a coherent object but then breaking it into independent parts or presenting an object but then abruptly replacing it with a different object. This allowed us to introduce the neural marker and behavioral consequence of an online resetting process in humans' VWM. Across seven experiments, we demonstrate that this resetting process involves abandoning the old VWM contents because they no longer correspond to the objects in the environment. Then, VWM encodes the novel information and reestablishes the correspondence between the new representations and the objects. The resetting process was marked by a unique neural signature: a sharp drop in the amplitude of the electrophysiological index of VWM contents (the contralateral delay activity), presumably indicating the loss of the existent object-to-representation mappings. This marker was missing when an updating process occurred. Moreover, when tracking moving items, VWM failed to detect salient changes in the object's shape when these changes occurred during the resetting process. This happened despite the object being fully visible, presumably because the mapping between the object and a VWM representation was lost. Importantly, we show that resetting, its neural marker, and the behavioral cost it entails, are specific to situations that involve a destruction of the objects-to-representations correspondence. Visual working memory (VWM) maintains task-relevant information in an online state. Previous studies showed that VWM representations are accessed and modified after changes in the environment. Here, we show that this updating process critically depends on an ongoing mapping between the

Development of nuclear power plant diagnosis technique using neural networks

International Nuclear Information System (INIS)

Horiguchi, Masahiro; Fukawa, Naohiro; Nishimura, Kazuo

1991-01-01

A nuclear power plant diagnosis technique has been developed, called transient phenomena analysis, which employs neural network. The neural networks identify malfunctioning equipment by recognizing the pattern of main plant parameters, making it possible to locate the cause of an abnormality when a plant is in a transient state. In a case where some piece of equipment shows abnormal behavior, many plant parameters either directly or indirectly related to that equipment change simultaneously. When an abrupt change in a plant parameter is detected, changes in the 49 main plant parameters are classified into three types and a characteristic change pattern consisting of 49 data is defined. The neural networks then judge the cause of the abnormality from this pattern. This neural-network-based technique can recognize 100 patterns that are characterized by the causes of plant abnormality. (author)

Neural responses to multimodal ostensive signals in 5-month-old infants.

Directory of Open Access Journals (Sweden)

Eugenio Parise

Full Text Available Infants' sensitivity to ostensive signals, such as direct eye contact and infant-directed speech, is well documented in the literature. We investigated how infants interpret such signals by assessing common processing mechanisms devoted to them and by measuring neural responses to their compounds. In Experiment 1, we found that ostensive signals from different modalities display overlapping electrophysiological activity in 5-month-old infants, suggesting that these signals share neural processing mechanisms independently of their modality. In Experiment 2, we found that the activation to ostensive signals from different modalities is not additive to each other, but rather reflects the presence of ostension in either stimulus stream. These data support the thesis that ostensive signals obligatorily indicate to young infants that communication is directed to them.

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.

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

Science.gov (United States)

Michaels, Jonathan A; Dann, Benjamin; Scherberger, Hansjörg

2016-11-01

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

Science.gov (United States)

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

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

X mapping in man: evidence against direct measurable linkage between ocular albinism and deutan colour blindness.

Science.gov (United States)

Pearce, W G; Sanger, R

1976-01-01

A Newfoundland kindred in which ocular albinism and deutan colour blindness are segregating provides strong evidence against the loci for these two X-borne characters being within direct measurable distance of each other. PMID:1085370

Neural responses to visual food cues according to weight status: a systematic review of functional magnetic resonance imaging studies.

Science.gov (United States)

Pursey, Kirrilly M; Stanwell, Peter; Callister, Robert J; Brain, Katherine; Collins, Clare E; Burrows, Tracy L

2014-01-01

Emerging evidence from recent neuroimaging studies suggests that specific food-related behaviors contribute to the development of obesity. The aim of this review was to report the neural responses to visual food cues, as assessed by functional magnetic resonance imaging (fMRI), in humans of differing weight status. Published studies to 2014 were retrieved and included if they used visual food cues, studied humans >18 years old, reported weight status, and included fMRI outcomes. Sixty studies were identified that investigated the neural responses of healthy weight participants (n = 26), healthy weight compared to obese participants (n = 17), and weight-loss interventions (n = 12). High-calorie food images were used in the majority of studies (n = 36), however, image selection justification was only provided in 19 studies. Obese individuals had increased activation of reward-related brain areas including the insula and orbitofrontal cortex in response to visual food cues compared to healthy weight individuals, and this was particularly evident in response to energy dense cues. Additionally, obese individuals were more responsive to food images when satiated. Meta-analysis of changes in neural activation post-weight loss revealed small areas of convergence across studies in brain areas related to emotion, memory, and learning, including the cingulate gyrus, lentiform nucleus, and precuneus. Differential activation patterns to visual food cues were observed between obese, healthy weight, and weight-loss populations. Future studies require standardization of nutrition variables and fMRI outcomes to enable more direct comparisons between studies.

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

Directory of Open Access Journals (Sweden)

Petros-Pavlos Ypsilantis

Full Text Available Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

Neural Correlates of Indicators of Sound Change in Cantonese: Evidence from Cortical and Subcortical Processes.

Science.gov (United States)

Maggu, Akshay R; Liu, Fang; Antoniou, Mark; Wong, Patrick C M

2016-01-01

Across time, languages undergo changes in phonetic, syntactic, and semantic dimensions. Social, cognitive, and cultural factors contribute to sound change, a phenomenon in which the phonetics of a language undergo changes over time. Individuals who misperceive and produce speech in a slightly divergent manner (called innovators ) contribute to variability in the society, eventually leading to sound change. However, the cause of variability in these individuals is still unknown. In this study, we examined whether such misperceptions are represented in neural processes of the auditory system. We investigated behavioral, subcortical (via FFR), and cortical (via P300) manifestations of sound change processing in Cantonese, a Chinese language in which several lexical tones are merging. Across the merging categories, we observed a similar gradation of speech perception abilities in both behavior and the brain (subcortical and cortical processes). Further, we also found that behavioral evidence of tone merging correlated with subjects' encoding at the subcortical and cortical levels. These findings indicate that tone-merger categories, that are indicators of sound change in Cantonese, are represented neurophysiologically with high fidelity. Using our results, we speculate that innovators encode speech in a slightly deviant neurophysiological manner, and thus produce speech divergently that eventually spreads across the community and contributes to sound change.

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.

A Neural Signature Encoding Decisions under Perceptual Ambiguity.

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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.

Artificial Astrocytes Improve Neural Network Performance

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Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-01-01

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

Artificial astrocytes improve neural network performance.

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Ana B Porto-Pazos

Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Artificial astrocytes improve neural network performance.

Science.gov (United States)

Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

2011-04-19

Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

Searching for evidence of a preferred rupture direction in small earthquakes at Parkfield

Science.gov (United States)

Kane, D. L.; Shearer, P. M.; Allmann, B.; Vernon, F. L.

2009-12-01

Theoretical modeling of strike-slip ruptures along a bimaterial interface suggests that the interface will have a preferred rupture direction and will produce asymmetric ground motion (Shi and Ben-Zion, 2006). This could have widespread implications for earthquake source physics and for hazard analysis on mature faults because larger ground motions would be expected in the direction of rupture propagation. Studies have shown that many large global earthquakes exhibit unilateral rupture, but a consistently preferred rupture direction along faults has not been observed. Some researchers have argued that the bimaterial interface model does not apply to natural faults, noting that the rupture of the M 6 2004 Parkfield earthquake propagated in the opposite direction from previous M 6 earthquakes along that section of the San Andreas Fault (Harris and Day, 2005). We analyze earthquake spectra from the Parkfield area to look for evidence of consistent rupture directivity along the San Andreas Fault. We separate the earthquakes into spatially defined clusters and quantify the differences in high-frequency energy among earthquakes recorded at each station. Propagation path effects are minimized in this analysis because we compare earthquakes located within a small volume and recorded by the same stations. By considering a number of potential end-member models, we seek to determine if a preferred rupture direction is present among small earthquakes at Parkfield.

Evidence of direct and indirect rebound effect in households in EU-27 countries

International Nuclear Information System (INIS)

Freire-González, Jaume

2017-01-01

This research estimates the direct and indirect rebound effect of energy efficiency in households for the EU-27 countries (the first twenty-seven Member States of the European Union). A hybrid methodology that combines econometric estimates, environmental extended input-output analysis and re-spending models has been developed. Although most of the economies present values below 100%, there are seven countries situated above this critical threshold. By weighting individual estimates by GDP, an average value for the overall EU-27 economy has been found between 73.62% and 81.16%. These results suggest that the energy policy at the European level should be rethought if efficiency measures pursue reducing energy consumption and tackling climate change. - Highlights: • Empirical evidence of direct and indirect rebound effect is provided for EU-27. • Most economies have a rebound effect below the threshold of 100% (20 of them). • Additional energy efficiency measures are needed even with low direct rebounds.

Modelling collective cell migration of neural crest.

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Szabó, András; Mayor, Roberto

2016-10-01

Collective cell migration has emerged in the recent decade as an important phenomenon in cell and developmental biology and can be defined as the coordinated and cooperative movement of groups of cells. Most studies concentrate on tightly connected epithelial tissues, even though collective migration does not require a constant physical contact. Movement of mesenchymal cells is more independent, making their emergent collective behaviour less intuitive and therefore lending importance to computational modelling. Here we focus on such modelling efforts that aim to understand the collective migration of neural crest cells, a mesenchymal embryonic population that migrates large distances as a group during early vertebrate development. By comparing different models of neural crest migration, we emphasize the similarity and complementary nature of these approaches and suggest a future direction for the field. The principles derived from neural crest modelling could aid understanding the collective migration of other mesenchymal cell types. Copyright © 2016 Elsevier Ltd. All rights reserved.

High school music classes enhance the neural processing of speech

OpenAIRE

Tierney, Adam; Krizman, Jennifer; Skoe, Erika; Johnston, Kathleen; Kraus, Nina

2013-01-01

Should music be a priority in public education? One argument for teaching music in school is that private music instruction relates to enhanced language abilities and neural function. However, the directionality of this relationship is unclear and it is unknown whether school-based music training can produce these enhancements. Here we show that two years of group music classes in high school enhance the subcortical encoding of speech. To tease apart the relationships between music and neural...

Primary neural leprosy: systematic review

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Jose Antonio Garbino

2013-06-01

Full Text Available The authors proposed a systematic review on the current concepts of primary neural leprosy by consulting the following online databases: MEDLINE, Lilacs/SciELO, and Embase. Selected studies were classified based on the degree of recommendation and levels of scientific evidence according to the “Oxford Centre for Evidence-based Medicine”. The following aspects were reviewed: cutaneous clinical and laboratorial investigations, i.e. skin clinical exam, smears, and biopsy, and Mitsuda's reaction; neurological investigation (anamnesis, electromyography and nerve biopsy; serological investigation and molecular testing, i.e. serological testing for the detection of the phenolic glycolipid 1 (PGL-I and the polymerase chain reaction (PCR; and treatment (classification criteria for the definition of specific treatment, steroid treatment, and cure criteria.

A neural link between affective understanding and interpersonal attraction

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Anders, Silke; de Jong, Roos; Beck, Christian; Haynes, John-Dylan; Ethofer, Thomas

2016-01-01

Being able to comprehend another person’s intentions and emotions is essential for successful social interaction. However, it is currently unknown whether the human brain possesses a neural mechanism that attracts people to others whose mental states they can easily understand. Here we show that the degree to which a person feels attracted to another person can change while they observe the other’s affective behavior, and that these changes depend on the observer’s confidence in having correctly understood the other’s affective state. At the neural level, changes in interpersonal attraction were predicted by activity in the reward system of the observer’s brain. Importantly, these effects were specific to individual observer–target pairs and could not be explained by a target’s general attractiveness or expressivity. Furthermore, using multivoxel pattern analysis (MVPA), we found that neural activity in the reward system of the observer’s brain varied as a function of how well the target’s affective behavior matched the observer’s neural representation of the underlying affective state: The greater the match, the larger the brain’s intrinsic reward signal. Taken together, these findings provide evidence that reward-related neural activity during social encounters signals how well an individual’s “neural vocabulary” is suited to infer another person’s affective state, and that this intrinsic reward might be a source of changes in interpersonal attraction. PMID:27044071

Analysis Resilient Algorithm on Artificial Neural Network Backpropagation

Science.gov (United States)

Saputra, Widodo; Tulus; Zarlis, Muhammad; Widia Sembiring, Rahmat; Hartama, Dedy

2017-12-01

Prediction required by decision makers to anticipate future planning. Artificial Neural Network (ANN) Backpropagation is one of method. This method however still has weakness, for long training time. This is a reason to improve a method to accelerate the training. One of Artificial Neural Network (ANN) Backpropagation method is a resilient method. Resilient method of changing weights and bias network with direct adaptation process of weighting based on local gradient information from every learning iteration. Predicting data result of Istanbul Stock Exchange training getting better. Mean Square Error (MSE) value is getting smaller and increasing accuracy.

Enhanced online convolutional neural networks for object tracking

Science.gov (United States)

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

2018-04-01

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

Direct economic burden of hepatitis B virus related diseases: evidence from Shandong, China

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Lu Jingjing

2013-01-01

Full Text Available Abstract Background Although the expenses of liver cirrhosis are covered by a critical illness fund under the current health insurance program in China, the economic burden associated with hepatitis B virus (HBV related diseases is not well addressed. In order to provide evidence to address the economic disease burden of HBV, we conducted a survey to investigate the direct economic burden of acute and chronic hepatitis B, cirrhosis and liver cancer caused by HBV-related disease. Methods From April 2010 to November 2010, we conducted a survey of inpatients with HBV-related diseases and who were hospitalized for seven or more days in one of the seven tertiary and six secondary hospitals in Shandong, China. Patients were recorded consecutively within a three-to-five month time period from each sampled hospital; an in-person survey was conducted to collect demographic and socio-economic information, as well as direct medical and nonmedical expenses during the last month and last year prior to the current hospitalization. Direct medical costs included total outpatient, inpatient, and self-treatment expenditures; direct nonmedical costs included spending on nutritional supplements, transportation, and nursing. Direct medical costs during the current hospitalization were also obtained from the hospital financial database. The direct economic cost was calculated as the sum of direct medical and nonmedical costs. Our results call for the importance of implementing clinical guideline, improving system accountability, and helping secondary and smaller hospitals to improve efficiency. This has important policy implication for the on-going hospital reform in China. Results Our data based on inpatients with HBV-related diseases suggested that the direct cost in US dollars for acute hepatitis B, severe hepatitis B, chronic hepatitis B, compensated cirrhosis, decompensated cirrhosis and primary liver cancer was $2954, $10834, $4552, $7400.28, $6936 and $10635

Direct economic burden of hepatitis B virus related diseases: evidence from Shandong, China.

