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Sample records for monitor neural activity

  1. High Accuracy Human Activity Monitoring using Neural network

    CERN Document Server

    Sharma, Annapurna; Chung, Wan-Young

    2011-01-01

    This paper presents the designing of a neural network for the classification of Human activity. A Triaxial accelerometer sensor, housed in a chest worn sensor unit, has been used for capturing the acceleration of the movements associated. All the three axis acceleration data were collected at a base station PC via a CC2420 2.4GHz ISM band radio (zigbee wireless compliant), processed and classified using MATLAB. A neural network approach for classification was used with an eye on theoretical and empirical facts. The work shows a detailed description of the designing steps for the classification of human body acceleration data. A 4-layer back propagation neural network, with Levenberg-marquardt algorithm for training, showed best performance among the other neural network training algorithms.

  2. Monitoring activity in neural circuits with genetically encoded indicators

    Directory of Open Access Journals (Sweden)

    Gerard Joseph Broussard

    2014-12-01

    Full Text Available Recent developments in genetically encoded indicators of neural activity (GINAs have greatly advanced the field of systems neuroscience. As they are encoded by DNA, GINAs can be targeted to genetically defined cellular populations. Combined with fluorescence microscopy, most notably multi-photon imaging, GINAs allow chronic simultaneous optical recordings from large populations of neurons or glial cells in awake, behaving mammals, particularly rodents. This large-scale recording of neural activity at multiple temporal and spatial scales has greatly advanced our understanding of the dynamics of neural circuitry underlying behavior—a critical first step toward understanding the complexities of brain function, such as sensorimotor integration and learning.Here, we summarize the recent development and applications of the major classes of GINAs. In particular, we take an in-depth look at the design of available GINA families with a particular focus on genetically encoded calcium indicators, sensors probing synaptic activity, and genetically encoded voltage indicators. Using the family of the genetically encoded calcium indicator GCaMP as an example, we review established sensor optimization pipelines. We also discuss practical considerations for end users of GINAs about experimental methods including approaches for gene delivery, imaging system requirements, and data analysis techniques. With the growing toolbox of GINAs and with new microscopy techniques pushing beyond their current limits, the age of light can finally achieve the goal of broad and dense sampling of neuronal activity across time and brain structures to obtain a dynamic picture of brain function.

  3. Monitoring the neural activity of the state of mental silence while practicing Sahaja yoga meditation.

    Science.gov (United States)

    Hernández, Sergio E; Suero, José; Rubia, Katya; González-Mora, José L

    2015-03-01

    To identify the neural correlates of the state of mental silence as experienced through Sahaja yoga meditation. Nineteen experienced meditators underwent functional magnetic resonance imaging during three short consecutive meditation periods, contrasted with a control relaxation condition. Relative to baseline, at the beginning of the meditation sessions there was a significant increase of activation in bilateral inferior frontal and temporal regions. Activation became progressively more reduced with deeper meditation stages and in the last meditation session it became localized to the right inferior frontal cortex/ right insula and right middle/superior temporal cortex. Furthermore, right inferior frontal activation was directly associated with the subjective depth of the mental silence experience. Meditators appear to pass through an initial intense neural self-control process necessary to silence their mind. After this they experience relatively reduced brain activation concomitant with the deepening of the state of mental silence over right inferior frontal cortex, probably reflecting an effortless process of attentional contemplation associated with this state.

  4. The neural basis of monitoring goal progress

    Directory of Open Access Journals (Sweden)

    Yael eBenn

    2014-09-01

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

  5. Fluorescence-based monitoring of in vivo neural activity using a circuit-tracing pseudorabies virus.

    Directory of Open Access Journals (Sweden)

    Andrea E Granstedt

    Full Text Available The study of coordinated activity in neuronal circuits has been challenging without a method to simultaneously report activity and connectivity. Here we present the first use of pseudorabies virus (PRV, which spreads through synaptically connected neurons, to express a fluorescent calcium indicator protein and monitor neuronal activity in a living animal. Fluorescence signals were proportional to action potential number and could reliably detect single action potentials in vitro. With two-photon imaging in vivo, we observed both spontaneous and stimulated activity in neurons of infected murine peripheral autonomic submandibular ganglia (SMG. We optically recorded the SMG response in the salivary circuit to direct electrical stimulation of the presynaptic axons and to physiologically relevant sensory stimulation of the oral cavity. During a time window of 48 hours after inoculation, few spontaneous transients occurred. By 72 hours, we identified more frequent and prolonged spontaneous calcium transients, suggestive of neuronal or tissue responses to infection that influence calcium signaling. Our work establishes in vivo investigation of physiological neuronal circuit activity and subsequent effects of infection with single cell resolution.

  6. Neural activity based biofeedback therapy for Autism spectrum disorder through wearable wireless textile EEG monitoring system

    Science.gov (United States)

    Sahi, Ahna; Rai, Pratyush; Oh, Sechang; Ramasamy, Mouli; Harbaugh, Robert E.; Varadan, Vijay K.

    2014-04-01

    Mu waves, also known as mu rhythms, comb or wicket rhythms are synchronized patterns of electrical activity involving large numbers of neurons, in the part of the brain that controls voluntary functions. Controlling, manipulating, or gaining greater awareness of these functions can be done through the process of Biofeedback. Biofeedback is a process that enables an individual to learn how to change voluntary movements for purposes of improving health and performance through the means of instruments such as EEG which rapidly and accurately 'feedback' information to the user. Biofeedback is used for therapeutic purpose for Autism Spectrum Disorder (ASD) by focusing on Mu waves for detecting anomalies in brain wave patterns of mirror neurons. Conventional EEG measurement systems use gel based gold cup electrodes, attached to the scalp with adhesive. It is obtrusive and wires sticking out of the electrodes to signal acquisition system make them impractical for use in sensitive subjects like infants and children with ASD. To remedy this, sensors can be incorporated with skull cap and baseball cap that are commonly used for infants and children. Feasibility of Textile based Sensor system has been investigated here. Textile based multi-electrode EEG, EOG and EMG monitoring system with embedded electronics for data acquisition and wireless transmission has been seamlessly integrated into fabric of these items for continuous detection of Mu waves. Textile electrodes were placed on positions C3, CZ, C4 according to 10-20 international system and their capability to detect Mu waves was tested. The system is ergonomic and can potentially be used for early diagnosis in infants and planning therapy for ASD patients.

  7. Rumination in major depressive disorder is associated with impaired neural activation during conflict monitoring.

    Science.gov (United States)

    Alderman, Brandon L; Olson, Ryan L; Bates, Marsha E; Selby, Edward A; Buckman, Jennifer F; Brush, Christopher J; Panza, Emily A; Kranzler, Amy; Eddie, David; Shors, Tracey J

    2015-01-01

    Individuals with major depressive disorder (MDD) often ruminate about past experiences, especially those with negative content. These repetitive thoughts may interfere with cognitive processes related to attention and conflict monitoring. However, the temporal nature of these processes as reflected in event-related potentials (ERPs) has not been well-described. We examined behavioral and ERP indices of conflict monitoring during a modified flanker task and the allocation of attention during an attentional blink (AB) task in 33 individuals with MDD and 36 healthy controls, and whether their behavioral performance and ERPs varied with level of rumination. N2 amplitude elicited by the flanker task was significantly reduced in participants with MDD compared to healthy controls. Level of self-reported rumination was also correlated with N2 amplitude. In contrast, P3 amplitude during the AB task was not significantly different between groups, nor was it correlated with rumination. No significant differences were found in behavioral task performance measures between groups or by rumination levels. These findings suggest that rumination in MDD is associated with select deficits in cognitive control, particularly related to conflict monitoring.

  8. Rumination in major depressive disorder is associated with impaired neural activation during conflict monitoring

    Directory of Open Access Journals (Sweden)

    Brandon L Alderman

    2015-05-01

    Full Text Available Individuals with major depressive disorder (MDD often ruminate about past experiences, especially those with negative content. These repetitive thoughts may interfere with cognitive processes related to attention and conflict monitoring. However, the temporal nature of these processes as reflected in event-related potentials (ERPs has not been well described. We examined behavioral and ERP indices of conflict monitoring during a modified flanker task and the allocation of attention during an attentional blink (AB task in 33 individuals with MDD and 36 healthy controls, and whether their behavioral performance and ERPs varied with level of rumination. N2 amplitude elicited by the flanker task was significantly reduced in participants with MDD compared to healthy controls. Level of self-reported rumination was also correlated with N2 amplitude. In contrast, P3 amplitude during the AB task was not significantly different between groups, nor was it correlated with rumination. No significant differences were found in behavioral task performance measures between groups or by rumination levels. These findings suggest that rumination in MDD is associated with select deficits in cognitive control, particularly related to conflict monitoring.

  9. Neural Net Safety Monitor Design

    Science.gov (United States)

    Larson, Richard R.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The

  10. Neural response dynamics of spiking and local field potential activity depend on CRT monitor refresh rate in the tree shrew primary visual cortex.

    Science.gov (United States)

    Veit, Julia; Bhattacharyya, Anwesha; Kretz, Robert; Rainer, Gregor

    2011-11-01

    Entrainment of neural activity to luminance impulses during the refresh of cathode ray tube monitor displays has been observed in the primary visual cortex (V1) of humans and macaque monkeys. This entrainment is of interest because it tends to temporally align and thus synchronize neural responses at the millisecond timescale. Here we show that, in tree shrew V1, both spiking and local field potential activity are also entrained at cathode ray tube refresh rates of 120, 90, and 60 Hz, with weakest but still significant entrainment even at 120 Hz, and strongest entrainment occurring in cortical input layer IV. For both luminance increments ("white" stimuli) and decrements ("black" stimuli), refresh rate had a strong impact on the temporal dynamics of the neural response for subsequent luminance impulses. Whereas there was rapid, strong attenuation of spikes and local field potential to prolonged visual stimuli composed of luminance impulses presented at 120 Hz, attenuation was nearly absent at 60-Hz refresh rate. In addition, neural onset latencies were shortest at 120 Hz and substantially increased, by ∼15 ms, at 60 Hz. In terms of neural response amplitude, black responses dominated white responses at all three refresh rates. However, black/white differences were much larger at 60 Hz than at higher refresh rates, suggesting a mechanism that is sensitive to stimulus timing. Taken together, our findings reveal many similarities between V1 of macaque and tree shrew, while underscoring a greater temporal sensitivity of the tree shrew visual system.

  11. Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

    Energy Technology Data Exchange (ETDEWEB)

    Ericson, Milton Nance [ORNL; McKnight, Timothy E [ORNL; Melechko, Anatoli Vasilievich [ORNL; Simpson, Michael L [ORNL; Morrison, Barclay [ORNL; Yu, Zhe [Columbia University

    2012-01-01

    Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.

  12. A study of reactor monitoring method with neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    2001-03-01

    The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

  13. Peripheral neural activity recording and stimulation system.

    Science.gov (United States)

    Loi, D; Carboni, C; Angius, G; Angotzi, G N; Barbaro, M; Raffo, L; Raspopovic, S; Navarro, X

    2011-08-01

    This paper presents a portable, embedded, microcontroller-based system for bidirectional communication (recording and stimulation) between an electrode, implanted in the peripheral nervous system, and a host computer. The device is able to record and digitize spontaneous and/or evoked neural activities and store them in data files on a PC. In addition, the system has the capability of providing electrical stimulation of peripheral nerves, injecting biphasic current pulses with programmable duration, intensity, and frequency. The recording system provides a highly selective band-pass filter from 800 Hz to 3 kHz, with a gain of 56 dB. The amplification range can be further extended to 96 dB with a variable gain amplifier. The proposed acquisition/stimulation circuitry has been successfully tested through in vivo measurements, implanting a tf-LIFE electrode in the sciatic nerve of a rat. Once implanted, the device showed an input referred noise of 0.83 μVrms, was capable of recording signals below 10 μ V, and generated muscle responses to injected stimuli. The results demonstrate the capability of processing and transmitting neural signals with very low distortion and with a power consumption lower than 1 W. A graphic, user-friendly interface has been developed to facilitate the configuration of the entire system, providing the possibility to activate stimulation and monitor recordings in real time.

  14. Windowed active sampling for reliable neural learning

    NARCIS (Netherlands)

    Barakova, E.I; Spaanenburg, L

    1998-01-01

    The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive pre-processing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these pro

  15. SSME Condition Monitoring Using Neural Networks and Plume Spectral Signatures

    Science.gov (United States)

    Hopkins, Randall; Benzing, Daniel

    1996-01-01

    For a variety of reasons, condition monitoring of the Space Shuttle Main Engine (SSME) has become an important concern for both ground tests and in-flight operation. The complexities of the SSME suggest that active, real-time condition monitoring should be performed to avoid large-scale or catastrophic failure of the engine. In 1986, the SSME became the subject of a plume emission spectroscopy project at NASA's Marshall Space Flight Center (MSFC). Since then, plume emission spectroscopy has recorded many nominal tests and the qualitative spectral features of the SSME plume are now well established. Significant discoveries made with both wide-band and narrow-band plume emission spectroscopy systems led MSFC to develop the Optical Plume Anomaly Detection (OPAD) system. The OPAD system is designed to provide condition monitoring of the SSME during ground-level testing. The operational health of the engine is achieved through the acquisition of spectrally resolved plume emissions and the subsequent identification of abnormal emission levels in the plume indicative of engine erosion or component failure. Eventually, OPAD, or a derivative of the technology, could find its way on to an actual space vehicle and provide in-flight engine condition monitoring. This technology step, however, will require miniaturized hardware capable of processing plume spectral data in real-time. An objective of OPAD condition monitoring is to determine how much of an element is present in the SSME plume. The basic premise is that by knowing the element and its concentration, this could be related back to the health of components within the engine. For example, an abnormal amount of silver in the plume might signify increased wear or deterioration of a particular bearing in the engine. Once an anomaly is identified, the engine could be shut down before catastrophic failure occurs. Currently, element concentrations in the plume are determined iteratively with the help of a non-linear computer

  16. Active voltammetric microsensors with neural signal processing.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    1998-12-11

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can

  17. Vibration monitoring of EDF rotating machinery using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. (Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering); Hamon, L.; Lefevre, F. (Electricite de France, 78 - Chatou (France). Direction des Etudes et Recherches)

    1991-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  18. Vibration monitoring of EDF rotating machinery using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. [Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering; Hamon, L.; Lefevre, F. [Electricite de France, 78 - Chatou (France). Direction des Etudes et Recherches

    1991-12-31

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  19. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  20. Neural activation in stress-related exhaustion

    DEFF Research Database (Denmark)

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

    2017-01-01

    The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty......, burnout level was positively associated with neural activation in the rostral prefrontal cortex, the posterior parietal cortex and the striatum, primarily in the 2-back condition. Following stress rehabilitation, the striatal activity decreased as a function of improved levels of burnout. No significant...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...

  1. Mindful awareness of feelings increases neural performance monitoring.

    Science.gov (United States)

    Saunders, Blair; Rodrigo, Achala H; Inzlicht, Michael

    2016-02-01

    Mindfulness has been associated with enhanced performance monitoring; however, little is known about the processes driving this apparent neurocognitive benefit. Here, we tested whether focusing present-moment awareness toward the nonjudgmental experience of emotion facilitates rapid neural responses to negative performance outcomes (i.e., mistakes). In particular, we compared whether directing present-moment awareness toward emotions or thoughts would enhance the neurophysiological correlates of performance monitoring: the error-related negativity (ERN) and the error positivity (Pe). Participants were randomly assigned to either a thought-focused or an emotion-focused group, and first they completed a preinduction go/no-go task. Subsequently, the groups followed inductions that promoted mindful attention toward either thoughts or emotions, before completing a final postinduction go/no-go session. The results indicated that emotion-focused participants demonstrated higher neural sensitivity to errors in the time course of the ERN, whereas focusing on thoughts had no effect on performance monitoring. In contrast, neither induction procedure altered the amplitude of the later Pe component. Although our manipulations also induced changes in behavior, the ERN effects remained significant after controlling for performance. Thus, our results suggest that mindfulness meditation boosts early neural performance monitoring (ERN amplitude), specifically through meditation's influence on affective processing.

  2. Vertically Aligned Carbon Nanofiber as Nano-Neuron Interface for Monitoring Neural Function

    OpenAIRE

    Yu, Zhe; McKnight, Timothy E.; Ericson, M. Nance; Melechko, Anatoli V.; Simpson, Michael L.; Morrison, Barclay

    2012-01-01

    Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber ...

  3. Genetic control of active neural circuits

    Directory of Open Access Journals (Sweden)

    Leon Reijmers

    2009-12-01

    Full Text Available The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory.

  4. Value activity monitoring

    OpenAIRE

    de Alencar Silva, P.

    2013-01-01

    Abstract: Current value modeling ontologies are grounded on the economic premise that profit sharing is a critical condition to be assessed during the configuration of a value constellation. Such a condition ought to be reinforced through a monitoring mechanism design, since a value model expresses only promises (but not assurances) of value creation. Hence there is a need to extend current value modeling ontologies with a monitoring ontology. This ontology will enable business practitioners ...

  5. A VLSI Neural Monitoring System With Ultra-Wideband Telemetry for Awake Behaving Subjects.

    Science.gov (United States)

    Greenwald, E; Mollazadeh, M; Hu, C; Wei Tang; Culurciello, E; Thakor, V

    2011-04-01

    Long-term monitoring of neuronal activity in awake behaving subjects can provide fundamental information about brain dynamics for neuroscience and neuroengineering applications. Here, we present a miniature, lightweight, and low-power recording system for monitoring neural activity in awake behaving animals. The system integrates two custom designed very-large-scale integrated chips, a neural interface module fabricated in 0.5 μm complementary metal-oxide semiconductor technology and an ultra-wideband transmitter module fabricated in a 0.5 μm silicon-on-sapphire (SOS) technology. The system amplifies, filters, digitizes, and transmits 16 channels of neural data at a rate of 1 Mb/s. The entire system, which includes the VLSI circuits, a digital interface board, a battery, and a custom housing, is small and lightweight (24 g) and, thus, can be chronically mounted on small animals. The system consumes 4.8 mA and records continuously for up to 40 h powered by a 3.7-V, 200-mAh rechargeable lithium-ion battery. Experimental benchtop characterizations as well as in vivo multichannel neural recordings from awake behaving rats are presented here.

  6. Transparent and Explicable Boiler Fouling Monitoring with Fuzzy Neural Newtwork

    Institute of Scientific and Technical Information of China (English)

    BinWu; You-TingShen

    1998-01-01

    Fouling on boiler beating surfaces is one of the important factors that damage boiler's economical performance and safety,with on-line monitoring of foiling states on boler heating surfaces,it is possible to optimize sootblower system,to visualize fouling states,to improve performance,as well as to remedy the insufficiency of experiment research in boiler heating surface fouling process.New method based on Fuzzy Neural Network(FNN) is presented to monitor fouling states on boiler heating surfaces on-line.Compared with regular methods,since FNN's reasoning process is transparent and comprehensible,it is possible to explain and comprehend reasoning process,which makes the FNN based system perform as an additional operation consulting system.

  7. Value activity monitoring

    NARCIS (Netherlands)

    de Alencar Silva, P.

    2013-01-01

    Current value modeling ontologies are grounded on the economic premise that profit sharing is a critical condition to be assessed during the configuration of a value constellation. Such a condition ought to be reinforced through a monitoring mechanism design, since a value model expresses only promi

  8. Review of Artificial Neural Networks (ANN) applied to corrosion monitoring

    Science.gov (United States)

    Mabbutt, S.; Picton, P.; Shaw, P.; Black, S.

    2012-05-01

    The assessment of corrosion within an engineering system often forms an important aspect of condition monitoring but it is a parameter that is inherently difficult to measure and predict. The electrochemical nature of the corrosion process allows precise measurements to be made. Advances in instruments, techniques and software have resulted in devices that can gather data and perform various analysis routines that provide parameters to identify corrosion type and corrosion rate. Although corrosion rates are important they are only useful where general or uniform corrosion dominates. However, pitting, inter-granular corrosion and environmentally assisted cracking (stress corrosion) are examples of corrosion mechanisms that can be dangerous and virtually invisible to the naked eye. Electrochemical noise (EN) monitoring is a very useful technique for detecting these types of corrosion and it is the only non-invasive electrochemical corrosion monitoring technique commonly available. Modern instrumentation is extremely sensitive to changes in the system and new experimental configurations for gathering EN data have been proven. In this paper the identification of localised corrosion by different data analysis routines has been reviewed. In particular the application of Artificial Neural Network (ANN) analysis to corrosion data is of key interest. In most instances data needs to be used with conventional theory to obtain meaningful information and relies on expert interpretation. Recently work has been carried out using artificial neural networks to investigate various types of corrosion data in attempts to predict corrosion behaviour with some success. This work aims to extend this earlier work to identify reliable electrochemical indicators of localised corrosion onset and propagation stages.

  9. Information transmission in oscillatory neural activity

    CERN Document Server

    Koepsell, Kilian

    2008-01-01

    Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al, 1999; Brenner et al, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.

  10. GMDH and neural networks applied in temperature sensors monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio, E-mail: ebueno@cefetsp.b [Instituto Federal de Educacao, Ciencia e Tecnologia, Braganca Paulista, SP (Brazil); Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Pereira, Iraci Martinez; Silva, Antonio Teixeira e, E-mail: martinez@ipen.b, E-mail: teixeira@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2009-07-01

    In this work a monitoring system was developed based on the Group Method of Data Handling (GMDH) and Neural Networks (ANNs) methodologies. This methodology was applied to the IEA-R1 research reactor at IPEN by using a database obtained from a theoretical model of the reactor. The IEA-R1 research reactor is a pool type reactor of 5 MW, cooled and moderated by light water, and uses graphite and beryllium as reflector. The theoretical model was developed using the Matlab GUIDE toolbox. The equations are based in the IEA-R1 mass and energy inventory balance and physical as well as operational aspects are taken into consideration. This methodology was developed by using the GMDH algorithm as input variables to the ANNs. The results obtained using the GMDH and ANNs were better than that obtained using only ANNs. (author)

  11. Contamination monitoring activities in Kanupp

    Energy Technology Data Exchange (ETDEWEB)

    Naqvi, S.S. [Karachi Nuclear Power Plant (Pakistan)

    1997-06-01

    The Karachi Nuclear Power Plant (Kanupp) is a 137 MWe pressurized heavy water reactor, designed and erected by the Canadian General Electric Company as a turn key project. The plant is in operation since it was commissioned in the year 1972. It is located at the Arabian Sea Coast about 15 miles to the west of Karachi. During its more than two decades of operation, the plant has generated about 8 billion units of electricity with an average life time availability factor of 60%. In Kanupp, radioactive contamination may exit due to the release of fission product, activation products etc., which may somehow escape from its confinement and may contaminate surface or other media such as air, water etc. In this paper, following items are described: main aspects of contamination, status of contamination monitoring, need of contamination monitoring, radiation protection activity, instruments, contamination, current status of contamination survey materials and their disposal, and environmental monitoring. (G.K.)

  12. The Monitoring of Red Tides Based on Modular Neural Networks Using Airborne Hyperspectral Remote Sensing

    Institute of Scientific and Technical Information of China (English)

    JI Guangrong; SUN Jie; ZHAO Wencang; ZHANG Hande

    2006-01-01

    This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction (LRC) is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm (AGA). The experiments showed that this method can monitor red tide rapidly and effectively.

  13. Active Job Monitoring in Pilots

    Science.gov (United States)

    Kuehn, Eileen; Fischer, Max; Giffels, Manuel; Jung, Christopher; Petzold, Andreas

    2015-12-01

    Recent developments in high energy physics (HEP) including multi-core jobs and multi-core pilots require data centres to gain a deep understanding of the system to monitor, design, and upgrade computing clusters. Networking is a critical component. Especially the increased usage of data federations, for example in diskless computing centres or as a fallback solution, relies on WAN connectivity and availability. The specific demands of different experiments and communities, but also the need for identification of misbehaving batch jobs, requires an active monitoring. Existing monitoring tools are not capable of measuring fine-grained information at batch job level. This complicates network-aware scheduling and optimisations. In addition, pilots add another layer of abstraction. They behave like batch systems themselves by managing and executing payloads of jobs internally. The number of real jobs being executed is unknown, as the original batch system has no access to internal information about the scheduling process inside the pilots. Therefore, the comparability of jobs and pilots for predicting run-time behaviour or network performance cannot be ensured. Hence, identifying the actual payload is important. At the GridKa Tier 1 centre a specific tool is in use that allows the monitoring of network traffic information at batch job level. This contribution presents the current monitoring approach and discusses recent efforts and importance to identify pilots and their substructures inside the batch system. It will also show how to determine monitoring data of specific jobs from identified pilots. Finally, the approach is evaluated.

  14. Multifractal detrended fluctuation analysis of optogenetic modulation of neural activity

    Science.gov (United States)

    Kumar, S.; Gu, L.; Ghosh, N.; Mohanty, S. K.

    2013-02-01

    Here, we introduce a computational procedure to examine whether optogenetically activated neuronal firing recordings could be characterized as multifractal series. Optogenetics is emerging as a valuable experimental tool and a promising approach for studying a variety of neurological disorders in animal models. The spiking patterns from cortical region of the brain of optogenetically-stimulated transgenic mice were analyzed using a sophisticated fluctuation analysis method known as multifractal detrended fluctuation analysis (MFDFA). We observed that the optogenetically-stimulated neural firings are consistent with a multifractal process. Further, we used MFDFA to monitor the effect of chemically induced pain (formalin injection) and optogenetic treatment used to relieve the pain. In this case, dramatic changes in parameters characterizing a multifractal series were observed. Both the generalized Hurst exponent and width of singularity spectrum effectively differentiates the neural activities during control and pain induction phases. The quantitative nature of the analysis equips us with better measures to quantify pain. Further, it provided a measure for effectiveness of the optogenetic stimulation in inhibiting pain. MFDFA-analysis of spiking data from other deep regions of the brain also turned out to be multifractal in nature, with subtle differences in the parameters during pain-induction by formalin injection and inhibition by optogenetic stimulation. Characterization of neuronal firing patterns using MFDFA will lead to better understanding of neuronal response to optogenetic activation and overall circuitry involved in the process.

  15. Monitoring crop cycles by SAR using a neural network trained by a model

    Science.gov (United States)

    del Frate, F.; Ferrazzoli, P.; Guerriero, L.; Strozzi, T.; Wegmüller, U.; Cookmartin, G.; Quegan, S.

    2002-01-01

    An algorithm, based on an electromagnetic model and a neural network, aimed at monitoring the multitemporal evolution of wheat fields, is described. Three different sites are used to validate the model, provide reference ground data, and test the algorithm.

  16. Models of neural networks with fuzzy activation functions

    Science.gov (United States)

    Nguyen, A. T.; Korikov, A. M.

    2017-02-01

    This paper investigates the application of a new form of neuron activation functions that are based on the fuzzy membership functions derived from the theory of fuzzy systems. On the basis of the results regarding neuron models with fuzzy activation functions, we created the models of fuzzy-neural networks. These fuzzy-neural network models differ from conventional networks that employ the fuzzy inference systems using the methods of neural networks. While conventional fuzzy-neural networks belong to the first type, fuzzy-neural networks proposed here are defined as the second-type models. The simulation results show that the proposed second-type model can successfully solve the problem of the property prediction for time – dependent signals. Neural networks with fuzzy impulse activation functions can be widely applied in many fields of science, technology and mechanical engineering to solve the problems of classification, prediction, approximation, etc.

  17. Face-induced expectancies influence neural mechanisms of performance monitoring.

    Science.gov (United States)

    Osinsky, Roman; Seeger, Jennifer; Mussel, Patrick; Hewig, Johannes

    2016-04-01

    In many daily situations, the consequences of our actions are predicted by cues that are often social in nature. For instance, seeing the face of an evaluator (e.g., a supervisor at work) may activate certain evaluative expectancies, depending on the history of prior encounters with that particular person. We investigated how such face-induced expectancies influence neurocognitive functions of performance monitoring. We recorded an electroencephalogram while participants completed a time-estimation task, during which they received performance feedback from a strict and a lenient evaluator. During each trial, participants first saw the evaluator's face before performing the task and, finally, receiving feedback. Therefore, faces could be used as predictive cues for the upcoming evaluation. We analyzed electrocortical signatures of performance monitoring at the stages of cue processing, task performance, and feedback reception. Our results indicate that, at the cue stage, seeing the strict evaluator's face results in an anticipatory preparation of fronto-medial monitoring mechanisms, as reflected by a sustained negative-going amplitude shift (i.e., the contingent negative variation). At the performance stage, face-induced expectancies of a strict evaluation rule led to increases of early performance monitoring signals (i.e., frontal-midline theta power). At the final stage of feedback reception, violations of outcome expectancies differentially affected the feedback-related negativity and frontal-midline theta power, pointing to a functional dissociation between these signatures. Altogether, our results indicate that evaluative expectancies induced by face-cues lead to adjustments of internal performance monitoring mechanisms at various stages of task processing.

  18. Identifying Emotions on the Basis of Neural Activation.

    Directory of Open Access Journals (Sweden)

    Karim S Kassam

    Full Text Available We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1 neural activation of the same individual in other trials, 2 neural activation of other individuals who experienced similar trials, and 3 neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

  19. The effect of the neural activity on topological properties of growing neural networks.

    Science.gov (United States)

    Gafarov, F M; Gafarova, V R

    2016-09-01

    The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

  20. Graphene microelectrode arrays for neural activity detection.

    Science.gov (United States)

    Du, Xiaowei; Wu, Lei; Cheng, Ji; Huang, Shanluo; Cai, Qi; Jin, Qinghui; Zhao, Jianlong

    2015-09-01

    We demonstrate a method to fabricate graphene microelectrode arrays (MEAs) using a simple and inexpensive method to solve the problem of opaque electrode positions in traditional MEAs, while keeping good biocompatibility. To study the interface differences between graphene-electrolyte and gold-electrolyte, graphene and gold electrodes with a large area were fabricated. According to the simulation results of electrochemical impedances, the gold-electrolyte interface can be described as a classical double-layer structure, while the graphene-electrolyte interface can be explained by a modified double-layer theory. Furthermore, using graphene MEAs, we detected the neural activities of neurons dissociated from Wistar rats (embryonic day 18). The signal-to-noise ratio of the detected signal was 10.31 ± 1.2, which is comparable to those of MEAs made with other materials. The long-term stability of the MEAs is demonstrated by comparing differences in Bode diagrams taken before and after cell culturing.

  1. Intelligent Monitoring System on Prediction of Building Damage Index using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Reni Suryanita

    2012-03-01

    Full Text Available An earthquake potentially destroys a tall building. The building damage can be indexed by FEMA into three categories namely Immediate Occupancy (IO, Life Safety (LS, and Collapse Prevention (CP. To determine the damage index, the building model has been simulated into structure analysis software. Acceleration data has been analyzed using non linear method in structure analysis program. The earthquake load is time history at surface, PGA=0105g. This work proposes an intelligent monitoring system utilizing Artificial Neural Network to predict the building damage index. The system also provides an alert system and notification to inform the status of the damage. Data learning is trained on ANN utilizing feed forward and back propagation algorithm. The alert system is designed to be able to activate the alarm sound, view the alert bar or text, and send notification via email to the security or management. The system is tested using sample data represented in three conditions involving IO, LS, and CP. The results show that the proposed intelligent monitoring system could provide prediction of up to 92% rate of accuracy and activate the alert. Implementation of the system in building monitoring would allow for rapid, intelligent and accurate prediction of the building damage index due to earthquake.

  2. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  3. A tailored biocompatible neural interface for long term monitoring in neural networks

    OpenAIRE

    Köhler, Per

    2016-01-01

    Neural interface electrodes that can record from neurons in the brain for long periods of time will be of great importance to unravel how the brain accomplishes its functions. However, current electrodes usually cause significant glia reactions and loss of neurons within the adjacent brain parenchyma. To address this challenge, a novel, polymer-based neural probe, with protrusions tailored to the target tissue, was developed to investigate which probe properties affect the development of a gl...

  4. Neural activity, memory, and dementias: serotonergic markers.

    Science.gov (United States)

    Meneses, Alfredo

    2017-04-01

    Dysfunctional memory seems to be a key component of diverse dementias and other neuropsychiatric disorders; unfortunately, no effective treatment exists for this, probably because of the absence of neural biomarkers accompanying it. Diverse neurotransmission systems have been implicated in memory, including serotonin or 5-hydroxytryptamine (5-HT). There are multiple serotonergic pharmacological tools, well-characterized downstream signaling in mammals' species and neural markers providing new insights into memory functions and dysfunctions. Serotonin in mammal species has multiple neural markers, including receptors (5-HT1-7), serotonin transporter, and volume transmission, which are present in brain areas involved in memory. Memory, amnesia, and forgetting modify serotonergic markers; this influence is bidirectional. Evidence shows insights and therapeutic targets and diverse approaches support the translatability of using neural markers and cerebral functions and dysfunctions, including memory formation and amnesia. For instance, 5-HT2A/2B/2C, 5-HT4, and 5-HT6 receptors are involved in tau protein hyperphosphorylation in Alzheimer's disease. In addition, at least, 5-HT1A, 5-HT4, 5-HT6, and 5-HT7 receptors as well as serotonin transporter seem to be useful neural markers and therapeutic targets. Hence, available evidence supports the notion that several mechanisms cooperate to achieve synaptic plasticity or memory, including changes in the number of neurotransmitter receptors and transporters. Considering that memory is a key component of dementias, hence reversing or reducing memory deficits might positively affect them?

  5. Synthesize, optimize, analyze, repeat (SOAR): Application of neural network tools to ECG patient monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Watrous, R.; Towell, G.; Glassman, M.S. [Siemens Corporate Research, Princeton, NJ (United States)

    1995-12-31

    Results are reported from the application of tools for synthesizing, optimizing and analyzing neural networks to an ECG Patient Monitoring task. A neural network was synthesized from a rule-based classifier and optimized over a set of normal and abnormal heartbeats. The classification error rate on a separate and larger test set was reduced by a factor of 2. When the network was analyzed and reduced in size by a factor of 40%, the same level of performance was maintained.

  6. Neural progenitor cells regulate microglia functions and activity.

    Science.gov (United States)

    Mosher, Kira I; Andres, Robert H; Fukuhara, Takeshi; Bieri, Gregor; Hasegawa-Moriyama, Maiko; He, Yingbo; Guzman, Raphael; Wyss-Coray, Tony

    2012-11-01

    We found mouse neural progenitor cells (NPCs) to have a secretory protein profile distinct from other brain cells and to modulate microglial activation, proliferation and phagocytosis. NPC-derived vascular endothelial growth factor was necessary and sufficient to exert at least some of these effects in mice. Thus, neural precursor cells may not only be shaped by microglia, but also regulate microglia functions and activity.

  7. Sociocultural patterning of neural activity during self-reflection

    Science.gov (United States)

    Ma, Yina; Bang, Dan; Wang, Chenbo; Allen, Micah; Frith, Chris; Roepstorff, Andreas; Han, Shihui

    2014-01-01

    Western cultures encourage self-construals independent of social contexts, whereas East Asian cultures foster interdependent self-construals that rely on how others perceive the self. How are culturally specific self-construals mediated by the human brain? Using functional magnetic resonance imaging, we monitored neural responses from adults in East Asian (Chinese) and Western (Danish) cultural contexts during judgments of social, mental and physical attributes of themselves and public figures to assess cultural influences on self-referential processing of personal attributes in different dimensions. We found that judgments of self vs a public figure elicited greater activation in the medial prefrontal cortex (mPFC) in Danish than in Chinese participants regardless of attribute dimensions for judgments. However, self-judgments of social attributes induced greater activity in the temporoparietal junction (TPJ) in Chinese than in Danish participants. Moreover, the group difference in TPJ activity was mediated by a measure of a cultural value (i.e. interdependence of self-construal). Our findings suggest that individuals in different sociocultural contexts may learn and/or adopt distinct strategies for self-reflection by changing the weight of the mPFC and TPJ in the social brain network. PMID:22956678

  8. OCT detection of neural activity in American cockroach nervous system

    Science.gov (United States)

    Gorczyńska, Iwona; Wyszkowska, Joanna; Bukowska, Danuta; Ruminski, Daniel; Karnowski, Karol; Stankiewicz, Maria; Wojtkowski, Maciej

    2013-03-01

    We show results of a project which focuses on detection of activity in neural tissue with Optical Coherence Tomography (OCT) methods. Experiments were performed in neural cords dissected from the American cockroach (Periplaneta americana L.). Functional OCT imaging was performed with ultrahigh resolution spectral / Fourier domain OCT system (axial resolution 2.5 μm). Electrical stimulation (voltage pulses) was applied to the sensory cercal nerve of the neural cord. Optical detection of functional activation of the sample was performed in the connective between the terminal abdominal ganglion and the fifth abdominal ganglion. Functional OCT data were collected over time with the OCT beam illuminating selected single point in the connectives (i.e. OCT M-scans were acquired). Phase changes of the OCT signal were analyzed to visualize occurrence of activation in the neural cord. Electrophysiology recordings (microelectrode method) were also performed as a reference method to demonstrate electrical response of the sample to stimulation.

  9. Condition monitoring of planetary gearbox by hardware implementation of artificial neural networks

    DEFF Research Database (Denmark)

    Dabrowski, Dariusz

    2016-01-01

    -stationary conditions and are exposed to extreme events. Also bucket-wheel excavators are equipped with high-power gearboxes that are exposed to shocks. Continuous monitoring of their condition is crucial in view of early failures, and to ensure safety of exploitation. Artificial neural networks allow for a quick...... environmental conditions. In this paper, a hardware implementation of an artificial neural network designed for condition monitoring of a planetary gearbox is presented. The implementation was done on a Field Programmable Gate Array (FPGA). It is characterized by much higher efficiency and stability than...

  10. Active Engine Mounting Control Algorithm Using Neural Network

    Directory of Open Access Journals (Sweden)

    Fadly Jashi Darsivan

    2009-01-01

    Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

  11. Condition monitoring of oil-impregnated paper bushings using extension neural network, Gaussian mixture and hidden Markov models

    CSIR Research Space (South Africa)

    Miya, WS

    2008-10-01

    Full Text Available In this paper, a comparison between Extension Neural Network (ENN), Gaussian Mixture Model (GMM) and Hidden Markov model (HMM) is conducted for bushing condition monitoring. The monitoring process is a two-stage implementation of a classification...

  12. Structural Health Monitoring Using Neural Network Based Vibrational System Identification

    CERN Document Server

    Sofge, Donald A

    2007-01-01

    Composite fabrication technologies now provide the means for producing high-strength, low-weight panels, plates, spars and other structural components which use embedded fiber optic sensors and piezoelectric transducers. These materials, often referred to as smart structures, make it possible to sense internal characteristics, such as delaminations or structural degradation. In this effort we use neural network based techniques for modeling and analyzing dynamic structural information for recognizing structural defects. This yields an adaptable system which gives a measure of structural integrity for composite structures.

  13. Understanding the brain by controlling neural activity

    OpenAIRE

    Krug, Kristine; Salzman, C. Daniel; Waddell, Scott

    2015-01-01

    Causal methods to interrogate brain function have been employed since the advent of modern neuroscience in the nineteenth century. Initially, randomly placed electrodes and stimulation of parts of the living brain were used to localize specific functions to these areas. Recent technical developments have rejuvenated this approach by providing more precise tools to dissect the neural circuits underlying behaviour, perception and cognition. Carefully controlled behavioural experiments have been...

  14. Death and rebirth of neural activity in sparse inhibitory networks

    OpenAIRE

    Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

    2016-01-01

    In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of the neural activity, as expected, but it can also promote neural reactivation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neurons' death). However, the random pruning of the connections is able to reverse the action of in...

  15. Neurometabolic coupling between neural activity, glucose, and lactate in activated visual cortex.

    Science.gov (United States)

    Li, Baowang; Freeman, Ralph D

    2015-11-01

    Neural activity is closely coupled with energy metabolism but details of the association remain to be identified. One basic area involves the relationships between neural activity and the main supportive substrates of glucose and lactate. This is of fundamental significance for the interpretation of non-invasive neural imaging. Here, we use microelectrodes with high spatial and temporal resolution to determine simultaneous co-localized changes in glucose, lactate, and neural activity during visual activation of the cerebral cortex in the cat. Tissue glucose and lactate concentration levels are measured with electrochemical microelectrodes while neural spiking activity and local field potentials are sampled by a microelectrode. These measurements are performed simultaneously while neurons are activated by visual stimuli of different contrast levels, orientations, and sizes. We find immediate decreases in tissue glucose concentration and simultaneous increases in lactate during neural activation. Both glucose and lactate signals return to their baseline levels instantly as neurons cease firing. No sustained changes or initial dips in glucose or lactate signals are elicited by visual stimulation. However, co-localized measurements of cerebral blood flow and neural activity demonstrate a clear delay in the cerebral blood flow signal such that it does not correlate temporally with the neural response. These results provide direct real-time evidence regarding the coupling between co-localized energy metabolism and neural activity during physiological stimulation. They are also relevant to a current question regarding the role of lactate in energy metabolism in the brain during neural activation. Dynamic changes in energy metabolites can be measured directly with high spatial and temporal resolution by use of enzyme-based microelectrodes. Here, to examine neuro-metabolic coupling during brain activation, we use combined microelectrodes to simultaneously measure

  16. Monitoring Scientific Developments from a Dynamic Perspective: Self-Organized Structuring To Map Neural Network Research.

    Science.gov (United States)

    Noyons, E. C. M.; van Raan, A. F. J.

    1998-01-01

    Using bibliometric mapping techniques, authors developed a methodology of self-organized structuring of scientific fields which was applied to neural network research. Explores the evolution of a data generated field structure by monitoring the interrelationships between subfields, the internal structure of subfields, and the dynamic features of…

  17. Monitoring the depth of anesthesia using entropy features and an artificial neural network.

    Science.gov (United States)

    Shalbaf, Reza; Behnam, Hamid; Sleigh, Jamie W; Steyn-Ross, Alistair; Voss, Logan J

    2013-08-15

    Monitoring the depth of anesthesia using an electroencephalogram (EEG) is a major ongoing challenge for anesthetists. The EEG is a recording of brain electrical activity, and it contains valuable information related to the different physiological states of the brain. This study proposes a novel automated method consisting of two steps for assessing anesthesia depth. Initially, the sample entropy and permutation entropy features were extracted from the EEG signal. Because EEG-derived parameters represent different aspects of the EEG features, it would be reasonable to use multiple parameters to assess the effect of the anesthetic. The sample entropy and permutation entropy features quantified the amount of complexity or irregularity in the EEG data and were conceptually simple, computationally efficient and artifact-resistant. Next, the extracted features were used as input for an artificial neural network, which was a data processing system based on the structure of a biological nervous system. The experimental results indicated that an overall accuracy of 88% could be obtained during sevoflurane anesthesia in 17 patients to classify the EEG data into awake, light, general and deep anesthetized states. In addition, this method yielded a classification accuracy of 92.4% to distinguish between awake and general anesthesia in an independent database of propofol and desflurane anesthesia in 129 patients. Considering the high accuracy of this method, a new EEG monitoring system could be developed to assist the anesthesiologist in estimating the depth of anesthesia in a rapid and accurate manner.

  18. Optical imaging of neural and hemodynamic brain activity

    Science.gov (United States)

    Schei, Jennifer Lynn

    Optical imaging technologies can be used to record neural and hemodynamic activity. Neural activity elicits physiological changes that alter the optical tissue properties. Specifically, changes in polarized light are concomitant with neural depolarization. We measured polarization changes from an isolated lobster nerve during action potential propagation using both reflected and transmitted light. In transmission mode, polarization changes were largest throughout the center of the nerve, suggesting that most of the optical signal arose from the inner nerve bundle. In reflection mode, polarization changes were largest near the edges, suggesting that most of the optical signal arose from the outer sheath. To overcome irregular cell orientation found in the brain, we measured polarization changes from a nerve tied in a knot. Our results show that neural activation produces polarization changes that can be imaged even without regular cell orientations. Neural activation expends energy resources and elicits metabolic delivery through blood vessel dilation, increasing blood flow and volume. We used spectroscopic imaging techniques combined with electrophysiological measurements to record evoked neural and hemodynamic responses from the auditory cortex of the rat. By using implantable optics, we measured responses across natural wake and sleep states, as well as responses following different amounts of sleep deprivation. During quiet sleep, evoked metabolic responses were larger compared to wake, perhaps because blood vessels were more compliant. When animals were sleep deprived, evoked hemodynamic responses were smaller following longer periods of deprivation. These results suggest that prolonged neural activity through sleep deprivation may diminish vascular compliance as indicated by the blunted vascular response. Subsequent sleep may allow vessels to relax, restoring their ability to deliver blood. These results also suggest that severe sleep deprivation or chronic

  19. Neural Network-Based Active Control for Offshore Platforms

    Institute of Scientific and Technical Information of China (English)

    周亚军; 赵德有

    2003-01-01

    A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.

  20. Social status modulates neural activity in the mentalizing network.

    Science.gov (United States)

    Muscatell, Keely A; Morelli, Sylvia A; Falk, Emily B; Way, Baldwin M; Pfeifer, Jennifer H; Galinsky, Adam D; Lieberman, Matthew D; Dapretto, Mirella; Eisenberger, Naomi I

    2012-04-15

    The current research explored the neural mechanisms linking social status to perceptions of the social world. Two fMRI studies provide converging evidence that individuals lower in social status are more likely to engage neural circuitry often involved in 'mentalizing' or thinking about others' thoughts and feelings. Study 1 found that college students' perception of their social status in the university community was related to neural activity in the mentalizing network (e.g., DMPFC, MPFC, precuneus/PCC) while encoding social information, with lower social status predicting greater neural activity in this network. Study 2 demonstrated that socioeconomic status, an objective indicator of global standing, predicted adolescents' neural activity during the processing of threatening faces, with individuals lower in social status displaying greater activity in the DMPFC, previously associated with mentalizing, and the amygdala, previously associated with emotion/salience processing. These studies demonstrate that social status is fundamentally and neurocognitively linked to how people process and navigate their social worlds. Copyright © 2012 Elsevier Inc. All rights reserved.

  1. Internal models for interpreting neural population activity during sensorimotor control.

    Science.gov (United States)

    Golub, Matthew D; Yu, Byron M; Chase, Steven M

    2015-01-01

    To successfully guide limb movements, the brain takes in sensory information about the limb, internally tracks the state of the limb, and produces appropriate motor commands. It is widely believed that this process uses an internal model, which describes our prior beliefs about how the limb responds to motor commands. Here, we leveraged a brain-machine interface (BMI) paradigm in rhesus monkeys and novel statistical analyses of neural population activity to gain insight into moment-by-moment internal model computations. We discovered that a mismatch between subjects' internal models and the actual BMI explains roughly 65% of movement errors, as well as long-standing deficiencies in BMI speed control. We then used the internal models to characterize how the neural population activity changes during BMI learning. More broadly, this work provides an approach for interpreting neural population activity in the context of how prior beliefs guide the transformation of sensory input to motor output.

  2. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network

    Institute of Scientific and Technical Information of China (English)

    Ali UYSAL; Raif BAYIR

    2013-01-01

    The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of de-tecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

  3. Neural mechanisms of time-based prospective memory: evidence for transient monitoring.

    Directory of Open Access Journals (Sweden)

    Kevin M Oksanen

    Full Text Available In daily life, we often need to remember to perform an action after, or at, a specific period of time (e.g., take pizza out of oven in 15 minutes. Surprisingly, little is known about the neural mechanisms that support this form of memory, termed time-based prospective memory (PM. Here we pioneer an fMRI paradigm that enables examination of both sustained and transient processes engaged during time-based PM. Participants were scanned while performing a demanding on-going task (n-back working memory, with and without an additional time-based PM demand. During the PM condition participants could access a hidden clock with a specific button-press response, while in the control condition, pseudo-clocks randomly appeared and were removed via the same response. Analyses tested for sustained activation associated with the PM condition, and also transient activation associated with clock-checks and the PM target response. Contrary to prior findings with event-based PM (i.e., remembering to perform a future action when a specific event occurs, no sustained PM-related activity was observed in anterior prefrontal cortex (aPFC or elsewhere in the brain; instead, transient clock-related activity was observed in this region. Critically, the activation was anticipatory, increasing before clock-check responses. Anticipatory activity prior to the PM target response was weaker in aPFC, but strong in pre-Supplementary Motor Area (pre-SMA; relative to clock-check responses, suggesting a functional double dissociation related to volitional decision-making. Together, the results suggest that aPFC-activity dynamics during time-based PM reflect a distinct transient monitoring process, enabling integration of the PM intention with current temporal information to facilitate scheduling of upcoming PM-related actions.

  4. Ant colony optimization and neural networks applied to nuclear power plant monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

  5. Neural activation toward erotic stimuli in homosexual and heterosexual males.

    Science.gov (United States)

    Kagerer, Sabine; Klucken, Tim; Wehrum, Sina; Zimmermann, Mark; Schienle, Anne; Walter, Bertram; Vaitl, Dieter; Stark, Rudolf

    2011-11-01

    Studies investigating sexual arousal exist, yet there are diverging findings on the underlying neural mechanisms with regard to sexual orientation. Moreover, sexual arousal effects have often been confounded with general arousal effects. Hence, it is still unclear which structures underlie the sexual arousal response in homosexual and heterosexual men. Neural activity and subjective responses were investigated in order to disentangle sexual from general arousal. Considering sexual orientation, differential and conjoint neural activations were of interest. The functional magnetic resonance imaging (fMRI) study focused on the neural networks involved in the processing of sexual stimuli in 21 male participants (11 homosexual, 10 heterosexual). Both groups viewed pictures with erotic content as well as aversive and neutral stimuli. The erotic pictures were subdivided into three categories (most sexually arousing, least sexually arousing, and rest) based on the individual subjective ratings of each participant. Blood oxygen level-dependent responses measured by fMRI and subjective ratings. A conjunction analysis revealed conjoint neural activation related to sexual arousal in thalamus, hypothalamus, occipital cortex, and nucleus accumbens. Increased insula, amygdala, and anterior cingulate gyrus activation could be linked to general arousal. Group differences emerged neither when viewing the most sexually arousing pictures compared with highly arousing aversive pictures nor compared with neutral pictures. Results suggest that a widespread neural network is activated by highly sexually arousing visual stimuli. A partly distinct network of structures underlies sexual and general arousal effects. The processing of preferred, highly sexually arousing stimuli recruited similar structures in homosexual and heterosexual males. © 2011 International Society for Sexual Medicine.

  6. Design of 3D Active Multichannel Silicon Neural Microelectrode

    Institute of Scientific and Technical Information of China (English)

    WANG Di; ZHANG Guoxiong; LI Xingfei

    2006-01-01

    To find a design method for 3D active multichannel silicon microelectrode,a microstructure of active neural recording system is presented,where two 2D probes,two integrated circuits and two spacers are microassembled on a 5 mm ×7 mm silicon platform,and 32 sites neural signals can be operated simultaneously.A theoretical model for measuring the neural signal by the silicon microelectrode is proposed based on the structure and fabrication process of a single-shank probe.The method of determining the dimensional parameters of the probe shank is discussed in the following three aspects,i.e.the structures of pallium and endocranium,coupled interconnecters noise,and strength characteristic of neural probe.The design criterion is to minimize the size of the neural probe as well as that the probe has enough stiffness to pierce the endocranium.The on-chip unity-gain bandpass amplifier has an overall gain of 42 dB over a bandwidth from 60 Hz to 10 kHz;and the DC-baseline stability circuit is of high input resistance above 30 MΩ to guarantee a cutoff frequency below 100 Hz.The circuit works in stimulating or recording modes.The conversion of the modes depends on the stimulating control signal.

  7. Neural activity of orbitofrontal cortex contributes to control of waiting.

    Science.gov (United States)

    Xiao, Xiong; Deng, Hanfei; Wei, Lei; Huang, Yanwang; Wang, Zuoren

    2016-09-01

    The willingness to wait for delayed reward and information is of fundamental importance for deliberative behaviors. The orbitofrontal cortex (OFC) is thought to be a core component of the neural circuitry underlying the capacity to control waiting. However, the neural correlates of active waiting and the causal role of the OFC in the control of waiting still remain largely unknown. Here, we trained rats to perform a waiting task (waiting for a pseudorandom time to obtain the water reward), and recorded neuronal ensembles in the OFC throughout the task. We observed that subset OFC neurons exhibited ramping activities throughout the waiting process. Receiver operating characteristic analysis showed that neural activities during the waiting period even predicted the trial outcomes (patient vs. impatient) on a trial-by-trial basis. Furthermore, optogenetic activation of the OFC during the waiting period improved the waiting performance, but did not influence rats' movement to obtain the reward. Taken together, these findings reveal that the neural activity in the OFC contributes to the control of waiting.

  8. 7 CFR 800.216 - Activities that shall be monitored.

    Science.gov (United States)

    2010-01-01

    ... merchandising activities identified in this section shall be monitored in accordance with the instructions. (b) Grain merchandising activities. Grain merchandising activities subject to monitoring for compliance with...) Recordkeeping activities. Elevator and merchandising recordkeeping activities subject to monitoring...

  9. Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

    Directory of Open Access Journals (Sweden)

    W. L. C. Rutten

    2006-01-01

    Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

  10. Condition Monitoring for DC-link Capacitors Based on Artificial Neural Network Algorithm

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    In power electronic systems, capacitor is one of the reliability critical components . Recently, the condition monitoring of capacitors to estimate their health status have been attracted by the academic research. Industry applications require more reliable power electronics products...... with preventive maintenance. However, the existing capacitor condition monitoring methods suffer from either increased hardware cost or low estimation accuracy, being the challenges to be adopted in industry applications. New development in condition monitoring technology with software solutions without extra...... hardware will reduce the cost, and therefore could be more promising for industry applications. A condition monitoring method based on Artificial Neural Network (ANN) algorithm is therefore proposed in this paper. The implementation of the ANN to the DC-link capacitor condition monitoring in a back...

  11. Artificial Neural Networks Applications: from Aircraft Design Optimization to Orbiting Spacecraft On-board Environment Monitoring

    Science.gov (United States)

    Jules, Kenol; Lin, Paul P.

    2002-01-01

    This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

  12. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  13. Controlling neural activity in Caenorhabditis elegans to evoke chemotactic behavior

    Science.gov (United States)

    Kocabas, Askin; Shen, Ching-Han; Guo, Zengcai V.; Ramanathan, Sharad

    2013-03-01

    Animals locate and track chemoattractive gradients in the environment to find food. With its simple nervous system, Caenorhabditis elegans is a good model system in which to understand how the dynamics of neural activity control this search behavior. To understand how the activity in its interneurons coordinate different motor programs to lead the animal to food, here we used optogenetics and new optical tools to manipulate neural activity directly in freely moving animals to evoke chemotactic behavior. By deducing the classes of activity patterns triggered during chemotaxis and exciting individual neurons with these patterns, we identified interneurons that control the essential locomotory programs for this behavior. Notably, we discovered that controlling the dynamics of activity in just one interneuron pair was sufficient to force the animal to locate, turn towards and track virtual light gradients.

  14. Neural activity predicts attitude change in cognitive dissonance.

    Science.gov (United States)

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

    2009-11-01

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

  15. Voice activity detection based on deep neural networks and Viterbi

    Science.gov (United States)

    Bai, Liang; Zhang, Zhen; Hu, Jun

    2017-09-01

    Voice Activity Detection (VAD) is important in speech processing. In the applications, the systems usually need to separate speech/non-speech parts, so that only the speech part can be dealt with. How to improve the performances of VAD in different noisy environments is an important issue in speech processing. Deep Neural network, which proves its efficiency in speech recognition, has been widely used in recent years. This paper studies the present typical VAD algorithms, and presents a new VAD algorithm based on deep neural networks and Viterbi algorithm. The result demonstrates the effectiveness of the deep neural network with Viterbi used in VAD. In addition, it shows the flexibility and the real-time performance of the algorithms.

  16. Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV

    Institute of Scientific and Technical Information of China (English)

    WANG Yu-jia; ZHANG Ming-jun

    2002-01-01

    A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.

  17. Neural activity when people solve verbal problems with insight.

    Directory of Open Access Journals (Sweden)

    Mark Jung-Beeman

    2004-04-01

    Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

  18. Monitoring the differentiation and migration patterns of neural cells derived from human embryonic stem cells using a microfluidic culture system.

    Science.gov (United States)

    Lee, Nayeon; Park, Jae Woo; Kim, Hyung Joon; Yeon, Ju Hun; Kwon, Jihye; Ko, Jung Jae; Oh, Seung-Hun; Kim, Hyun Sook; Kim, Aeri; Han, Baek Soo; Lee, Sang Chul; Jeon, Noo Li; Song, Jihwan

    2014-06-01

    Microfluidics can provide unique experimental tools to visualize the development of neural structures within a microscale device, which is followed by guidance of neurite growth in the axonal isolation compartment. We utilized microfluidics technology to monitor the differentiation and migration of neural cells derived from human embryonic stem cells (hESCs). We co-cultured hESCs with PA6 stromal cells, and isolated neural rosette-like structures, which subsequently formed neurospheres in suspension culture. Tuj1-positive neural cells, but not nestin-positive neural precursor cells (NPCs), were able to enter the microfluidics grooves (microchannels), suggesting that neural cell-migratory capacity was dependent upon neuronal differentiation stage. We also showed that bundles of axons formed and extended into the microchannels. Taken together, these results demonstrated that microfluidics technology can provide useful tools to study neurite outgrowth and axon guidance of neural cells, which are derived from human embryonic stem cells.

  19. Active Noise Feedback Control Using a Neural Network

    OpenAIRE

    Zhang Qizhi; Jia Yongle

    2001-01-01

    The active noise control (ANC) is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR) filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with ...

  20. Evaluating a genetically encoded optical sensor of neural activity using electrophysiology in intact adult fruit flies

    Directory of Open Access Journals (Sweden)

    Gilles Laurent

    2007-11-01

    Full Text Available Genetically encoded optical indicators hold the promise of enabling non-invasive monitoring of activity in identified neurons in behaving organisms. However, the interpretation of images of brain activity produced using such sensors is not straightforward. Several recent studies of sensory coding used G-CaMP 1.3-a calcium sensor-as an indicator of neural activity; some of these studies characterized the imaged neurons as having narrow tuning curves, a conclusion not always supported by parallel electrophysiological studies. To better understand the possible cause of these conflicting results, we performed simultaneous in vivo 2-photon imaging and electrophysiological recording of G-CaMP 1.3 expressing neurons in the antennal lobe (AL of intact fruitflies. We find that G-CaMP has a relatively high threshold, that its signal often fails to capture spiking response kinetics, and that it can miss even high instantaneous rates of activity if those are not sustained. While G-CaMP can be misleading, it is clearly useful for the identification of promising neural targets: when electrical activity is well above the sensor's detection threshold, its signal is fairly well correlated with mean firing rate and G-CaMP does not appear to alter significantly the responses of neurons that express it. The methods we present should enable any genetically encoded sensor, activator, or silencer to be evaluated in an intact neural circuit in vivo in Drosophila.