Science.gov (United States)

Lu, Jingjing; Xu, Aiqiang; Wang, Jian; Zhang, Li; Song, Lizhi; Li, Renpeng; Zhang, Shunxiang; Zhuang, Guihua; Lu, Mingshan

2013-01-31

Although the expenses of liver cirrhosis are covered by a critical illness fund under the current health insurance program in China, the economic burden associated with hepatitis B virus (HBV) related diseases is not well addressed. In order to provide evidence to address the economic disease burden of HBV, we conducted a survey to investigate the direct economic burden of acute and chronic hepatitis B, cirrhosis and liver cancer caused by HBV-related disease. From April 2010 to November 2010, we conducted a survey of inpatients with HBV-related diseases and who were hospitalized for seven or more days in one of the seven tertiary and six secondary hospitals in Shandong, China. Patients were recorded consecutively within a three-to-five month time period from each sampled hospital; an in-person survey was conducted to collect demographic and socio-economic information, as well as direct medical and nonmedical expenses during the last month and last year prior to the current hospitalization. Direct medical costs included total outpatient, inpatient, and self-treatment expenditures; direct nonmedical costs included spending on nutritional supplements, transportation, and nursing. Direct medical costs during the current hospitalization were also obtained from the hospital financial database. The direct economic cost was calculated as the sum of direct medical and nonmedical costs. Our results call for the importance of implementing clinical guideline, improving system accountability, and helping secondary and smaller hospitals to improve efficiency. This has important policy implication for the on-going hospital reform in China. Our data based on inpatients with HBV-related diseases suggested that the direct cost in US dollars for acute hepatitis B, severe hepatitis B, chronic hepatitis B, compensated cirrhosis, decompensated cirrhosis and primary liver cancer was $2954, $10834, $4552, $7400.28, $6936 and $10635, respectively. These costs ranged from 30.72% (for acute

Predictive Acoustic Tracking with an Adaptive Neural Mechanism

DEFF Research Database (Denmark)

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

model of the lizard peripheral auditory system to extract information regarding sound direction. This information is utilised by a neural machinery to learn the acoustic signal’s velocity through fast and unsupervised correlation-based learning adapted from differential Hebbian learning. This approach...

Effects of direct social experience on trust decisions and neural reward circuitry

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Dominic S. Fareri

2012-10-01

Full Text Available The human striatum is integral for reward-processing and supports learning by linking experienced outcomes with prior expectations. Recent endeavors implicate the striatum in processing outcomes of social interactions, such as social approval/rejection, as well as in learning reputations of others. Interestingly, social impressions often influence our behavior with others during interactions. Information about an interaction partner’s moral character acquired from biographical information hinders updating of expectations after interactions via top down modulation of reward circuitry. An outstanding question is whether initial impressions formed through experience similarly modulate the ability to update social impressions at the behavioral and neural level. We investigated the role of experienced social information on trust behavior and reward-related BOLD activity. Participants played a computerized ball tossing game with three fictional partners manipulated to be perceived as good, bad or neutral. Participants then played an iterated trust game as investors with these same partners while undergoing fMRI. Unbeknownst to participants, partner behavior in the trust game was random and unrelated to their ball-tossing behavior. Participants’ trust decisions were influenced by their prior experience in the ball tossing game, investing less often with the bad partner compared to the good and neutral. Reinforcement learning models revealed that participants were more sensitive to updating their beliefs about good and bad partners when experiencing outcomes consistent with initial experience. Increased striatal and anterior cingulate BOLD activity for positive versus negative trust game outcomes emerged, which further correlated with model-derived prediction-error (PE learning signals. These results suggest that initial impressions formed from direct social experience can be continually shaped by consistent information through reward learning

Effects of Direct Social Experience on Trust Decisions and Neural Reward Circuitry

Science.gov (United States)

Fareri, Dominic S.; Chang, Luke J.; Delgado, Mauricio R.

2012-01-01

The human striatum is integral for reward-processing and supports learning by linking experienced outcomes with prior expectations. Recent endeavors implicate the striatum in processing outcomes of social interactions, such as social approval/rejection, as well as in learning reputations of others. Interestingly, social impressions often influence our behavior with others during interactions. Information about an interaction partner’s moral character acquired from biographical information hinders updating of expectations after interactions via top down modulation of reward circuitry. An outstanding question is whether initial impressions formed through experience similarly modulate the ability to update social impressions at the behavioral and neural level. We investigated the role of experienced social information on trust behavior and reward-related BOLD activity. Participants played a computerized ball-tossing game with three fictional partners manipulated to be perceived as good, bad, or neutral. Participants then played an iterated trust game as investors with these same partners while undergoing fMRI. Unbeknownst to participants, partner behavior in the trust game was random and unrelated to their ball-tossing behavior. Participants’ trust decisions were influenced by their prior experience in the ball-tossing game, investing less often with the bad partner compared to the good and neutral. Reinforcement learning models revealed that participants were more sensitive to updating their beliefs about good and bad partners when experiencing outcomes consistent with initial experience. Increased striatal and anterior cingulate BOLD activity for positive versus negative trust game outcomes emerged, which further correlated with model-derived prediction error learning signals. These results suggest that initial impressions formed from direct social experience can be continually shaped by consistent information through reward learning mechanisms. PMID:23087604

A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human population.

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Roman Bauer

Full Text Available Glioma is the most common form of primary brain tumor. Demographically, the risk of occurrence increases until old age. Here we present a novel computational model to reproduce the probability of glioma incidence across the lifespan. Previous mathematical models explaining glioma incidence are framed in a rather abstract way, and do not directly relate to empirical findings. To decrease this gap between theory and experimental observations, we incorporate recent data on cellular and molecular factors underlying gliomagenesis. Since evidence implicates the adult neural stem cell as the likely cell-of-origin of glioma, we have incorporated empirically-determined estimates of neural stem cell number, cell division rate, mutation rate and oncogenic potential into our model. We demonstrate that our model yields results which match actual demographic data in the human population. In particular, this model accounts for the observed peak incidence of glioma at approximately 80 years of age, without the need to assert differential susceptibility throughout the population. Overall, our model supports the hypothesis that glioma is caused by randomly-occurring oncogenic mutations within the neural stem cell population. Based on this model, we assess the influence of the (experimentally indicated decrease in the number of neural stem cells and increase of cell division rate during aging. Our model provides multiple testable predictions, and suggests that different temporal sequences of oncogenic mutations can lead to tumorigenesis. Finally, we conclude that four or five oncogenic mutations are sufficient for the formation of glioma.

On the Nature of the Intrinsic Connectivity of the Cat Motor Cortex: Evidence for a Recurrent Neural Network Topology

DEFF Research Database (Denmark)

Capaday, Charles; Ethier, C; Brizzi, L

2009-01-01

and functional significance of the intrinsic horizontal connections between neurons in the motor cortex (MCx) remain to be clarified. To further elucidate the nature of this intracortical connectivity pattern, experiments were done on the MCx of three cats. The anterograde tracer biocytin was ejected......Capaday C, Ethier C, Brizzi L, Sik A, van Vreeswijk C, Gingras D. On the nature of the intrinsic connectivity of the cat motor cortex: evidence for a recurrent neural network topology. J Neurophysiol 102: 2131-2141, 2009. First published July 22, 2009; doi: 10.1152/jn.91319.2008. The details...... iontophoretically in layers II, III, and V. Some 30-50 neurons within a radius of similar to 250 mu m were thus stained. The functional output of the motor cortical point at which biocytin was injected, and of the surrounding points, was identified by microstimulation and electromyographic recordings. The axonal...

Stimulus-dependent maximum entropy models of neural population codes.

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Einat Granot-Atedgi

Full Text Available Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.

GH mediates exercise-dependent activation of SVZ neural precursor cells in aged mice.

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Daniel G Blackmore

Full Text Available Here we demonstrate, both in vivo and in vitro, that growth hormone (GH mediates precursor cell activation in the subventricular zone (SVZ of the aged (12-month-old brain following exercise, and that GH signaling stimulates precursor activation to a similar extent to exercise. Our results reveal that both addition of GH in culture and direct intracerebroventricular infusion of GH stimulate neural precursor cells in the aged brain. In contrast, no increase in neurosphere numbers was observed in GH receptor null animals following exercise. Continuous infusion of a GH antagonist into the lateral ventricle of wild-type animals completely abolished the exercise-induced increase in neural precursor cell number. Given that the aged brain does not recover well after injury, we investigated the direct effect of exercise and GH on neural precursor cell activation following irradiation. This revealed that physical exercise as well as infusion of GH promoted repopulation of neural precursor cells in irradiated aged animals. Conversely, infusion of a GH antagonist during exercise prevented recovery of precursor cells in the SVZ following irradiation.

GH Mediates Exercise-Dependent Activation of SVZ Neural Precursor Cells in Aged Mice

Science.gov (United States)

Blackmore, Daniel G.; Vukovic, Jana; Waters, Michael J.; Bartlett, Perry F.

2012-01-01

Here we demonstrate, both in vivo and in vitro, that growth hormone (GH) mediates precursor cell activation in the subventricular zone (SVZ) of the aged (12-month-old) brain following exercise, and that GH signaling stimulates precursor activation to a similar extent to exercise. Our results reveal that both addition of GH in culture and direct intracerebroventricular infusion of GH stimulate neural precursor cells in the aged brain. In contrast, no increase in neurosphere numbers was observed in GH receptor null animals following exercise. Continuous infusion of a GH antagonist into the lateral ventricle of wild-type animals completely abolished the exercise-induced increase in neural precursor cell number. Given that the aged brain does not recover well after injury, we investigated the direct effect of exercise and GH on neural precursor cell activation following irradiation. This revealed that physical exercise as well as infusion of GH promoted repopulation of neural precursor cells in irradiated aged animals. Conversely, infusion of a GH antagonist during exercise prevented recovery of precursor cells in the SVZ following irradiation. PMID:23209615

The dissociable neural dynamics of cognitive conflict and emotional conflict control: An ERP study.

Science.gov (United States)

Xue, Song; Li, Yu; Kong, Xia; He, Qiaolin; Liu, Jia; Qiu, Jiang

2016-04-21

This study investigated differences in the neural time-course of cognitive conflict and emotional conflict control, using event-related potentials (ERPs). Although imaging studies have provided some evidence that distinct, dissociable neural systems underlie emotional and nonemotional conflict resolution, no ERP study has directly compared these two types of conflict. Therefore, the present study used a modified face-word Stroop task to explore the electrophysiological correlates of cognitive and emotional conflict control. The behavioral data showed that the difference in response time of congruency (incongruent condition minus the congruent condition) was larger in the cognitive conflict task than in the emotional conflict task, which indicated that cognitive conflict was stronger than the emotional conflict in the present tasks. Analysis of the ERP data revealed a main effect of task type on N2, which may be associated with top-down attention. The N450 results showed an interaction between cognitive and emotional conflict, which might be related to conflict detection. In addition, we found the incongruent condition elicited a larger SP than the congruent condition, which might be related to conflict resolution. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Neural-Network Object-Recognition Program

Science.gov (United States)

Spirkovska, L.; Reid, M. B.

1993-01-01

HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

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.

What Can Psychiatric Disorders Tell Us about Neural Processing of the Self?

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Zhao, Weihua; Luo, Lizhu; Li, Qin; Kendrick, Keith M

2013-01-01

Many psychiatric disorders are associated with abnormal self-processing. While these disorders also have a wide-range of complex, and often heterogeneous sets of symptoms involving different cognitive, emotional, and motor domains, an impaired sense of self can contribute to many of these. Research investigating self-processing in healthy subjects has facilitated identification of changes in specific neural circuits which may cause altered self-processing in psychiatric disorders. While there is evidence for altered self-processing in many psychiatric disorders, here we will focus on four of the most studied ones, schizophrenia, autism spectrum disorder (ASD), major depression, and borderline personality disorder (BPD). We review evidence for dysfunction in two different neural systems implicated in self-processing, namely the cortical midline system (CMS) and the mirror neuron system (MNS), as well as contributions from altered inter-hemispheric connectivity (IHC). We conclude that while abnormalities in frontal-parietal activity and/or connectivity in the CMS are common to all four disorders there is more disruption of integration between frontal and parietal regions resulting in a shift toward parietal control in schizophrenia and ASD which may contribute to the greater severity and delusional aspects of their symptoms. Abnormalities in the MNS and in IHC are also particularly evident in schizophrenia and ASD and may lead to disturbances in sense of agency and the physical self in these two disorders. A better future understanding of how changes in the neural systems sub-serving self-processing contribute to different aspects of symptom abnormality in psychiatric disorders will require that more studies carry out detailed individual assessments of altered self-processing in conjunction with measurements of neural functioning.

Morphological neural networks

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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.

Novel maximum-margin training algorithms for supervised neural networks.

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Ludwig, Oswaldo; Nunes, Urbano

2010-06-01

This paper proposes three novel training methods, two of them based on the backpropagation approach and a third one based on information theory for multilayer perceptron (MLP) binary classifiers. Both backpropagation methods are based on the maximal-margin (MM) principle. The first one, based on the gradient descent with adaptive learning rate algorithm (GDX) and named maximum-margin GDX (MMGDX), directly increases the margin of the MLP output-layer hyperplane. The proposed method jointly optimizes both MLP layers in a single process, backpropagating the gradient of an MM-based objective function, through the output and hidden layers, in order to create a hidden-layer space that enables a higher margin for the output-layer hyperplane, avoiding the testing of many arbitrary kernels, as occurs in case of support vector machine (SVM) training. The proposed MM-based objective function aims to stretch out the margin to its limit. An objective function based on Lp-norm is also proposed in order to take into account the idea of support vectors, however, overcoming the complexity involved in solving a constrained optimization problem, usually in SVM training. In fact, all the training methods proposed in this paper have time and space complexities O(N) while usual SVM training methods have time complexity O(N (3)) and space complexity O(N (2)) , where N is the training-data-set size. The second approach, named minimization of interclass interference (MICI), has an objective function inspired on the Fisher discriminant analysis. Such algorithm aims to create an MLP hidden output where the patterns have a desirable statistical distribution. In both training methods, the maximum area under ROC curve (AUC) is applied as stop criterion. The third approach offers a robust training framework able to take the best of each proposed training method. The main idea is to compose a neural model by using neurons extracted from three other neural networks, each one previously trained by

Grand Research Plan for Neural Circuits of Emotion and Memory--current status of neural circuit studies in China.

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Zhu, Yuan-Gui; Cao, He-Qi; Dong, Er-Dan

2013-02-01

During recent years, major advances have been made in neuroscience, i.e., asynchronous release, three-dimensional structural data sets, saliency maps, magnesium in brain research, and new functional roles of long non-coding RNAs. Especially, the development of optogenetic technology provides access to important information about relevant neural circuits by allowing the activation of specific neurons in awake mammals and directly observing the resulting behavior. The Grand Research Plan for Neural Circuits of Emotion and Memory was launched by the National Natural Science Foundation of China. It takes emotion and memory as its main objects, making the best use of cutting-edge technologies from medical science, life science and information science. In this paper, we outline the current status of neural circuit studies in China and the technologies and methodologies being applied, as well as studies related to the impairments of emotion and memory. In this phase, we are making efforts to repair the current deficiencies by making adjustments, mainly involving four aspects of core scientific issues to investigate these circuits at multiple levels. Five research directions have been taken to solve important scientific problems while the Grand Research Plan is implemented. Future research into this area will be multimodal, incorporating a range of methods and sciences into each project. Addressing these issues will ensure a bright future, major discoveries, and a higher level of treatment for all affected by debilitating brain illnesses.