  1. Dynamic neural activity during stress signals resilient coping.

    Science.gov (United States)

    Sinha, Rajita; Lacadie, Cheryl M; Constable, R Todd; Seo, Dongju

    2016-08-02

    Active coping underlies a healthy stress response, but neural processes supporting such resilient coping are not well-known. Using a brief, sustained exposure paradigm contrasting highly stressful, threatening, and violent stimuli versus nonaversive neutral visual stimuli in a functional magnetic resonance imaging (fMRI) study, we show significant subjective, physiologic, and endocrine increases and temporally related dynamically distinct patterns of neural activation in brain circuits underlying the stress response. First, stress-specific sustained increases in the amygdala, striatum, hypothalamus, midbrain, right insula, and right dorsolateral prefrontal cortex (DLPFC) regions supported the stress processing and reactivity circuit. Second, dynamic neural activation during stress versus neutral runs, showing early increases followed by later reduced activation in the ventrolateral prefrontal cortex (VLPFC), dorsal anterior cingulate cortex (dACC), left DLPFC, hippocampus, and left insula, suggested a stress adaptation response network. Finally, dynamic stress-specific mobilization of the ventromedial prefrontal cortex (VmPFC), marked by initial hypoactivity followed by increased VmPFC activation, pointed to the VmPFC as a key locus of the emotional and behavioral control network. Consistent with this finding, greater neural flexibility signals in the VmPFC during stress correlated with active coping ratings whereas lower dynamic activity in the VmPFC also predicted a higher level of maladaptive coping behaviors in real life, including binge alcohol intake, emotional eating, and frequency of arguments and fights. These findings demonstrate acute functional neuroplasticity during stress, with distinct and separable brain networks that underlie critical components of the stress response, and a specific role for VmPFC neuroflexibility in stress-resilient coping.

  2. Early interfaced neural activity from chronic amputated nerves

    Directory of Open Access Journals (Sweden)

    Kshitija Garde

    2009-05-01

    Full Text Available Direct interfacing of transected peripheral nerves with advanced robotic prosthetic devices has been proposed as a strategy for achieving natural motor control and sensory perception of such bionic substitutes, thus fully functionally replacing missing limbs in amputees. Multi-electrode arrays placed in the brain and peripheral nerves have been used successfully to convey neural control of prosthetic devices to the user. However, reactive gliosis, micro hemorrhages, axonopathy and excessive inflammation, currently limit their long-term use. Here we demonstrate that enticement of peripheral nerve regeneration through a non-obstructive multi-electrode array, after either acute or chronic nerve amputation, offers a viable alternative to obtain early neural recordings and to enhance long-term interfacing of nerve activity. Non restrictive electrode arrays placed in the path of regenerating nerve fibers allowed the recording of action potentials as early as 8 days post-implantation with high signal-to-noise ratio, as long as 3 months in some animals, and with minimal inflammation at the nerve tissue-metal electrode interface. Our findings suggest that regenerative on-dependent multi-electrode arrays of open design allow the early and stable interfacing of neural activity from amputated peripheral nerves and might contribute towards conveying full neural control and sensory feedback to users of robotic prosthetic devices. .

  3. TRIGA control rod position and reactivity transient Monitoring by Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rosa, R.; Palomba, M.; Sepielli, M. [ENEA - Casaccia TRIGA Reactor (Italy)

    2008-10-29

    Plant sensors drift or malfunction and operator actions in nuclear reactor control can be supported by sensor on-line monitoring, and data validation through soft-computing process. On-line recalibration can often avoid manual calibration or drifting component replacement. DSP requires prompt response to the modified conditions. Artificial Neural Network (ANN) and Fuzzy logic ensure: prompt response, link with field measurement and physical system behaviour, data incoming interpretation, and detection of discrepancy for mis-calibration or sensor faults. ANN (Artificial Neural Network) is a system based on the operation of biological neural networks. Although computing is day by day advancing, there are certain tasks that a program made for a common microprocessor is unable to perform. A software implementation of an ANN can be made with Pros and Cons. Pros: A neural network can perform tasks that a linear program can not; When an element of the neural network fails, it can continue without any problem by their parallel nature; A neural network learns and does not need to be reprogrammed; It can be implemented in any application; It can be implemented without any problem. Cons: The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated; it requires high processing time for large neural networks; and the neural network needs training to operate. Three possibilities of training exist: Supervised learning: the network is trained providing input and matching output patterns; Unsupervised learning: input patterns are not a priori classified and the system must develop its own representation of the input stimuli; Reinforcement Learning: intermediate form of the above two types of learning, the learning machine does some action on the environment and gets a feedback response from the environment. Two TRIGAN ANN applications are considered: control rod position and fuel temperature. The outcome obtained in this

  4. Perception Neural Networks for Active Noise Control Systems

    Directory of Open Access Journals (Sweden)

    Wang Xiaoli

    2012-11-01

    Full Text Available In a response to a growing demand for environments of 70dB or less noise levels, many industrial sectors have focused with some form of noise control system. Active noise control (ANC has proven to be the most effective technology. This paper mainly investigates application of neural network on self-adaptation system in active noise control (ANC. An active silencing control system is made which adopts a motional feedback loudspeaker as not a noise controlling source but a detecting sensor. The working fundamentals and the characteristics of the motional feedback loudspeaker are analyzed in detail. By analyzing each acoustical path, identification based adaptive linear neural network is built. This kind of identifying method can be achieved conveniently. The estimated result of each sound channel matches well with its real sound character, respectively.

  5. Active Noise Feedback Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Zhang Qizhi

    2001-01-01

    Full Text Available The active noise control (ANC is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. An on-line learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.

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

    Science.gov (United States)

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

    2016-01-15

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

  7. Rapid regulation of sialidase activity in response to neural activity and sialic acid removal during memory processing in rat hippocampus.

    Science.gov (United States)

    Minami, Akira; Meguro, Yuko; Ishibashi, Sayaka; Ishii, Ami; Shiratori, Mako; Sai, Saki; Horii, Yuuki; Shimizu, Hirotaka; Fukumoto, Hokuto; Shimba, Sumika; Taguchi, Risa; Takahashi, Tadanobu; Otsubo, Tadamune; Ikeda, Kiyoshi; Suzuki, Takashi

    2017-04-07

    Sialidase cleaves sialic acids on the extracellular cell surface as well as inside the cell and is necessary for normal long-term potentiation (LTP) at mossy fiber-CA3 pyramidal cell synapses and for hippocampus-dependent spatial memory. Here, we investigated in detail the role of sialidase in memory processing. Sialidase activity measured with 4-methylumbelliferyl-α-d-N-acetylneuraminic acid (4MU-Neu5Ac) or 5-bromo-4-chloroindol-3-yl-α-d-N-acetylneuraminic acid (X-Neu5Ac) and Fast Red Violet LB was increased by high-K(+)-induced membrane depolarization. Sialidase activity was also increased by chemical LTP induction with forskolin and activation of BDNF signaling, non-NMDA receptors, or NMDA receptors. The increase in sialidase activity with neural excitation appears to be caused not by secreted sialidase or by an increase in sialidase expression but by a change in the subcellular localization of sialidase. Astrocytes as well as neurons are also involved in the neural activity-dependent increase in sialidase activity. Sialidase activity visualized with a benzothiazolylphenol-based sialic acid derivative (BTP3-Neu5Ac), a highly sensitive histochemical imaging probe for sialidase activity, at the CA3 stratum lucidum of rat acute hippocampal slices was immediately increased in response to LTP-inducible high-frequency stimulation on a time scale of seconds. To obtain direct evidence for sialic acid removal on the extracellular cell surface during neural excitation, the extracellular free sialic acid level in the hippocampus was monitored using in vivo microdialysis. The free sialic acid level was increased by high-K(+)-induced membrane depolarization. Desialylation also occurred during hippocampus-dependent memory formation in a contextual fear-conditioning paradigm. Our results show that neural activity-dependent desialylation by sialidase may be involved in hippocampal memory processing. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  8. Persistent activity in neural networks with dynamic synapses.

    Directory of Open Access Journals (Sweden)

    Omri Barak

    2007-02-01

    Full Text Available Persistent activity states (attractors, observed in several neocortical areas after the removal of a sensory stimulus, are believed to be the neuronal basis of working memory. One of the possible mechanisms that can underlie persistent activity is recurrent excitation mediated by intracortical synaptic connections. A recent experimental study revealed that connections between pyramidal cells in prefrontal cortex exhibit various degrees of synaptic depression and facilitation. Here we analyze the effect of synaptic dynamics on the emergence and persistence of attractor states in interconnected neural networks. We show that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states. This analysis raises the possibility that the framework of attractor neural networks can be extended to represent time-dependent stimuli.

  9. Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-09-01

    Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.

  10. Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes.

    Science.gov (United States)

    Takahashi, Maria Beatriz; Leme, Jaci; Caricati, Celso Pereira; Tonso, Aldo; Fernández Núñez, Eutimio Gustavo; Rocha, José Celso

    2015-06-01

    Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV-Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV-Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 10(5) ± 1.90 10(5) cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV-VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

  11. Monitoring Biological Activity at Geothermal Power Plants

    Energy Technology Data Exchange (ETDEWEB)

    Peter Pryfogle

    2005-09-01

    The economic impact of microbial growth in geothermal power plants has been estimated to be as high as $500,000 annually for a 100 MWe plant. Many methods are available to monitor biological activity at these facilities; however, very few plants have any on-line monitoring program in place. Metal coupon, selective culturing (MPN), total organic carbon (TOC), adenosine triphosphate (ATP), respirometry, phospholipid fatty acid (PLFA), and denaturing gradient gel electrophoresis (DGGE) characterizations have been conducted using water samples collected from geothermal plants located in California and Utah. In addition, the on-line performance of a commercial electrochemical monitor, the BIoGEORGE?, has been evaluated during extended deployments at geothermal facilities. This report provides a review of these techniques, presents data on their application from laboratory and field studies, and discusses their value in characterizing and monitoring biological activities at geothermal power plants.

  12. Activated sludge process based on artificial neural network

    Institute of Scientific and Technical Information of China (English)

    张文艺; 蔡建安

    2002-01-01

    Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.

  13. Development of Si neural probe with piezoresistive force sensor for minimally invasive and precise monitoring of insertion forces

    Science.gov (United States)

    Harashima, Takuya; Morikawa, Takumi; Kino, Hisashi; Fukushima, Takafumi; Tanaka, Tetsu

    2017-04-01

    A Si neural probe is one of the most important tools for neurophysiology and brain science because of its various functions such as optical stimulation and drug delivery. However, the Si neural probe is not robust compared with a metal tetrode, and could be broken by mechanical stress caused by insertion to the brain. Therefore, the Si neural probe becomes more useful if it has a stress sensor that can measure mechanical forces applied to the probe so as not to be broken. In this paper, we proposed and fabricated the Si neural probe with a piezoresistive force sensor for minimally invasive and precise monitoring of insertion forces. The fabricated piezoresistive force sensor accurately measured forces and successfully detected insertion events without buckling or bending in the shank of the Si neural probe. This Si neural probe with a piezoresistive force sensor has become one of the most versatile tools for neurophysiology and brain science.

  14. Semi-real-time monitoring of cracking on couplings by neural network analysis of acoustic emission signals

    Science.gov (United States)

    Godinez-Azcuaga, Valery F.; Shu, Fong; Finlayson, Richard D.; O'Donnell, Bruce W.

    2004-07-01

    This paper presents the results obtained during the development of a semi-real-time monitoring methodology based on Neural Network Pattern Recognition of Acoustic Emission (AE) signals for early detection of cracks in couplings used in aircraft and engine drive systems. AE signals were collected in order to establish a baseline of a gear-testing fixture background noise and its variations due to rotational speed and torque. Also, simulated cracking signals immersed in background noise were collected. EDM notches were machined in the driving gear and the load on the gearbox was increased until damaged was induced. Using these data, a Neural Network Signal Classifier (NNSC) was implemented and tested. The testing showed that the NNSC was capable of correctly identifying six different classes of AE signals corresponding to different gearbox operation conditions. Also, a semi-real-time classification software was implemented. This software includes functions that allow the user to view and classify AE data from a dynamic process as they are recorded at programmable time intervals. The software is capable of monitoring periodic statistics of AE data, which can be used as an indicator of damage presence and severity in a dynamic system. The semi-real-time classification software was successfully tested in situations where a delay of 10 seconds between data acquisition and classification was achieved with a hit rate of 50 hits/second per channel on eight active AE channels.

  15. Technology of remote monitoring for nuclear activity monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Kwack, Ehn Ho; Kim, Jong Soo; Yoon, Wan Ki; Park, Sung Sik; Na, Won Woo; An, Jin Soo; Cha, Hong Ryul; Kim, Jung Soo

    2000-05-01

    In a view of safeguards monitoring at nuclear facilities, the monitoring is changing to remote method so that this report is described to remote monitoring(RM) applying on commercial NPP in Korea. To enhance IAEA safeguards efficiency and effectiveness, IAEA is taking into account of remote monitoring system(RMS) and testing as a field trial. IRMP(International Remote Monitoring Project) in participating many nations for development of RMS is proceeding their project such as technical exchange and research etc. In case of our country are carrying out the research relevant RM since acceptance RMS at 7th ROK-IAEA safeguards implementation review meeting. With a view to enhancement the RMS, installation location and element technology of the RM equipment are evaluated in a view of safeguards in Korea LWRs, and proposed a procedure for national inspection application through remote data evaluation from Younggwang-3 NPP. These results are large valuable to use of national inspection at time point extending installation to all Korea PWR NPP. In case of CANDU, neutron, gamma measurement and basic concept of network using optical fiber scintillating detector as remote verification method for dry storage canister are described. Also RM basic design of spent fuel transfer campaign is described that unattended RM without inspector instead of performing in participating together with IAEA and national inspector. The transfer campaign means the spent fuel storage pond to dry storage canister for about two months every year. Therefore, positively participation of IAEA strength safeguards project will be increased transparency for our nuclear activity as well as contributed to national relevant industry.

  16. BP-Neural-Network-Based Tool Wear Monitoring by Using Wavelet Decomposition of the Power Spectrum

    Institute of Scientific and Technical Information of China (English)

    ZHENG Jian-ming; XI Chang-qing; LI Yan; XIAO Ji-ming

    2004-01-01

    In a drilling process, the power spectrum of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the power spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers using wavelet transform and extraction of the low frequency decomposition coefficient us the envelope information of the power spectrum. Intelligent identification of the tool wear status is achieved in the drilling process through fusing the wavelet decomposition coefficient of the power spectrum by using a BP ( Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural networks show high identification precision and the ability of extension.

  17. Artificial neural networks for monitoring the gas turbine; Artificiella neuronnaet foer gasturbinoevervakning

    Energy Technology Data Exchange (ETDEWEB)

    Fast, Magnus; Thern, Marcus [Inst. foer Energivetenskaper, Lunds Univ. (Sweden)

    2011-10-15

    Through available historical operational data from gas turbines, fast, accurate, easy to use and reliable models can be developed. These models can be used for monitoring of gas turbines and assist in the transition from today's time-based maintenance to condition based maintenance. For the end user this means that, because only operational data is needed, they can easily develop their own tools independent of the manufacturer. Traditionally these types of models are constructed with physical relations for e.g., mass, energy and momentum. To develop a model with physical relations is often laborious and requires classified information which the end user does not have access to. Research has shown that by producing models using operational data a very high model precision can be achieved. When implementing these models in a power plant computer system the gas turbine's performance can be monitored in real time. This can facilitate fault detection at an early stage, and if necessary, stop the gas turbine before major damage occurs. For the power plant owner, this means that the gas turbine reliability is increased since the need for maintenance is minimized and the downtime is reduced. It also means that a measure of the gas turbine's overall status is continuously available, with respect to e.g. degradation, which helps in the planning of service intervals. The tool used is called artificial neural networks (ANN), a collective name for a number of algorithms for information processing that attempts to mimic the nerve cell function. Just like real networks of neurons in a brain, these artificial neural networks have the ability to learn. In this case, neural networks are trained to mimic the behavior of gas turbines by introducing them to data from real gas turbines. After a neural network is trained it represents a very accurate model of the gas turbine that it is trained to emulate.

  18. Neural correlates of error monitoring in adolescents prospectively predict initiation of tobacco use

    Directory of Open Access Journals (Sweden)

    Andrey P. Anokhin

    2015-12-01

    Full Text Available Deficits in self-regulation of behavior can play an important role in the initiation of substance use and progression to regular use and dependence. One of the distinct component processes of self-regulation is error monitoring, i.e. detection of a conflict between the intended and actually executed action. Here we examined whether a neural marker of error monitoring, Error-Related Negativity (ERN, predicts future initiation of tobacco use. ERN was assessed in a prospective longitudinal sample at ages 12, 14, and 16 using a flanker task. ERN amplitude showed a significant increase with age during adolescence. Reduced ERN amplitude at ages 14 and 16, as well as slower rate of its developmental changes significantly predicted initiation of tobacco use by age 18 but not transition to regular tobacco use or initiation of marijuana and alcohol use. The present results suggest that attenuated development of the neural mechanisms of error monitoring during adolescence can increase the risk for initiation of tobacco use. The present results also suggest that the role of distinct neurocognitive component processes involved in behavioral regulation may be limited to specific stages of addiction.

  19. Multiview fusion for activity recognition using deep neural networks

    Science.gov (United States)

    Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

    2016-07-01

    Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

  20. Performance evaluation of salivary amylase activity monitor.

    Science.gov (United States)

    Yamaguchi, Masaki; Kanemori, Takahiro; Kanemaru, Masashi; Takai, Noriyasu; Mizuno, Yasufumi; Yoshida, Hiroshi

    2004-10-15

    In order to quantify psychological stress and to distinguish eustress and distress, we have been investigating the establishment of a method that can quantify salivary amylase activity (SMA). Salivary glands not only act as amplifiers of a low level of norepinephrine, but also respond more quickly and sensitively to psychological stress than cortisol levels. Moreover, the time-course changes of the salivary amylase activity have a possibility to distinguish eustress and distress. Thus, salivary amylase activity can be utilized as an excellent index for psychological stress. However, in dry chemistry system, a method for quantification of the enzymatic activity still needs to be established that can provide with sufficient substrate in a testing tape as well as can control enzymatic reaction time. Moreover, it is necessary to develop a method that has the advantages of using saliva, such as ease of collection, rapidity of response, and able to use at any time. In order to establish an easy method to monitor the salivary amylase activity, a salivary transcription device was fabricated to control the enzymatic reaction time. A fabricated salivary amylase activity monitor consisted of three devices, the salivary transcription device, a testing-strip and an optical analyzer. By adding maltose as a competitive inhibitor to a substrate Ga1-G2-CNP, a broad-range activity testing-strip was fabricated that could measure the salivary amylase activity with a range of 0-200 kU/l within 150 s. The calibration curve of the monitor for the salivary amylase activity showed R2=0.941, indicating that it was possible to use this monitor for the analysis of the salivary amylase activity without the need to determine the salivary volume quantitatively. In order to evaluate the assay variability of the monitor, salivary amylase activity was measured using Kraepelin psychodiagnostic test as a psychological stressor. A significant difference of salivary amylase activity was recognized

  1. A neural network model for olfactory glomerular activity prediction

    Science.gov (United States)

    Soh, Zu; Tsuji, Toshio; Takiguchi, Noboru; Ohtake, Hisao

    2012-12-01

    Recently, the importance of odors and methods for their evaluation have seen increased emphasis, especially in the fragrance and food industries. Although odors can be characterized by their odorant components, their chemical information cannot be directly related to the flavors we perceive. Biological research has revealed that neuronal activity related to glomeruli (which form part of the olfactory system) is closely connected to odor qualities. Here we report on a neural network model of the olfactory system that can predict glomerular activity from odorant molecule structures. We also report on the learning and prediction ability of the proposed model.

  2. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation.

    Directory of Open Access Journals (Sweden)

    Tatia M C Lee

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

  3. Distinct neural activity associated with focused-attention meditation and loving-kindness meditation.

    Science.gov (United States)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2012-01-01

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

  5. Active system monitoring applied on wind turbines

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Parbo, Henrik

    2009-01-01

    A concept for active system monitoring (ASM) applied on wind turbines is presented in this paper. The concept is based on an injection of a small periodic auxiliary signal in the system. An investigation of the signature from the auxiliary input in residual (error) signals can then be applied...

  6. GMDH and neural networks applied in monitoring and fault detection in sensors in nuclear power plants

    Energy Technology Data Exchange (ETDEWEB)

    Bueno, Elaine Inacio [Instituto Federal de Educacao, Ciencia e Tecnologia, Guarulhos, SP (Brazil); Pereira, Iraci Martinez; Silva, Antonio Teixeira e, E-mail: martinez@ipen.b, E-mail: teixeira@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2011-07-01

    In this work a new monitoring and fault detection methodology was developed using GMDH (Group Method of Data Handling) algorithm and artificial neural networks (ANNs) which was applied in the IEA-R1 research reactor at IPEN. The monitoring and fault detection system was developed in two parts: the first was dedicated to preprocess information, using GMDH algorithm; and the second to the process information using ANNs. The preprocess information was divided in two parts. In the first part, the GMDH algorithm was used to generate a better database estimate, called matrix z, which was used to train the ANNs. In the second part the GMDH was used to study the best set of variables to be used to train the ANNs, resulting in a best monitoring variable estimative. The methodology was developed and tested using five different models: one theoretical model and for models using different sets of reactor variables. After an exhausting study dedicated to the sensors monitoring, the fault detection in sensors was developed by simulating faults in the sensors database using values of +5%, +10%, +15% and +20% in these sensors database. The good results obtained through the present methodology shows the viability of using GMDH algorithm in the study of the best input variables to the ANNs, thus making possible the use of these methods in the implementation of a new monitoring and fault detection methodology applied in sensors. (author)

  7. Motor neuron activation in peripheral nerves using infrared neural stimulation

    Science.gov (United States)

    Peterson, E. J.; Tyler, D. J.

    2014-02-01

    Objective. Localized activation of peripheral axons may improve selectivity of peripheral nerve interfaces. Infrared neural stimulation (INS) employs localized delivery to activate neural tissue. This study investigated INS to determine whether localized delivery limited functionality in larger mammalian nerves. Approach. The rabbit sciatic nerve was stimulated extraneurally with 1875 nm wavelength infrared light, electrical stimulation, or a combination of both. Infrared-sensitive regions (ISR) of the nerve surface and electromyogram (EMG) recruitment of the Medial Gastrocnemius, Lateral Gastrocnemius, Soleus, and Tibialis Anterior were the primary output measures. Stimulation applied included infrared-only, electrical-only, and combined infrared and electrical. Main results. 81% of nerves tested were sensitive to INS, with 1.7 ± 0.5 ISR detected per nerve. INS was selective to a single muscle within 81% of identified ISR. Activation energy threshold did not change significantly with stimulus power, but motor activation decreased significantly when radiant power was decreased. Maximum INS levels typically recruited up to 2-9% of any muscle. Combined infrared and electrical stimulation differed significantly from electrical recruitment in 7% of cases. Significance. The observed selectivity of INS indicates that it may be useful in augmenting rehabilitation, but significant challenges remain in increasing sensitivity and response magnitude to improve the functionality of INS.

  8. Differences in "bottom-up" and "top-down" neural activity in current and former cigarette smokers: Evidence for neural substrates which may promote nicotine abstinence through increased cognitive control.

    Science.gov (United States)

    Nestor, Liam; McCabe, Ella; Jones, Jennifer; Clancy, Luke; Garavan, Hugh

    2011-06-15

    Drug-related stimuli, through conditioning, are thought to acquire incentive motivational properties that code possible reward availability and elicit an attentional bias, possibly through increased "bottom-up" neural processing. The processes underlying this attentional bias are considered important in the maintenance of addiction, and crucially, in relapse among substance users attempting to remain abstinent. Equally, impaired "top-down" cognitive control may impair the ability to restrain "bottom-up" pre-potent behaviours, such as drug use, following exposure to drug-related stimuli. Two experiments sought to identify the neural loci of bottom-up/top-down processing during fMRI. Experiment 1 utilised an attentional bias paradigm to examine the behavioural and neural responses to neutral, emotionally evocative and smoking-related cues in control (n=13), ex-smoking (n=10 - abstinent >12months) and smoking (n=13 - mean >6.5years of use) groups. Experiment 2 used a go/no-go paradigm to examine the neural correlates of motor response inhibition and error monitoring in the same sample. The results of Experiment 1 demonstrated that, across conditions, current smokers had significantly less neural activity in cortical but significantly more activity in subcortical areas compared to both controls and ex-smokers. Ex-smokers exhibited more neural activity than both control and smoker groups in prefrontal cortical regions. Similarly, Experiment 2 revealed that smokers had reduced neural activity in prefrontal cortical regions during motor response inhibition compared to controls while ex-smokers demonstrated greater neural activity in prefrontal cortical regions compared to both controls and smokers during error monitoring. The results reveal cortical and subcortical differences between current smokers and controls and a general pattern of increased prefrontal cortical activity in ex-smokers. These findings may suggest that elevated topdown control might be an important

  9. Supervised learning for neural manifold using spatiotemporal brain activity

    Science.gov (United States)

    Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

    2015-12-01

    Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

  10. Estimation of spatiotemporal neural activity using radial basis function networks.

    Science.gov (United States)

    Anderson, R W; Das, S; Keller, E L

    1998-12-01

    We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.

  11. Artificial Neural Network-Based Monitoring of the Fuel Assembly Temperature Sensor and FPGA Implementation

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2015-07-01

    Numerous methods have been developed around the world to model the dynamic behavior and detect a faulty operating mode of a temperature sensor. In this context, we present in this study a new method based on the dependence between the fuel assembly temperature profile on control rods positions, and the coolant flow rate in a nuclear reactor. This seems to be possible since the insertion of control rods at different axial positions and variations in flow rate of the reactor coolant results in different produced thermal power in the reactor. This is closely linked to the instant fuel rod temperature profile. In a first step, we selected parameters to be used and confirmed the adequate correlation between the chosen parameters and those to be estimated by the proposed monitoring system. In the next step, we acquired and de-noised the data of corresponding parameters, the qualified data is then used to design and train the artificial neural network. The effective data denoising was done by using the wavelet transform to remove a various kind of artifacts such as inherent noise. With the suitable choice of wavelet level and smoothing method, it was possible for us to remove all the non-required artifacts with a view to verify and analyze the considered signal. In our work, several potential mother wavelet functions (Haar, Daubechies, Bi-orthogonal, Reverse Bi-orthogonal, Discrete Meyer and Symlets) were investigated to find the most similar function with the being processed signals. To implement the proposed monitoring system for the fuel rod temperature sensor (03 wire RTD sensor), we used the Bayesian artificial neural network 'BNN' technique to model the dynamic behavior of the considered sensor, the system correlate the estimated values with the measured for the concretization of the proposed system we propose an FPGA (field programmable gate array) implementation. The monitoring system use the correlation. (authors)

  12. Classification of the extracellular fields produced by activated neural structures

    Directory of Open Access Journals (Sweden)

    Perry Danielle

    2005-09-01

    Full Text Available Abstract Background Classifying the types of extracellular potentials recorded when neural structures are activated is an important component in understanding nerve pathophysiology. Varying definitions and approaches to understanding the factors that influence the potentials recorded during neural activity have made this issue complex. Methods In this article, many of the factors which influence the distribution of electric potential produced by a traveling action potential are discussed from a theoretical standpoint with illustrative simulations. Results For an axon of arbitrary shape, it is shown that a quadrupolar potential is generated by action potentials traveling along a straight axon. However, a dipole moment is generated at any point where an axon bends or its diameter changes. Next, it is shown how asymmetric disturbances in the conductivity of the medium surrounding an axon produce dipolar potentials, even during propagation along a straight axon. Next, by studying the electric fields generated by a dipole source in an insulating cylinder, it is shown that in finite volume conductors, the extracellular potentials can be very different from those in infinite volume conductors. Finally, the effects of impulses propagating along axons with inhomogeneous cable properties are analyzed. Conclusion Because of the well-defined factors affecting extracellular potentials, the vague terms far-field and near-field potentials should be abandoned in favor of more accurate descriptions of the potentials.

  13. Wireless system for seismic activity monitoring

    OpenAIRE

    Безвесільна, Олена Миколаївна; Козько, Констянтин Сергійович

    2014-01-01

    The article examines the concepts and principles of sensor networks operations, especially the one that is used to monitor seismic activity and potential natural disasters. It also describes the operating principle of the geographically distributed wireless system, represented by block diagrams of typical sensor nodes and base station, as well as constructive electrical circuit sensor node and the frequency generator radio transmissions the base station and sensor nodes, we formulate to calcu...

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

    DEFF Research Database (Denmark)

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

    2011-01-01

    Neural oscillations in different frequency bands have been observed in a range of sensorimotor tasks and have been linked to coupling of spatially distinct neurons. The goal of this study was to detect a general motor network that is activated during phasic and tonic movements and to study the task......-dependent modulation of frequency coupling within this network. To this end we recorded 122-multichannel EEG in 13 healthy subjects while they performed three simple motor tasks. EEG data source modeling using individual MR images was carried out with a multiple source beamformer approach. A bilateral motor network...... connecting frontal, cerebellar and central motor regions, was consistently activated throughout the motor tasks. Quantification of observed spectral responses using dynamic causal modeling revealed strong coupling in the c-band (30–48 Hz) between frontal and central motor regions when a slow finger movement...

  15. Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

    Science.gov (United States)

    Wielgosz, Maciej; Skoczeń, Andrzej; Mertik, Matej

    2017-09-01

    The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyzes voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE = 0 . 00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer.

  16. Condition Monitoring and Faults Diagnosis for Synchronous Generator Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Omer Elfaki Elbashir

    2013-09-01

    Full Text Available Early detection and diagnosis of incipient fault is desirable for on line condition assessment production quality assurance and improved operational efficiency of synchronous generator running of power supply. Artificial Intelligent techniques are increasly used for condition monitoring and fault diagnosis of machines. In this paper, Artificial Neural Network (ANN approach employed for fault diagnosis in the generator, based on monitoring generator currents to give indication of the winding faults. Feed-forward Network, error back propagation training algorithm are used to perform the generator faults diagnosis and their values. NN which has been trained for all possible operating condition of the machine used to classify the incoming data. The inputs of the NN are the stator and rotor currents, and the output represents the running condition of the generator. The training of the NN achieved by the data through a mathematical model based approach to simulate the generator faults at various degree of severity.This paper evaluates through simulation line currents magnitude of the generator .The final results have been represented on a monitoring unit, built using matlab program, to give early warning of the generator failure.

  17. Social decisions affect neural activity to perceived dynamic gaze.

    Science.gov (United States)

    Latinus, Marianne; Love, Scott A; Rossi, Alejandra; Parada, Francisco J; Huang, Lisa; Conty, Laurence; George, Nathalie; James, Karin; Puce, Aina

    2015-11-01

    Gaze direction, a cue of both social and spatial attention, is known to modulate early neural responses to faces e.g. N170. However, findings in the literature have been inconsistent, likely reflecting differences in stimulus characteristics and task requirements. Here, we investigated the effect of task on neural responses to dynamic gaze changes: away and toward transitions (resulting or not in eye contact). Subjects performed, in random order, social (away/toward them) and non-social (left/right) judgment tasks on these stimuli. Overall, in the non-social task, results showed a larger N170 to gaze aversion than gaze motion toward the observer. In the social task, however, this difference was no longer present in the right hemisphere, likely reflecting an enhanced N170 to gaze motion toward the observer. Our behavioral and event-related potential data indicate that performing social judgments enhances saliency of gaze motion toward the observer, even those that did not result in gaze contact. These data and that of previous studies suggest two modes of processing visual information: a 'default mode' that may focus on spatial information; a 'socially aware mode' that might be activated when subjects are required to make social judgments. The exact mechanism that allows switching from one mode to the other remains to be clarified.

  18. Condition monitoring of 3G cellular networks through competitive neural models.

    Science.gov (United States)

    Barreto, Guilherme A; Mota, João C M; Souza, Luis G M; Frota, Rewbenio A; Aguayo, Leonardo

    2005-09-01

    We develop an unsupervised approach to condition monitoring of cellular networks using competitive neural algorithms. Training is carried out with state vectors representing the normal functioning of a simulated CDMA2000 network. Once training is completed, global and local normality profiles (NPs) are built from the distribution of quantization errors of the training state vectors and their components, respectively. The global NP is used to evaluate the overall condition of the cellular system. If abnormal behavior is detected, local NPs are used in a component-wise fashion to find abnormal state variables. Anomaly detection tests are performed via percentile-based confidence intervals computed over the global and local NPs. We compared the performance of four competitive algorithms [winner-take-all (WTA), frequency-sensitive competitive learning (FSCL), self-organizing map (SOM), and neural-gas algorithm (NGA)] and the results suggest that the joint use of global and local NPs is more efficient and more robust than current single-threshold methods.

  19. Neural analysis of seismic data: applications to the monitoring of Mt. Vesuvius

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    Antonietta M. Esposito

    2013-11-01

    Full Text Available The computing techniques currently available for the seismic monitoring allow advanced analysis. However, the correct event classification remains a critical aspect for the reliability of real time automatic analysis. Among the existing methods, neural networks may be considered efficient tools for detection and discrimination, and may be integrated into intelligent systems for the automatic classification of seismic events. In this work we apply an unsupervised technique for analysis and classification of seismic signals recorded in the Mt. Vesuvius area in order to improve the automatic event detection. The examined dataset contains about 1500 records divided into four typologies of events: earthquakes, landslides, artificial explosions, and “other” (any other signals not included in the previous classes. First, the Linear Predictive Coding (LPC and a waveform parametrization have been applied to achieve a significant and compact data encoding. Then, the clustering is obtained using a Self-Organizing Map (SOM neural network which does not require an a-priori classification of the seismic signals, groups those with similar structures, providing a simple framework for understanding the relationships between them. The resulting SOM map is separated into different areas, each one containing the events of a defined type. This means that the SOM discriminates well the four classes of seismic signals. Moreover, the system will classify a new input pattern depending on its position on the SOM map. The proposed approach can be an efficient instrument for the real time automatic analysis of seismic data, especially in the case of possible volcanic unrest.

  20. Monitoring the Freshness of Moroccan Sardines with a Neural-Network Based Electronic Nose

    Directory of Open Access Journals (Sweden)

    Benachir Bouchikhi

    2006-10-01

    Full Text Available An electronic nose was developed and used as a rapid technique to classify thefreshness of sardine samples according to the number of days spent under cold storage (4 ±1°C, in air. The volatile compounds present in the headspace of weighted sardine sampleswere introduced into a sensor chamber and the response signals of the sensors wererecorded as a function of time. Commercially available gas sensors based on metal oxidesemiconductors were used and both static and dynamic features from the sensorconductance response were input to the pattern recognition engine. Data analysis wasperformed by three different pattern recognition methods such as probabilistic neuralnetworks (PNN, fuzzy ARTMAP neural networks (FANN and support vector machines(SVM. The objective of this study was to find, among these three pattern recognitionmethods, the most suitable one for accurately identifying the days of cold storage undergoneby sardine samples. The results show that the electronic nose can monitor the freshness ofsardine samples stored at 4°C, and that the best classification and prediction are obtainedwith SVM neural network. The SVM approach shows improved classificationperformances, reducing the amount of misclassified samples down to 3.75 %.

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

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    H. MEKKI

    2015-03-01

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

  2. Neural network model for the on-line monitoring of a crystallization process

    Directory of Open Access Journals (Sweden)

    Guardani R.

    2001-01-01

    Full Text Available This paper presents the results of the application of a recently developed technique, based on Neural Networks (NN, in the recognition of angular distribution patterns of light scattered by particles in suspension, for the purpose of estimating concentration and crystal size distribution (CSD in a precipitation process based on the addition of antisolvent (a model system consisting of sodium chloride, water and ethanol. In the first step, in NN model was fitted, using particles with different size distributions and concentrations. Then the model was used to monitor the process, thus enabling a fast and reliable estimation of supersaturation and CSD. Such information, which is difficult to obtain by any other means, can be used in the study of fundamental aspects of crystallization and precipitation processes.

  3. Wireless power transfer and data communication for neural implants case study : epilepsy monitoring

    CERN Document Server

    Yilmaz, Gürkan

    2017-01-01

    This book presents new circuits and systems for implantable biomedical applications targeting neural recording. The authors describe a system design adapted to conform to the requirements of an epilepsy monitoring system. Throughout the book, these requirements are reflected in terms of implant size, power consumption, and data rate. In addition to theoretical background which explains the relevant technical challenges, the authors provide practical, step-by-step solutions to these problems. Readers will gain understanding of the numerical values in such a system, enabling projections for feasibility of new projects. Provides complete, system-level perspective for implantable batteryless biomedical system; Extends design example to implementation and long term in-vitro validation; Discusses system design concerns regarding wireless power transmission and wireless data communication, particularly for systems in which both are performed on the same channel/frequency; Presents fully-integrated, implantable syste...

  4. A High Input Impedance Low Noise Integrated Front-End Amplifier for Neural Monitoring.

    Science.gov (United States)

    Zhou, Zhijun; Warr, Paul A

    2016-12-01

    Within neural monitoring systems, the front-end amplifier forms the critical element for signal detection and pre-processing, which determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a novel combined feedback loop-controlled approach is proposed to compensate for input leakage currents generated by low noise amplifiers when in integrated circuit form alongside signal leakage into the input bias network. This loop topology ensures the Front-End Amplifier (FEA) maintains a high input impedance across all manufacturing and operational variations. Measured results from a prototype manufactured on the AMS 0.35 [Formula: see text] CMOS technology is provided. This FEA consumes 3.1 [Formula: see text] in 0.042 [Formula: see text], achieves input impedance of 42 [Formula: see text], and 18.2 [Formula: see text] input-referred noise.

  5. Impaired activity-dependent neural circuit assembly and refinement in autism spectrum disorder genetic models

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    Caleb Andrew Doll

    2014-02-01

    Full Text Available Early-use activity during circuit-specific critical periods refines brain circuitry by the coupled processes of eliminating inappropriate synapses and strengthening maintained synapses. We theorize these activity-dependent developmental processes are specifically impaired in autism spectrum disorders (ASDs. ASD genetic models in both mouse and Drosophila have pioneered our insights into normal activity-dependent neural circuit assembly and consolidation, and how these developmental mechanisms go awry in specific genetic conditions. The monogenic Fragile X syndrome (FXS, a common cause of heritable ASD and intellectual disability, has been particularly well linked to defects in activity-dependent critical period processes. The Fragile X Mental Retardation Protein (FMRP is positively activity-regulated in expression and function, in turn regulates excitability and activity in a negative feedback loop, and appears to be required for the activity-dependent remodeling of synaptic connectivity during early-use critical periods. The Drosophila FXS model has been shown to functionally conserve the roles of human FMRP in synaptogenesis, and has been centrally important in generating our current mechanistic understanding of the FXS disease state. Recent advances in Drosophila optogenetics, transgenic calcium reporters, highly-targeted transgenic drivers for individually-identified neurons, and a vastly improved connectome of the brain are now being combined to provide unparalleled opportunities to both manipulate and monitor activity-dependent processes during critical period brain development in defined neural circuits. The field is now poised to exploit this new Drosophila transgenic toolbox for the systematic dissection of activity-dependent mechanisms in normal versus ASD brain development, particularly utilizing the well-established Drosophila FXS disease model.

  6. Global Stability Analysis for Periodic Solution in Discontinuous Neural Networks with Nonlinear Growth Activations

    Directory of Open Access Journals (Sweden)

    Wu Huaiqin

    2009-01-01

    Full Text Available This paper considers a new class of additive neural networks where the neuron activations are modelled by discontinuous functions with nonlinear growth. By Leray-Schauder alternative theorem in differential inclusion theory, matrix theory, and generalized Lyapunov approach, a general result is derived which ensures the existence and global asymptotical stability of a unique periodic solution for such neural networks. The obtained results can be applied to neural networks with a broad range of activation functions assuming neither boundedness nor monotonicity, and also show that Forti's conjecture for discontinuous neural networks with nonlinear growth activations is true.

  7. Self-reported empathy and neural activity during action imitation and observation in schizophrenia

    Directory of Open Access Journals (Sweden)

    William P. Horan

    2014-01-01

    Conclusions: Although patients with schizophrenia demonstrated largely normal patterns of neural activation across the finger movement and facial expression tasks, they reported decreased self perceived empathy and failed to show the typical relationship between neural activity and self-reported empathy seen in controls. These findings suggest that patients show a disjunction between automatic neural responses to low level social cues and higher level, integrative social cognitive processes involved in self-perceived empathy.

  8. Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

    Directory of Open Access Journals (Sweden)

    Yasir Hassan Ali

    2015-01-01

    Full Text Available The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ. The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

  9. Active sensors for health monitoring of aging aerospace structures

    Energy Technology Data Exchange (ETDEWEB)

    GIURGIUTIU,VICTOR; REDMOND,JAMES M.; ROACH,DENNIS P.; RACKOW,KIRK A.

    2000-02-29

    A project to develop non-intrusive active sensors that can be applied on existing aging aerospace structures for monitoring the onset and progress of structural damage (fatigue cracks and corrosion) is presented. The state of the art in active sensors structural health monitoring and damage detection is reviewed. Methods based on (a) elastic wave propagation and (b) electro-mechanical (E/M) impedance technique are cited and briefly discussed. The instrumentation of these specimens with piezoelectric active sensors is illustrated. The main detection strategies (E/M impedance for local area detection and wave propagation for wide area interrogation) are discussed. The signal processing and damage interpretation algorithms are tuned to the specific structural interrogation method used. In the high-frequency E/M impedance approach, pattern recognition methods are used to compare impedance signatures taken at various time intervals and to identify damage presence and progression from the change in these signatures. In the wave propagation approach, the acousto-ultrasonic methods identifying additional reflection generated from the damage site and changes in transmission velocity and phase are used. Both approaches benefit from the use of artificial intelligence neural networks algorithms that can extract damage features based on a learning process. Design and fabrication of a set of structural specimens representative of aging aerospace structures is presented. Three built-up specimens (pristine, with cracks, and with corrosion damage) are used. The specimen instrumentation with active sensors fabricated at the University of South Carolina is illustrated. Preliminary results obtained with the E/M impedance method on pristine and cracked specimens are presented.

  10. Interpreting collective neural activity underlying spatial navigation in virtual reality

    Science.gov (United States)

    Meshulam, Leenoy; Gauthier, Jeff; Tank, David; Bialek, William

    2015-03-01

    Traditionally, cognitive- demanding processes like spatial navigation were studied by recording the activity of single neurons. However, recent technological progress allows imaging the simultaneous activity of large neuronal populations in awake behaving animals. This progress in experimental work calls for a similar adjustments of the modeling frameworks. To achieve a description of the ``real thermodynamics'' of the neural system, we construct maximum entropy models for optical imaging data taken in vivo, from the hippocampus of mice navigating in a virtual reality environment. This provides a natural extension of statistical mechanics applicable to brain activity, by focusing on the interactions between cells rather than on single cell's activity. We aim to determine how the topology of the energy landscape predicted by the model corresponds to the location of the animal in the environment. Since large subpopulations of the neurons in this area are spatially modulated, we expect the landscape to exhibit a large ``valley'' structure of local minima, corresponding to the animal's position along the environment. Such a finding is especially of interest because the location information emerges solely from the activity patterns that are accessible to the brain.

  11. Deterministic dynamics of neural activity during absence seizures in rats

    Science.gov (United States)

    Ouyang, Gaoxiang; Li, Xiaoli; Dang, Chuangyin; Richards, Douglas A.

    2009-04-01

    The study of brain electrical activities in terms of deterministic nonlinear dynamics has recently received much attention. Forbidden ordinal patterns (FOP) is a recently proposed method to investigate the determinism of a dynamical system through the analysis of intrinsic ordinal properties of a nonstationary time series. The advantages of this method in comparison to others include simplicity and low complexity in computation without further model assumptions. In this paper, the FOP of the EEG series of genetic absence epilepsy rats from Strasbourg was examined to demonstrate evidence of deterministic dynamics during epileptic states. Experiments showed that the number of FOP of the EEG series grew significantly from an interictal to an ictal state via a preictal state. These findings indicated that the deterministic dynamics of neural networks increased significantly in the transition from the interictal to the ictal states and also suggested that the FOP measures of the EEG series could be considered as a predictor of absence seizures.

  12. Activation of endogenous neural stem cells for multiple sclerosis therapy

    Directory of Open Access Journals (Sweden)

    Iliana eMichailidou

    2015-01-01

    Full Text Available Multiple sclerosis (MS is a chronic inflammatory disorder of the central nervous system, leading to severe neurological deficits. Current MS treatment regimens, consist of immunomodulatory agents aiming to reduce the rate of relapses. However, these agents are usually insufficient to treat chronic neurological disability.A promising perspective for future therapy of MS is the regeneration of lesions with replacement of the damaged oligodendrocytes or neurons. Therapies targeting to the enhancement of endogenous remyelination, aim to promote the activation of either the parenchymal oligodendrocyte progenitor cells or the subventricular zone-derived neural stem cells (NSCs. Less studied but highly potent, is the strategy of neuronal regeneration with endogenous NSCs that although being linked to numerous limitations, is anticipated to ameliorate cognitive disability in MS. Focusing on the forebrain, this review highlights the role of NSCs in the regeneration of MS lesions.

  13. Monitoring active volcanoes: The geochemical approach

    Directory of Open Access Journals (Sweden)

    Takeshi Ohba

    2011-06-01

    Full Text Available

    The geochemical surveillance of an active volcano aims to recognize possible signals that are related to changes in volcanic activity. Indeed, as a consequence of the magma rising inside the volcanic "plumbing system" and/or the refilling with new batches of magma, the dissolved volatiles in the magma are progressively released as a function of their relative solubilities. When approaching the surface, these fluids that are discharged during magma degassing can interact with shallow aquifers and/or can be released along the main volcano-tectonic structures. Under these conditions, the following main degassing processes represent strategic sites to be monitored.

    The main purpose of this special volume is to collect papers that cover a wide range of topics in volcanic fluid geochemistry, which include geochemical characterization and geochemical monitoring of active volcanoes using different techniques and at different sites. Moreover, part of this volume has been dedicated to the new geochemistry tools.

  14. Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons.

    Science.gov (United States)

    Ma, Ying; Shaik, Mohammed A; Kozberg, Mariel G; Kim, Sharon H; Portes, Jacob P; Timerman, Dmitriy; Hillman, Elizabeth M C

    2016-12-27

    Brain hemodynamics serve as a proxy for neural activity in a range of noninvasive neuroimaging techniques including functional magnetic resonance imaging (fMRI). In resting-state fMRI, hemodynamic fluctuations have been found to exhibit patterns of bilateral synchrony, with correlated regions inferred to have functional connectivity. However, the relationship between resting-state hemodynamics and underlying neural activity has not been well established, making the neural underpinnings of functional connectivity networks unclear. In this study, neural activity and hemodynamics were recorded simultaneously over the bilateral cortex of awake and anesthetized Thy1-GCaMP mice using wide-field optical mapping. Neural activity was visualized via selective expression of the calcium-sensitive fluorophore GCaMP in layer 2/3 and 5 excitatory neurons. Characteristic patterns of resting-state hemodynamics were accompanied by more rapidly changing bilateral patterns of resting-state neural activity. Spatiotemporal hemodynamics could be modeled by convolving this neural activity with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Simultaneous imaging and electrophysiology confirmed that Thy1-GCaMP signals are well-predicted by multiunit activity. Neurovascular coupling between resting-state neural activity and hemodynamics was robust and fast in awake animals, whereas coupling in urethane-anesthetized animals was slower, and in some cases included lower-frequency (neural activity. The patterns of bilaterally-symmetric spontaneous neural activity revealed by wide-field Thy1-GCaMP imaging may depict the neural foundation of functional connectivity networks detected in resting-state fMRI.

  15. Cerebral oxygen delivery and consumption during evoked neural activity

    Directory of Open Access Journals (Sweden)

    Alberto L Vazquez

    2010-06-01

    Full Text Available Increases in neural activity evoke increases in the delivery and consumption of oxygen. Beyond observations of cerebral tissue and blood oxygen, the role and properties of cerebral oxygen delivery and consumption during changes in brain function are not well understood. This work overviews the current knowledge of functional oxygen delivery and consumption and introduces recent and preliminary findings to explore the mechanisms by which oxygen is delivered to tissue as well as the temporal dynamics of oxygen metabolism. Vascular oxygen tension measurements have shown that a relatively large amount of oxygen exits pial arterioles prior to capillaries. Additionally, increases in cerebral blood flow (CBF induced by evoked neural activation are accompanied by arterial vasodilation and also by increases in arteriolar oxygenation. This increase contributes not only to the down-stream delivery of oxygen to tissue, but also to delivery of additional oxygen to extra-vascular spaces surrounding the arterioles. On the other hand, the changes in tissue oxygen tension due to functional increases in oxygen consumption have been investigated using a method to suppress the evoked CBF response. The functional decreases in tissue oxygen tension induced by increases in oxygen consumption are slow to evoked changes in CBF under control conditions. Preliminary findings obtained using flavoprotein autofluorescence imaging suggest cellular oxidative metabolism changes at a faster rate than the average changes in tissue oxygen. These issues are important in the determination of the dynamic changes in tissue oxygen metabolism from hemoglobin-based imaging techniques such as blood oxygenation-level dependent functional magnetic resonance imaging (fMRI.

  16. Decorrelation of Neural-Network Activity by Inhibitory Feedback

    Science.gov (United States)

    Einevoll, Gaute T.; Diesmann, Markus

    2012-01-01

    Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between

  17. Studying modulation on simultaneously activated SSVEP neural networks by a cognitive task.

    Science.gov (United States)

    Wu, Zhenghua

    2014-01-01

    Since the discovery of steady-state visually evoked potential (SSVEP), it has been used in many fields. Numerous studies suggest that there exist three SSVEP neural networks in different frequency bands. An obvious phenomenon has been observed, that the amplitude and phase of SSVEP can be modulated by a cognitive task. Previous works have studied this modulation on separately activated SSVEP neural networks by a cognitive task. If two or more SSVEP neural networks are activated simultaneously in the process of a cognitive task, is the modulation on different SSVEP neural networks the same? In this study, two different SSVEP neural networks were activated simultaneously by two different frequency flickers, with a working memory task irrelevant to the flickers being conducted at the same time. The modulated SSVEP waves were compared with each other and to those only under one flicker in previous studies. The comparison results show that the cognitive task can modulate different SSVEP neural networks with a similar style.

  18. Can simple interactions capture complex features of neural activity underlying behavior in a virtual reality environment?

    Science.gov (United States)

    Meshulam, Leenoy; Gauthier, Jeffrey; Brody, Carlos; Tank, David; Bialek, William

    The complex neural interactions which are abundant in most recordings of neural activity are relatively poorly understood. A prime example of such interactions can be found in the in vivo neural activity which underlies complex behaviors of mice, imaged in brain regions such as hippocampus and parietal cortex. Experimental techniques now allow us to accurately follow these neural interactions in the simultaneous activity of large neuronal populations of awake behaving animals. Here, we demonstrate that pairwise maximum entropy models can predict a surprising number of properties of the neural activity. The models, that are constrained with activity rates and interactions between pairs of neurons, are well fit to the activity `states' in the hippocampus and cortex of mice performing cognitive tasks while navigating in a virtual reality environment.

  19. Tropospheric ozone column retrieval from the Ozone Monitoring Instrument by means of a neural network algorithm

    Directory of Open Access Journals (Sweden)

    P. Sellitto

    2011-05-01

    Full Text Available Monitoring tropospheric ozone from space is of critical importance in order to gain more thorough knowledge on phenomena affecting air quality and the greenhouse effect. Deriving information on tropospheric ozone from UV/VIS nadir satellite spectrometers is difficult owing to the weak sensitivity of the measured radiance spectra to variations of ozone in the troposphere. Here we propose an alternative method of analysis to retrieve tropospheric ozone columns from Ozone Monitoring Instrument radiances by means of a Neural Network algorithms. An extended set of ozone sonde measurements at northern mid-latitudes has been considered as the training and test data set. The design of the algorithm is extensively discussed. Our retrievals are compared to both tropospheric ozone residuals and optimal estimation retrievals over a similar independent test data set. Results show that our algorithm has comparable accuracy with respect to both correlative methods and its performance is slightly better over a subset containing only European ozone sonde stations. Possible sources of errors are analyzed. Finally, the capabilities of our algorithm to derive information on boundary layer ozone are studied and the results critically discussed.

  20. Neural Network Hydrological Modelling: Linear Output Activation Functions?

    Science.gov (United States)

    Abrahart, R. J.; Dawson, C. W.

    2005-12-01

    The power to represent non-linear hydrological processes is of paramount importance in neural network hydrological modelling operations. The accepted wisdom requires non-polynomial activation functions to be incorporated in the hidden units such that a single tier of hidden units can thereafter be used to provide a 'universal approximation' to whatever particular hydrological mechanism or function is of interest to the modeller. The user can select from a set of default activation functions, or in certain software packages, is able to define their own function - the most popular options being logistic, sigmoid and hyperbolic tangent. If a unit does not transform its inputs it is said to possess a 'linear activation function' and a combination of linear activation functions will produce a linear solution; whereas the use of non-linear activation functions will produce non-linear solutions in which the principle of superposition does not hold. For hidden units, speed of learning and network complexities are important issues. For the output units, it is desirable to select an activation function that is suited to the distribution of the target values: e.g. binary targets (logistic); categorical targets (softmax); continuous-valued targets with a bounded range (logistic / tanh); positive target values with no known upper bound (exponential; but beware of overflow); continuous-valued targets with no known bounds (linear). It is also standard practice in most hydrological applications to use the default software settings and to insert a set of identical non-linear activation functions in the hidden layer and output layer processing units. Mixed combinations have nevertheless been reported in several hydrological modelling papers and the full ramifications of such activities requires further investigation and assessment i.e. non-linear activation functions in the hidden units connected to linear or clipped-linear activation functions in the output unit. There are two

  1. Determination of Activation Functions in A Feedforward Neural Network by using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Oğuz ÜSTÜN

    2009-03-01

    Full Text Available In this study, activation functions of all layers of the multilayered feedforward neural network have been determined by using genetic algorithm. The main criteria that show the efficiency of the neural network is to approximate to the desired output with the same number nodes and connection weights. One of the important parameter to determine this performance is to choose a proper activation function. In the classical neural network designing, a network is designed by choosing one of the generally known activation function. In the presented study, a table has been generated for the activation functions. The ideal activation function for each node has been chosen from this table by using the genetic algorithm. Two dimensional regression problem clusters has been used to compare the performance of the classical static neural network and the genetic algorithm based neural network. Test results reveal that the proposed method has a high level approximation capacity.

  2. EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

    Science.gov (United States)

    Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

    2016-12-01

    Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

  3. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

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

  4. SPR imaging combined with cyclic voltammetry for the detection of neural activity

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-03-01

    Full Text Available Surface plasmon resonance (SPR detects changes in refractive index at a metal-dielectric interface. In this study, SPR imaging (SPRi combined with cyclic voltammetry (CV was applied to detect neural activity in isolated bullfrog sciatic nerves. The neural activities induced by chemical and electrical stimulation led to an SPR response, and the activities were recorded in real time. The activities of different parts of the sciatic nerve were recorded and compared. The results demonstrated that SPR imaging combined with CV is a powerful tool for the investigation of neural activity.

  5. Quality Assurance Project Plan for Facility Effluent Monitoring Plan activities

    Energy Technology Data Exchange (ETDEWEB)

    Frazier, T.P.

    1994-10-20

    This Quality Assurance Project Plan addresses the quality assurance requirements for the activities associated with the Facility Effluent Monitoring Plans, which are part of the overall Hanford Site Environmental Protection Plan. This plan specifically applies to the sampling and analysis activities and continuous monitoring performed for all Facility Effluent Monitoring Plan activities conducted by Westinghouse Hanford Company. It is generic in approach and will be implemented in conjunction with the specific requirements of the individual Facility Effluent Monitoring Plans.

  6. A new neutron monitor with silver activation

    CERN Document Server

    Luszik-Bhadra, M; Hohmann, E

    2010-01-01

    A moderator-type neutron monitor has been developed, which registers delayed beta rays from neutron-induced silver activation and which is able to measure dose equivalent in pulsed fields with peak dose rates of several thousand Sv h(-1). The monitor uses four silicon diodes in the centre of a polyethylene moderator, 30 cm in diameter. Two of the diodes are covered by natural silver foils and two of them by tin foils. The latter are used to subtract photon-induced pulses. For registering signals, a pulse height threshold is set at 662 key, which minimizes the effect of Cs-137 and lower energy radiation and - in addition - enhances the detection of beta rays from the shorter half-life silver isotope Ag-110 (25 s) as compared to the longer half-life isotope Ag-108 (144 s). The results of measurements in neutron and photon calibration fields, of MCNPX neutron response calculations and of first measurements in a high-intensity pulsed field at the PSI accelerator are shown. (c) 2010 Elsevier Ltd. All rights reserv...

  7. Assessing physical activity using wearable monitors: measures of physical activity

    National Research Council Canada - National Science Library

    Butte, Nancy F; Ekelund, Ulf; Westerterp, Klaas R

    2012-01-01

    .... Six main categories of wearable monitors are currently available to investigators: pedometers, load transducers/foot-contact monitors, accelerometers, HR monitors, combined accelerometer and HR monitors, and multiple sensor systems...

  8. Sensory entrainment mechanisms in auditory perception: neural synchronization and cortico-striatal activation

    Directory of Open Access Journals (Sweden)

    Catia M Sameiro-Barbosa

    2016-08-01

    Full Text Available The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations and, potentially, neural activation in the cortico-striatal system.