Ordered self-assembled monolayers terminated with different chemical functional groups direct neural stem cell linage behaviours

International Nuclear Information System (INIS)

Yao, Shenglian; Liu, Xi; He, Jin; Wang, Xiumei; Wang, Ying; Cui, Fu-Zhai

2016-01-01

Neural stem cells (NSCs) have been a promising candidate for stem cell-based nerve tissue regeneration. Therefore, the design of idea biomaterials that deliver precise regulatory signals to control stem cell fate is currently a crucial issue that depends on a profound understanding of the interactions between NSCs with the surrounding micro-environment. In this work, self-assembled monolayers of alkanethiols on gold with different chemical groups, including hydroxyl (−OH), amino (−NH 2 ), carboxyl (−COOH) and methyl (−CH 3 ), were used as a simple model to study the effects of surface chemistry on NSC fate decisions. Contact angle measurement and x-ray photoelectron spectroscopy (XPS) examination implied that all types of alkanethiols self-assembled on gold into a close-packed phase structure with similar molecular densities. In this study, we evaluated NSC adhesion, migration and differentiation in response to different chemical functional groups cultured under serum-free conditions. Our studies showed that NSCs exhibited certain phenotypes with extreme sensitivity to surface chemical groups. Compared with other functional groups, the SAMs with hydroxyl end-groups provided the best micro-environment in promoting NSC migration and maintaining an undifferentiated or neuronal differentiation state.  −NH 2 surfaces directed neural stem cells into astrocytic lineages, while NSCs on  −COOH and  −CH 3 surfaces had a similar potency to differentiate into three nerve lineages. To further investigate the possible signaling pathway, the gene expression of integrin β1 and β4 were examined. The results indicated that a high expression of β1 integrin would probably have a tight correlation with the expression of nestin, which implied the stemness of NSCs, while β4 integrin seemed to correspond to the differentiated NSCs. The results presented here give useful information for the future design of biomaterials to regulate the preservation

Factor structure of suggestibility revisited: new evidence for direct and indirect suggestibility

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Romuald Polczyk

2016-05-01

Full Text Available Background Yielding to suggestions can be viewed as a relatively stable individual trait, called suggestibility. It has been long proposed that there are two kinds of suggestible influence, and two kinds of suggestibility corresponding to them: direct and indirect. Direct suggestion involves overt unhidden influence, while indirect suggestion concerns influence that is hidden, and the participant does not know that the suggestibility is being measured. So far however, empirical evidence for the existence of the two factors has been scarce. In the present study, more sophisticated and reliable tools for measuring suggestibility were applied than in the previous research, in the hope that better measurement would reveal the factor structure of suggestibility. Two tests of direct suggestibility were used: the Harvard Group Scale of Hypnotic Susceptibility, Form A, measuring hypnotic susceptibility, and the Barber Suggestibility Scale, measuring non-hypnotic direct imaginative suggestibility. Three tests served to measure indirect suggestibility: the Sensory Suggestibility Scale, measuring indirect suggestibility relating to perception; the Gudjonsson Suggestibility Scale, measuring the tendency to yield to suggestive questions and changing answers after negative feedback; and the Emotional Dialogs Tests, measuring the tendency to perceive nonexistent aggression. Participants and procedure In sum, 115 participants were tested, 69 women, 49 men, mean age 22.20 years, SD = 2.20. Participants were tested in two sessions, lasting for a total of four hours. Results Confirmatory factor analyses confirmed the existence of two uncorrelated factors of suggestibility: direct and indirect. Conclusions Suggestibility may indeed involve two factors, direct and indirect, and failure to discover them in previous research may be due to methodological problems.

Direct behavioral evidence for retronasal olfaction in rats.

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Shree Hari Gautam

Full Text Available The neuroscience of flavor perception is becoming increasingly important to understand abnormal feeding behaviors and associated chronic diseases such as obesity. Yet, flavor research has mainly depended on human subjects due to the lack of an animal model. A crucial step towards establishing an animal model of flavor research is to determine whether the animal uses the retronasal mode of olfaction, an essential element of flavor perception. We designed a go- no go behavioral task to test the rat's ability to detect and discriminate retronasal odorants. In this paradigm, tasteless aqueous solutions of odorants were licked by water-restricted head-fixed rats from a lick spout. Orthonasal contamination was avoided by employing a combination of a vacuum around the lick-spout and blowing clean air toward the nose. Flow models support the effectiveness of both approaches. The licked odorants were successfully discriminated by rats. Moreover, the tasteless odorant amyl acetate was reliably discriminated against pure distilled water in a concentration-dependent manner. The results from this retronasal odor discrimination task suggest that rats are capable of smelling retronasally. This direct behavioral evidence establishes the rat as a useful animal model for flavor research.

Explaining neural signals in human visual cortex with an associative learning model.

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Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Body-specific motor imagery of hand actions: neural evidence from right- and left-handers

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Roel M Willems

2009-11-01

Full Text Available If motor imagery uses neural structures involved in action execution, then the neural correlates of imagining an action should differ between individuals who tend to execute the action differently. Here we report fMRI data showing that motor imagery is influenced by the way people habitually perform motor actions with their particular bodies; that is, motor imagery is ‘body-specific’ (Casasanto, 2009. During mental imagery for complex hand actions, activation of cortical areas involved in motor planning and execution was left-lateralized in right-handers but right-lateralized in left-handers. We conclude that motor imagery involves the generation of an action plan that is grounded in the participant’s motor habits, not just an abstract representation at the level of the action’s goal. People with different patterns of motor experience form correspondingly different neurocognitive representations of imagined actions.

A functional neuroimaging study assessing gender differences in the neural mechanisms underlying the ability to resist impulsive desires.

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Diekhof, Esther K; Keil, Maria; Obst, Katrin U; Henseler, Ilona; Dechent, Peter; Falkai, Peter; Gruber, Oliver

2012-09-14

There is ample evidence of gender differences in neural processes and behavior. Differences in reward-related behaviors have been linked to either temporary or permanent organizational influences of gonadal hormones on the mesolimbic dopamine system and reward-related activation. Still, little is known about the association between biological gender and the neural underpinnings of the ability to resist reward-related impulses. Here we assessed with functional magnetic resonance imaging which neural processes enable men and women to successfully control their desire for immediate reward when this is required by a higher-order goal (i.e., during a 'desire-reason dilemma'; Diekhof and Gruber, 2010). Thirty-two participants (16 females) were closely matched for age, personality characteristics (e.g., novelty seeking) and behavioral performance in the 'desire-reason task'. On the neural level, men and women showed similarities in the general response of the nucleus accumbens and of the ventral tegmental area to predictors of immediate reward, but they differed in additional brain mechanisms that enabled self-controlled decisions against the preference for immediate reward. Firstly, men exhibited a stronger reduction of activation in the ventral pallidum, putamen, temporal pole and pregenual anterior cingulate cortex during the 'desire-reason dilemma'. Secondly, connectivity analyses revealed a significant change in the direction of the connectivity between anteroventral prefrontal cortex and nucleus accumbens during decisions counteracting the reward-related impulse when comparing men and women. Together, these findings support the view of a sexual dimorphism that manifested in the recruitment of gender-specific neural resources during the successful deployment of self-control. Copyright © 2012 Elsevier B.V. All rights reserved.

Neural Correlates of Direct Access Trading in a Real Stock Market: An fMRI Investigation.

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Raggetti, GianMario; Ceravolo, Maria G; Fattobene, Lucrezia; Di Dio, Cinzia

2017-01-01

Background: While financial decision making has been barely explored, no study has previously investigated the neural correlates of individual decisions made by professional traders involved in real stock market negotiations, using their own financial resources. Aim: We sought to detect how different brain areas are modulated by factors like age, expertise, psychological profile (speculative risk seeking or aversion) and, eventually, size and type (Buy/Sell) of stock negotiations, made through Direct Access Trading (DAT) platforms. Subjects and methods: Twenty male traders underwent fMRI while negotiating in the Italian stock market using their own preferred trading platform. Results: At least 20 decision events were collected during each fMRI session. Risk averse traders performed a lower number of financial transactions with respect to risk seekers, with a lower average economic value, but with a higher rate of filled proposals. Activations were observed in cortical and subcortical areas traditionally involved in decision processes, including the ventrolateral and dorsolateral prefrontal cortex (vlPFC, dlPFC), the posterior parietal cortex (PPC), the nucleus accumbens (NAcc), and dorsal striatum. Regression analysis indicated an important role of age in modulating activation of left NAcc, while traders' expertise was negatively related to activation of vlPFC. High value transactions were associated with a stronger activation of the right PPC when subjects' buy rather than sell. The success of the trading activity, based on a large number of filled transactions, was related with higher activation of vlPFC and dlPFC. Independent of chronological and professional age, traders differed in their attitude to DAT, with distinct brain activity profiles being detectable during fMRI sessions. Those subjects who described themselves as very self-confident, showed a lower or absent activation of both the caudate nucleus and the dlPFC, while more reflexive traders showed

Neural Correlates of Direct Access Trading in a Real Stock Market: An fMRI Investigation

Directory of Open Access Journals (Sweden)

GianMario Raggetti

2017-09-01

Full Text Available Background: While financial decision making has been barely explored, no study has previously investigated the neural correlates of individual decisions made by professional traders involved in real stock market negotiations, using their own financial resources.Aim: We sought to detect how different brain areas are modulated by factors like age, expertise, psychological profile (speculative risk seeking or aversion and, eventually, size and type (Buy/Sell of stock negotiations, made through Direct Access Trading (DAT platforms.Subjects and methods: Twenty male traders underwent fMRI while negotiating in the Italian stock market using their own preferred trading platform.Results: At least 20 decision events were collected during each fMRI session. Risk averse traders performed a lower number of financial transactions with respect to risk seekers, with a lower average economic value, but with a higher rate of filled proposals. Activations were observed in cortical and subcortical areas traditionally involved in decision processes, including the ventrolateral and dorsolateral prefrontal cortex (vlPFC, dlPFC, the posterior parietal cortex (PPC, the nucleus accumbens (NAcc, and dorsal striatum. Regression analysis indicated an important role of age in modulating activation of left NAcc, while traders' expertise was negatively related to activation of vlPFC. High value transactions were associated with a stronger activation of the right PPC when subjects' buy rather than sell. The success of the trading activity, based on a large number of filled transactions, was related with higher activation of vlPFC and dlPFC. Independent of chronological and professional age, traders differed in their attitude to DAT, with distinct brain activity profiles being detectable during fMRI sessions. Those subjects who described themselves as very self-confident, showed a lower or absent activation of both the caudate nucleus and the dlPFC, while more reflexive traders

Weather forecasting based on hybrid neural model

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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.

Behavioral and neuroimaging evidence for overreliance on habit learning in alcohol-dependent patients.

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Sjoerds, Z; de Wit, S; van den Brink, W; Robbins, T W; Beekman, A T F; Penninx, B W J H; Veltman, D J

2013-12-17

Substance dependence is characterized by compulsive drug-taking despite negative consequences. Animal research suggests an underlying imbalance between goal-directed and habitual action control with chronic drug use. However, this imbalance, and its associated neurophysiological mechanisms, has not yet been experimentally investigated in human drug abusers. The aim of the present study therefore was to assess the balance between goal-directed and habit-based learning and its neural correlates in abstinent alcohol-dependent (AD) patients. A total of 31 AD patients and 19 age, gender and education matched healthy controls (HC) underwent functional magnetic resonance imaging (fMRI) during completion of an instrumental learning task designed to study the balance between goal-directed and habit learning. Task performance and task-related blood oxygen level-dependent activations in the brain were compared between AD patients and healthy matched controls. Findings were additionally associated with duration and severity of alcohol dependence. The results of this study provide evidence for an overreliance on stimulus-response habit learning in AD compared with HC, which was accompanied by decreased engagement of brain areas implicated in goal-directed action (ventromedial prefrontal cortex and anterior putamen) and increased recruitment of brain areas implicated in habit learning (posterior putamen) in AD patients. In conclusion, this is the first human study to provide experimental evidence for a disturbed balance between goal-directed and habitual control by use of an instrumental learning task, and to directly implicate cortical dysfunction to overreliance on inflexible habits in AD patients.

Large-scale multielectrode recording and stimulation of neural activity

International Nuclear Information System (INIS)

Sher, A.; Chichilnisky, E.J.; Dabrowski, W.; Grillo, A.A.; Grivich, M.; Gunning, D.; Hottowy, P.; Kachiguine, S.; Litke, A.M.; Mathieson, K.; Petrusca, D.

2007-01-01

Large circuits of neurons are employed by the brain to encode and process information. How this encoding and processing is carried out is one of the central questions in neuroscience. Since individual neurons communicate with each other through electrical signals (action potentials), the recording of neural activity with arrays of extracellular electrodes is uniquely suited for the investigation of this question. Such recordings provide the combination of the best spatial (individual neurons) and temporal (individual action-potentials) resolutions compared to other large-scale imaging methods. Electrical stimulation of neural activity in turn has two very important applications: it enhances our understanding of neural circuits by allowing active interactions with them, and it is a basis for a large variety of neural prosthetic devices. Until recently, the state-of-the-art in neural activity recording systems consisted of several dozen electrodes with inter-electrode spacing ranging from tens to hundreds of microns. Using silicon microstrip detector expertise acquired in the field of high-energy physics, we created a unique neural activity readout and stimulation framework that consists of high-density electrode arrays, multi-channel custom-designed integrated circuits, a data acquisition system, and data-processing software. Using this framework we developed a number of neural readout and stimulation systems: (1) a 512-electrode system for recording the simultaneous activity of as many as hundreds of neurons, (2) a 61-electrode system for electrical stimulation and readout of neural activity in retinas and brain-tissue slices, and (3) a system with telemetry capabilities for recording neural activity in the intact brain of awake, naturally behaving animals. We will report on these systems, their various applications to the field of neurobiology, and novel scientific results obtained with some of them. We will also outline future directions

Neural Integration of Information Specifying Human Structure from Form, Motion, and Depth

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Jackson, Stuart; Blake, Randolph

2010-01-01

Recent computational models of biological motion perception operate on ambiguous two-dimensional representations of the body (e.g., snapshots, posture templates) and contain no explicit means for disambiguating the three-dimensional orientation of a perceived human figure. Are there neural mechanisms in the visual system that represent a moving human figure’s orientation in three dimensions? To isolate and characterize the neural mechanisms mediating perception of biological motion, we used an adaptation paradigm together with bistable point-light (PL) animations whose perceived direction of heading fluctuates over time. After exposure to a PL walker with a particular stereoscopically defined heading direction, observers experienced a consistent aftereffect: a bistable PL walker, which could be perceived in the adapted orientation or reversed in depth, was perceived predominantly reversed in depth. A phase-scrambled adaptor produced no aftereffect, yet when adapting and test walkers differed in size or appeared on opposite sides of fixation aftereffects did occur. Thus, this heading direction aftereffect cannot be explained by local, disparity-specific motion adaptation, and the properties of scale and position invariance imply higher-level origins of neural adaptation. Nor is disparity essential for producing adaptation: when suspended on top of a stereoscopically defined, rotating globe, a context-disambiguated “globetrotter” was sufficient to bias the bistable walker’s direction, as were full-body adaptors. In sum, these results imply that the neural signals supporting biomotion perception integrate information on the form, motion, and three-dimensional depth orientation of the moving human figure. Models of biomotion perception should incorporate mechanisms to disambiguate depth ambiguities in two-dimensional body representations. PMID:20089892

Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

KAUST Repository

Merzaban, Jasmeen S.