  9. Application of Artificial Neural Network in Active Vibration Control of Diesel Engine

    Institute of Scientific and Technical Information of China (English)

    SUN Cheng-shun; ZHANG Jian-wu

    2005-01-01

    Artificial Neural Network (ANN) is applied to diesel twostage vibration isolating system and an AVC (Active Vibration Control) system is developed. Both identifier and controller are constructed by three-layer BP neural network. Besides computer simulation, experiment research is carried out on both analog bench and diesel bench. The results of simulation and experiment show a diminished response of vibration.

  10. Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems.

    Science.gov (United States)

    Zhang, Yingying; Wang, Juncheng; Vorontsov, A M; Hou, Guangli; Nikanorova, M N; Wang, Hongliang

    2014-01-01

    The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.

  11. In vivo optoacoustic monitoring of calcium activity in the brain (Conference Presentation)

    Science.gov (United States)

    Deán-Ben, Xose Luís.; Gottschalk, Sven; Sela, Gali; Lauri, Antonella; Kneipp, Moritz; Ntziachristos, Vasilis; Westmeyer, Gil G.; Shoham, Shy; Razansky, Daniel

    2017-03-01

    Non-invasive observation of spatio-temporal neural activity of large neural populations distributed over the entire brain of complex organisms is a longstanding goal of neuroscience [1,2]. Recently, genetically encoded calcium indicators (GECIs) have revolutionized neuroimaging by enabling mapping the activity of entire neuronal populations in vivo [3]. Visualization of these powerful sensors with fluorescence microscopy has however been limited to superficial regions while deep brain areas have so far remained unreachable [4]. We have developed a volumetric multispectral optoacoustic tomography platform for imaging neural activation deep in scattering brains [5]. The developed methodology can render 100 volumetric frames per second across scalable fields of view ranging between 50-1000 mm3 with respective spatial resolution of 35-150µm. Experiments performed in immobilized and freely swimming larvae and in adult zebrafish brains expressing the genetically-encoded calcium indicator GCaMP5G demonstrated, for the first time, the fundamental ability to directly track neural dynamics using optoacoustics while overcoming the depth barrier of optical imaging in scattering brains [6]. It was further possible to monitor calcium transients in a scattering brain of a living adult transgenic zebrafish expressing GCaMP5G calcium indicator [7]. Fast changes in optoacoustic traces associated to GCaMP5G activity were detectable in the presence of other strongly absorbing endogenous chromophores, such as hemoglobin. The results indicate that the optoacoustic signal traces generally follow the GCaMP5G fluorescence dynamics and further enable overcoming the longstanding optical-diffusion penetration barrier associated to scattering in biological tissues [6]. The new functional optoacoustic neuroimaging method can visualize neural activity at penetration depths and spatio-temporal resolution scales not covered with the existing neuroimaging techniques. Thus, in addition to the well

  12. A putatively novel form of spontaneous coordination in neural activity.

    Science.gov (United States)

    Hermer-Vazquez, Raymond; Hermer-Vazquez, Linda; Srinivasan, Sridhar

    2009-04-06

    We simultaneously recorded local field potentials from three sites along the olfactory-entorhinal axis in rats lightly anesthetized with isoflurane, as part of another experiment. While analyzing the initial data from that experiment with spectrograms, we discovered a potentially novel form of correlated neural activity, with near-simultaneous occurrence across the three widely separated brain sites. After validating their existence further, we named these events Synchronous Frequency Bursts (SFBs). Here we report our initial investigations into their properties and their potential functional significance. In Experiment 1, we found that SFBs have highly regular properties, consisting of brief (approximately 250 ms), high amplitude bursts of LFP energy spanning frequency ranges from the delta band (1-4 Hz) to at least the low gamma band (30-50 Hz). SFBs occurred almost simultaneously across recording sites, usually with onsets sites. While the SFBs had fairly typical, exponentially decaying power spectral density plots, their coherence structure was unusual, with high peaks in several narrow frequency ranges and little coherence in other bands. In Experiment 2, we found that SFBs occurred far more often under light anesthesia than deeper anesthetic states, and were especially prevalent as the animals regained consciousness. Finally, in Experiment 3 we showed that SFBs occur simultaneously at a significant rate across brain sites from putatively different functional subsystems--olfactory versus motor pathways. We suggest that SFBs do not carry information per se, but rather, play a role in coordinating activity in different frequency bands, potentially brain-wide, as animals progress from sleep or anesthesia toward full consciousness.

  13. Activity in part of the neural correlates of consciousness reflects integration.

    Science.gov (United States)

    Eriksson, Johan

    2017-07-25

    Integration is commonly viewed as a key process for generating conscious experiences. Accordingly, there should be increased activity within the neural correlates of consciousness when demands on integration increase. We used fMRI and "informational masking" to isolate the neural correlates of consciousness and measured how the associated brain activity changed as a function of required integration. Integration was manipulated by comparing the experience of hearing simple reoccurring tones to hearing harmonic tone triplets. The neural correlates of auditory consciousness included superior temporal gyrus, lateral and medial frontal regions, cerebellum, and also parietal cortex. Critically, only activity in left parietal cortex increased significantly as a function of increasing demands on integration. We conclude that integration can explain part of the neural activity associated with the generation conscious experiences, but that much of associated brain activity apparently reflects other processes. Copyright © 2017. Published by Elsevier Inc.

  14. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models.

    Science.gov (United States)

    Adams, Matthew D; Kanaroglou, Pavlos S

    2016-03-01

    Air pollution poses health concerns at the global scale. The challenge of managing air pollution is significant because of the many air pollutants, insufficient funds for monitoring and abatement programs, and political and social challenges in defining policy to limit emissions. Some governments provide citizens with air pollution health risk information to allow them to limit their exposure. However, many regions still have insufficient air pollution monitoring networks to provide real-time mapping. Where available, these risk mapping systems either provide absolute concentration data or the concentrations are used to derive an Air Quality Index, which provides the air pollution risk for a mix of air pollutants with a single value. When risk information is presented as a single value for an entire region it does not inform on the spatial variation within the region. Without an understanding of the local variation residents can only make a partially informed decision when choosing daily activities. The single value is typically provided because of a limited number of active monitoring units in the area. In our work, we overcome this issue by leveraging mobile air pollution monitoring techniques, meteorological information and land use information to map real-time air pollution health risks. We propose an approach that can provide improved health risk information to the public by applying neural network models within a framework that is inspired by land use regression. Mobile air pollution monitoring campaigns were conducted across Hamilton from 2005 to 2013. These mobile air pollution data were modelled with a number of predictor variables that included information on the surrounding land use characteristics, the meteorological conditions, air pollution concentrations from fixed location monitors, and traffic information during the time of collection. Fine particulate matter and nitrogen dioxide were both modelled. During the model fitting process we reserved

  15. Smart interactive electronic system for monitoring the electromagnetic activities of biological systems

    Science.gov (United States)

    Popa, Sorin G.; Shahinpoor, Mohsen

    2001-08-01

    A novel electronic device capable of sensing and monitoring the myoelectric, polarization wave and electromagnetic activities of the biological systems and in particular the human body is presented. It is known that all the physical and chemical processes within biological systems are associated with polarization, depolarization waves from the brain, neural signals and myoelectric processes that manifest themselves in ionic and dipole motion. The technology developed in our laboratory is based on certain charge motion sensitive electronics. The electronic system developed is capable of sensing the electromagnetic activities of biological systems. The information obtained is then processed by specialized software in order to interpret it from physical and chemical point of view.

  16. How consumer physical activity monitors could transform human physiology research.

    Science.gov (United States)

    Wright, Stephen P; Hall Brown, Tyish S; Collier, Scott R; Sandberg, Kathryn

    2017-03-01

    A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O2, and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing.

  17. Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks.

    Science.gov (United States)

    Luo, Junwen; Nikolic, Konstantin; Evans, Benjamin D; Dong, Na; Sun, Xiaohan; Andras, Peter; Yakovlev, Alex; Degenaar, Patrick

    2016-08-17

    We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.

  18. Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks.

    Science.gov (United States)

    Junwen Luo; Nikolic, Konstantin; Evans, Benjamin D; Na Dong; Xiaohan Sun; Andras, Peter; Yakovlev, Alex; Degenaar, Patrick

    2017-02-01

    We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.

  19. Neural activity control of neural stem cells and SVZ niche response to brain injury

    OpenAIRE

    Páez González, Patricia

    2014-01-01

    Patricia Paez-Gonzalez Kuo Lab, Dept. of Cell Biology, Duke University Medical Center, NC,USA. Date: 11/16/2014 Utilizing stem cells in the adult brain hold great promise for regenerative medicine. Harnessing ability of adult neural stem cells (NSCs) to generate new neurons or other types of brain cells may provide much needed therapies for patients suffering from brain injuries or neuro-degenerative diseases such as Parkinson’s, Scizophrenia, or Alzheimer’s disease. However...

  20. Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

    Science.gov (United States)

    Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

    2017-01-01

    Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

  1. Filtered-X Radial Basis Function Neural Networks for Active Noise Control

    Directory of Open Access Journals (Sweden)

    Bambang Riyanto

    2004-05-01

    Full Text Available This paper presents active control of acoustic noise using radial basis function (RBF networks and its digital signal processor (DSP real-time implementation. The neural control system consists of two stages: first, identification (modeling of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.

  2. Nordic monitoring on diet, physical activity and overweight

    DEFF Research Database (Denmark)

    Fagt, Sisse; Andersen, Lene Frost; Anderssen, Sigmund A.;

    In 2007, a Nordic working group was established with the aim to describe a future Nordic monitoring system on diet, physical activity and overweight. The monitoring system should be simple and at relatively low cost. Therefore it has been decided to conduct the moni-toring as a telephone interview...

  3. A neural network architecture for implementation of expert systems for real time monitoring

    Science.gov (United States)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  4. Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks.

    Science.gov (United States)

    Perchiazzi, Gaetano; Rylander, Christian; Pellegrini, Mariangela; Larsson, Anders; Hedenstierna, Göran

    2016-04-11

    Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEPapp) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEPtot) is the sum of PEEPapp and intrinsic PEEP (PEEPi), a hidden component. To measure PEEPtot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). We hypothesized that artificial neural networks (ANN) could estimate the PEEPtot from flow and pressure tracings during ongoing mechanical ventilation. Ten pigs were mechanically ventilated, and the time constant of their respiratory system (τRS) was measured. We shortened their expiratory time (TE) according to multiples of τRS, obtaining different respiratory patterns (Rpat). Pressure (PAW) and flow (V'AW) at the airway opening during ongoing mechanical ventilation were simultaneously recorded, with and without the addition of external resistance. The last breath of each Rpat included an EEHM, which was used to compute the reference PEEPtot. The entire protocol was repeated after the induction of ALI with i.v. injection of oleic acid, and 382 tracings were obtained. The ANN had to extract the PEEPtot, from the tracings without an EEHM. ANN agreement with reference PEEPtot was assessed with the Bland-Altman method. Bland Altman analysis of estimation error by ANN showed -0.40 ± 2.84 (expressed as bias ± precision) and ±5.58 as limits of agreement (data expressed as cmH2O). The ANNs estimated the PEEPtot well at different levels of PEEPapp under dynamic conditions, opening up new possibilities in monitoring PEEPi in critically ill patients who require ventilator treatment.

  5. The modulating effect of personality traits on neural error monitoring: evidence from event-related FMRI.

    Directory of Open Access Journals (Sweden)

    Zrinka Sosic-Vasic

    Full Text Available The present study investigated the association between traits of the Five Factor Model of Personality (Neuroticism, Extraversion, Openness for Experiences, Agreeableness, and Conscientiousness and neural correlates of error monitoring obtained from a combined Eriksen-Flanker-Go/NoGo task during event-related functional magnetic resonance imaging in 27 healthy subjects. Individual expressions of personality traits were measured using the NEO-PI-R questionnaire. Conscientiousness correlated positively with error signaling in the left inferior frontal gyrus and adjacent anterior insula (IFG/aI. A second strong positive correlation was observed in the anterior cingulate gyrus (ACC. Neuroticism was negatively correlated with error signaling in the inferior frontal cortex possibly reflecting the negative inter-correlation between both scales observed on the behavioral level. Under present statistical thresholds no significant results were obtained for remaining scales. Aligning the personality trait of Conscientiousness with task accomplishment striving behavior the correlation in the left IFG/aI possibly reflects an inter-individually different involvement whenever task-set related memory representations are violated by the occurrence of errors. The strong correlations in the ACC may indicate that more conscientious subjects were stronger affected by these violations of a given task-set expressed by individually different, negatively valenced signals conveyed by the ACC upon occurrence of an error. Present results illustrate that for predicting individual responses to errors underlying personality traits should be taken into account and also lend external validity to the personality trait approach suggesting that personality constructs do reflect more than mere descriptive taxonomies.

  6. The modulating effect of personality traits on neural error monitoring: evidence from event-related FMRI.

    Science.gov (United States)

    Sosic-Vasic, Zrinka; Ulrich, Martin; Ruchsow, Martin; Vasic, Nenad; Grön, Georg

    2012-01-01

    The present study investigated the association between traits of the Five Factor Model of Personality (Neuroticism, Extraversion, Openness for Experiences, Agreeableness, and Conscientiousness) and neural correlates of error monitoring obtained from a combined Eriksen-Flanker-Go/NoGo task during event-related functional magnetic resonance imaging in 27 healthy subjects. Individual expressions of personality traits were measured using the NEO-PI-R questionnaire. Conscientiousness correlated positively with error signaling in the left inferior frontal gyrus and adjacent anterior insula (IFG/aI). A second strong positive correlation was observed in the anterior cingulate gyrus (ACC). Neuroticism was negatively correlated with error signaling in the inferior frontal cortex possibly reflecting the negative inter-correlation between both scales observed on the behavioral level. Under present statistical thresholds no significant results were obtained for remaining scales. Aligning the personality trait of Conscientiousness with task accomplishment striving behavior the correlation in the left IFG/aI possibly reflects an inter-individually different involvement whenever task-set related memory representations are violated by the occurrence of errors. The strong correlations in the ACC may indicate that more conscientious subjects were stronger affected by these violations of a given task-set expressed by individually different, negatively valenced signals conveyed by the ACC upon occurrence of an error. Present results illustrate that for predicting individual responses to errors underlying personality traits should be taken into account and also lend external validity to the personality trait approach suggesting that personality constructs do reflect more than mere descriptive taxonomies.

  7. Parasympathetic neural activity accounts for the lowering of exercise heart rate at high altitude

    DEFF Research Database (Denmark)

    Boushel, Robert Christopher; Calbet, J A; Rådegran, G

    2001-01-01

    In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied.......In chronic hypoxia, both heart rate (HR) and cardiac output (Q) are reduced during exercise. The role of parasympathetic neural activity in lowering HR is unresolved, and its influence on Q and oxygen transport at high altitude has never been studied....

  8. Identification of non-linear models of neural activity in bold fmri

    DEFF Research Database (Denmark)

    Jacobsen, Daniel Jakup; Madsen, Kristoffer Hougaard; Hansen, Lars Kai

    2006-01-01

    Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These ....... These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-one-out cross validation, using a Metropolis-Hastings algorithm for sampling of the posterior parameter distribution....

  9. Distance modulation of neural activity in the visual cortex.

    Science.gov (United States)

    Dobbins, A C; Jeo, R M; Fiser, J; Allman, J M

    1998-07-24

    Humans use distance information to scale the size of objects. Earlier studies demonstrated changes in neural response as a function of gaze direction and gaze distance in the dorsal visual cortical pathway to parietal cortex. These findings have been interpreted as evidence of the parietal pathway's role in spatial representation. Here, distance-dependent changes in neural response were also found to be common in neurons in the ventral pathway leading to inferotemporal cortex of monkeys. This result implies that the information necessary for object and spatial scaling is common to all visual cortical areas.

  10. Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant

    Energy Technology Data Exchange (ETDEWEB)

    Fast, M. [Division of Thermal Power Engineering, Department of Energy Sciences, Lund University, P.O. Box 118, S-221 00 Lund (Sweden); Palme, T. [Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, N-4036 Stavanger (Norway)

    2010-02-15

    The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server. The plant comprised a Siemens SGT800 gas turbine with a heat recovery steam generator as well as a bio-fueled boiler and its steam cycle. Steam from the heat recovery steam generator and the bio-fueled boiler expanded together in a common steam turbine, producing both electricity and heat. The artificial neural network models were trained with operational data from the components of the combined heat and power plant. Accurate predictions from the ANN (Artificial neural network) models in combination with an undemanding integration in the power plant's computer system were some of the main conclusions from this study. (author)

  11. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The fe

  12. Voltage Estimation in Active Distribution Grids Using Neural Networks

    DEFF Research Database (Denmark)

    Pertl, Michael; Heussen, Kai; Gehrke, Oliver

    2016-01-01

    the observability of distribution systems has to be improved. To increase the situational awareness of the power system operator data driven methods can be employed. These methods benefit from newly available data sources such as smart meters. This paper presents a voltage estimation method based on neural networks...

  13. Neural Activity during Encoding Predicts False Memories Created by Misinformation

    Science.gov (United States)

    Okado, Yoko; Stark, Craig E. L.

    2005-01-01

    False memories are often demonstrated using the misinformation paradigm, in which a person's recollection of a witnessed event is altered after exposure to misinformation about the event. The neural basis of this phenomenon, however, remains unknown. The authors used fMRI to investigate encoding processes during the viewing of an event and…

  14. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The fe

  15. Volcanic monitoring from space using neural networks approach. Simultaneous ash and sulfur dioxide retrievals using multispectral measurements

    Science.gov (United States)

    Piscini, A.; Corradini, S.; Chini, M.; Merucci, L.; Stramondo, S.; Picchiani, M.; Del Frate, F.

    2012-04-01

    In this work a Multi Layer Perceptron Neural Networks (MLPNN) approach has been used for a simultaneous volcanic ash and sulfur dioxide retrievals considering the MODIS measurements. As test case the 2010 Eyjafjallajokull eruption have been considered. A network was built for each parameter to be retrieved. Additionally, for volcanic ash, a network for the classification of "ash image pixels" was implemented, which was then used to mask the estimates. Several network topologies were compared in terms of their performance. Concerning the training phase and networks testing, a set of MODIS images was selected covering the Eyjafjallajokull May events. The classification NNs were trained with the volcanic ash classification map obtained with the Brightness Temperature Difference algorithm, assumed as benchmark. The neural networks for the quantitative estimation of the parameters associated with volcanic ash, mass, effective radius, aerosol optical depth and SO2, were instead trained with maps obtained using consolidated estimation algorithms based on simulated radiances at the top of the atmosphere, generated in turn applying a radiative transfer model to remote sensing data. The networks proved to be very effective in solving the inversion problem related to the estimation of the parameters of the volcanic cloud, settling the crucial issue related to false alarms in the detection of volcanic ash. Furthermore, once the training phase is complete, NNs provide a faster inversion technique, useful for the applications. From this point of view the technique satisfies the need to respond quickly as a result of disastrous natural hazards, such as volcanic eruptions. Future activities include testing the effectiveness of the technique under different lighting conditions (night images) and on other types of multispectral data, such as that provided by high temporal resolution sensors like SEVIRI-MSG, on board the METEOSAT second Generation satellites. The latter would be

  16. Lack of telomerase activity in rabbit bone marrow stromal cells during differentiation along neural pathway

    Institute of Scientific and Technical Information of China (English)

    CHEN Zhen-zhou; XU Ru-xiang; JIANG Xiao-dan; TENG Xiao-hua; LI Gui-tao; ZHOU Yü-xi

    2006-01-01

    Objective: To investigate telomerase activity in rabbit bone marrow stromal cells (BMSCs) during their committed differentiation in vitro along neural pathway and the effect of glial cell line-derived neurotrophic factor (GDNF) on the expression of telomerase.Methods: BMSCs were acquired from rabbit marrow and divided into control group, GDNF (10 ng/ml) group.No. ZL02134314. 4) supplemented with 10% fetal bovine serum (FBS) was used to induce BMSCs differentiation along neural pathway. Fluorescent immunocytochemistry was employed to identify the expressions of Nestin, neuronspecific endase (NSE), and gial fibrillary acidic protein (GFAP). The growth curves of the cells and the status of cell cycles were analyzed, respectively. During the differentiation, telomerase activitys were detected using the telomeric repeat amplification protocol-enzyme-linked immunosorbent assay (TRAP-ELISA).Results: BMSCs were successfully induced to differentiate along neural pathway and expressed specific markers of fetal neural epithelium, mature neuron and glial cells. Telomerase activities were undetectable in BMSCs during differentiation along neural pathway. Similar changes of cell growth curves, cell cycle status and telomerase expression were observed in the two groups.Conclusions: Rabbit BMSCs do not display telomerase activity during differentiation along neural pathway. GDNF shows little impact on proliferation and telomerase activity of BMSCs.

  17. Instructional physical activity monitor video in english and spanish

    Science.gov (United States)

    The ActiGraph activity monitor is a widely used method for assessing physical activity. Compliance with study procedures in critical. A common procedure is for the research team to meet with participants and demonstrate how and when to attach and remove the monitor and convey how many wear-days are ...

  18. Monitoring bat activity at the Dutch EEZ in 2014

    NARCIS (Netherlands)

    Lagerveld, S.; Jonge Poerink, B.; Vries, de P.

    2015-01-01

    IMARES conducted studies in 2012 and 2013 to monitor offshore bat activity with passive acoustic ultrasonic recorders. In the follow-up project reported here, more data on the offshore occurrence of bats was collected in 2014. Using the same methodology as in 2012 and 2013, bat activity was monitore

  19. Quality Assurance Project Plan for Facility Effluent Monitoring Plan activities

    Energy Technology Data Exchange (ETDEWEB)

    Nickels, J.M.

    1991-06-01

    This Quality Assurance Project Plan addresses the quality assurance requirements for the Facility Monitoring Plans of the overall site-wide environmental monitoring plan. This plan specifically applies to the sampling and analysis activities and continuous monitoring performed for all Facility Effluent Monitoring Plan activities conducted by Westinghouse Hanford Company. It is generic in approach and will be implemented in conjunction with the specific requirements of individual Facility Effluent Monitoring Plans. This document is intended to be a basic road map to the Facility Effluent Monitoring Plan documents (i.e., the guidance document for preparing Facility Effluent Monitoring Plans, Facility Effluent Monitoring Plan determinations, management plan, and Facility Effluent Monitoring Plans). The implementing procedures, plans, and instructions are appropriate for the control of effluent monitoring plans requiring compliance with US Department of Energy, US Environmental Protection Agency, state, and local requirements. This Quality Assurance Project Plan contains a matrix of organizational responsibilities, procedural resources from facility or site manuals used in the Facility Effluent Monitoring Plans, and a list of the analytes of interest and analytical methods for each facility preparing a Facility Effluent Monitoring Plan. 44 refs., 1 figs., 2 tabs.

  20. Neural correlates of the relationship between discourse coherence and sensory monitoring in schizophrenia.

    Science.gov (United States)

    Tagamets, Malle A; Cortes, Carlos R; Griego, Jacqueline A; Elvevåg, Brita

    2014-06-01

    Unusual language use is a core feature of psychosis, but the nature and significance of this are not understood. In particular, thought disorder in schizophrenia (SZ) is characterized by markedly bizarre speech, but the cognitive components that contribute to this and the brain correlates of these components are unknown. A number of studies have demonstrated language abnormalities in single word processing, but few have examined speech in SZ at the discourse level. This has been at least partly due to the difficulty in quantifying content of discourse. Recently, methods in computational linguistics have been found to be useful for detecting differences in semantic coherence during discourse between different clinical groups. We build on this work by demonstrating how these methods can be combined with funtional magnetic resonance imaging (fMRI) in order to tease apart factors that underlie free discourse and its deviations, and how they relate to brain activity. Eleven volunteers with SZ and eleven controls participated in an interview during which they were asked to talk as much as they could about 'religious belief'. These same participants underwent fMRI during a word monitoring task, during which modality of monitoring was manipulated by varying the congruence of auditory and visual stimuli. Semantic coherence scores, measured from free discourse, were examined for their relationship to brain activations during fMRI. In healthy controls, regions associated with executive function were related to coherence. In persons with SZ, coherence was mainly related to auditory and visual regions, depending on the modality of monitoring, but superior/middle temporal cortex related to coherence regardless of task. These findings are consistent with existing evidence for a role of superior temporal cortex in thought disorder, and demonstrate that computational measures of semantic content capture objective measures of coherence in speech that can be usefully related to

  1. High baseline activity in inferior temporal cortex improves neural and behavioral discriminability during visual categorization

    Directory of Open Access Journals (Sweden)

    Nazli eEmadi

    2014-11-01

    Full Text Available Spontaneous firing is a ubiquitous property of neural activity in the brain. Recent literature suggests that this baseline activity plays a key role in perception. However, it is not known how the baseline activity contributes to neural coding and behavior. Here, by recording from the single neurons in the inferior temporal cortex of monkeys performing a visual categorization task, we thoroughly explored the relationship between baseline activity, the evoked response, and behavior. Specifically we found that a low-frequency (< 8 Hz oscillation in the spike train, prior and phase-locked to the stimulus onset, was correlated with increased gamma power and neuronal baseline activity. This enhancement of the baseline activity was then followed by an increase in the neural selectivity and the response reliability and eventually a higher behavioral performance.

  2. A fast learning algorithm of neural network with tunable activation function

    Institute of Scientific and Technical Information of China (English)

    SHEN Yanjun; WANG Bingwen

    2004-01-01

    This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.

  3. Active Vibration Control of the Smart Plate Using Artificial Neural Network Controller

    Directory of Open Access Journals (Sweden)

    Mohit

    2015-01-01

    Full Text Available The active vibration control (AVC of a rectangular plate with single input and single output approach is investigated using artificial neural network. The cantilever plate of finite length, breadth, and thickness having piezoelectric patches as sensors/actuators fixed at the upper and lower surface of the metal plate is considered for examination. The finite element model of the cantilever plate is utilized to formulate the whole strategy. The compact RIO and MATLAB simulation software are exercised to get the appropriate results. The cantilever plate is subjected to impulse input and uniform white noise disturbance. The neural network is trained offline and tuned with LQR controller. The various training algorithms to tune the neural network are exercised. The best efficient algorithm is finally considered to tune the neural network controller designed for active vibration control of the smart plate.

  4. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

    Science.gov (United States)

    Truccolo, Wilson; Eden, Uri T; Fellows, Matthew R; Donoghue, John P; Brown, Emery N

    2005-02-01

    Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

  5. Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review.

    Directory of Open Access Journals (Sweden)

    Valerie A J Block

    Full Text Available To perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.Studies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures, energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.137 studies met inclusion criteria in multiple sclerosis (MS (61 studies; stroke (41; Parkinson's Disease (PD (20; dementia (11; traumatic brain injury (2 and ataxia (1. Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.These studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

  6. In vivo blockade of neural activity alters dendritic development of neonatal CA1 pyramidal cells.

    Science.gov (United States)

    Groc, Laurent; Petanjek, Zdravko; Gustafsson, Bengt; Ben-Ari, Yehezkel; Hanse, Eric; Khazipov, Roustem

    2002-11-01

    During development, neural activity has been proposed to promote neuronal growth. During the first postnatal week, the hippocampus is characterized by an oscillating neural network activity and a rapid neuronal growth. In the present study we tested in vivo, by injecting tetanus toxin into the hippocampus of P1 rats, whether this neural activity indeed promotes growth of pyramidal cells. We have previously shown that tetanus toxin injection leads to a strong reduction in the frequency of spontaneous GABA and glutamatergic synaptic currents, and to a complete blockade of the early neural network activity during the first postnatal week. Morphology of neurobiotin-filled CA1 pyramidal cells was analyzed at the end of the first postnatal week (P6-10). In activity-reduced neurons, the total length of basal dendritic tree was three times less than control. The number, but not the length, of basal dendritic branches was affected. The growth impairment was restricted to the basal dendrites. The apical dendrite, the axons, or the soma grew normally during activity deprivation. Thus, the in vivo neural activity in the neonate hippocampus seems to promote neuronal growth by initiating novel branches.

  7. Explorative data analysis for changes in neural activity

    Science.gov (United States)

    Blythe, Duncan A. J.; Meinecke, Frank C.; von Bünau, Paul; Müller, Klaus-Robert

    2013-04-01

    Neural recordings are non-stationary time series, i.e. their properties typically change over time. Identifying specific changes, e.g., those induced by a learning task, can shed light on the underlying neural processes. However, such changes of interest are often masked by strong unrelated changes, which can be of physiological origin or due to measurement artifacts. We propose a novel algorithm for disentangling such different causes of non-stationarity and in this manner enable better neurophysiological interpretation for a wider set of experimental paradigms. A key ingredient is the repeated application of Stationary Subspace Analysis (SSA) using different temporal scales. The usefulness of our explorative approach is demonstrated in simulations, theory and EEG experiments with 80 brain-computer interfacing subjects.

  8. The impact of cancer on the neural activity

    CERN Document Server

    Iarosz, Kelly Cristiane; Baptista, Murilo da Silva; Protachevicz, Paulo Ricardo

    2015-01-01

    We study the impact of the decrease in the neural population on the neuronal firing rate. We propose a cellular automaton model from cancerous growth in a brain tissue and the death of neurons due absence of cells that help support to neurons. We use this model to study how the firing rate changes when the neuronal networks is under different external stimuli and the cancerous cells have different proliferation rate. Our work shows that the cancer proliferation decreases the neuronal firing rate.

  9. Active control of vibration using a neural network.

    Science.gov (United States)

    Snyder, S D; Tanaka, N

    1995-01-01

    Feedforward control of sound and vibration using a neural network-based control system is considered, with the aim being to derive an architecture/algorithm combination which is capable of supplanting the commonly used finite impulse response filter/filtered-x least mean square (LMS) linear arrangement for certain nonlinear problems. An adaptive algorithm is derived which enables stable adaptation of the neural controller for this purpose, while providing the capacity to maintain causality within the control scheme. The algorithm is shown to be simply a generalization of the linear filtered-x LMS algorithm. Experiments are undertaken which demonstrate the utility of the proposed arrangement, showing that it performs as well as a linear control system for a linear control problem and better for a nonlinear control problem. The experiments also lead to the conclusion that more work is required to improve the predictability and consistency of the performance before the neural network controller becomes a practical alternative to the current linear feedforward systems.

  10. In vivo monitoring of glial scar proliferation on chronically implanted neural electrodes by fiber optical coherence tomography

    Directory of Open Access Journals (Sweden)

    Yijing eXie

    2014-08-01

    Full Text Available In neural prosthetics and stereotactic neurosurgery, intracortical electrodes are often utilized for delivering therapeutic electrical pulses, and recording neural electrophysiological signals. Unfortunately, neuroinflammation impairs the neuron-electrode-interface by developing a compact glial encapsulation around the implants in long term. At present, analyzing this immune reaction is only feasible with post-mortem histology; currently no means for specific in vivo monitoring exist and most applicable imaging modalities can not provide information in deep brain regions. Optical coherence tomography (OCT is a well established imaging modality for in vivo studies, providing cellular resolution and up to 1.2 mm imaging depth in brain tissue. A fiber based spectral domain OCT was shown to be capable of minimally invasive brain imaging. In the present study, we propose to use a fiber based spectral domain OCT to monitor the progression of the tissue's immune response through scar encapsulation progress in a rat animal model. A fine fiber catheter was implanted in rat brain together with a flexible polyimide microelectrode in sight both of which used as a foreign body to induce the brain tissue immune reaction. OCT signals were collected from animals up to 12 weeks after implantation and thus gliotic scarring in vivo monitored for that time. Preliminary data showed a significant enhancement of the OCT backscattering signal during the first three weeks after implantation, and increased attenuation factor of the sampled tissue due to the glial scar formation.

  11. The affective impact of financial skewness on neural activity and choice.

    Science.gov (United States)

    Wu, Charlene C; Bossaerts, Peter; Knutson, Brian

    2011-02-15

    Few finance theories consider the influence of "skewness" (or large and asymmetric but unlikely outcomes) on financial choice. We investigated the impact of skewed gambles on subjects' neural activity, self-reported affective responses, and subsequent preferences using functional magnetic resonance imaging (FMRI). Neurally, skewed gambles elicited more anterior insula activation than symmetric gambles equated for expected value and variance, and positively skewed gambles also specifically elicited more nucleus accumbens (NAcc) activation than negatively skewed gambles. Affectively, positively skewed gambles elicited more positive arousal and negatively skewed gambles elicited more negative arousal than symmetric gambles equated for expected value and variance. Subjects also preferred positively skewed gambles more, but negatively skewed gambles less than symmetric gambles of equal expected value. Individual differences in both NAcc activity and positive arousal predicted preferences for positively skewed gambles. These findings support an anticipatory affect account in which statistical properties of gambles--including skewness--can influence neural activity, affective responses, and ultimately, choice.

  12. Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

    Institute of Scientific and Technical Information of China (English)

    WEI Du-Qu; LUO Xiao-Shu; ZOU Yan-Li

    2008-01-01

    We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N.Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs.It is found that for a given appropriate coupling strength,there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced.On the other hand,for a given intermediate system size level,there ex/sts an optimal value of coupling strength such that the intensity of firing activity reaches its maximum.These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network.

  13. Integrative activity of neural networks may code virtual spaces with internal representations.

    Science.gov (United States)

    Strelnikov, Kuzma

    2014-10-01

    It was shown recently in neuroimaging that spatial differentiation of brain activity provides novel information about brain function. This confirms the integrative organisation of brain activity, but given present technical limitations of neuroimaging approaches, the exact role of integrative activity remains unclear. We trained a neural network to integrate information using random numbers so as to imitate the "centre-periphery" pattern of brain activity in neuroimaging. Only the hierarchical organisation of the network permitted the learning of fast and reliable integration. We presented images to the trained network and, by spatial differentiation of the network activity, obtained virtual spaces with the presented images. Thus, our study established the necessity of the hierarchical organisation of neural networks for integration and demonstrated that the role of neural integration in the brain may be to create virtual spaces with internal representations of the objects.

  14. Light-induced Notch activity controls neurogenic and gliogenic potential of neural progenitors.

    Science.gov (United States)

    Kim, Kyung-Tai; Song, Mi-Ryoung

    2016-10-28

    Oscillations in Notch signaling are essential for reserving neural progenitors for cellular diversity in developing brains. Thus, steady and prolonged overactivation of Notch signaling is not suitable for generating neurons. To acquire greater temporal control of Notch activity and mimic endogenous oscillating signals, here we adopted a light-inducible transgene system to induce active form of Notch NICD in neural progenitors. Alternating Notch activity saved more progenitors that are prone to produce neurons creating larger number of mixed clones with neurons and progenitors in vitro, compared to groups with no light or continuous light stimulus. Furthermore, more upper layer neurons and astrocytes arose upon intermittent Notch activity, indicating that dynamic Notch activity maintains neural progeny and fine-tune neuron-glia diversity.

  15. Serotonin activates overall feeding by activating two separate neural pathways in Caenorhabditis elegans.

    Science.gov (United States)

    Song, Bo-mi; Avery, Leon

    2012-02-08

    Food intake in the nematode Caenorhabditis elegans requires two distinct feeding motions, pharyngeal pumping and isthmus peristalsis. Bacteria, the natural food of C. elegans, activate both feeding motions (Croll, 1978; Horvitz et al., 1982; Chiang et al., 2006). The mechanisms by which bacteria activate the feeding motions are largely unknown. To understand the process, we studied how serotonin, an endogenous pharyngeal pumping activator whose action is triggered by bacteria, activates feeding motions. Here, we show that serotonin, like bacteria, activates overall feeding by activating isthmus peristalsis as well as pharyngeal pumping. During active feeding, the frequencies and the timing of onset of the two motions were distinct, but each isthmus peristalsis was coupled to the preceding pump. We found that serotonin activates the two feeding motions mainly by activating two separate neural pathways in response to bacteria. For activating pumping, the SER-7 serotonin receptor in the MC motor neurons in the feeding organ activated cholinergic transmission from MC to the pharyngeal muscles by activating the Gsα signaling pathway. For activating isthmus peristalsis, SER-7 in the M4 (and possibly M2) motor neuron in the feeding organ activated the G(12)α signaling pathway in a cell-autonomous manner, which presumably activates neurotransmission from M4 to the pharyngeal muscles. Based on our results and previous calcium imaging of pharyngeal muscles (Shimozono et al., 2004), we propose a model that explains how the two feeding motions are separately regulated yet coupled. The feeding organ may have evolved this way to support efficient feeding.

  16. Fractal patterns of neural activity exist within the suprachiasmatic nucleus and require extrinsic network interactions.

    Science.gov (United States)

    Hu, Kun; Meijer, Johanna H; Shea, Steven A; vanderLeest, Henk Tjebbe; Pittman-Polletta, Benjamin; Houben, Thijs; van Oosterhout, Floor; Deboer, Tom; Scheer, Frank A J L

    2012-01-01

    The mammalian central circadian pacemaker (the suprachiasmatic nucleus, SCN) contains thousands of neurons that are coupled through a complex network of interactions. In addition to the established role of the SCN in generating rhythms of ~24 hours in many physiological functions, the SCN was recently shown to be necessary for normal self-similar/fractal organization of motor activity and heart rate over a wide range of time scales--from minutes to 24 hours. To test whether the neural network within the SCN is sufficient to generate such fractal patterns, we studied multi-unit neural activity of in vivo and in vitro SCNs in rodents. In vivo SCN-neural activity exhibited fractal patterns that are virtually identical in mice and rats and are similar to those in motor activity at time scales from minutes up to 10 hours. In addition, these patterns remained unchanged when the main afferent signal to the SCN, namely light, was removed. However, the fractal patterns of SCN-neural activity are not autonomous within the SCN as these patterns completely broke down in the isolated in vitro SCN despite persistence of circadian rhythmicity. Thus, SCN-neural activity is fractal in the intact organism and these fractal patterns require network interactions between the SCN and extra-SCN nodes. Such a fractal control network could underlie the fractal regulation observed in many physiological functions that involve the SCN, including motor control and heart rate regulation.

  17. Temporal coupling between stimulus-evoked neural activity and hemodynamic responses from individual cortical columns

    Energy Technology Data Exchange (ETDEWEB)

    Bruyns-Haylett, Michael; Zheng Ying; Berwick, Jason; Jones, Myles [The Centre for Signal Processing in Neuroimaging and Systems Neuroscience (SPINSN), Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP (United Kingdom)], E-mail: m.jones@sheffield.ac.uk

    2010-04-21

    Using previously published data from the whisker barrel cortex of anesthetized rodents (Berwick et al 2008 J. Neurophysiol. 99 787-98) we investigated whether highly spatially localized stimulus-evoked cortical hemodynamics responses displayed a linear time-invariant (LTI) relationship with neural activity. Presentation of stimuli to individual whiskers of 2 s and 16 s durations produced hemodynamics and neural activity spatially localized to individual cortical columns. Two-dimensional optical imaging spectroscopy (2D-OIS) measured hemoglobin responses, while multi-laminar electrophysiology recorded neural activity. Hemoglobin responses to 2 s stimuli were deconvolved with underlying evoked neural activity to estimate impulse response functions which were then convolved with neural activity evoked by 16 s stimuli to generate predictions of hemodynamic responses. An LTI system more adequately described the temporal neuro-hemodynamics coupling relationship for these spatially localized sensory stimuli than in previous studies that activated the entire whisker cortex. An inability to predict the magnitude of an initial 'peak' in the total and oxy- hemoglobin responses was alleviated when excluding responses influenced by overlying arterial components. However, this did not improve estimation of the hemodynamic responses return to baseline post-stimulus cessation.

  18. Dynamical Behaviors of Multiple Equilibria in Competitive Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2016-03-01

    This paper addresses the problem of coexistence and dynamical behaviors of multiple equilibria for competitive neural networks. First, a general class of discontinuous nonmonotonic piecewise linear activation functions is introduced for competitive neural networks. Then based on the fixed point theorem and theory of strict diagonal dominance matrix, it is shown that under some conditions, such n -neuron competitive neural networks can have 5(n) equilibria, among which 3(n) equilibria are locally stable and the others are unstable. More importantly, it is revealed that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibria and locally stable equilibria than the ones with other activation functions, such as the continuous Mexican-hat-type activation function and discontinuous two-level activation function. Furthermore, the 3(n) locally stable equilibria given in this paper are located in not only saturated regions, but also unsaturated regions, which is different from the existing results on multistability of neural networks with multiple level activation functions. A simulation example is provided to illustrate and validate the theoretical findings.

  19. Monitoring of FR Cnc Flaring Activity

    CERN Document Server

    Golovin, A; Pavlenko, E; Kuznyetsova, Yu; Krushevska, V; Sergeev, A

    2007-01-01

    Being excited by the detection of the first ever-observed optical flare in FR Cnc, we decided to continue photometrical monitoring of this object. The observations were carried out at Crimean Astrophysical Observatory (Crimea, Ukraine; CrAO - hereafter) and at the Terskol Observatory (Russia, Northern Caucasus). The obtained lightcurves are presented and discussed. No distinguishable flares were detected that could imply that flares on FR Cnc are very rare event.

  20. Beyond conflict monitoring: Cognitive control and the neural basis of thinking before you act.

    Science.gov (United States)

    Brown, Joshua W

    2013-06-01

    Cognitive control refers to the processes by which individual cognitive functions are coordinated in the service of higher level goals. The anterior cingulate cortex (ACC) in the middle front of the brain monitors performance, and it is activated when the need for control is greater, as in difficult situations or when errors occur. Since the late 1990s, the ACC has been thought to signal when there is internal conflict between competing action plans, so that the conflict can be resolved. More recently, an alternative model has reconceptualized the computational role of ACC as predicting and evaluating the likely outcomes of a planned action before actions are made. This new predicted response outcome (PRO) model accounts for a broader range of findings and suggests that the ACC might support the cognitive operations by which individuals can "think before you act" in order to avoid risky or otherwise poor choices.

  1. Multistability and Instability of Neural Networks With Discontinuous Nonmonotonic Piecewise Linear Activation Functions.

    Science.gov (United States)

    Nie, Xiaobing; Zheng, Wei Xing

    2015-11-01

    In this paper, we discuss the coexistence and dynamical behaviors of multiple equilibrium points for recurrent neural networks with a class of discontinuous nonmonotonic piecewise linear activation functions. It is proved that under some conditions, such n -neuron neural networks can have at least 5(n) equilibrium points, 3(n) of which are locally stable and the others are unstable, based on the contraction mapping theorem and the theory of strict diagonal dominance matrix. The investigation shows that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibrium points and more locally stable equilibrium points than the ones with continuous Mexican-hat-type activation function or discontinuous two-level activation functions. An illustrative example with computer simulations is presented to verify the theoretical analysis.

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

    Science.gov (United States)

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

    2016-01-01

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

  3. Perceptual similarity of visual patterns predicts dynamic neural activation patterns measured with MEG.

    Science.gov (United States)

    Wardle, Susan G; Kriegeskorte, Nikolaus; Grootswagers, Tijl; Khaligh-Razavi, Seyed-Mahdi; Carlson, Thomas A

    2016-05-15

    Perceptual similarity is a cognitive judgment that represents the end-stage of a complex cascade of hierarchical processing throughout visual cortex. Previous studies have shown a correspondence between the similarity of coarse-scale fMRI activation patterns and the perceived similarity of visual stimuli, suggesting that visual objects that appear similar also share similar underlying patterns of neural activation. Here we explore the temporal relationship between the human brain's time-varying representation of visual patterns and behavioral judgments of perceptual similarity. The visual stimuli were abstract patterns constructed from identical perceptual units (oriented Gabor patches) so that each pattern had a unique global form or perceptual 'Gestalt'. The visual stimuli were decodable from evoked neural activation patterns measured with magnetoencephalography (MEG), however, stimuli differed in the similarity of their neural representation as estimated by differences in decodability. Early after stimulus onset (from 50ms), a model based on retinotopic organization predicted the representational similarity of the visual stimuli. Following the peak correlation between the retinotopic model and neural data at 80ms, the neural representations quickly evolved so that retinotopy no longer provided a sufficient account of the brain's time-varying representation of the stimuli. Overall the strongest predictor of the brain's representation was a model based on human judgments of perceptual similarity, which reached the limits of the maximum correlation with the neural data defined by the 'noise ceiling'. Our results show that large-scale brain activation patterns contain a neural signature for the perceptual Gestalt of composite visual features, and demonstrate a strong correspondence between perception and complex patterns of brain activity.

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

    Directory of Open Access Journals (Sweden)

    Jon Touryan

    2017-07-01

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

  5. Increased neural activity of a mushroom body neuron subtype in the brains of forager honeybees.

    Directory of Open Access Journals (Sweden)

    Taketoshi Kiya

    Full Text Available Honeybees organize a sophisticated society, and the workers transmit information about the location of food sources using a symbolic dance, known as 'dance communication'. Recent studies indicate that workers integrate sensory information during foraging flight for dance communication. The neural mechanisms that account for this remarkable ability are, however, unknown. In the present study, we established a novel method to visualize neural activity in the honeybee brain using a novel immediate early gene, kakusei, as a marker of neural activity. The kakusei transcript was localized in the nuclei of brain neurons and did not encode an open reading frame, suggesting that it functions as a non-coding nuclear RNA. Using this method, we show that neural activity of a mushroom body neuron subtype, the small-type Kenyon cells, is prominently increased in the brains of dancer and forager honeybees. In contrast, the neural activity of the two mushroom body neuron subtypes, the small-and large-type Kenyon cells, is increased in the brains of re-orienting workers, which memorize their hive location during re-orienting flights. These findings demonstrate that the small-type Kenyon cell-preferential activity is associated with foraging behavior, suggesting its involvement in information integration during foraging flight, which is an essential basis for dance communication.

  6. Adaptive RBF Neural Network Control for Three-Phase Active Power Filter

    Directory of Open Access Journals (Sweden)

    Juntao Fei

    2013-05-01

    Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.

  7. Role of low voltage activated calcium channels in neuritogenesis and active migration of embryonic neural progenitor cells.

    Science.gov (United States)

    Louhivuori, Lauri M; Louhivuori, Verna; Wigren, Henna-Kaisa; Hakala, Elina; Jansson, Linda C; Nordström, Tommy; Castrén, Maija L; Akerman, Karl E

    2013-04-15

    The central role of calcium influx and electrical activity in embryonic development raises important questions about the role and regulation of voltage-dependent calcium influx. Using cultured neural progenitor cell (NPC) preparations, we recorded barium currents through voltage-activated channels using the whole-cell configuration of the patch-clamp technique and monitored intracellular free calcium concentrations with Fura-2 digital imaging. We found that NPCs as well as expressing high-voltage-activated (HVA) calcium channels express functional low-threshold voltage-dependent calcium channels in the very early stages of differentiation (5 h to 1 day). The size of the currents recorded at -50 versus -20 mV after 1 day in differentiation was dependent on the nature of the charge carrier. Peak currents measured at -20 mV in the presence 10 mM Ca2+ instead of 10 mM Ba2+ had a tendency to be smaller, whereas the nature of the divalent species did not influence the amplitude measured at -50 mV. The T-type channel blockers mibefradil and NNC 55-0396 significantly reduced the calcium responses elicited by depolarizing with extracellular potassium, while the overall effect of the HVA calcium channel blockers was small at differentiation day 1. At differentiation day 20, the calcium responses were effectively blocked by nifedipine. Time-lapse imaging of differentiating neurospheres cultured in the presence of low-voltage-activated (LVA) blockers showed a significant decrease in the number of active migrating neuron-like cells and neurite extensions. Together, these data provide evidence that LVA calcium channels are involved in the physiology of differentiating and migrating NPCs.

  8. Monitoring activities review of the Radiological Environmental Surveillance Program

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, P.D.

    1992-03-01

    The 1992 Monitoring Activities Review (MAR) is directed at the Radiological Environment Surveillance Program (RESP) activities at the Radioactive Waste Management Complex (RWMC) of Idaho Engineering Laboratory (INEL). MAR panelists studied RESP documents and discussed their concerns with Environmental Monitoring Unit (EMU) staff and other panel members. These concerns were subsequently consolidated into a collection of recommendations with supporting discussions. Recommendations focus on specific monitoring activities, as well as the overall program. The MAR report also contains pertinent comments that should not require further action.

  9. Neural networks with non-smooth and impact activations

    CERN Document Server

    Akhmet, M U

    2011-01-01

    In this paper, we consider a model of impulsive recurrent neural networks with piecewise constant delay. The dynamics are presented by differential equations with discontinuities such as impulses at fixed moments and piecewise constant argument of generalized type. Sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point are obtained. By employing Green's function we derive new result of existence of the periodic solution. The global asymptotic stability of the solution is investigated. Examples with numerical simulations are given to validate the theoretical results.

  10. [The sociological monitoring as a tool to evaluate preventive activities].

    Science.gov (United States)

    Mazus, A I; Leven, I I; Vinogradova, O V; Zelenev, V V; Makarenko, O V

    2009-01-01

    The monitoring of conditions of HIV-infection spreading includes qualitative research methods to reveal specified information from people relating immediately to the problem of HIV-infection prevalence. The acquired information can be used both for monitoring of the conditions of HIV-infection spreading (morbidity, prevalence, mortality) and adjustment of preventive activities at the level of specific administrative territory.

  11. DEVELOPMENT ANALYZERS TRANSACTIONS IN MONITORING THE BUSINESS ACTIVITIES OF ORGANIZATIONS

    Directory of Open Access Journals (Sweden)

    L. E. Sovik

    2013-01-01

    Full Text Available In the article there are marked the features and prerequisites of implementation in food production technologies devoted to monitor business activity in the realtime. The methodical approach to the development of analyzers transactional business processes of the organization is offered, monitoring scheme for one of the basic types of business events in the procurement process is constructed.

  12. Salicylate induced tinnitus: behavioral measures and neural activity in auditory cortex of awake rats.

    Science.gov (United States)

    Yang, Guang; Lobarinas, Edward; Zhang, Liyan; Turner, Jeremy; Stolzberg, Daniel; Salvi, Richard; Sun, Wei

    2007-04-01

    Neurophysiological studies of salicylate-induced tinnitus have generally been carried out under anesthesia, a condition that abolishes the perception of tinnitus and depresses neural activity. To overcome these limitations, measurement of salicylate induced tinnitus were obtained from rats using schedule induced polydipsia avoidance conditioning (SIPAC) and gap pre-pulse inhibition of acoustic startle (GPIAS). Both behavioral measures indicated that tinnitus was present after treatment with 150 and 250 mg/kg of salicylate; measurements with GPIAS indicated that the pitch of the tinnitus was near 16 kHz. Chronically implanted microwire electrode arrays were used to monitor the local field potentials and spontaneous discharge rate from multiunit clusters in the auditory cortex of awake rats before and after treatment with 150 mg/kg of salicylate. The amplitude of the local field potential elicited with 60 dB SPL tone bursts increased significantly 2h after salicylate treatment particularly at 16-20 kHz; frequencies associated with the tinnitus pitch. Field potential amplitudes had largely recovered 1-2 days post-salicylate when behavioral results showed that tinnitus was absent. The mean spontaneous spike recorded from the same multiunit cluster pre- and post-salicylate decreased from 22 spikes/s before treatment to 14 spikes/s 2h post-salicylate and recovered 1 day post-treatment. These preliminary physiology data suggest that salicylate induced tinnitus is associated with sound evoked hyperactivity in auditory cortex and spontaneous hypoactivity.

  13. Protein Palmitoylation Regulates Neural Stem Cell Differentiation by Modulation of EID1 Activity.

    Science.gov (United States)

    Chen, Xueran; Du, Zhaoxia; Li, Xian; Wang, Liyan; Wang, Fuwu; Shi, Wei; Hao, Aijun

    2016-10-01

    The functional significance of palmitoylation in the switch between self-renewal and differentiation of neural stem cells (NSCs) is not well defined, and the underlying mechanisms of protein palmitoylation are not well understood. Here, mouse NSCs were used as a model system and cell behavior was monitored in the presence of the protein palmitoylation inhibitor 2-bromopalmitate (2BRO). Our data show that 2BRO impaired the differentiation of NSCs into both neurons and glia and impaired NSC cell cycle exit. Moreover, the results show that palmitoylation modified E1A-like inhibitor of differentiation one (EID1) and this modification regulated EID1 degradation and CREB-binding protein (CBP)/p300 histone acetyltransferase activity at the switch between self-renewal and differentiation of NSCs. Our results extended the cellular role of palmitoylation, suggesting that it acts as a regulator in the acetylation-dependent gene expression network, and established the epigenetic regulatory function of palmitoylation in the switch between maintenance of multipotency and differentiation in NSCs.

  14. Wireless micropower instrumentation for multimodal acquisition of electrical and chemical neural activity.

    Science.gov (United States)

    Mollazadeh, M; Murari, K; Cauwenberghs, G; Thakor, N

    2009-12-01

    The intricate coupling between electrical and chemical activity in neural pathways of the central nervous system, and the implication of this coupling in neuropathologies, such as Parkinson's disease, motivates simultaneous monitoring of neurochemical and neuropotential signals. However, to date, neurochemical sensing has been lacking in integrated clinical instrumentation as well as in brain-computer interfaces (BCI). Here, we present an integrated system capable of continuous acquisition of data modalities in awake, behaving subjects. It features one channel each of a configurable neuropotential and a neurochemical acquisition system. The electrophysiological channel is comprised of a 40-dB gain, fully differential amplifier with tunable bandwidth from 140 Hz to 8.2 kHz. The amplifier offers input-referred noise below 2 muV rms for all bandwidth settings. The neurochemical module features a picoampere sensitivity potentiostat with a dynamic range spanning six decades from picoamperes to microamperes. Both systems have independent on-chip, configurable DeltaSigma analog-to-digital converters (ADCs) with programmable digital gain and resolution. The system was also interfaced to a wireless power harvesting and telemetry module capable of powering up the circuits, providing clocks for ADC operation, and telemetering out the data at up to 32 kb/s over 3.5 cm with a bit-error rate of less than 10(-5). Characterization and experimental results from the electrophysiological and neurochemical modules as well as the full system are presented.

  15. A wireless recording system that utilizes Bluetooth technology to transmit neural activity in freely moving animals.

    Science.gov (United States)

    Hampson, Robert E; Collins, Vernell; Deadwyler, Sam A

    2009-09-15

    A new wireless transceiver is described for recording individual neuron firing from behaving rats utilizing Bluetooth transmission technology and a processor onboard for discrimination of neuronal waveforms and associated time stamps. This universal brain activity transmitter (UBAT) is attached to rodents via a backpack and amplifier headstage and can transmit 16 channels of captured neuronal firing data via a Bluetooth transceiver chip over very large and unconstrained distances. The onboard microprocessor of the UBAT allows flexible online control over waveform isolation criteria via transceiver instruction and the two-way communication capacity allows for closed-loop applications between neural events and behavioral or physiological processes which can be modified by transceiver instructions. A detailed description of the multiplexer processing of channel data as well as examples of neuronal recordings in different behavioral testing contexts is provided to demonstrate the capacity for robust transmission within almost any laboratory environment. A major advantage of the UBAT is the long transmission range and lack of object-based line of sight interference afforded by Bluetooth technology, allowing flexible recording capabilities within multiple experimental paradigms without interruption. Continuous recordings over very large distance separations from the monitor station are demonstrated providing experimenters with recording advantages not previously available with other telemetry devices.

  16. Brain Activity Monitoring for Assessing Satisfaction

    Directory of Open Access Journals (Sweden)

    Paola Johanna Rodríguez Carrillo

    2015-06-01

    Full Text Available Satisfaction is a dimension of usability for which quantitative metrics cannot be calculated during user interactions. Measurement is subjective and depends on the ability to interpret questionnaires and on the memory of the user. This paper represents an attempt to develop an automatic quantitative metric of satisfaction, developed using a Brain Computer Interface to monitor the mental states (Attention/Meditation of users. Based on these results, we are able to establish a correlation between the state of Attention and the users' level of satisfaction.

  17. Strategies influence neural activity for feedback learning across child and adolescent development.

    Science.gov (United States)

    Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

    2014-09-01

    Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development.

  18. Evaluation of Neural Networks Performance in Active Cancellation of Acoustic Noise

    Directory of Open Access Journals (Sweden)

    Mehrshad Salmasi,

    2014-12-01

    Full Text Available Active Noise Control (ANC works on the principle of destructive interference between the primary disturbance field heard as undesired noise and the secondary field which is generated from control actuators. In the simplest system, the disturbance field can be a simple sine wave, and the secondary field is the same sine wave but 180 degrees out of phase. This research presents an investigation on the use of different types of neural networks in active noise control. Performance of the multilayer perceptron (MLP, Elman and generalized regression neural networks (GRNN in active cancellation of acoustic noise signals is investigated and compared in this paper. Acoustic noise signals are selected from a Signal Processing Information Base (SPIB database. In order to compare the networks appropriately, similar structures and similar training and test samples are deduced for neural networks. The simulation results show that MLP, GRNN, and Elman neural networks present proper performance in active cancellation of acoustic noise. It is concluded that Elman and MLP neural networks have better performance than GRNN in noise attenuation. It is demonstrated that designed ANC system achieve good noise reduction in low frequencies.

  19. Interest of Monitoring Diaphragmatic Electrical Activity in the Pediatric Intensive Care Unit

    Directory of Open Access Journals (Sweden)

    Laurence Ducharme-Crevier

    2013-01-01

    Full Text Available The monitoring of electrical activity of the diaphragm (EAdi is a new minimally invasive bedside technology that was developed for the neurally adjusted ventilatory assist (NAVA mode of ventilation. In addition to its role in NAVA ventilation, this technology provides the clinician with previously unavailable and essential information on diaphragm activity. In this paper, we review the clinical interests of EAdi in the pediatric intensive care setting. Firstly, the monitoring of EAdi allows the clinician to tailor the ventilatory settings on an individual basis, avoiding frequent overassistance leading potentially to diaphragmatic atrophy. Increased inspiratory EAdi levels can also suggest insufficient support, while a strong tonic activity may reflect the patient efforts to increase its lung volume. EAdi monitoring also allows detection of patient-ventilator asynchrony. It can play a role in evaluation of extubation readiness. Finally, EAdi monitoring provides the clinician with better understanding of the ventilatory capacity of patients with acute neuromuscular disease. Further studies are warranted to evaluate the clinical impact of these potential benefits.