2015-09-13

Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs locally, thus mandating optimization of vascular delivery of the cells to involved sites. Here, we analyzed NSCs for expression of molecular effectors of cell migration and found that these cells are natively devoid of E-selectin ligands. Using glycosyltransferase-programmed stereosubstitution (GPS), we glycan engineered the cell surface of NSCs ("GPS-NSCs") with resultant enforced expression of the potent E-selectin ligand HCELL (hematopoietic cell E-/L-selectin ligand) and of an E-selectin-binding glycoform of neural cell adhesion molecule ("NCAM-E"). Following intravenous (i.v.) injection, short-term homing studies demonstrated that, compared with buffer-treated (control) NSCs, GPS-NSCs showed greater neurotropism. Administration of GPS-NSC significantly attenuated the clinical course of experimental autoimmune encephalomyelitis (EAE), with markedly decreased inflammation and improved oligodendroglial and axonal integrity, but without evidence of long-term stem cell engraftment. Notably, this effect of NSC is not a universal property of adult stem cells, as administration of GPS-engineered mouse hematopoietic stem/progenitor cells did not improve EAE clinical course. These findings highlight the utility of cell surface glycan engineering to boost stem cell delivery in neuroinflammatory conditions and indicate that, despite the use of a neural tissue-specific progenitor cell population, neural repair in EAE results from endogenous repair and not from direct, NSC-derived cell replacement.

Exploring the role of neural mirroring in children with autism spectrum disorder.

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Ruysschaert, Lieselot; Warreyn, Petra; Wiersema, Jan R; Oostra, Ann; Roeyers, Herbert

2014-04-01

Investigating the underlying neural mechanisms of autism spectrum disorder (ASD) has recently been influenced by the discovery of mirror neurons. These neurons, active during both observation and execution of actions, are thought to play a crucial role in imitation and other social-communicative skills that are often impaired in ASD. In the current electroencephalographic study, we investigated mu suppression, indicating neural mirroring in children with ASD between the ages of 24 and 48 months and age-matched typically developing children, during observation of goal-directed actions and non-goal-directed mimicked hand movements, as well as during action execution. Results revealed no significant group differences with significant central mu suppression in the ASD children and control children during both execution and observation of goal-directed actions and during observation of hand movements. Furthermore, no significant correlations between mu suppression on one hand and quality of imitation, age, and social communication questionnaire scores on the other hand were found. These findings challenge the "broken mirror" hypothesis of ASD, suggesting that impaired neural mirroring is not a distinctive feature of ASD. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Neural synchrony in cortical networks: history, concept and current status

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Peter Uhlhaas

2009-07-01

Full Text Available Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

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

The neural basis of body form and body action agnosia.

Science.gov (United States)

Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria

2008-10-23

Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.

Shared or separate mechanisms for self-face and other-face processing? Evidence from adaptation.

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Brendan eRooney

2012-03-01

Full Text Available Evidence that self-face recognition is dissociable from general face recognition hasimportant implications both for models of social cognition and for our understanding offace recognition. In two studies, we examine how adaptation affects the perception ofpersonally familiar faces, and we use a visual adaptation paradigm to investigatewhether the neural mechanisms underlying the recognition of one’s own and other facesare shared or separate. In Study 1 we show that the representation of personally familiarfaces is rapidly updated by visual experience with unfamiliar faces, so that theperception of one’s own face and a friend’s face is altered by a brief period ofadaptation to distorted unfamiliar faces. In Study 2, participants adapted to images oftheir own and a friend’s face distorted in opposite directions; the contingent aftereffectswe observe are indicative of separate neural populations, but we suggest that thesereflect coding of facial identity rather than of the categories ‘self’ and ‘other’.

RBF Neural Network Approach for Identification and Control of DC Motors

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EA Feilat

2012-12-01

Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Direct behavioral and neurophysiological evidence for retronasal olfaction in mice.

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Michelle R Rebello

Full Text Available The neuroscience of flavor perception is hence becoming increasingly important to understand food flavor perception that guides food selection, ingestion and appreciation. We recently provided evidence that rats can use the retronasal mode of olfaction, an essential element of human flavor perception. We showed that in rats, like humans, odors can acquire a taste. We and others also defined how the input of the olfactory bulb (OB -not functionally imageable in humans- codes retronasal smell in anesthetized rat. The powerful awake transgenic mouse, however, would be a valuable additional model in the study of flavor neuroscience. We used a go/no-go behavioral task to test the mouse's ability to detect and discriminate the retronasal odor amyl acetate. In this paradigm a tasteless aqueous odor solution was licked by water-restricted head-fixed mice from a lick spout. Orthonasal contamination was avoided. The retronasal odor was successfully discriminated by mice against pure distilled water in a concentration-dependent manner. Bulbectomy removed the mice's ability to discriminate the retronasal odor but not tastants. The OB showed robust optical calcium responses to retronasal odorants in these awake mice. These results suggest that mice, like rats, are capable of smelling retronasally. This direct neuro-behavioral evidence establishes the mouse as a useful additional animal model for flavor research.

End-to-End Neural Optical Music Recognition of Monophonic Scores

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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.

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

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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

Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

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Ayman Hamdy Kassem

2011-01-01

Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

Neural Substrates of Semantic Prospection – Evidence from the Dementias

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Irish, Muireann; Eyre, Nadine; Dermody, Nadene; O’Callaghan, Claire; Hodges, John R.; Hornberger, Michael; Piguet, Olivier

2016-01-01

The ability to envisage personally relevant events at a future time point represents an incredibly sophisticated cognitive endeavor and one that appears to be intimately linked to episodic memory integrity. Far less is known regarding the neurocognitive mechanisms underpinning the capacity to envisage non-personal future occurrences, known as semantic future thinking. Moreover the degree of overlap between the neural substrates supporting episodic and semantic forms of prospection remains unclear. To this end, we sought to investigate the capacity for episodic and semantic future thinking in Alzheimer’s disease (n = 15) and disease-matched behavioral-variant frontotemporal dementia (n = 15), neurodegenerative disorders characterized by significant medial temporal lobe (MTL) and frontal pathology. Participants completed an assessment of past and future thinking across personal (episodic) and non-personal (semantic) domains, as part of a larger neuropsychological battery investigating episodic and semantic processing, and their performance was contrasted with 20 age- and education-matched healthy older Controls. Participants underwent whole-brain T1-weighted structural imaging and voxel-based morphometry analysis was conducted to determine the relationship between gray matter integrity and episodic and semantic future thinking. Relative to Controls, both patient groups displayed marked future thinking impairments, extending across episodic and semantic domains. Analyses of covariance revealed that while episodic future thinking deficits could be explained solely in terms of episodic memory proficiency, semantic prospection deficits reflected the interplay between episodic and semantic processing. Distinct neural correlates emerged for each form of future simulation with differential involvement of prefrontal, lateral temporal, and medial temporal regions. Notably, the hippocampus was implicated irrespective of future thinking domain, with the suggestion of

Neural Substrates of Semantic Prospection – Evidence from the Dementias

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Muireann eIrish

2016-05-01

Full Text Available The ability to envisage personally relevant events at a future time point represents an incredibly sophisticated cognitive endeavor and one that appears to be intimately linked to episodic memory integrity. Far less is known regarding the neurocognitive mechanisms underpinning the capacity to envisage non-personal future occurrences, known as semantic future thinking. Moreover the degree of overlap between the neural substrates supporting episodic and semantic forms of prospection remains unclear. To this end, we sought to investigate the capacity for episodic and semantic future thinking in Alzheimer’s disease (n = 15 and disease-matched behavioral-variant frontotemporal dementia (n = 15, neurodegenerative disorders characterized by significant medial temporal lobe and frontal pathology. Participants completed an assessment of past and future thinking across personal (episodic and non-personal (semantic domains, as part of a larger neuropsychological battery investigating episodic and semantic processing, and their performance was contrasted with 20 age- and education-matched healthy older Controls. Participants underwent whole-brain T1 weighted structural imaging and voxel-based morphometry analysis was conducted to determine the relationship between grey matter integrity and episodic and semantic future thinking. Relative to Controls, both patient groups displayed marked future thinking impairments, extending across episodic and semantic domains. Analyses of covariance revealed that while episodic future thinking deficits could be explained solely in terms of episodic memory proficiency, semantic prospection deficits reflected the interplay between episodic and semantic processing. Distinct neural correlates emerged for each form of future simulation with differential involvement of prefrontal, lateral temporal and medial temporal regions. Notably, the hippocampus was implicated irrespective of future thinking domain, with the suggestion of

Sleep-dependent directional coupling between human neocortex and hippocampus.

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Wagner, Tobias; Axmacher, Nikolai; Lehnertz, Klaus; Elger, Christian E; Fell, Jürgen

2010-02-01

Complex interactions between neocortex and hippocampus are the neural basis of memory formation. Two-step theories of memory formation suggest that initial encoding of novel information depends on the induction of rapid plasticity within the hippocampus, and is followed by a second sleep-dependent step of memory consolidation. These theories predict information flow from the neocortex into the hippocampus during waking state and in the reverse direction during sleep. However, experimental evidence that interactions between hippocampus and neocortex have a predominant direction which reverses during sleep rely on cross-correlation analysis of data from animal experiments and yielded inconsistent results. Here, we investigated directional coupling in intracranial EEG data from human subjects using a phase-modeling approach which is well suited to reveal functional interdependencies in oscillatory data. In general, we observed that the anterior hippocampus predominantly drives nearby and remote brain regions. Surprisingly, however, the influence of neocortical regions on the hippocampus significantly increased during sleep as compared to waking state. These results question the standard model of hippocampal-neocortical interactions and suggest that sleep-dependent consolidation is accomplished by an active retrieval of hippocampal information by the neocortex. Copyright 2009 Elsevier Srl. All rights reserved.

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

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Mermillod, Martial; Grynberg, Delphine; Pio-Lopez, Léo; Rychlowska, Magdalena; Beffara, Brice; Harquel, Sylvain; Vermeulen, Nicolas; Niedenthal, Paula M; Dutheil, Frédéric; Droit-Volet, Sylvie

2017-01-01

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

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

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Martial Mermillod

2018-01-01

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

Bilingualism increases neural response consistency and attentional control: evidence for sensory and cognitive coupling.

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Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

2014-01-01

Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.

Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

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Xin Wang

2016-01-01

Full Text Available In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

Modification of surface/neuron interfaces for neural cell-type specific responses: a review

International Nuclear Information System (INIS)

Chen, Cen; Kong, Xiangdong; Lee, In-Seop

2016-01-01

Surface/neuron interfaces have played an important role in neural repair including neural prostheses and tissue engineered scaffolds. This comprehensive literature review covers recent studies on the modification of surface/neuron interfaces. These interfaces are identified in cases both where the surfaces of substrates or scaffolds were in direct contact with cells and where the surfaces were modified to facilitate cell adhesion and controlling cell-type specific responses. Different sources of cells for neural repair are described, such as pheochromocytoma neuronal-like cell, neural stem cell (NSC), embryonic stem cell (ESC), mesenchymal stem cell (MSC) and induced pluripotent stem cell (iPS). Commonly modified methods are discussed including patterned surfaces at micro- or nano-scale, surface modification with conducting coatings, and functionalized surfaces with immobilized bioactive molecules. These approaches to control cell-type specific responses have enormous potential implications in neural repair. (paper)

Polarity-specific high-level information propagation in neural networks.

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Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals.

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.

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Güçlü, Umut; van Gerven, Marcel A J

2015-07-08

Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream. Copyright © 2015 the authors 0270-6474/15/3510005-10$15.00/0.

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

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Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Direct evidence of strain transfer for InAs island growth on compliant Si substrates

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Marçal, L. A. B.; Magalhães-Paniago, R.; Malachias, Angelo, E-mail: angeloms@fisica.ufmg.br [Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, CEP 31270-901, Belo Horizonte (Brazil); Richard, M.-I. [European Synchrotron (ESRF), ID01 beamline, CS 40220, 38043 Grenoble Cedex 9 (France); Aix-Marseille University, IM2NP-CNRS, Faculté des Sciences de St Jérôme, 13397 Marseille (France); Cavallo, F. [Center for High Technology Materials, University of New Mexico, 1313 Goddard St., Albuquerque, New Mexico 87106 (United States); University of Wisconsin-Madison, 1500 Engineering Drive, Madison, Wisconsin 53706 (United States); Lagally, M. G. [University of Wisconsin-Madison, 1500 Engineering Drive, Madison, Wisconsin 53706 (United States); Schmidt, O. G. [Institute for Integrative Nanosciences, IFW-Dresden, D-01171 Dresden (Germany); Schülli, T. Ü. [European Synchrotron (ESRF), ID01 beamline, CS 40220, 38043 Grenoble Cedex 9 (France); Deneke, Ch. [Laboratório Nacional de Nanotecnologia (LNNano/CNPEM), C.P. 6192, CEP 13083-970, Campinas (Brazil)

2015-04-13

Semiconductor heteroepitaxy on top of thin compliant layers has been explored as a path to make inorganic electronics mechanically flexible as well as to integrate materials that cannot be grown directly on rigid substrates. Here, we show direct evidences of strain transfer for InAs islands on freestanding Si thin films (7 nm). Synchrotron X-ray diffraction measurements using a beam size of 300 × 700 nm{sup 2} can directly probe the strain status of the compliant substrate underneath deposited islands. Using a recently developed diffraction mapping technique, three-dimensional reciprocal space maps were reconstructed around the Si (004) peak for specific illuminated positions of the sample. The strain retrieved was analyzed using continuous elasticity theory via Finite-element simulations. The comparison of experiment and simulations yields the amount of strain from the InAs islands, which is transferred to the compliant Si thin film.