  20. Control of a Shunt Active Power Filter with Neural Networks—Theory and Practical Results

    Science.gov (United States)

    Villalva, Marcelo G.; Filho, Ernesto Ruppert

    This paper presents theoretical studies and practical results obtained with a four-wire shunt active power filter fully controlled with neural networks. The paper is focused on a current compensation method based on adaptive linear elements (adalines), which are powerful and easy-to-use neural networks. The reader will find here an introduction about these networks, an explanatory section about the achievement of Fourier series with adalines, and the full description of an adaline-based selective current compensator. The paper also brings a quick discussion about the use of a feedforward neural network in the current controller of the active filter, as well as simulation and experimental results obtained with the prototype of an active power filter.

  1. New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations.

    Science.gov (United States)

    Cai, Zuowei; Huang, Lihong; Zhang, Lingling

    2015-05-01

    This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback controller and using some analytic techniques, new testable algebraic criteria are obtained to realize two different kinds of global exponential synchronization of the drive-response system. Moreover, we give the estimated rate of exponential synchronization which depends on the delays and system parameters. The obtained results extend some previous works on synchronization of delayed neural networks not only with continuous activations but also with discontinuous activations. Finally, numerical examples are provided to show the correctness of our analysis via computer simulations. Our method and theoretical results have a leading significance in the design of synchronized neural network circuits involving discontinuous factors and time-varying delays.

  2. Dose-dependent effects of cannabis on the neural correlates of error monitoring in frequent cannabis users.

    Science.gov (United States)

    Kowal, Mikael A; van Steenbergen, Henk; Colzato, Lorenza S; Hazekamp, Arno; van der Wee, Nic J A; Manai, Meriem; Durieux, Jeffrey; Hommel, Bernhard

    2015-11-01

    Cannabis has been suggested to impair the capacity to recognize discrepancies between expected and executed actions. However, there is a lack of conclusive evidence regarding the acute impact of cannabis on the neural correlates of error monitoring. In order to contribute to the available knowledge, we used a randomized, double-blind, between-groups design to investigate the impact of administration of a low (5.5 mg THC) or high (22 mg THC) dose of vaporized cannabis vs. placebo on the amplitudes of the error-related negativity (ERN) and error positivity (Pe) in the context of the Flanker task, in a group of frequent cannabis users (required to use cannabis minimally 4 times a week, for at least 2 years). Subjects in the high dose group (n=18) demonstrated a significantly diminished ERN in comparison to the placebo condition (n=19), whereas a reduced Pe amplitude was observed in both the high and low dose (n=18) conditions, as compared to placebo. The results suggest that a high dose of cannabis may affect the neural correlates of both the conscious (late), as well as the initial automatic processes involved in error monitoring, while a low dose of cannabis might impact only the conscious (late) processing of errors.

  3. Vascular Endothelial Growth Factor Receptor 3 Controls Neural Stem Cell Activation in Mice and Humans

    Directory of Open Access Journals (Sweden)

    Jinah Han

    2015-02-01

    Full Text Available Neural stem cells (NSCs continuously produce new neurons within the adult mammalian hippocampus. NSCs are typically quiescent but activated to self-renew or differentiate into neural progenitor cells. The molecular mechanisms of NSC activation remain poorly understood. Here, we show that adult hippocampal NSCs express vascular endothelial growth factor receptor (VEGFR 3 and its ligand VEGF-C, which activates quiescent NSCs to enter the cell cycle and generate progenitor cells. Hippocampal NSC activation and neurogenesis are impaired by conditional deletion of Vegfr3 in NSCs. Functionally, this is associated with compromised NSC activation in response to VEGF-C and physical activity. In NSCs derived from human embryonic stem cells (hESCs, VEGF-C/VEGFR3 mediates intracellular activation of AKT and ERK pathways that control cell fate and proliferation. These findings identify VEGF-C/VEGFR3 signaling as a specific regulator of NSC activation and neurogenesis in mammals.

  4. Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Marlon Navia

    2015-09-01

    Full Text Available Several systems have been proposed to monitor wireless sensor networks (WSN. These systems may be active (causing a high degree of intrusion or passive (low observability inside the nodes. This paper presents the implementation of an active hybrid (hardware and software monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART, serial peripheral interface (SPI, and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference, about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.

  5. Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks.

    Science.gov (United States)

    Navia, Marlon; Campelo, Jose C; Bonastre, Alberto; Ors, Rafael; Capella, Juan V; Serrano, Juan J

    2015-09-18

    Several systems have been proposed to monitor wireless sensor networks (WSN). These systems may be active (causing a high degree of intrusion) or passive (low observability inside the nodes). This paper presents the implementation of an active hybrid (hardware and software) monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part) which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART), serial peripheral interface (SPI), and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference), about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.

  6. Neural activation during submaximal contractions seems more reflective of neuromuscular ageing than maximal voluntary activation

    Directory of Open Access Journals (Sweden)

    Gil eScaglioni

    2016-02-01

    Full Text Available This study aimed at testing the hypothesis that differences in neural activation strategy during submaximal but not maximal plantarflexions exist between young and older men. Eleven young men (YM, 26±4 yr and 13 OM (76±3 yr volunteered for the investigation. Maximal voluntary torque (MVT was 38.2%, lower (P<0.001 in OM than in YM, while voluntary activation was equivalent (~97%. The relationship between the interpolated twitch-torque and the voluntary torque (IT-VT relationship was composite (curvilinear+exponential for both age-groups. However, the OM showed accentuated concavity, as attested by the occurrence of the deviation from linearity at a lower contraction intensity (OM: 54.9 vs. YM: 71.9% MVT. In conclusion, ageing does not affect the capacity to fully activate the plantar flexors during maximal performances, but it alters the activation pattern for submaximal levels of effort. The greater age-related concavity of the IT-VT relationship suggests that, during submaximal contractions, OM need to reach a level of activation higher than YM to develop an equivalent relative torque.

  7. Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

    Science.gov (United States)

    Li, Shuai; Li, Yangming

    2013-10-28

    The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

  8. Active imaging for monitoring and technical diagnostics

    Directory of Open Access Journals (Sweden)

    Marek Piszczek

    2014-08-01

    Full Text Available The article presents the results of currently running work in the field of active imaging. The term active refers to both the image acquisition methods, so-called methods of the spatio-temporal framing and active visualization method applying augmented reality. Also results of application of the HMD and 6DoF modules as well as the experimental laser photography device are given. The device works by methods of spatio-temporal framing and it has been developed at the IOE WAT. In terms of image acquisition - active imaging involves the use of illumination of the observed scene. In the field of information visualization - active imaging directly concerns the issues of interaction human-machine environment. The results show the possibility of using the described techniques, among others, rescue (fire brigade, security of mass events (police or the protection of critical infrastructure as well as broadly understood diagnostic problems. Examples presented in the article show a wide range of possible uses of the methods both in observational techniques and measurement. They are relatively innovative solutions and require elaboration of series of hardware and algorithmic issues. However, already at this stage it is clear that active acquisition and visualization methods indicate a high potential for this type of information solutions.[b]Keywords[/b]: active imaging, augmented reality, digital image processing

  9. Visualizing the Hidden Activity of Artificial Neural Networks.

    Science.gov (United States)

    Rauber, Paulo E; Fadel, Samuel G; Falcao, Alexandre X; Telea, Alexandru C

    2017-01-01

    In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.

  10. Computational modeling of neural activities for statistical inference

    CERN Document Server

    Kolossa, Antonio

    2016-01-01

    This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .

  11. Information content of neural networks with self-control and variable activity

    Science.gov (United States)

    Bollé, D.; Amari, S. I.; Dominguez Carreta, D. R. C.; Massolo, G.

    2001-02-01

    A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations.

  12. Reduced respiratory neural activity elicits a long-lasting decrease in the CO2 threshold for apnea in anesthetized rats.

    Science.gov (United States)

    Baertsch, N A; Baker, T L

    2017-01-01

    Two critical parameters that influence breathing stability are the levels of arterial pCO2 at which breathing ceases and subsequently resumes - termed the apneic and recruitment thresholds (AT and RT, respectively). Reduced respiratory neural activity elicits a chemoreflex-independent, long-lasting increase in phrenic burst amplitude, a form of plasticity known as inactivity-induced phrenic motor facilitation (iPMF). The physiological significance of iPMF is unknown. To determine if iPMF and neural apnea have long-lasting physiological effects on breathing, we tested the hypothesis that patterns of neural apnea that induce iPMF also elicit changes in the AT and RT. Phrenic nerve activity and end-tidal CO2 were recorded in urethane-anesthetized, ventilated rats to quantify phrenic nerve burst amplitude and the AT and RT before and after three patterns of neural apnea that differed in their duration and ability to elicit iPMF: brief intermittent neural apneas, a single brief "massed" neural apnea, or a prolonged neural apnea. Consistent with our hypothesis, we found that patterns of neural apnea that elicited iPMF also resulted in changes in the AT and RT. Specifically, intermittent neural apneas progressively decreased the AT with each subsequent neural apnea, which persisted for at least 60min. Similarly, a prolonged neural apnea elicited a long-lasting decrease in the AT. In both cases, the magnitude of the AT decrease was proportional to iPMF. In contrast, the RT was transiently decreased following prolonged neural apnea, and was not proportional to iPMF. No changes in the AT or RT were observed following a single brief neural apnea. Our results indicate that the AT and RT are differentially altered by neural apnea and suggest that specific patterns of neural apnea that elicit plasticity may stabilize breathing via a decrease in the AT.

  13. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces

    OpenAIRE

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stim...

  14. Autonomic neural control and implications for remote medical monitoring in space.

    Science.gov (United States)

    Cooke, William H

    2007-07-01

    Long-duration space travel or extended stays on the moon or Mars will pose new challenges for maintaining and monitoring the health status of astronauts. Remote medical monitoring systems will need to be developed for a number of applications, including providing decision support for care-givers in the event of traumatic injury in space. The focus of this brief review is to introduce potential methods of monitoring astronaut status remotely from simple ECG recordings.

  15. Fundamental Active Current Adaptive Linear Neural Networks for Photovoltaic Shunt Active Power Filters

    Directory of Open Access Journals (Sweden)

    Muhammad Ammirrul Atiqi Mohd Zainuri

    2016-05-01

    Full Text Available This paper presents improvement of a harmonics extraction algorithm, known as the fundamental active current (FAC adaptive linear element (ADALINE neural network with the integration of photovoltaic (PV to shunt active power filters (SAPFs as active current source. Active PV injection in SAPFs should reduce dependency on grid supply current to supply the system. In addition, with a better and faster harmonics extraction algorithm, the SAPF should perform well, especially under dynamic PV and load conditions. The role of the actual injection current from SAPF after connecting PVs will be evaluated, and the better effect of using FAC ADALINE will be confirmed. The proposed SAPF was simulated and evaluated in MATLAB/Simulink first. Then, an experimental laboratory prototype was also developed to be tested with a PV simulator (CHROMA 62100H-600S, and the algorithm was implemented using a TMS320F28335 Digital Signal Processor (DSP. From simulation and experimental results, significant improvements in terms of total harmonic distortion (THD, time response and reduction of source power from grid have successfully been verified and achieved.

  16. Memory monitoring performance and PFC activity are associated with 5-HTTLPR genotype in older adults

    Science.gov (United States)

    Pacheco, Jennifer; Beevers, Christopher G.; McGeary, John E.; Schnyer, David M.

    2012-01-01

    Older adults show extensive variability in cognitive performance, including episodic memory. A portion of this variability could potentially be explained by genetic factors. Recent literature shows that the neurotransmitter serotonin plays an important role in memory processes, as enhancements of brain serotonin have led to memory improvement. Here, we have begun to explore genetic contributions to the performance and underlying brain activity associated with source memory monitoring. Using a source recognition memory task during fMRI scanning, this study offers evidence that older adults who carry a short allele (S-car) of the serotonin transporter linked polymorphic region (5-HTTLPR) in the SLC6A4 gene show specific deficits in source memory monitoring relative to older adults who are homozygous for the long allele (LL). These deficits are accompanied by less neural activity in regions of prefrontal cortex that have been shown to support accurate memory monitoring. Moreover, while the older adult LL group’s behavioral performance does not differ from younger adults, their brain activation reveals evidence of compensatory activation that likely supports their higher performance level. These results provide preliminary evidence that the long-allele homozygous profile is cognitively beneficial to older adults, particularly for memory functioning. PMID:22705442

  17. Application of neural networks with orthogonal activation functions in control of dynamical systems

    Science.gov (United States)

    Nikolić, Saša S.; Antić, Dragan S.; Milojković, Marko T.; Milovanović, Miroslav B.; Perić, Staniša Lj.; Mitić, Darko B.

    2016-04-01

    In this article, we present a new method for the synthesis of almost and quasi-orthogonal polynomials of arbitrary order. Filters designed on the bases of these functions are generators of generalised quasi-orthogonal signals for which we derived and presented necessary mathematical background. Based on theoretical results, we designed and practically implemented generalised first-order (k = 1) quasi-orthogonal filter and proved its quasi-orthogonality via performed experiments. Designed filters can be applied in many scientific areas. In this article, generated functions were successfully implemented in Nonlinear Auto Regressive eXogenous (NARX) neural network as activation functions. One practical application of the designed orthogonal neural network is demonstrated through the example of control of the complex technical non-linear system - laboratory magnetic levitation system. Obtained results were compared with neural networks with standard activation functions and orthogonal functions of trigonometric shape. The proposed network demonstrated superiority over existing solutions in the sense of system performances.

  18. Circular polarization intrinsic optical signal recording of stimulus-evoked neural activity.

    Science.gov (United States)

    Lu, Rong-Wen; Zhang, Qiu-Xiang; Yao, Xin-Cheng

    2011-05-15

    Linear polarization intrinsic optical signal (LP-IOS) measurement can provide sensitive detection of neural activities in stimulus-activated neural tissues. However, the LP-IOS magnitude and signal-to-noise ratio (SNR) are highly correlated with the nerve orientation relative to the polarization plane of the incident light. Because of the complexity of orientation dependency, LP-IOS optimization and outcome interpretation are time consuming and complicated. In this study, we demonstrate the feasibility of circular polarization intrinsic optical signal (CP-IOS) measurement. Our theoretical modeling and experimental investigation indicate that CP-IOS magnitude and SNR are independent from the nerve orientation. Therefore, CP-IOS promises a practical method for polarization IOS imaging of complex neural systems.

  19. Application of Neural Network in Simple Tool Wear Monitoring and Indentification System in MDF Milling

    Directory of Open Access Journals (Sweden)

    Marcin Zbieć

    2011-03-01

    Full Text Available This paper deals with simple neural network-based diagnostic system, applied to tool wear prediction in MDF milling. Ten tools were used for the test, and each one was consequently worn in the process of MDF milling. During the wearing process, the key process parameters were measured, such as cutting and thrust forces, temperature and power consumption. The neural network-based system was used for tool wear prediction of all the tools except the fi rst one, based on data collected during the previous attempts. The test has shown that the proposed system has good prediction accuracy and that it could be a useful tool in the optimization of the woodworking process.

  20. Simultaneous and automated monitoring of the multimetal biosorption processes by potentiometric sensor array and artificial neural network.

    Science.gov (United States)

    Wilson, D; del Valle, M; Alegret, S; Valderrama, C; Florido, A

    2013-09-30

    In this communication, a new methodology for the simultaneous and automated monitoring of biosorption processes of multimetal mixtures of polluting heavy metals on vegetable wastes based on flow-injection potentiometry (FIP) and electronic tongue detection (ET) is presented. A fixed-bed column filled with grape stalks from wine industry wastes is used as the biosorption setup to remove the metal mixtures from the influent solution. The monitoring system consists in a computer controlled-FIP prototype with the ET based on an array of 9 flow-through ion-selective electrodes and electrodes with generic response to divalent ions placed in series, plus an artificial neural network response model. The cross-response to Cu(2+), Cd(2+), Zn(2+), Pb(2+) and Ca(2+) (as target ions) is used, and only when dynamic treatment of the kinetic components of the transient signal is incorporated, a correct operation of the system is achieved. For this purpose, the FIA peaks are transformed via use of Fourier treatment, and selected coefficients are used to feed an artificial neural network response model. Real-time monitoring of different binary (Cu(2+)/ Pb(2+)), (Cu(2+)/ Zn(2+)) and ternary mixtures (Cu(2+)/ Pb(2+)/ Zn(2+)), (Cu(2+)/ Zn(2+)/ Cd(2+)), simultaneous to the release of Ca(2+) in the effluent solution, are achieved satisfactorily using the reported system, obtaining the corresponding breakthrough curves, and showing the ion-exchange mechanism among the different metals. Analytical performance is verified against conventional spectroscopic techniques, with good concordance of the obtained breakthrough curves and modeled adsorption parameters.

  1. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  2. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

    Science.gov (United States)

    Winkler, David A; Le, Tu C

    2017-01-01

    Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems have been overcome by modern mathematical techniques. QSAR studies have almost always used so-called "shallow" neural networks in which there is a single hidden layer between the input and output layers. Recently, a new and potentially paradigm-shifting type of neural network based on Deep Learning has appeared. Deep learning methods have generated impressive improvements in image and voice recognition, and are now being applied to QSAR and QSAR modelling. This paper describes the differences in approach between deep and shallow neural networks, compares their abilities to predict the properties of test sets for 15 large drug data sets (the kaggle set), discusses the results in terms of the Universal Approximation theorem for neural networks, and describes how DNN may ameliorate or remove troublesome "activity cliffs" in QSAR data sets. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Management plan for Facility Effluent Monitoring Plan activities

    Energy Technology Data Exchange (ETDEWEB)

    Nickels, J.M.; Pratt, D.R.

    1991-08-01

    The DOE/RL 89-19, United States Department of Energy-Richland Operations Office Environmental Protection Implementation Plan (1989), requires the Hanford Site to prepare an Environmental Monitoring Plan (EMP) by November 9, 1991. The DOE/EH-0173T, Environmental Regulatory Guide for Radiological Effluent Monitoring and Environmental Surveillance (1991), provides additional guidance and requires implementation of the EMP within 36 months of the effective data of the rule. DOE Order 5400.1, General Environmental Protection Program, requires each US Department of Energy (DOE) site, facility, or activity that uses, generates, releases, or manages significant quantities of hazardous materials to prepare an EMP. This EMP is to identify and discuss two major activities: (1) effluent monitoring and (2) environmental surveillance. At the Hanford Site, the site-wide EMP will consist of the following elements: (1) A conceptual plan addressing effluent monitoring and environmental surveillance; (2) Pacific Northwest Laboratory (PNL) site-wide environmental surveillance program; (3) Westinghouse Hanford Company (Westinghouse Hanford) effluent monitoring program consisting of the near-field operations environmental monitoring activities and abstracts of each Facility Effluent Monitoring Plan (FEMP). This management plan addresses the third of these three elements of the EMP, the FEMPs.

  4. Neural activity in the macaque putamen associated with saccades and behavioral outcome.

    Directory of Open Access Journals (Sweden)

    Jessica M Phillips

    Full Text Available It is now widely accepted that the basal ganglia nuclei form segregated, parallel loops with neocortical areas. The prevalent view is that the putamen is part of the motor loop, which receives inputs from sensorimotor areas, whereas the caudate, which receives inputs from frontal cortical eye fields and projects via the substantia nigra pars reticulata to the superior colliculus, belongs to the oculomotor loop. Tracer studies in monkeys and functional neuroimaging studies in human subjects, however, also suggest a potential role for the putamen in oculomotor control. To investigate the role of the putamen in saccadic eye movements, we recorded single neuron activity in the caudal putamen of two rhesus monkeys while they alternated between short blocks of pro- and anti-saccades. In each trial, the instruction cue was provided after the onset of the peripheral stimulus, thus the monkeys could either generate an immediate response to the stimulus based on the internal representation of the rule from the previous trial, or alternatively, could await the visual rule-instruction cue to guide their saccadic response. We found that a subset of putamen neurons showed saccade-related activity, that the preparatory mode (internally- versus externally-cued influenced the expression of task-selectivity in roughly one third of the task-modulated neurons, and further that a large proportion of neurons encoded the outcome of the saccade. These results suggest that the caudal putamen may be part of the neural network for goal-directed saccades, wherein the monitoring of saccadic eye movements, context and performance feedback may be processed together to ensure optimal behavioural performance and outcomes are achieved during ongoing behaviour.

  5. Evaluation of Activity Recognition Algorithms for Employee Performance Monitoring

    OpenAIRE

    2012-01-01

    Successful Human Resource Management plays a key role in success of any organization. Traditionally, human resource managers rely on various information technology solutions such as Payroll and Work Time Systems incorporating RFID and biometric technologies. This research evaluates activity recognition algorithms for employee performance monitoring. An activity recognition algorithm has been implemented that categorized the activity of employee into following in to classes: job activities and...

  6. The mechanism of neurally mediated syncope assessed by an ambulatory radionuclide monitoring system and heart rate variability indices during head-up tilt

    Energy Technology Data Exchange (ETDEWEB)

    Hosaka, Haruhiko [Self Defense Force Central Hospital, Tokyo (Japan); Takase, Bonpei; Ohsuzu, Fumitaka [National Defense Medical Coll., Tokorozawa, Saitama (Japan); Kurita, Akira [National Defense Medical Coll., Tokorozawa, Saitama (Japan). Research Center

    2002-11-01

    Previously, we tested the hypothesis that the great decline in left ventricular volume during head-up tilt test could trigger ventricular mechanoreceptor activation, using ambulatory radionuclide monitoring system (C-VEST system). The aim of this study is to investigate the mechanism of tilt-induced syncope further, based on our previous report. We measured the temporal changes in left ventricular volume, ejection fraction, cardiac output, and heart rate variability indices during head-up tilt test in 34 patients with syncope of an undetermined etiology. Twenty-two patients and a positive response (P group). Twelve patients showed a negative response (N group). Before syncope, left ventricular volume declined (P group, diastolic volume; -7.9{+-}6.8%: systolic volume; -23.3{+-}33.8%: N group, diastolic volume; -2.5{+-}1.9%: systolic volume; 0.6{+-}9.5%: p<0.05), ejection fraction increased (P group, 3.9{+-}2.5%; N group, -3.5{+-}7.2%; p<0.005), and high frequency spectra increased (P group, 12.0{+-}20.3%; N group, 3.1{+-}9.7%; p<0.05), more extremely in the P group than in the N group. The value of the high frequency spectra before the head-up tilt test was significantly higher in the P group than in the N group (P group, 5.8{+-}0.9 ms; N group, 5.0{+-}1.1 ms; p<0.05). The precise evaluation of left ventricular volume by ambulatory radionuclide monitoring system combined with a heart rate variability analysis is considered to be useful for clarifying the pathophysiology of neurally mediated syncope. Patients with neurally mediated syncope have higher baseline parasympathetic tone than normal population. (author)

  7. Using Perfusion fMRI to Measure Continuous Changes in Neural Activity with Learning

    Science.gov (United States)

    Olson, Ingrid R.; Rao, Hengyi; Moore, Katherine Sledge; Wang, Jiongjiong; Detre, John A.; Aguirre, Geoffrey K.

    2006-01-01

    In this study, we examine the suitability of a relatively new imaging technique, "arterial spin labeled perfusion imaging," for the study of continuous, gradual changes in neural activity. Unlike BOLD imaging, the perfusion signal is stable over long time-scales, allowing for accurate assessment of continuous performance. In addition, perfusion…

  8. Neural activity supporting the formation of associative memory versus source memory.

    Science.gov (United States)

    Park, Heekyeong; Shannon, Vale; Biggan, John; Spann, Catherine

    2012-08-30

    The ability to form a new association with discontiguous elements constitutes the very crux of episodic memory. However, it is not fully understood whether different types of associations rely on common neural correlates for encoding associations. In the present study, we investigated whether the formation of associative memory (associations between items) and source memory (associations between an item and its context) recruits common neural activity during encoding, or whether each type of association requires different neural activity for subsequent memory. During study, participants were visually presented a list of object pairs in the scanner while the names of objects were simultaneously presented either in a male or female voice. Participants completed a post-scan recognition test for associative and source memories for object pairs and their contexts. Associative memory was predicted in the left inferior prefrontal cortex, the fusiform gyrus and the medial temporal lobe including both perirhinal and parahippocampal cortices and the posterior hippocampus. Encoding activity for source memory was identified in the right insula and the right anterior hippocampus. Further, neural activity in the right posterior hippocampus was recruited for successful formation of both associative and source memories. Collectively, these findings highlight the pivotal role of the hippocampus in successful encoding of associative and source memories and add more weight to the role of the perirhinal cortex in associative encoding of objects. The present findings have implications for roles of the medial temporal lobe sub-regions for successful formation of associative and source memories.

  9. Modeling electrocortical activity through improved local approximations of integral neural field equations.

    NARCIS (Netherlands)

    Coombes, S.; Venkov, N.A.; Shiau, L.; Bojak, I.; Liley, D.T.; Laing, C.R.

    2007-01-01

    Neural field models of firing rate activity typically take the form of integral equations with space-dependent axonal delays. Under natural assumptions on the synaptic connectivity we show how one can derive an equivalent partial differential equation (PDE) model that properly treats the axonal dela

  10. Evaluation of neural networks to identify types of activity using accelerometers

    NARCIS (Netherlands)

    Vries, S.I. de; Garre, F.G.; Engbers, L.H.; Hildebrandt, V.H.; Buuren, S. van

    2011-01-01

    Purpose: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults. Methods: Forty-nine subjects (21 men and 28 women; age range

  11. Differential neural activity patterns for spatial relations in humans: a MEG study.

    Science.gov (United States)

    Scott, Nicole M; Leuthold, Arthur; Sera, Maria D; Georgopoulos, Apostolos P

    2016-02-01

    Children learn the words for above-below relations earlier than for left-right relations, despite treating these equally well in a simple visual categorization task. Even as adults--conflicts in congruency, such as when a stimulus is depicted in a spatially incongruent manner with respect to salient global cues--can be challenging. Here we investigated the neural correlates of encoding and maintaining in working memory above-below and left-right relational planes in 12 adults using magnetoencephalography in order to discover whether above-below relations are represented by the brain differently than left-right relations. Adults performed perfectly on the task behaviorally, so any differences in neural activity were attributed to the stimuli's cognitive attributes. In comparing above-below to left-right relations during stimulus encoding, we found the greatest differences in neural activity in areas associated with space and movement. In comparing congruent to incongruent trials, we found the greatest differential activity in premotor areas. For both contrasts, brain areas involved in the encoding phase were also involved in the maintenance phase, which provides evidence that those brain areas are particularly important in representing the relational planes or congruency types throughout the trial. When comparing neural activity associated with the relational planes during working memory, additional right posterior areas were implicated, whereas the congruent-incongruent contrast implicated additional bilateral frontal and temporal areas. These findings are consistent with the hypothesis left-right relations are represented differently than above-below relations.

  12. Identification of children's activity type with accelerometer-based neural networks

    NARCIS (Netherlands)

    Vries, S.I. de; Engels, M.; Garre, F.G.

    2011-01-01

    Purpose: The study's purpose was to identify children's physical activity type using artificial neural network (ANN) models based on uniaxial or triaxial accelerometer data from the hip or the ankle. Methods: Fifty-eight children (31 boys and 27 girls, age range = 9-12 yr) performed the following ac

  13. Evaluation of neural networks to identify types of activity using accelerometers

    NARCIS (Netherlands)

    Vries, S.I. de; Garre, F.G.; Engbers, L.H.; Hildebrandt, V.H.; Buuren, S. van

    2011-01-01

    Purpose: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults. Methods: Forty-nine subjects (21 men and 28 women; age range

  14. Distributed dynamical computation in neural circuits with propagating coherent activity patterns.

    Directory of Open Access Journals (Sweden)

    Pulin Gong

    2009-12-01

    Full Text Available Activity in neural circuits is spatiotemporally organized. Its spatial organization consists of multiple, localized coherent patterns, or patchy clusters. These patterns propagate across the circuits over time. This type of collective behavior has ubiquitously been observed, both in spontaneous activity and evoked responses; its function, however, has remained unclear. We construct a spatially extended, spiking neural circuit that generates emergent spatiotemporal activity patterns, thereby capturing some of the complexities of the patterns observed empirically. We elucidate what kind of fundamental function these patterns can serve by showing how they process information. As self-sustained objects, localized coherent patterns can signal information by propagating across the neural circuit. Computational operations occur when these emergent patterns interact, or collide with each other. The ongoing behaviors of these patterns naturally embody both distributed, parallel computation and cascaded logical operations. Such distributed computations enable the system to work in an inherently flexible and efficient way. Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation.

  15. An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function

    Directory of Open Access Journals (Sweden)

    Onursal Çetin

    2015-06-01

    Full Text Available Objective: Implementation of multilayer neural network (MLNN with sigmoid activation function for the diagnosis of hepatitis disease. Methods: Artificial neural networks (ANNs are efficient tools currently in common use for medical diagnosis. In hardware based architectures activation functions play an important role in ANN behavior. Sigmoid function is the most frequently used activation function because of its smooth response. Thus, sigmoid function and its close approximations were implemented as activation function. The dataset is taken from the UCI machine learning database. Results: For the diagnosis of hepatitis disease, MLNN structure was implemented and Levenberg Morquardt (LM algorithm was used for learning. Our method of classifying hepatitis disease produced an accuracy of 91.9% to 93.8% via 10 fold cross validation. Conclusion: When compared to previous work that diagnosed hepatitis disease using artificial neural networks and the identical data set, our results are promising in order to reduce the size and cost of neural network based hardware. Thus, hardware based diagnosis systems can be developed effectively by using approximations of sigmoid function.