Directed neuronal differentiation of human embryonic stem cells

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Noggle Scott A

2003-10-01

Full Text Available Abstract Background We have developed a culture system for the efficient and directed differentiation of human embryonic stem cells (HESCs to neural precursors and neurons. HESC were maintained by manual passaging and were differentiated to a morphologically distinct OCT-4+/SSEA-4- monolayer cell type prior to the derivation of embryoid bodies. Embryoid bodies were grown in suspension in serum free conditions, in the presence of 50% conditioned medium from the human hepatocarcinoma cell line HepG2 (MedII. Results A neural precursor population was observed within HESC derived serum free embryoid bodies cultured in MedII conditioned medium, around 7–10 days after derivation. The neural precursors were organized into rosettes comprised of a central cavity surrounded by ring of cells, 4 to 8 cells in width. The central cells within rosettes were proliferating, as indicated by the presence of condensed mitotic chromosomes and by phosphoHistone H3 immunostaining. When plated and maintained in adherent culture, the rosettes of neural precursors were surrounded by large interwoven networks of neurites. Immunostaining demonstrated the expression of nestin in rosettes and associated non-neuronal cell types, and a radial expression of Map-2 in rosettes. Differentiated neurons expressed the markers Map-2 and Neurofilament H, and a subpopulation of the neurons expressed tyrosine hydroxylase, a marker for dopaminergic neurons. Conclusion This novel directed differentiation approach led to the efficient derivation of neuronal cultures from HESCs, including the differentiation of tyrosine hydroxylase expressing neurons. HESC were morphologically differentiated to a monolayer OCT-4+ cell type, which was used to derive embryoid bodies directly into serum free conditions. Exposure to the MedII conditioned medium enhanced the derivation of neural precursors, the first example of the effect of this conditioned medium on HESC.

Optimal neural networks for protein-structure prediction

International Nuclear Information System (INIS)

Head-Gordon, T.; Stillinger, F.H.

1993-01-01

The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

microRNA expression in the neural retina: Focus on Müller glia.

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Quintero, Heberto; Lamas, Mónica

2018-03-01

The neural retina hosts a unique specialized type of macroglial cell that not only preserves retinal homeostasis, function, and integrity but also may serve as a source of new neurons during regenerative processes: the Müller cell. Precise microRNA-driven mechanisms of gene regulation impel and direct the processes of Müller glia lineage acquisition from retinal progenitors during development, the triggering of their response to retinal degeneration and, in some cases, Müller cell reprogramming and regenerative events. In this review we survey the recent reports describing, through functional assays, the regulatory role of microRNAs in Müller cell physiology, differentiation potential, and retinal pathology. We discuss also the evidence based on expression analysis that points out the relevance of a Müller glia-specific microRNA signature that would orchestrate these processes. © 2017 Wiley Periodicals, Inc.

Bilingualism provides a neural reserve for aging populations.

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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.

Learning-parameter adjustment in neural networks

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Heskes, Tom M.; Kappen, Bert

1992-06-01

We present a learning-parameter adjustment algorithm, valid for a large class of learning rules in neural-network literature. The algorithm follows directly from a consideration of the statistics of the weights in the network. The characteristic behavior of the algorithm is calculated, both in a fixed and a changing environment. A simple example, Widrow-Hoff learning for statistical classification, serves as an illustration.

Attribution of intentional causation influences the perception of observed movements: Behavioural evidence and neural correlates

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James W Moore

2013-01-01

Full Text Available Recent research on human agency suggests that intentional causation is associated with a subjective compression in the temporal interval between actions and their effects. That is, intentional movements and their causal effects are perceived as closer together in time than equivalent unintentional movements and their causal effects. This so-called intentional binding effect is consistently found for one’s own self-generated actions. It has also been suggested that intentional binding occurs when observing intentional movements of others. However, this evidence is undermined by limitations of the paradigm used. In the current study we aimed to overcome these limitations using a more rigorous design in combination with functional Magnetic Resonance Imaging (fMRI to explore the neural underpinnings of intentional binding of observed movements. In particular, we aimed to identify brain areas sensitive to the interaction between intentionality and causality attributed to the observed action. Our behavioural results confirmed the occurrence of intentional binding for observed movements using this more rigorous paradigm. Our fMRI results highlighted a collection of brain regions whose activity was sensitive to the interaction between intentionality and causation. Intriguingly, these brain regions have previously been implicated in the sense of agency over one’s own movements. We discuss the implications of these results for intentional binding specifically, and the sense of agency more generally.

Handwritten Digits Recognition Using Neural Computing

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Călin Enăchescu

2009-12-01

Full Text Available In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classification task is performed using a Convolutional Neural Network (CNN. CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters, and with robustness to distortions and simple geometric transformations.The main contributions of this paper are related to theoriginal methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background.By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology database.

An ERP study of structural anomalies in native and semantic free artificial grammar: evidence for shared processing mechanisms.

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Tabullo, Ángel; Sevilla, Yamila; Segura, Enrique; Zanutto, Silvano; Wainselboim, Alejandro

2013-08-21

Artificial grammars have been widely applied to the study of sequential learning in language, but few studies have directly compared the neural correlates of artificial and native grammar processing. In this study, we examined Event Related Potentials (ERPs) elicited by structural anomalies in semantic-free artificial grammar sequences and sentences in the subjects' native language (Spanish). Although ERPs differed during early stages, we observed similar posterior negativities (N400) and P600 effects in a late stage. We interpret these results as evidence of at least partially shared neural mechanisms for processing of language and artificial grammars. We suggest that in both the natural and artificial grammars, the N400 and P600 components we observed can be explained as the result of unfulfilled predictions about incoming stimuli. Copyright © 2013 Elsevier B.V. All rights reserved.

Learning-induced neural plasticity of speech processing before birth.

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Partanen, Eino; Kujala, Teija; Näätänen, Risto; Liitola, Auli; Sambeth, Anke; Huotilainen, Minna

2013-09-10

Learning, the foundation of adaptive and intelligent behavior, is based on plastic changes in neural assemblies, reflected by the modulation of electric brain responses. In infancy, auditory learning implicates the formation and strengthening of neural long-term memory traces, improving discrimination skills, in particular those forming the prerequisites for speech perception and understanding. Although previous behavioral observations show that newborns react differentially to unfamiliar sounds vs. familiar sound material that they were exposed to as fetuses, the neural basis of fetal learning has not thus far been investigated. Here we demonstrate direct neural correlates of human fetal learning of speech-like auditory stimuli. We presented variants of words to fetuses; unlike infants with no exposure to these stimuli, the exposed fetuses showed enhanced brain activity (mismatch responses) in response to pitch changes for the trained variants after birth. Furthermore, a significant correlation existed between the amount of prenatal exposure and brain activity, with greater activity being associated with a higher amount of prenatal speech exposure. Moreover, the learning effect was generalized to other types of similar speech sounds not included in the training material. Consequently, our results indicate neural commitment specifically tuned to the speech features heard before birth and their memory representations.

Morphogenetic movements during cranial neural tube closure in the chick embryo and the effect of homocysteine

NARCIS (Netherlands)

Brouns, M.R.; Afman, L.A.; VanHauten, B.A.M.; Hekking, J.W.M.; Kohler, E.S.; Straaten, van H.W.M.

2005-01-01

In order to unravel morphogenetic mechanisms involved in neural tube closure, critical cell movements that are fundamental to remodelling of the cranial neural tube in the chick embryo were studied in vitro by quantitative time-lapse video microscopy. Two main directions of movements were observed.

Morphogenetic movements during cranial neural tube closure in the chick embryo and the effect of homocysteine.

NARCIS (Netherlands)

Brouns, M.R.; Afman, L.A.; Vanhauten, B.A.M.; Hekking, J.W.M.; Köhler, E.S.; Straaten, H.W.M. van

2005-01-01

In order to unravel morphogenetic mechanisms involved in neural tube closure, critical cell movements that are fundamental to remodelling of the cranial neural tube in the chick embryo were studied in vitro by quantitative time-lapse video microscopy. Two main directions of movements were observed.

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

Probabilistic neural network algorithm for using radon emanations as an earthquake precursor

International Nuclear Information System (INIS)

Gupta, Dhawal; Shahani, D.T.

2014-01-01

The investigation throughout the world in past two decades provides evidence which indicate that significance variation of radon and other soil gases occur in association with major geophysical events such as earthquake. The traditional statistical algorithm includes regression to remove the effect of the meteorological parameters from the raw radon and anomalies are calculated either taking the periodicity in seasonal variations or periodicity computed using Fast Fourier Transform. In case of neural networks the regression step is avoided. A neural network model can be found which can learn the behavior of radon with respect to meteorological parameter in order that changing emission patterns may be adapted to by the model on its own. The output of this neural model is the estimated radon values. This estimated radon value is used to decide whether anomalous behavior of radon has occurred and a valid precursor may be identified. The neural network model developed using Radial Basis function network gave a prediction rate of 87.7%. The same was accompanied by huge false alarms. The present paper deals with improved neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period. This neural network model reduces the false alarms to zero and gave same prediction rate as RBF networks. (author)

Neural correlates of uncertain decision making: ERP evidence from the Iowa Gambling Task

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Ji-fang eCui

2013-11-01

Full Text Available In our daily life, it is very common to make decisions in uncertain situations. The Iowa Gambling Task (IGT has been widely used in laboratory studies because of its good simulation of uncertainty in real life activities. The present study aimed to examine the neural correlates of uncertain decision making with the IGT. Twenty-six university students completed this study. An adapted IGT was administered to them, and the EEG data were recorded. The adapted IGT we used allowed us to analyze the choice evaluation, response selection, and feedback evaluation stages of uncertain decision making within the same paradigm. In the choice evaluation stage, the advantageous decks evoked larger P3 amplitude in the left hemisphere, while the disadvantageous decks evoked larger P3 in the right hemisphere. In the response selection stage, the response of pass (the card was not turned over; the participants neither won nor lost money evoked larger negativity preceding the response compared to that of play (the card was turned over; the participant either won or lost money. In the feedback evaluation stage, feedback-related negativity was only sensitive to the valence (win/loss but not the magnitude (large/small of the outcome, and P3 was sensitive to both the valence and the magnitude of the outcome. These results were consistent with the notion that a positive somatic state was represented in the left hemisphere and a negative somatic state was represented in the right hemisphere. There were also anticipatory ERP effects that guided the participants’ responses and provided evidence for the somatic marker hypothesis with more precise timing.

Evidence for a neural dual-process account for adverse effects of cognitive control.

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Zink, Nicolas; Stock, Ann-Kathrin; Colzato, Lorenza; Beste, Christian

2018-06-09

Advantageous effects of cognitive control are well-known, but cognitive control may also have adverse effects, for example when it suppresses the implicit processing of stimulus-response (S-R) bindings that could benefit task performance. Yet, the neurophysiological and functional neuroanatomical structures associated with adverse effects of cognitive control are poorly understood. We used an extreme group approach to compare individuals who exhibit adverse effects of cognitive control to individuals who do not by combining event-related potentials (ERPs), source localization, time-frequency analysis and network analysis methods. While neurophysiological correlates of cognitive control (i.e. N2, N450, theta power and theta-mediated neuronal network efficiency) and task-set updating (P3) both reflect control demands and implicit information processing, differences in the degree of adverse cognitive control effects are associated with two independent neural mechanisms: Individuals, who show adverse behavioral effects of cognitive control, show reduced small-world properties and thus reduced efficiency in theta-modulated networks when they fail to effectively process implicit information. In contrast to this, individuals who do not display adverse control effects show enhanced task-set updating mechanism when effectively processing implicit information, which is reflected by the P3 ERP component and associated with the temporo-parietal junction (TPJ, BA 40) and medial frontal gyrus (MFG; BA 8). These findings suggest that implicit S-R contingencies, which benefit response selection without cognitive control, are always 'picked up', but may fail to be integrated with task representations to guide response selection. This provides evidence for a neurophysiological and functional neuroanatomical "dual-process" account of adverse cognitive control effects.

Direct analysis in real time mass spectrometry for analysis of sexual assault evidence.

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Musah, Rabi A; Cody, Robert B; Dane, A John; Vuong, Angela L; Shepard, Jason R E

2012-05-15

Sexual assault crimes are vastly underreported and suffer from alarmingly low prosecution and conviction rates. The key scientific method to aid in prosecution of such cases is forensic DNA analysis, where biological evidence such as semen collected using a rape test kit is used to determine a suspect's DNA profile. However, the growing awareness by criminals of the importance of DNA in the prosecution of sexual assaults has resulted in increased condom use by assailants as a means to avoid leaving behind their DNA. Thus, other types of trace evidence are important to help corroborate victims' accounts, exonerate the innocent, link suspects to the crime, or confirm penetration. Direct Analysis in Real Time Mass Spectrometry (DART-MS) was employed for the comprehensive characterization of non-DNA trace evidence associated with sexual assault. The ambient ionization method associated with DART-MS is extremely rapid and samples are processed instantaneously, without the need for extraction, sample preparation, or other means that might compromise forensic evidence for future analyses. In a single assay, we demonstrated the ability to identify lubricant formulations associated with sexual assault, such as the spermicide nonoxynol-9, compounds used in condom manufacture, and numerous other trace components as probative evidence. In addition, the method can also serve to identify compounds within trace biological residues, such as fatty acids commonly identified in latent fingerprints. Characterization of lubricant residues as probative evidence serves to establish a connection between the victim and the perpetrator, and the availability of these details may lead to higher rates of prosecution and conviction, as well as more severe penalties. The methodology described here opens the way for the adoption of a comprehensive, rapid, and sensitive analysis for use in crime labs, while providing knowledge that can inform and guide criminal justice policy and practice

Are all spatial reference frames egocentric? Reinterpreting evidence for allocentric, object-centered, or world-centered reference frames

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Flavia eFilimon

2015-12-01

Full Text Available The use and neural representation of egocentric spatial reference frames is well documented. In contrast, whether the brain represents spatial relationships between objects in allocentric, object-centered, or world-centered coordinates is debated. Here, I review behavioral, neuropsychological, neurophysiological (neuronal recording, and neuroimaging evidence for and against allocentric, object-centered, or world-centered spatial reference frames. Based on theoretical considerations, simulations, and empirical findings from spatial navigation, spatial judgments, and goal-directed movements, I suggest that all spatial representations may in fact be dependent on egocentric reference frames.

Direct evidence for impact winter following the Cretaceous-Paleogene bolide impact

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Vellekoop, J.; Sluijs, A.; Smit, J.; Schouten, S.; Sinninghe Damsté, J. S.; Brinkhuis, H.

2012-12-01

The Cretaceous/Paleogene (K/Pg) boundary, ~65.5 Ma, marks a mass-extinction event related the impact of a large asteroid on the Yucatan peninsula, Mexico. Model scenarios predict that the explosive injection of dust and sulfate aerosols into the stratosphere blocked incoming solar radiation, resulting in a cooling pulse of months to several decades, a so-called 'impact winter', but thus far, proxy records lack sufficient resolution to evaluate this hypothesis. We report on a major, short-lived drop in sea surface temperatures (SSTs) recorded in an unusually well preserved and stratigraphically expanded K/Pg boundary site in Texas, USA, based on TEX86 paleothermometry. Critically, the cooling directly post-dates impact-related tsunami deposits, and coincides with the deposition of extraterrestrial iridium representing aerosol fall out, restricting the age of the cooling to the first months to decades after impact. We interpret this cooling to reflect the first direct evidence for the "impact winter" at the K/Pg boundary. The combination of darkness and cooling must have been a key contributory element in the extinctions of many biological clades, including the dinosaurs, flying reptiles and marine reptiles.

Musicians' Enhanced Neural Differentiation of Speech Sounds Arises Early in Life: Developmental Evidence from Ages 3 to 30

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Strait, Dana L.; O'Connell, Samantha; Parbery-Clark, Alexandra; Kraus, Nina

2014-01-01

The perception and neural representation of acoustically similar speech sounds underlie language development. Music training hones the perception of minute acoustic differences that distinguish sounds; this training may generalize to speech processing given that adult musicians have enhanced neural differentiation of similar speech syllables compared with nonmusicians. Here, we asked whether this neural advantage in musicians is present early in life by assessing musically trained and untrained children as young as age 3. We assessed auditory brainstem responses to the speech syllables /ba/ and /ga/ as well as auditory and visual cognitive abilities in musicians and nonmusicians across 3 developmental time-points: preschoolers, school-aged children, and adults. Cross-phase analyses objectively measured the degree to which subcortical responses differed to these speech syllables in musicians and nonmusicians for each age group. Results reveal that musicians exhibit enhanced neural differentiation of stop consonants early in life and with as little as a few years of training. Furthermore, the extent of subcortical stop consonant distinction correlates with auditory-specific cognitive abilities (i.e., auditory working memory and attention). Results are interpreted according to a corticofugal framework for auditory learning in which subcortical processing enhancements are engendered by strengthened cognitive control over auditory function in musicians. PMID:23599166

Predicting tool life in turning operations using neural networks and image processing

Science.gov (United States)

Mikołajczyk, T.; Nowicki, K.; Bustillo, A.; Yu Pimenov, D.

2018-05-01

A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

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.

How necessary is the vasculature in the life of neural stem and progenitor cells? Evidence from evolution, development and the adult nervous system.

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CHRISTOS eKOUTSAKIS

2016-02-01

Full Text Available Augmenting evidence suggests that such is the functional dependence of neural stem cells (NSCs on the vasculature that they normally reside in perivascular niches. Two examples are the neurovascular and the oligovascular niches of the adult brain, specialized microenvironments where NSCs or oligodendrocyte progenitor cells survive and remain mitotically active in close proximity to blood vessels. In addition, the often observed co-ordination of angiogenesis and neurogenesis led to these processes being described as coupled. Here, we adopt an evo-devo approach to argue that some stages in the life of a NSC, such as specification and commitment, are independent of the vasculature, while stages such as proliferation and migration are largely dependent on blood vessels. We also explore available evidence on the possible involvement of the vasculature in other phenomena such as the diversification of NSCs during evolution and we provide original data on the senescence of NSCs in the subependymal zone stem cell niche. Finally, we will comment on the other side of the story; on how much is the vasculature dependent on NSCs and their progeny.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Science.gov (United States)

Kang, Eunhee; Min, Junhong; Ye, Jong Chul

2017-10-01

Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 "Low-Dose CT Grand Challenge." To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from

Optimization of multilayer neural network parameters for speaker recognition

Science.gov (United States)

Tovarek, Jaromir; Partila, Pavol; Rozhon, Jan; Voznak, Miroslav; Skapa, Jan; Uhrin, Dominik; Chmelikova, Zdenka

2016-05-01

This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

OpenAIRE

Hochberg, Leigh R.; Bacher, Daniel; Jarosiewicz, Beata; Masse, Nicolas Y.; Simeral, John D.; Vogel, Joern; Haddadin, Sami; Liu, Jie; Cash, Sydney S.; van der Smagt, Patrick; Donoghue, John P.

2012-01-01

Paralysis following spinal cord injury, brainstemstroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface ...

Direction of Wolf-Rayet stars in a very powerful far-infrared galaxy - Direct evidence for a starburst

International Nuclear Information System (INIS)

Armus, L.; Heckman, T.M.; Miley, G.K.

1988-01-01

Spectra covering the wavelength range 4476-7610 A are presented for the powerful far-infrared galaxy IRAS 01003-2238. The broad emission band centered at a rest wavelength of roughly 4660 A, and other broad weaker features are interpreted, as arising from the combined effect of approximately 100,000 late Wolf-Rayet stars of the WN subtype. This represents perhaps the most direct evidence to date for the presence of a large number of hot massive stars in the nucleus of a very powerful far-infrared galaxy. The high number of Wolf-Rayet stars in relation to the number of O-type stars may be interpreted as arguing against continuous steady state star formation in 01003-2238, in favor of a recent burst of star formation occurring approximately 100 million yrs ago. 24 references

Evidence of nonvagal neural stimulation of canine gastric acid secretion.

Science.gov (United States)

Tansy, M F; Probst, S J; Martin, J S

1975-06-01

In this study, we confirmed our original findings that central vagus stimulation is significantly associated with a subsequent increase in gastric mucus secretion. Central vagus stimulation following phenoxybenzamine hydrochloride administration was associated significantly with protracted elevations in secretory volume and titratable acid. We were unable to conclude that phenoxybenzamine itself in several pharmacologic dosages was associated with an increase in titratable acid. The acid secretory responses could be abolished by transection of the splanchnic nerves. Electrical stimulation of the peripheral part of the splanchnic nerve following administration of phenoxybenzamine was also associated with significant increases in secretory volume and titrable acidity. These secretory responses were not blocked by atropine but were diminished by burimamide. It is concluded that, in the dog, a largely heretofore unsuspected second neural pathway exists which is capable of influencing gastric acid secretion.

The allocation of attention to learning of goal-directed actions: A cognitive neuroscience framework focusing on the basal ganglia

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Liz eFranz

2012-12-01

Full Text Available The present paper builds on the idea that attention is largely in service of our actions. A framework and model which captures the allocation of attention for learning of goal-directed actions is proposed and developed. This framework highlights an evolutionary model based on the notion that rudimentary brain functions have become embedded into increasingly higher levels of networks which all contribute to adaptive learning. Background literature is presented alongside key evidence based on experimental studies in the so-called ‘split-brain’ (surgically divided cerebral hemispheres with a key focus on bimanual actions. The proposed multilevel cognitive-neural system of attention is built upon key processes of a highly-adaptive basal-ganglia-thalamic-cortical system. Although overlap with other existing findings and models is acknowledged where appropriate, the proposed framework is an original synthesis of cognitive experimental findings with supporting evidence of a neural system and a carefully formulated model of attention. It is the hope that this new synthesis will be informative in fields of cognition and other fields of brain sciences and will lead to new avenues for experimentation across domains.

Inactivity-induced respiratory plasticity: Protecting the drive to breathe in disorders that reduce respiratory neural activity☆

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Strey, K.A.; Baertsch, N.A.; Baker-Herman, T.L.

2013-01-01

Multiple forms of plasticity are activated following reduced respiratory neural activity. For example, in ventilated rats, a central neural apnea elicits a rebound increase in phrenic and hypoglossal burst amplitude upon resumption of respiratory neural activity, forms of plasticity called inactivity-induced phrenic and hypoglossal motor facilitation (iPMF and iHMF), respectively. Here, we provide a conceptual framework for plasticity following reduced respiratory neural activity to guide future investigations. We review mechanisms giving rise to iPMF and iHMF, present new data suggesting that inactivity-induced plasticity is observed in inspiratory intercostals (iIMF) and point out gaps in our knowledge. We then survey conditions relevant to human health characterized by reduced respiratory neural activity and discuss evidence that inactivity-induced plasticity is elicited during these conditions. Understanding the physiological impact and circumstances in which inactivity-induced respiratory plasticity is elicited may yield novel insights into the treatment of disorders characterized by reductions in respiratory neural activity. PMID:23816599

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

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

2017-10-17

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

Neural Evidence for a Distinction between Short-Term Memory and the Focus of Attention

Science.gov (United States)

Lewis-Peacock, Jarrod A.; Drysdale, Andrew T.; Oberauer, Klaus; Postle, Bradley R.

2012-01-01

It is widely assumed that the short-term retention of information is accomplished via maintenance of an active neural trace. However, we demonstrate that memory can be preserved across a brief delay despite the apparent loss of sustained representations. Delay period activity may, in fact, reflect the focus of attention, rather than STM. We…

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 mesenchyme is responsible for neural fold elevation. In this study we provide evidence that such expansions may play an important role in cranial neural fold elevation by pushing the folds towards the dorsal midline to assist in their elevation. For mesenchymal expansion to result in fold elevation, hyaluronate (HA) and mesenchymal cells must be non-randomly distributed within the mesenchyme. Patterns of mesenchymal cell distribution and cell proliferation were analyzed using the computer-assisted method of smoothed spatial averaging. The distribution of Alcian blue-stained and 3 H-glucosamine-labelled HA was also analyzed during cranial neural fold elevation using established image processing techniques. Analysis of the distribution of 3 H-thymidine-labelled mesenchymal cells indicated that differential mitotic activity was not responsible for decreased mesenchymal cell density. Likewise, analysis of distribution patterns of 3 H-glucosamine-labelled HA indicated that decreased HA concentration was not produced by regional differences in HA synthesis. These results suggest that decreases in mesenchymal cell density and HA concentration that occur during neural fold elevation are produced by mesenchymal expansion

Neural correlates of executive functions in patients with obesity.

Science.gov (United States)

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

2018-01-01

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

The Neural Basis of Changing Social Norms through Persuasion

OpenAIRE

Yomogida, Yukihito; Matsumoto, Madoka; Aoki, Ryuta; Sugiura, Ayaka; Phillips, Adam N.; Matsumoto, Kenji

2017-01-01

Social norms regulate behavior, and changes in norms have a great impact on society. In most modern societies, norms change through interpersonal communication and persuasive messages found in media. Here, we examined the neural basis of persuasion-induced changes in attitude toward and away from norms using fMRI. We measured brain activity while human participants were exposed to persuasive messages directed toward specific norms. Persuasion directed toward social norms specifically activate...

Contingency management: New directions and remaining challenges for an evidence-based intervention

Science.gov (United States)

Rash, Carla J.; Stitzer, Maxine; Weinstock, Jeremiah

2016-01-01

This article introduces the special issue on contingency management (CM), an efficacious intervention for the treatment of substance use disorders with low uptake in clinical settings that is not commensurate with evidence for efficacy. In this special issue of the Journal of Substance Abuse Treatment, we present 16 articles representing the latest research in efficacy, implementation, and technological advances related to CM. Combined, this collection of articles highlights the diverse populations, settings, and applications of CM in the treatment of substance use disorders. We conclude by highlighting directions for future research, particularly those that may increase CM’s appeal and uptake in routine clinical care. PMID:27746057

Emotion identification and aging: Behavioral and neural age-related changes.

Science.gov (United States)

Gonçalves, Ana R; Fernandes, Carina; Pasion, Rita; Ferreira-Santos, Fernando; Barbosa, Fernando; Marques-Teixeira, João

2018-05-01

Aging is known to alter the processing of facial expressions of emotion (FEE), however the impact of this alteration is less clear. Additionally, there is little information about the temporal dynamics of the neural processing of facial affect. We examined behavioral and neural age-related changes in the identification of FEE using event-related potentials. Furthermore, we analyze the relationship between behavioral/neural responses and neuropsychological functioning. To this purpose, 30 younger adults, 29 middle-aged adults and 26 older adults identified FEE. The behavioral results showed a similar performance between groups. The neural results showed no significant differences between groups for the P100 component and an increased N170 amplitude in the older group. Furthermore, a pattern of asymmetric activation was evident in the N170 component. Results also suggest deficits in facial feature decoding abilities, reflected by a reduced N250 amplitude in older adults. Neuropsychological functioning predicts P100 modulation, but does not seem to influence emotion identification ability. The findings suggest the existence of a compensatory function that would explain the age-equivalent performance in emotion identification. The study may help future research addressing behavioral and neural processes involved on processing of FEE in neurodegenerative conditions. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

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.

Compositionality in Neural Control:An Interdisciplinary Study of Scribbling Movements in Primates

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Moshe eAbeles

2013-09-01

Full Text Available This article discusses the compositional structure of hand movements by analyzing and modeling neural and behavioral data obtained from experiments where a monkey (Macaca fascicularis performed scribbling movements induced by a search task. Using geometrically based approaches to movement segmentation, it is shown that the hand trajectories are composed of elementary segments that are primarily parabolic in shape. The segments could be categorized into a small number of classes on the basis of decreasing intra-class variance over the course of training. A separate classification of the neural data employing a hidden Markov model showed a coincidence of the neural states with the behavioral categories. An additional analysis of both types of data by a data mining method provided evidence that the neural activity patterns underlying the behavioral primitives were formed by sets of specific and precise spike patterns. A geometric description of the movement trajectories, together with precise neural timing data indicates a compositional variant of a realistic synfire chain model. This model reproduces the typical shapes and temporal properties of the trajectories; hence the structure and composition of the primitives may reflect meaningful behavior.

Neural crest stem cell population in craniomaxillofacial development and tissue repair

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M La Noce

2014-10-01

Full Text Available Neural crest cells, delaminating from the neural tube during migration, undergo an epithelial-mesenchymal transition and differentiate into several cell types strongly reinforcing the mesoderm of the craniofacial body area – giving rise to bone, cartilage and other tissues and cells of this human body area. Recent studies on craniomaxillofacial neural crest-derived cells have provided evidence for the tremendous plasticity of these cells. Actually, neural crest cells can respond and adapt to the environment in which they migrate and the cranial mesoderm plays an important role toward patterning the identity of the migrating neural crest cells. In our experience, neural crest-derived stem cells, such as dental pulp stem cells, can actively proliferate, repair bone and give rise to other tissues and cytotypes, including blood vessels, smooth muscle, adipocytes and melanocytes, highlighting that their use in tissue engineering is successful. In this review, we provide an overview of the main pathways involved in neural crest formation, delamination, migration and differentiation; and, in particular, we concentrate our attention on the translatability of the latest scientific progress. Here we try to suggest new ideas and strategies that are needed to fully develop the clinical use of these cells. This effort should involve both researchers/clinicians and improvements in good manufacturing practice procedures. It is important to address studies towards clinical application or take into consideration that studies must have an effective therapeutic prospect for humans. New approaches and ideas must be concentrated also toward stem cell recruitment and activation within the human body, overcoming the classical grafting.

Using neural networks and extreme value distributions to model electricity pool prices: Evidence from the Australian National Electricity Market 1998–2013

International Nuclear Information System (INIS)

Dev, Priya; Martin, Michael A.

2014-01-01

Highlights: • Neural nets are unable to properly capture spiky price behavior found in the electricity market. • We modeled electricity price data from the Australian National Electricity Market over 15 years. • Neural nets need to be augmented with other modeling techniques to capture price spikes. • We fit a Generalized Pareto Distribution to price spikes using a peaks-over-thresholds approach. - Abstract: Competitors in the electricity supply industry desire accurate predictions of electricity spot prices to hedge against financial risks. Neural networks are commonly used for forecasting such prices, but certain features of spot price series, such as extreme price spikes, present critical challenges for such modeling. We investigate the predictive capacity of neural networks for electricity spot prices using Australian National Electricity Market data. Following neural net modeling of the data, we explore extreme price spikes through extreme value modeling, fitting a Generalized Pareto Distribution to price peaks over an estimated threshold. While neural nets capture the smoother aspects of spot price data, they are unable to capture local, volatile features that characterize electricity spot price data. Price spikes can be modeled successfully through extreme value modeling

predicting water levels at kainji dam using artificial neural networks

African Journals Online (AJOL)

2013-03-01

Mar 1, 2013 ... Apart from insufficient number of power generation plants, existing ones are ... ture and/or functional aspects of biological neural net-. Nigerian Journal of ... Their model used the radiosonde-based 700-hPa wind direction and ...

Foreign direct investment and policy framework: New Granger causality evidence from African countries

Directory of Open Access Journals (Sweden)

Rafiu Adewale Aregbeshola

2014-11-01

Full Text Available The strategic importance of foreign direct investment in the contemporary economies has been tremendous.While various countries (developed and developing economies have benefitted from the direct and spillovereffects of FDI, which range from improved technology and knowledge diffusion through to individual andcorporate capability enhancement, FDI outflow remains largely channelled to the developed countries, andthe rapidly developing countries in Asia and South America. Evidence suggests that the developmentenhancingeffects of FDI are felt more highly in the developing economies, such as economies in Africa.However, FDI inflow to the developing economies has been very low. Using data generated from the AfricanDevelopment Indicators (ADI between 1980 and 2008 in econometric estimations, this paper finds thatgovernment policies (especially fiscal and monetary policies play significant roles in facilitating FDI inflow tothe African countries studied. The study thereby suggests an improved regulatory framework to make Africamore attractive to inflow of FDI.

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.

Musical aptitude and second language pronunciation skills in school-aged children: neural and behavioral evidence.

Science.gov (United States)

Milovanov, Riia; Huotilainen, Minna; Välimäki, Vesa; Esquef, Paulo A A; Tervaniemi, Mari

2008-02-15

The main focus of this study was to examine the relationship between musical aptitude and second language pronunciation skills. We investigated whether children with superior performance in foreign language production represent musical sound features more readily in the preattentive level of neural processing compared with children with less-advanced production skills. Sound processing accuracy was examined in elementary school children by means of event-related potential (ERP) recordings and behavioral measures. Children with good linguistic skills had better musical skills as measured by the Seashore musicality test than children with less accurate linguistic skills. The ERP data accompany the results of the behavioral tests: children with good linguistic skills showed more pronounced sound-change evoked activation with the music stimuli than children with less accurate linguistic skills. Taken together, the results imply that musical and linguistic skills could partly be based on shared neural mechanisms.

Outputs of paired Gabor filters summed across the background frame of reference predict the direction of movement

Science.gov (United States)

Lawton, Teri B.

1989-01-01

A cortical neural network that computes the visibility of shifts in the direction of movement is proposed. The network computes: (1) the magnitude of the position difference between the test and background patterns, (2) localized contrast differences at different spatial scales analyzed by computing temporal gradients of the difference and sum of the outputs of paired even- and odd-symmetric bandpass filters convolved with the input pattern, and (3) using global processes that pool the output from paired even- and odd-symmetric simple and complex cells across the spatial extent of the background frame of reference the direction a test pattern moved relative to a textured background. Evidence that magnocellular pathways are used to discriminate the direction of movement is presented. Since magnocellular pathways are used to discriminate the direction of movement, this task is not affected by small pattern changes such as jitter, short presentations, blurring, and different background contrasts that result when the veiling illumination in a scene changes.

Wind Power Plant Prediction by Using Neural Networks: Preprint

Energy Technology Data Exchange (ETDEWEB)

Liu, Z.; Gao, W.; Wan, Y. H.; Muljadi, E.

2012-08-01

This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.

Field-theoretic approach to fluctuation effects in neural networks

International Nuclear Information System (INIS)

Buice, Michael A.; Cowan, Jack D.

2007-01-01

A well-defined stochastic theory for neural activity, which permits the calculation of arbitrary statistical moments and equations governing them, is a potentially valuable tool for theoretical neuroscience. We produce such a theory by analyzing the dynamics of neural activity using field theoretic methods for nonequilibrium statistical processes. Assuming that neural network activity is Markovian, we construct the effective spike model, which describes both neural fluctuations and response. This analysis leads to a systematic expansion of corrections to mean field theory, which for the effective spike model is a simple version of the Wilson-Cowan equation. We argue that neural activity governed by this model exhibits a dynamical phase transition which is in the universality class of directed percolation. More general models (which may incorporate refractoriness) can exhibit other universality classes, such as dynamic isotropic percolation. Because of the extremely high connectivity in typical networks, it is expected that higher-order terms in the systematic expansion are small for experimentally accessible measurements, and thus, consistent with measurements in neocortical slice preparations, we expect mean field exponents for the transition. We provide a quantitative criterion for the relative magnitude of each term in the systematic expansion, analogous to the Ginsburg criterion. Experimental identification of dynamic universality classes in vivo is an outstanding and important question for neuroscience

Neural theory for the perception of causal actions.

Science.gov (United States)

Fleischer, Falk; Christensen, Andrea; Caggiano, Vittorio; Thier, Peter; Giese, Martin A

2012-07-01

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.

Evidence of direct smooth muscle relaxant effects of the fibrate gemfibrozil.

Science.gov (United States)

Phelps, Laura E; Peuler, Jacob D

2010-01-01

Fibrates are commonly employed to treat abnormal lipid metabolism via their unique ability to stimulate peroxisome proliferator-activated receptor alpha (PPARalpha). Interestingly, they also decrease systemic arterial pressure, despite recent evidence that PPAR alpha may contribute to expression of renin and related hypertension. Yet, mechanisms responsible for their potential antihypertensive activity remain unresolved. Rapid decreases in arterial pressure following bolus intravenous injections of bezafibrate strongly suggest they may relax arterial smooth muscle directly. But since bezafibrate is highly susceptible to photodegradation in aqueous media, it has never been critically tested for this possibility in vitro with isolated arterial smooth muscle preparations. Accordingly, we tested gemfibrozil which is resistant to photodegradation. We examined it over a therapeutically-relevant range (50-400 microM) for both acute and delayed relaxant effects on contractions of the isolated rat tail artery; contractions induced by either depolarizing its smooth muscle cell membranes with high potassium or stimulating its membrane-bound receptors with norepinephrine and arginine-vasopressin. We also examined these same gemfibrozil levels for effects on spontaneously-occurring phasic rhythmic contractile activity, typically not seen in arteries under in vitro conditions but commonly exhibited by smooth muscle of uterus, duodenum and bladder. We found that gemfibrozil significantly relaxed all induced forms of contraction in the rat tail artery, acutely at the higher test levels and after a delay of a few hours at the lower test levels. The highest test level of gemfibrozil (400 microM) also completely abolished spontaneously-occurring contractile activity of the isolated uterus and duodenum and markedly suppressed it in the bladder. This is the first evidence that a fibrate drug can directly relax smooth muscle contractions, either induced by various contractile agents or

A Biophysical Neural Model To Describe Spatial Visual Attention

International Nuclear Information System (INIS)

Hugues, Etienne; Jose, Jorge V.

2008-01-01

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations

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)

Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes

Directory of Open Access Journals (Sweden)

Marcia M. Lastre Valdes

2014-06-01

Full Text Available In this paper a review and analysis of the major theories and models that address the prediction of corporate bankruptcy and insolvency is made. Neural networks are a tool of most recent appearance, although in recent years have received considerable attention from the academic and professional world, and have started to be implemented in different models testing organizations insolvency based on neural computation. The purpose of this paper is to yield evidence of the usefulness of Artificial Neural Networks in the problem of bankruptcy prediction insolence or so compare its predictive ability with the methods commonly used in that context. The findings suggest that high predictive capabilities can be achieved  using artificial neural networks, with qualitative and quantitative variables.

Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Barhen, J.; Cogswell, R.; Protopopescu, V.

1999-07-31

A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

Stochastic synchronization of coupled neural networks with intermittent control

International Nuclear Information System (INIS)

Yang Xinsong; Cao Jinde

2009-01-01

In this Letter, we study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Weiner process, and adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this Letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.

Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

Science.gov (United States)

Xu, Bin; Yang, Chenguang; Pan, Yongping

2015-10-01

This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

Direct evidence of strong local ferroelectric ordering in a thermoelectric semiconductor

Energy Technology Data Exchange (ETDEWEB)

Aggarwal, Leena; Sekhon, Jagmeet S.; Arora, Ashima; Sheet, Goutam, E-mail: goutam@iisermohali.ac.in [Department of Physical Sciences, Indian Institute of Science Education and Research Mohali (IISER M), Sector 81, S. A. S. Nagar, Manauli PO-140306 (India); Guin, Satya N.; Negi, Devendra S.; Datta, Ranjan; Biswas, Kanishka, E-mail: kanishka@jncasr.ac.in [New Chemistry Unit and International Centre for Materials Science, Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur, Bangalore 560064 (India)

2014-09-15

It is thought that the proposed new family of multi-functional materials, namely, the ferroelectric thermoelectrics may exhibit enhanced functionalities due to the coupling of the thermoelectric parameters with ferroelectric polarization in solids. Therefore, the ferroelectric thermoelectrics are expected to be of immense technological and fundamental significance. As a first step towards this direction, it is most important to identify the existing high performance thermoelectric materials exhibiting ferroelectricity. Herein, through the direct measurement of local polarization switching, we show that the recently discovered thermoelectric semiconductor AgSbSe{sub 2} has local ferroelectric ordering. Using piezo-response force microscopy, we demonstrate the existence of nanometer scale ferroelectric domains that can be switched by external electric field. These observations are intriguing as AgSbSe{sub 2} crystalizes in cubic rock-salt structure with centro-symmetric space group (Fm–3m), and therefore, no ferroelectricity is expected. However, from high resolution transmission electron microscopy measurement, we found the evidence of local superstructure formation which, we believe, leads to local distortion of the centro-symmetric arrangement in AgSbSe{sub 2} and gives rise to the observed ferroelectricity. Stereochemically active 5S{sup 2} lone-pair of Sb may also give rise to local structural distortion thereby creating ferroelectricity in AgSbSe{sub 2}.

Direct evidence for an orbital magnetic quadrupole twist mode in nuclei

International Nuclear Information System (INIS)

Reitz, B.; Frekers, D.

2002-02-01

The reactions 58 Ni(e,e') and 58 Ni(p,p') have been studied at kinematics favorable for the excitation of J π = 2 - states by isovector spin-flip transitions with ΔL = 1. There are states at an excitation energy E x ∼ 10 MeV which are strongly excited in electron scattering but not in proton scattering, suggesting a predominantly orbital character. This is taken as direct evidence for the so-called twist mode in nuclei in which different layers of nuclear fluid in the upper and lower hemisphere counterrotate against each other. Microscopic quasiparticle-phonon model calculations which predict sizable orbital M2 strength at his excitation energy yield indeed a current flow pattern of the strongest transitions consistent with a twist-like motion. (orig.)

Direction of movement is encoded in the human primary motor cortex.

Directory of Open Access Journals (Sweden)

Carolien M Toxopeus

Full Text Available The present study investigated how direction of hand movement, which is a well-described parameter in cerebral organization of motor control, is incorporated in the somatotopic representation of the manual effector system in the human primary motor cortex (M1. Using functional magnetic resonance imaging (fMRI and a manual step-tracking task we found that activation patterns related to movement in different directions were spatially disjoint within the representation area of the hand on M1. Foci of activation related to specific movement directions were segregated within the M1 hand area; activation related to direction 0° (right was located most laterally/superficially, whereas directions 180° (left and 270° (down elicited activation more medially within the hand area. Activation related to direction 90° was located between the other directions. Moreover, by investigating differences between activations related to movement along the horizontal (0°+180° and vertical (90°+270° axis, we found that activation related to the horizontal axis was located more anterolaterally/dorsally in M1 than for the vertical axis, supporting that activations related to individual movement directions are direction- and not muscle related. Our results of spatially segregated direction-related activations in M1 are in accordance with findings of recent fMRI studies on neural encoding of direction in human M1. Our results thus provide further evidence for a direct link between direction as an organizational principle in sensorimotor transformation and movement execution coded by effector representations in M1.

Super-linear Precision in Simple Neural Population Codes

Science.gov (United States)

Schwab, David; Fiete, Ila

2015-03-01

A widely used tool for quantifying the precision with which a population of noisy sensory neurons encodes the value of an external stimulus is the Fisher Information (FI). Maximizing the FI is also a commonly used objective for constructing optimal neural codes. The primary utility and importance of the FI arises because it gives, through the Cramer-Rao bound, the smallest mean-squared error achievable by any unbiased stimulus estimator. However, it is well-known that when neural firing is sparse, optimizing the FI can result in codes that perform very poorly when considering the resulting mean-squared error, a measure with direct biological relevance. Here we construct optimal population codes by minimizing mean-squared error directly and study the scaling properties of the resulting network, focusing on the optimal tuning curve width. We then extend our results to continuous attractor networks that maintain short-term memory of external stimuli in their dynamics. Here we find similar scaling properties in the structure of the interactions that minimize diffusive information loss.

Neural Networks Integrated Circuit for Biomimetics MEMS Microrobot

Directory of Open Access Journals (Sweden)

Ken Saito

2014-06-01

Full Text Available In this paper, we will propose the neural networks integrated circuit (NNIC which is the driving waveform generator of the 4.0, 2.7, 2.5 mm, width, length, height in size biomimetics microelectromechanical systems (MEMS microrobot. The microrobot was made from silicon wafer fabricated by micro fabrication technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the ant-like switching behavior. The NNIC generates the driving waveform using synchronization phenomena such as biological neural networks. The driving waveform can operate the actuators of the MEMS microrobot directly. Therefore, the NNIC bare chip realizes the robot control without using any software programs or A/D converters. The microrobot performed forward and backward locomotion, and also changes direction by inputting an external single trigger pulse. The locomotion speed of the microrobot was 26.4 mm/min when the step width was 0.88 mm. The power consumption of the system was 250 mWh when the room temperature was 298 K.

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

Neural processing of race during imitation: self-similarity versus social status

Science.gov (United States)

Reynolds Losin, Elizabeth A.; Cross, Katy A.; Iacoboni, Marco; Dapretto, Mirella

2017-01-01

People preferentially imitate others who are similar to them or have high social status. Such imitative biases are thought to have evolved because they increase the efficiency of cultural acquisition. Here we focused on distinguishing between self-similarity and social status as two candidate mechanisms underlying neural responses to a person’s race during imitation. We used fMRI to measure neural responses when 20 African American (AA) and 20 European American (EA) young adults imitated AA, EA and Chinese American (CA) models and also passively observed their gestures and faces. We found that both AA and EA participants exhibited more activity in lateral fronto-parietal and visual regions when imitating AAs compared to EAs or CAs. These results suggest that racial self-similarity is not likely to modulate neural responses to race during imitation, in contrast with findings from previous neuroimaging studies of face perception and action observation. Furthermore, AA and EA participants associated AAs with lower social status than EAs or CAs, suggesting that the social status associated with different racial groups may instead modulate neural activity during imitation of individuals from those groups. Taken together, these findings suggest that neural responses to race during imitation are driven by socially-learned associations rather than self-similarity. This may reflect the adaptive role of imitation in social learning, where learning from higher-status models can be more beneficial. This study provides neural evidence consistent with evolutionary theories of cultural acquisition. PMID:23813738

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.

The Neural Correlates of Abstract and Concrete Words: Evidence from Brain-Damaged Patients

OpenAIRE

Papagno, Costanza; Martello, Giorgia; Mattavelli, Giulia

2013-01-01

Neuropsychological and activation studies on the neural correlates of abstract and concrete words have produced contrasting results. The present study explores the anatomical substrates of abstract/concrete words in 22 brain-damaged patients with a single vascular lesion either in the right or left hemisphere. One hundred and twenty (60 concrete and 60 abstract) noun triplets were used for a semantic similarity judgment task. We found a significant interaction in word type × group since left ...

Anomaly based intrusion detection for a biometric identification system using neural networks

CSIR Research Space (South Africa)

Mgabile, T

2012-10-01

Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

Proceedings of the workshop cum symposium on applications of neural networks in nuclear science and industry

International Nuclear Information System (INIS)

1993-01-01

The Workshop cum Symposium on Application of Neural Networks in Nuclear Science and Industry was held at Bombay during November 24-26. 1993. The past decade has seen many important advances in the design and technology of artificial neural networks in research and industry. Neural networks is an interdisciplinary field covering a broad spectrum of applications in surveillance, diagnosis of nuclear power plants, nuclear spectroscopy, speech and written text recognition, robotic control, signal processing etc. The objective of the symposium was to promote awareness of advances in neural network research and applications. It was also aimed at conducting the review of the present status and giving direction for future technological developments. Contributed papers have been organized into the following groups: a) neural network architectures, learning algorithms and modelling, b) computer vision and image processing, c) signal processing, d) neural networks and fuzzy systems, e) nuclear applications and f) neural networks and allied applications. Papers relevant to INIS are indexed separately. (M.K.V.)

Globally exponential stability condition of a class of neural networks with time-varying delays

International Nuclear Information System (INIS)

Liao, T.-L.; Yan, J.-J.; Cheng, C.-J.; Hwang, C.-C.

2005-01-01

In this Letter, the globally exponential stability for a class of neural networks including Hopfield neural networks and cellular neural networks with time-varying delays is investigated. Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived. The condition is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation. Furthermore, the exponential stability degree is more easily assigned than those reported in the literature. Some examples are given to demonstrate validity and excellence of the presented stability condition herein

Neural Dynamics Associated with Semantic and Episodic Memory for Faces: Evidence from Multiple Frequency Bands

Science.gov (United States)

Zion-Golumbic, Elana; Kutas, Marta; Bentin, Shlomo

2010-01-01

Prior semantic knowledge facilitates episodic recognition memory for faces. To examine the neural manifestation of the interplay between semantic and episodic memory, we investigated neuroelectric dynamics during the creation (study) and the retrieval (test) of episodic memories for famous and nonfamous faces. Episodic memory effects were evident…

Criticality and avalanches in neural networks

International Nuclear Information System (INIS)

Zare, Marzieh; Grigolini, Paolo

2013-01-01

Highlights: • Temporal criticality is used as criticality indicator. • The Mittag–Leffler function is proposed as a proper form of temporal complexity. • The distribution of avalanche size becomes scale free in the supercritical state. • The scale-free distribution of avalanche sizes is an epileptic manifestation. -- Abstract: Experimental work, both in vitro and in vivo, reveals the occurrence of neural avalanches with an inverse power law distribution in size and time duration. These properties are interpreted as an evident manifestation of criticality, thereby suggesting that the brain is an operating near criticality complex system: an attractive theoretical perspective that according to Gerhard Werner may help to shed light on the origin of consciousness. However, a recent experimental observation shows no clear evidence for power-law scaling in awake and sleeping brain of mammals, casting doubts on the assumption that the brain works at criticality. This article rests on a model proposed by our group in earlier publications to generate neural avalanches with the time duration and size distribution matching the experimental results on neural networks. We now refine the analysis of the time distance between consecutive firing bursts and observe the deviation of the corresponding distribution from the Poisson statistics, as the system moves from the non-cooperative to the cooperative regime. In other words, we make the assumption that the genuine signature of criticality may emerge from temporal complexity rather than from the size and time duration of avalanches. We argue that the Mittag–Leffler (ML) exponential function is a satisfactory indicator of temporal complexity, namely of the occurrence of non-Poisson and renewal events. The assumption that the onset of criticality corresponds to the birth of renewal non-Poisson events establishes a neat distinction between the ML function and the power law avalanches generating regime. We find that

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.

A neural network method to correct bidirectional effects in water-leaving radiance

Science.gov (United States)

Fan, Yongzhen; Li, Wei; Voss, Kenneth J.; Gatebe, Charles K.; Stamnes, Knut

2017-02-01

The standard method to convert the measured water-leaving radiances from the observation direction to the nadir direction developed by Morel and coworkers requires knowledge of the chlorophyll concentration (CHL). Also, the standard method was developed for open ocean water, which makes it unsuitable for turbid coastal waters. We introduce a neural network method to convert the water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method does not require any prior knowledge of the water constituents or the inherent optical properties (IOPs). This method is fast, accurate and can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both open ocean and coastal waters. In open ocean or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In turbid coastal waters, especially sediment-dominated waters, a significant improvement was obtained compared to the standard method.

The Use of Evidence in Public Debates in the Media: The Case of Swiss Direct-Democratic Campaigns in the Health Policy Sector

Science.gov (United States)

Stucki, Iris

2016-01-01

This article analyses the reporting of evidence in Swiss direct-democratic campaigns in the health policy sector, assuming that an informed public helps democracy function successfully. A content analysis of the media's news reporting shows that of 5030 media items retrieved, a reference to evidence is found in 6.8%. The voter receives evidence in…

Neural Mechanisms of Circadian Regulation of Natural and Drug Reward

Directory of Open Access Journals (Sweden)

Lauren M. DePoy

2017-01-01

Full Text Available Circadian rhythms are endogenously generated near 24-hour variations of physiological and behavioral functions. In humans, disruptions to the circadian system are associated with negative health outcomes, including metabolic, immune, and psychiatric diseases, such as addiction. Animal models suggest bidirectional relationships between the circadian system and drugs of abuse, whereby desynchrony, misalignment, or disruption may promote vulnerability to drug use and the transition to addiction, while exposure to drugs of abuse may entrain, disrupt, or perturb the circadian timing system. Recent evidence suggests natural (i.e., food and drug rewards may influence overlapping neural circuitry, and the circadian system may modulate the physiological and behavioral responses to these stimuli. Environmental disruptions, such as shifting schedules or shorter/longer days, influence food and drug intake, and certain mutations of circadian genes that control cellular rhythms are associated with altered behavioral reward. We highlight the more recent findings associating circadian rhythms to reward function, linking environmental and genetic evidence to natural and drug reward and related neural circuitry.

Application of neural networks to group technology

Science.gov (United States)

Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

1991-08-01

Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

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

Evidence for parallel consolidation of motion direction and orientation into visual short-term memory.

Science.gov (United States)

Rideaux, Reuben; Apthorp, Deborah; Edwards, Mark

2015-02-12

Recent findings have indicated the capacity to consolidate multiple items into visual short-term memory in parallel varies as a function of the type of information. That is, while color can be consolidated in parallel, evidence suggests that orientation cannot. Here we investigated the capacity to consolidate multiple motion directions in parallel and reexamined this capacity using orientation. This was achieved by determining the shortest exposure duration necessary to consolidate a single item, then examining whether two items, presented simultaneously, could be consolidated in that time. The results show that parallel consolidation of direction and orientation information is possible, and that parallel consolidation of direction appears to be limited to two. Additionally, we demonstrate the importance of adequate separation between feature intervals used to define items when attempting to consolidate in parallel, suggesting that when multiple items are consolidated in parallel, as opposed to serially, the resolution of representations suffer. Finally, we used facilitation of spatial attention to show that the deterioration of item resolution occurs during parallel consolidation, as opposed to storage. © 2015 ARVO.

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...

Foreground removal from Planck Sky Model temperature maps using a MLP neural network

DEFF Research Database (Denmark)

Nørgaard-Nielsen, Hans Ulrik; Hebert, K.

2009-01-01

with no systematic errors. To demonstrate the feasibility of a simple multilayer perceptron (MLP) neural network for extracting the CMB temperature signal, we have analyzed a specific data set, namely the Planck Sky Model maps, developed for evaluation of different component separation methods before including them...... in the Planck data analysis pipeline. It is found that a MLP neural network can provide a CMB map of about 80% of the sky to a very high degree uncorrelated with the foreground components. Also the derived power spectrum shows little evidence for systematic errors....

Barratt Impulsivity and Neural Regulation of Physiological Arousal.

Directory of Open Access Journals (Sweden)

Sheng Zhang

Full Text Available Theories of personality have posited an increased arousal response to external stimulation in impulsive individuals. However, there is a dearth of studies addressing the neural basis of this association.We recorded skin conductance in 26 individuals who were assessed with Barratt Impulsivity Scale (BIS-11 and performed a stop signal task during functional magnetic resonance imaging. Imaging data were processed and modeled with Statistical Parametric Mapping. We used linear regressions to examine correlations between impulsivity and skin conductance response (SCR to salient events, identify the neural substrates of arousal regulation, and examine the relationship between the regulatory mechanism and impulsivity.Across subjects, higher impulsivity is associated with greater SCR to stop trials. Activity of the ventromedial prefrontal cortex (vmPFC negatively correlated to and Granger caused skin conductance time course. Furthermore, higher impulsivity is associated with a lesser strength of Granger causality of vmPFC activity on skin conductance, consistent with diminished control of physiological arousal to external stimulation. When men (n = 14 and women (n = 12 were examined separately, however, there was evidence suggesting association between impulsivity and vmPFC regulation of arousal only in women.Together, these findings confirmed the link between Barratt impulsivity and heightened arousal to salient stimuli in both genders and suggested the neural bases of altered regulation of arousal in impulsive women. More research is needed to explore the neural processes of arousal regulation in impulsive individuals and in clinical conditions that implicate poor impulse control.

Barratt Impulsivity and Neural Regulation of Physiological Arousal.

Science.gov (United States)

Zhang, Sheng; Hu, Sien; Hu, Jianping; Wu, Po-Lun; Chao, Herta H; Li, Chiang-shan R

2015-01-01

Theories of personality have posited an increased arousal response to external stimulation in impulsive individuals. However, there is a dearth of studies addressing the neural basis of this association. We recorded skin conductance in 26 individuals who were assessed with Barratt Impulsivity Scale (BIS-11) and performed a stop signal task during functional magnetic resonance imaging. Imaging data were processed and modeled with Statistical Parametric Mapping. We used linear regressions to examine correlations between impulsivity and skin conductance response (SCR) to salient events, identify the neural substrates of arousal regulation, and examine the relationship between the regulatory mechanism and impulsivity. Across subjects, higher impulsivity is associated with greater SCR to stop trials. Activity of the ventromedial prefrontal cortex (vmPFC) negatively correlated to and Granger caused skin conductance time course. Furthermore, higher impulsivity is associated with a lesser strength of Granger causality of vmPFC activity on skin conductance, consistent with diminished control of physiological arousal to external stimulation. When men (n = 14) and women (n = 12) were examined separately, however, there was evidence suggesting association between impulsivity and vmPFC regulation of arousal only in women. Together, these findings confirmed the link between Barratt impulsivity and heightened arousal to salient stimuli in both genders and suggested the neural bases of altered regulation of arousal in impulsive women. More research is needed to explore the neural processes of arousal regulation in impulsive individuals and in clinical conditions that implicate poor impulse control.

Training Deep Spiking Neural Networks Using Backpropagation.

Science.gov (United States)

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

2016-01-01

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

Imidacloprid toxicity impairs spatial memory of echolocation bats through neural apoptosis in hippocampal CA1 and medial entorhinal cortex areas.

Science.gov (United States)

Hsiao, Chun-Jen; Lin, Ching-Lung; Lin, Tian-Yu; Wang, Sheue-Er; Wu, Chung-Hsin

2016-04-13

It has been reported that the decimation of honey bees was because of pesticides of imidacloprid. The imidacloprid is a wildly used neonicotinoid insecticide. However, whether imidacloprid toxicity interferes with the spatial memory of echolocation bats is still unclear. Thus, we compared the spatial memory of Formosan leaf-nosed bats, Hipposideros terasensis, before and after chronic treatment with a low dose of imidacloprid. We observed that stereotyped flight patterns of echolocation bats that received chronic imidacloprid treatment were quite different from their originally learned paths. We further found that neural apoptosis in hippocampal CA1 and medial entorhinal cortex areas of echolocation bats that received imidacloprid treatment was significantly enhanced in comparison with echolocation bats that received sham treatment. Thus, we suggest that imidacloprid toxicity may interfere with the spatial memory of echolocation bats through neural apoptosis in hippocampal CA1 and medial entorhinal cortex areas. The results provide direct evidence that pesticide toxicity causes a spatial memory disorder in echolocation bats. This implies that agricultural pesticides may pose severe threats to the survival of echolocation bats.

Behavioral and neural evidence of increased attention to the bottom half of the face in deaf signers

Science.gov (United States)

Mitchell, Teresa V.; Letourneau, Susan M.; Maslin, Melissa T.

2013-01-01

Purpose This study examined the effects of deafness and sign language use on the distribution of attention across the top and bottom halves of faces. Methods In a composite face task, congenitally deaf signers and typically hearing controls made same/different judgments of the top or bottom halves of faces presented with the halves aligned or spatially misaligned, while event-related potentials (ERPs) were recorded. Results Both groups were more accurate when judging misaligned than aligned faces, which indicates holistic face processing. Misalignment affected all ERP components examined, with effects on the N170 resembling those of face inversion. Hearing adults were similarly accurate when judging the top and bottom halves of the faces, but deaf signers were more accurate when attending to the bottom than the top. Attending to the top elicited faster P1 and N170 latencies for both groups; within the deaf group, this effect was greatest for individuals who produced the highest accuracies when attending to the top. Conclusions These findings dovetail with previous research by providing behavioral and neural evidence of increased attention to the bottom half of the face in deaf signers, and by documenting that these effects generalize to a speeded task, in the absence of gaze shifts, with neutral facial expressions. PMID:23142816

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

Securing Digital Images Integrity using Artificial Neural Networks