  16. Differences in Neural Activation as a Function of Risk-taking Task Parameters

    Directory of Open Access Journals (Sweden)

    Eliza eCongdon

    2013-09-01

    Full Text Available Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART, which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analogue Risk Taking (BART task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step towards characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.

  17. The methodical statutes monitoring of activity by innovative structures

    OpenAIRE

    Stoianovskii, Andrii; Baranovska, Sofia; Stoianovska, Iryna

    2012-01-01

    In the article it is suggested to perfect methodical recommendations in relation to monitoring of activity of innovative structures, which, among other, allow to mark off the results of activity of leading organ of management and contractors of innovative projects, registered in her limits an innovative structure.

  18. Fabric-based integrated energy devices for wearable activity monitors.

    Science.gov (United States)

    Jung, Sungmook; Lee, Jongsu; Hyeon, Taeghwan; Lee, Minbaek; Kim, Dae-Hyeong

    2014-09-01

    A wearable fabric-based integrated power-supply system that generates energy triboelectrically using human activity and stores the generated energy in an integrated supercapacitor is developed. This system can be utilized as either a self-powered activity monitor or as a power supply for external wearable sensors. These demonstrations give new insights for the research of wearable electronics.

  19. A Constraint Satisfaction Neural Network and Heuristic Combined Approach for Concurrent Activities Scheduling

    Institute of Scientific and Technical Information of China (English)

    YAN JiHong(闫纪红); WU Cheng(吴澄)

    2003-01-01

    Scheduling activities in concurrent product development process is of great sig-nificance to shorten development lead time and minimize the cost. Moreover, it can eliminate theunnecessary redesign periods and guarantee that serial activities can be executed as concurrently aspossible. This paper presents a constraint satisfaction neural network and heuristic combined ap-proach for concurrent activities scheduling. In the combined approach, the neural network is usedto obtain a feasible starting time of all the activities based on sequence constraints, the heuris-tic algorithm is used to obtain a feasible solution of the scheduling problem based on resourceconstraints. The feasible scheduling solution is obtained by a gradient optimization function. Sim-ulations have shown that the proposed combined approach is efficient and feasible with respect toconcurrent activities scheduling.

  20. Global robust dissipativity of interval recurrent neural networks with time-varying delay and discontinuous activations.

    Science.gov (United States)

    Duan, Lian; Huang, Lihong; Guo, Zhenyuan

    2016-07-01

    In this paper, the problems of robust dissipativity and robust exponential dissipativity are discussed for a class of recurrent neural networks with time-varying delay and discontinuous activations. We extend an invariance principle for the study of the dissipativity problem of delay systems to the discontinuous case. Based on the developed theory, some novel criteria for checking the global robust dissipativity and global robust exponential dissipativity of the addressed neural network model are established by constructing appropriate Lyapunov functionals and employing the theory of Filippov systems and matrix inequality techniques. The effectiveness of the theoretical results is shown by two examples with numerical simulations.

  1. Cognitive-affective neural plasticity following active-controlled mindfulness intervention

    DEFF Research Database (Denmark)

    Allen, Micah Galen

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

  2. Global robust dissipativity of interval recurrent neural networks with time-varying delay and discontinuous activations

    Science.gov (United States)

    Duan, Lian; Huang, Lihong; Guo, Zhenyuan

    2016-07-01

    In this paper, the problems of robust dissipativity and robust exponential dissipativity are discussed for a class of recurrent neural networks with time-varying delay and discontinuous activations. We extend an invariance principle for the study of the dissipativity problem of delay systems to the discontinuous case. Based on the developed theory, some novel criteria for checking the global robust dissipativity and global robust exponential dissipativity of the addressed neural network model are established by constructing appropriate Lyapunov functionals and employing the theory of Filippov systems and matrix inequality techniques. The effectiveness of the theoretical results is shown by two examples with numerical simulations.

  3. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

    Directory of Open Access Journals (Sweden)

    Mehmet Şimşir

    2016-01-01

    Full Text Available Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

  4. Simultaneous imaging of neural activity in three dimensions

    Directory of Open Access Journals (Sweden)

    Sean eQuirin

    2014-04-01

    Full Text Available We introduce a scanless optical method to image neuronal activity in three dimensions simultaneously. Using a spatial light modulator and a custom-designed phase mask, we illuminate and collect light simultaneously from different focal planes and perform calcium imaging of neuronal activity in vitro and in vivo. This method, combining structured illumination with volume projection imaging, could be used as a technological platform for brain activity mapping.

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

    Science.gov (United States)

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

    2017-02-01

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

  6. Navigation of autonomous mobile robot using different activation functions of wavelet neural network

    Directory of Open Access Journals (Sweden)

    Panigrahi Pratap Kumar

    2015-03-01

    Full Text Available An autonomous mobile robot is a robot which can move and act autonomously without the help of human assistance. Navigation problem of mobile robot in unknown environment is an interesting research area. This is a problem of deducing a path for the robot from its initial position to a given goal position without collision with the obstacles. Different methods such as fuzzy logic, neural networks etc. are used to find collision free path for mobile robot. This paper examines behavior of path planning of mobile robot using three activation functions of wavelet neural network i.e. Mexican Hat, Gaussian and Morlet wavelet functions by MATLAB. The simulation result shows that WNN has faster learning speed with respect to traditional artificial neural network.

  7. Complete stability of delayed recurrent neural networks with Gaussian activation functions.

    Science.gov (United States)

    Liu, Peng; Zeng, Zhigang; Wang, Jun

    2017-01-01

    This paper addresses the complete stability of delayed recurrent neural networks with Gaussian activation functions. By means of the geometrical properties of Gaussian function and algebraic properties of nonsingular M-matrix, some sufficient conditions are obtained to ensure that for an n-neuron neural network, there are exactly 3(k) equilibrium points with 0≤k≤n, among which 2(k) and 3(k)-2(k) equilibrium points are locally exponentially stable and unstable, respectively. Moreover, it concludes that all the states converge to one of the equilibrium points; i.e., the neural networks are completely stable. The derived conditions herein can be easily tested. Finally, a numerical example is given to illustrate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. RBF neural network and active circles based algorithm for contours extraction

    Institute of Scientific and Technical Information of China (English)

    Zhou Zhiheng; Zeng Delu; Xie Shengli

    2007-01-01

    For the contours extraction from the images, active contour model and self-organizing map based approach are popular nowadays. But they are still confronted with the problems that the optimization of energy function will trap in local minimums and the contour evolutions greatly depend on the initial contour selection. Addressing to these problems, a contours extraction algorithm based on RBF neural network is proposed here. A series of circles with adaptive radius and center is firstly used to search image feature points that are scattered enough. After the feature points are clustered, a group of radial basis functions are constructed. Using the pixels' intensities and gradients as the input vector, the final object contour can be obtained by the predicting ability of the neural network. The RBF neural network based algorithm is tested on three kinds of images, such as changing topology, complicated background, and blurring or noisy boundary. Simulation results show that the proposed algorithm performs contours extraction greatly.

  9. Age-related changes in neural activity associated with familiarity, recollection and false recognition.

    Science.gov (United States)

    Duarte, Audrey; Graham, Kim S; Henson, Richard N

    2010-10-01

    Older adults often exhibit elevated false recognition for events that never occurred, while simultaneously experiencing difficulty in recognizing events that actually occurred. It has been proposed that reduced recollection in conjunction with an over-reliance on familiarity may contribute to this pattern of results. This explanation is somewhat inconsistent, however, with recent evidence suggesting that familiarity and associated neural activity are reduced in healthy aging. Alternatively, given that illusory memory may be based, in part, on veridical memory processes (recollection/familiarity), one might predict that older adults exhibit enhanced false alarm rates because the neural signatures associated with true recognition (hits) and false recognition (false alarms) are less distinguishable in old than in young adults. Here, we used event-related fMRI to measure the effects of aging on neural activity associated with recollection, familiarity and familiarity-based false alarms for objects in young and older adults. Compared to young adults, older adults exhibited elevated false alarm rates and impaired behavioral indices of recollection and familiarity. Imaging data showed that older adults exhibited reduced recollection effects in the left parietoccipital cortex. Furthermore, while similar regions in frontal, parietal, lateral and inferior temporal cortices contributed to familiarity-based true and false recognition, reduced familiarity-related activity in frontal and inferior temporal regions in the older adults resulted in decreased differentiation between true and false recognition effects in this group. Our results suggest that reductions in neural activity associated with both recollection and familiarity for studied items may contribute to elevated false recognition in older adults, via reduced differentiation between the neural activity associated with true and false memory.

  10. Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network

    Directory of Open Access Journals (Sweden)

    Achmad Arwan

    2016-07-01

    Full Text Available AbstrakProduksi, order, penjualan, dan pengiriman adalah serangkaian event yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari event tersebut dicatat dalam event log. Complex Event Processing adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan event log dimasukkan ke input jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event.Kata kunci: complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. AbstractProductions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results

  11. Evaluation of Activity Recognition Algorithms for Employee Performance Monitoring

    Directory of Open Access Journals (Sweden)

    Mehreen Mumtaz

    2012-09-01

    Full Text Available Successful Human Resource Management plays a key role in success of any organization. Traditionally, human resource managers rely on various information technology solutions such as Payroll and Work Time Systems incorporating RFID and biometric technologies. This research evaluates activity recognition algorithms for employee performance monitoring. An activity recognition algorithm has been implemented that categorized the activity of employee into following in to classes: job activities and non-job related activities. Finally, the algorithm will compute the time which employee spent in job related and non-job related activities. This paper presents a novel architecture based upon video analytics that can facilitate Human Resource Managers in real time.

  12. Social power and approach-related neural activity

    NARCIS (Netherlands)

    M.A.S. Boksem (Maarten); R. Smolders (Ruud); D. de Cremer (David)

    2009-01-01

    textabstractIt 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

  13. Local active information storage as a tool to understand distributed neural information processing.

    Science.gov (United States)

    Wibral, Michael; Lizier, Joseph T; Vögler, Sebastian; Priesemann, Viola; Galuske, Ralf

    2014-01-01

    Every act of information processing can in principle be decomposed into the component operations of information storage, transfer, and modification. Yet, while this is easily done for today's digital computers, the application of these concepts to neural information processing was hampered by the lack of proper mathematical definitions of these operations on information. Recently, definitions were given for the dynamics of these information processing operations on a local scale in space and time in a distributed system, and the specific concept of local active information storage was successfully applied to the analysis and optimization of artificial neural systems. However, no attempt to measure the space-time dynamics of local active information storage in neural data has been made to date. Here we measure local active information storage on a local scale in time and space in voltage sensitive dye imaging data from area 18 of the cat. We show that storage reflects neural properties such as stimulus preferences and surprise upon unexpected stimulus change, and in area 18 reflects the abstract concept of an ongoing stimulus despite the locally random nature of this stimulus. We suggest that LAIS will be a useful quantity to test theories of cortical function, such as predictive coding.

  14. Effect of short-term escitalopram treatment on neural activation during emotional processing.

    Science.gov (United States)

    Maron, Eduard; Wall, Matt; Norbury, Ray; Godlewska, Beata; Terbeck, Sylvia; Cowen, Philip; Matthews, Paul; Nutt, David J

    2016-01-01

    Recent functional magnetic resonance (fMRI) imaging studies have revealed that subchronic medication with escitalopram leads to significant reduction in both amygdala and medial frontal gyrus reactivity during processing of emotional faces, suggesting that escitalopram may have a distinguishable modulatory effect on neural activation as compared with other serotonin-selective antidepressants. In this fMRI study we aimed to explore whether short-term medication with escitalopram in healthy volunteers is associated with reduced neural response to emotional processing, and whether this effect is predicted by drug plasma concentration. The neural response to fearful and happy faces was measured before and on day 7 of treatment with escitalopram (10mg) in 15 healthy volunteers and compared with those in a control unmedicated group (n=14). Significantly reduced activation to fearful, but not to happy facial expressions was observed in the bilateral amygdala, cingulate and right medial frontal gyrus following escitalopram medication. This effect was not correlated with plasma drug concentration. In accordance with previous data, we showed that escitalopram exerts its rapid direct effect on emotional processing via attenuation of neural activation in pathways involving medial frontal gyrus and amygdala, an effect that seems to be distinguishable from that of other SSRIs.

  15. Convergence of inhibitory neural inputs regulate motor activity in the murine and monkey stomach.

    Science.gov (United States)

    Shaylor, Lara A; Hwang, Sung Jin; Sanders, Kenton M; Ward, Sean M

    2016-11-01

    Inhibitory motor neurons regulate several gastric motility patterns including receptive relaxation, gastric peristaltic motor patterns, and pyloric sphincter opening. Nitric oxide (NO) and purines have been identified as likely candidates that mediate inhibitory neural responses. However, the contribution from each neurotransmitter has received little attention in the distal stomach. The aims of this study were to identify the roles played by NO and purines in inhibitory motor responses in the antrums of mice and monkeys. By using wild-type mice and mutants with genetically deleted neural nitric oxide synthase (Nos1(-/-)) and P2Y1 receptors (P2ry1(-/-)) we examined the roles of NO and purines in postjunctional inhibitory responses in the distal stomach and compared these responses to those in primate stomach. Activation of inhibitory motor nerves using electrical field stimulation (EFS) produced frequency-dependent inhibitory junction potentials (IJPs) that produced muscle relaxations in both species. Stimulation of inhibitory nerves during slow waves terminated pacemaker events and associated contractions. In Nos1(-/-) mice IJPs and relaxations persisted whereas in P2ry1(-/-) mice IJPs were absent but relaxations persisted. In the gastric antrum of the non-human primate model Macaca fascicularis, similar NO and purine neural components contributed to inhibition of gastric motor activity. These data support a role of convergent inhibitory neural responses in the regulation of gastric motor activity across diverse species.

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

    Directory of Open Access Journals (Sweden)

    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.

  17. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches

    Science.gov (United States)

    Ma, Ying; Shaik, Mohammed A.; Kozberg, Mariel G.; Thibodeaux, David N.; Zhao, Hanzhi T.; Yu, Hang

    2016-01-01

    Although modern techniques such as two-photon microscopy can now provide cellular-level three-dimensional imaging of the intact living brain, the speed and fields of view of these techniques remain limited. Conversely, two-dimensional wide-field optical mapping (WFOM), a simpler technique that uses a camera to observe large areas of the exposed cortex under visible light, can detect changes in both neural activity and haemodynamics at very high speeds. Although WFOM may not provide single-neuron or capillary-level resolution, it is an attractive and accessible approach to imaging large areas of the brain in awake, behaving mammals at speeds fast enough to observe widespread neural firing events, as well as their dynamic coupling to haemodynamics. Although such wide-field optical imaging techniques have a long history, the advent of genetically encoded fluorophores that can report neural activity with high sensitivity, as well as modern technologies such as light emitting diodes and sensitive and high-speed digital cameras have driven renewed interest in WFOM. To facilitate the wider adoption and standardization of WFOM approaches for neuroscience and neurovascular coupling research, we provide here an overview of the basic principles of WFOM, considerations for implementation of wide-field fluorescence imaging of neural activity, spectroscopic analysis and interpretation of results. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’. PMID:27574312

  18. Chronic monitoring of lower urinary tract activity via a sacral dorsal root ganglia interface

    Science.gov (United States)

    Khurram, Abeer; Ross, Shani E.; Sperry, Zachariah J.; Ouyang, Aileen; Stephan, Christopher; Jiman, Ahmad A.; Bruns, Tim M.

    2017-06-01

    Objective. Our goal is to develop an interface that integrates chronic monitoring of lower urinary tract (LUT) activity with stimulation of peripheral pathways. Approach. Penetrating microelectrodes were implanted in sacral dorsal root ganglia (DRG) of adult male felines. Peripheral electrodes were placed on or in the pudendal nerve, bladder neck and near the external urethral sphincter. Supra-pubic bladder catheters were implanted for saline infusion and pressure monitoring. Electrode and catheter leads were enclosed in an external housing on the back. Neural signals from microelectrodes and bladder pressure of sedated or awake-behaving felines were recorded under various test conditions in weekly sessions. Electrodes were also stimulated to drive activity. Main results. LUT single- and multi-unit activity was recorded for 4-11 weeks in four felines. As many as 18 unique bladder pressure single-units were identified in each experiment. Some channels consistently recorded bladder afferent activity for up to 41 d, and we tracked individual single-units for up to 23 d continuously. Distension-evoked and stimulation-driven (DRG and pudendal) bladder emptying was observed, during which LUT sensory activity was recorded. Significance. This chronic implant animal model allows for behavioral studies of LUT neurophysiology and will allow for continued development of a closed-loop neuroprosthesis for bladder control.

  19. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  20. Use of neural networks for monitoring surface water quality changes in a neotropical urban stream.

    Science.gov (United States)

    da Costa, Andréa Oliveira Souza; Silva, Priscila Ferreira; Sabará, Millôr Godoy; da Costa, Esly Ferreira

    2009-08-01

    This paper reports the using of neural networks for water quality analysis in a tropical urban stream before (2002) and after sewerage building and the completion of point-source control-based sanitation program (2003). Mathematical modeling divided water quality data in two categories: (a) input of some in situ water quality variables (temperature, pH, O2 concentration, O2 saturation and electrical conductivity) and (b) water chemical composition (N-NO2(-); N-NO3(-); N-NH4(+) Total-N; P-PO4(3-); K+; Ca2+; Mg+2; Cu2+; Zn2+ and Fe+3) as the output from tested models. Stream water data come from fortnightly sampling in five points along the Ipanema stream (Southeast Brazil, Minas Gerais state) plus two points downstream and upstream Ipanema discharge into Doce River. Once the best models are consistent with variables behavior we suggest that neural networking shows potential as a methodology to enhance guidelines for urban streams restoration, conservation and management.

  1. Activity monitoring in sleep research, medicine and psychopharmacology.

    Science.gov (United States)

    Klösch, G; Gruber, G; Anderer, P; Saletu, B

    2001-04-17

    Motor activity as a diagnostic parameter has become an important feature in many fields of medicine and psychology. The concept of mobility and immobility implies the assumption that mental and behaviour disorders involve abnormal activity that can be measured to characterise the disorder itself, to diagnose its presence and to document the impact of treatment. In sleep research, activity monitoring by wrist actigraphs has proven its usefulness as an efficient method to assess the rest-activity cycle over long time periods and to estimate sleep-related features such as sleep efficiency and total sleep time. But like many other techniques and devices, activity monitoring has some limitations and drawbacks. This paper describes the basic features of wrist actigraphy in measuring nocturnal and daytime motor activity.

  2. An overview of existing raptor contaminant monitoring activities in Europe.

    Science.gov (United States)

    Gómez-Ramírez, P; Shore, R F; van den Brink, N W; van Hattum, B; Bustnes, J O; Duke, G; Fritsch, C; García-Fernández, A J; Helander, B O; Jaspers, V; Krone, O; Martínez-López, E; Mateo, R; Movalli, P; Sonne, C

    2014-06-01

    Biomonitoring using raptors as sentinels can provide early warning of the potential impacts of contaminants on humans and the environment and also a means of tracking the success of associated mitigation measures. Examples include detection of heavy metal-induced immune system impairment, PCB-induced altered reproductive impacts, and toxicity associated with lead in shot game. Authorisation of such releases and implementation of mitigation is now increasingly delivered through EU-wide directives but there is little established pan-European monitoring to quantify outcomes. We investigated the potential for EU-wide coordinated contaminant monitoring using raptors as sentinels. We did this using a questionnaire to ascertain the current scale of national activity across 44 European countries. According to this survey, there have been 52 different contaminant monitoring schemes with raptors over the last 50years. There were active schemes in 15 (predominantly western European) countries and 23 schemes have been running for >20years; most monitoring was conducted for >5years. Legacy persistent organic compounds (specifically organochlorine insecticides and PCBs), and metals/metalloids were monitored in most of the 15 countries. Fungicides, flame retardants and anticoagulant rodenticides were also relatively frequently monitored (each in at least 6 countries). Common buzzard (Buteo buteo), common kestrel (Falco tinnunculus), golden eagle (Aquila chrysaetos), white-tailed sea eagle (Haliaeetus albicilla), peregrine falcon (Falco peregrinus), tawny owl (Strix aluco) and barn owl (Tyto alba) were most commonly monitored (each in 6-10 countries). Feathers and eggs were most widely analysed although many schemes also analysed body tissues. Our study reveals an existing capability across multiple European countries for contaminant monitoring using raptors. However, coordination between existing schemes and expansion of monitoring into Eastern Europe is needed. This would enable

  3. An investigation of the relationship between activation of a social cognitive neural network and social functioning.

    Science.gov (United States)

    Pinkham, Amy E; Hopfinger, Joseph B; Ruparel, Kosha; Penn, David L

    2008-07-01

    Previous work examining the neurobiological substrates of social cognition in healthy individuals has reported modulation of a social cognitive network such that increased activation of the amygdala, fusiform gyrus, and superior temporal sulcus are evident when individuals judge a face to be untrustworthy as compared with trustworthy. We examined whether this pattern would be present in individuals with schizophrenia who are known to show reduced activation within these same neural regions when processing faces. Additionally, we sought to determine how modulation of this social cognitive network may relate to social functioning. Neural activation was measured using functional magnetic resonance imaging with blood oxygenation level dependent contrast in 3 groups of individuals--nonparanoid individuals with schizophrenia, paranoid individuals with schizophrenia, and healthy controls--while they rated faces as either trustworthy or untrustworthy. Analyses of mean percent signal change extracted from a priori regions of interest demonstrated that both controls and nonparanoid individuals with schizophrenia showed greater activation of this social cognitive network when they rated a face as untrustworthy relative to trustworthy. In contrast, paranoid individuals did not show a significant difference in levels of activation based on how they rated faces. Further, greater activation of this social cognitive network to untrustworthy faces was significantly and positively correlated with social functioning. These findings indicate that impaired modulation of neural activity while processing social stimuli may underlie deficits in social cognition and social dysfunction in schizophrenia.

  4. Active Sites Environmental Monitoring Program: Mid-FY 1991 report

    Energy Technology Data Exchange (ETDEWEB)

    Ashwood, T.L.; Wickliff, D.S.; Morrissey, C.M.

    1991-10-01

    This report summarizes the activities of the Active Sites Environmental Monitoring Program (ASEMP) from October 1990 through March 1991. The ASEMP was established in 1989 by Solid Waste Operations and the Environmental Sciences Division to provide early detection and performance monitoring at active low-level radioactive waste (LLW) disposal sites in Solid Waste Storage Area (SWSA) 6 and transuranic (TRU) waste storage sites in SWSA 5 as required by chapters II and III of US Department of Energy Order 5820.2A. Monitoring results continue to demonstrate the no LLW is being leached from the storage vaults on the tumulus pads. Loading of vaults on Tumulus II began during this reporting period and 115 vaults had been loaded by the end of March 1991.

  5. Gain control of gamma frequency activation by a novel feed forward disinhibitory loop: implications for normal and epileptic neural activity

    Directory of Open Access Journals (Sweden)

    Zeinab eBirjandian

    2013-11-01

    Full Text Available The inhibition of excitatory (pyramidal neurons directly dampens their activity resulting in a suppression of neural network output. The inhibition of inhibitory cells is more complex. Inhibitory drive is known to gate neural network synchrony, but there is also a widely held view that it may augment excitability by reducing inhibitory cell activity, a process termed disinhibition. Surprisingly, however, disinhibition has never been demonstrated to be an important mechanism that augments or drives the activity of excitatory neurons in a functioning neural circuit. Using voltage sensitive dye imaging (VSDI we show that 20-80 Hz stimulus trains, (beta-gamma activation, of the olfactory cortex pyramidal cells in layer II leads to a subsequent reduction in inhibitory interneuron activity that augments the efficacy of the initial stimulus. This disinhibition occurs with a lag of about 150-250 ms after the initial excitation of the layer 2 pyramidal cell layer. In addition activation of the endopiriform nucleus also arises just before the disinhibitory phase with a lag of about 40-80 ms. Preventing the spread of action potentials from layer II stopped the excitation of the endopiriform nucleus, abolished the disinhibitory activity and reduced the excitation of layer II cells. After the induction of experimental epilepsy the disinhibition was more intense with a concomitant increase in excitatory cell activity. Our observations provide the first evidence of feed forward disinhibition loop that augments excitatory neurotransmission, a mechanism that could play an important role in the development of epileptic seizures.

  6. Neural mass modeling of power-line magnetic fields effects on brain activity

    Directory of Open Access Journals (Sweden)

    Julien eModolo

    2013-04-01

    Full Text Available Neural mass models are an appropriate framework to study brain activity, combining a high degree of biological realism while being mathematically tractable. These models have been used, with a certain success, to simulate brain electric (electroencephalography, EEG and metabolic (functional magnetic resonance imaging, fMRI activity. However, concrete applications of neural mass models have remained limited to date. Motivated by experimental results obtained in humans, we propose in this paper a neural mass model designed to study the interaction between power-line magnetic fields (60 Hz in North America and brain activity. The model includes pyramidal cells; dendrite-projecting, slow GABAergic neurons; soma-projecting, fast GABAergic neurons; and glutamatergic interneurons. A simple phenomenological model of interaction between the induced electric field and neuron membranes is also considered, along with a model of post-synaptic calcium concentration and associated changes in synaptic weights Simulated EEG signals are produced in a simple protocol, both in the absence and presence of a 60 Hz magnetic field. These results are discussed based on results obtained previously in humans. Notably, results highlight that 1 EEG alpha (8-12 Hz power can be modulated by weak membrane depolarizations induced by the exposure; 2 the level of input noise has a significant impact on EEG alpha power modulation; and 3 neural mass network size results in a different alpha rhythm modulation than when an individual neural mass is considered. Results obtained from the model shed new light on the effects of power-line magnetic fields on brain activity, and will provide guidance in future human experiments. This may represent a valuable contribution to international regulation agencies setting guidelines on magnetic field values to which the general public and workers can be exposed.

  7. Prediction Model of Antibacterial Activities for Inorganic Antibacterial Agents Based on Artificial Neural Networks

    Institute of Scientific and Technical Information of China (English)

    刘雪峰; 张利; 涂铭旌

    2004-01-01

    Quantitatively evaluation of antibacterial activities of inorganic antibacterial agents is an urgent problem to be solved. Using experimental data by an orthogonal design, a prediction model of the relation between conditions of preparing inorganic antibacterial agents and their antibacterial activities has been developed. This is accomplished by introducing BP artificial neural networks in the study of inorganic antibacterial agents..It provides a theoretical support for the development and research on inorganic antibacterial agents.

  8. Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks

    OpenAIRE

    Vicente, Henrique; Roseiro, José C.; Arteiro, José M.; Neves, José; Caldeira, A. Teresa

    2013-01-01

    Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to the chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria, isolated from Quercus suber. Artificial Neural Networks were used to maximize the percentage of inhibition triggered by antifungal activity of bioactive compounds p...

  9. Constructive feedforward neural networks using hermite polynomial activation functions.

    Science.gov (United States)

    Ma, Liying; Khorasani, K

    2005-07-01

    In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.

  10. Dynamic neural activity during stress signals resilient coping

    OpenAIRE

    Sinha, Rajita; Lacadie, Cheryl M; Constable, R. Todd; Seo, Dongju

    2016-01-01

    We live in a time of increasing terror, stress, and trauma, and yet humans show a remarkable ability to cope under high stress states. How the brain supports such active resilient coping is not well-understood. Findings showed high stress levels are accompanied by dynamic brain signals in circuits representing the stress reaction, adaptation, and behavioral control responses. In addition, a ventromedial prefrontal cortical region showed initial decreases in brain activation, but then mobilize...

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

    Science.gov (United States)

    Zhang, Sheng; Li, Chiang-Shan Ray

    2010-01-15

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

  12. Context-dependent olfactory learning monitored by activities of salivary neurons in cockroaches.

    Science.gov (United States)

    Matsumoto, Chihiro Sato; Matsumoto, Yukihisa; Watanabe, Hidehiro; Nishino, Hiroshi; Mizunami, Makoto

    2012-01-01

    Context-dependent discrimination learning, a sophisticated form of nonelemental associative learning, has been found in many animals, including insects. The major purpose of this research is to establish a method for monitoring this form of nonelemental learning in rigidly restrained insects for investigation of underlying neural mechanisms. We report context-dependent olfactory learning (occasion-setting problem solving) of salivation, which can be monitored as activity changes of salivary neurons in immobilized cockroaches, Periplaneta americana. A group of cockroaches was trained to associate peppermint odor (conditioned stimulus, CS) with sucrose solution reward (unconditioned stimulus, US) while vanilla odor was presented alone without pairing with the US under a flickering light condition (1.0 Hz) and also trained to associate vanilla odor with sucrose reward while peppermint odor was presented alone under a steady light condition. After training, the responses of salivary neurons to the rewarded peppermint odor were significantly greater than those to the unrewarded vanilla odor under steady illumination and those to the rewarded vanilla odor was significantly greater than those to the unrewarded peppermint odor in the presence of flickering light. Similar context-dependent responses were observed in another group of cockroaches trained with the opposite stimulus arrangement. This study demonstrates context-dependent olfactory learning of salivation for the first time in any vertebrate and invertebrate species, which can be monitored by activity changes of salivary neurons in restrained cockroaches. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Endogenous testosterone levels are associated with neural activity in men with schizophrenia during facial emotion processing.

    Science.gov (United States)

    Ji, Ellen; Weickert, Cynthia Shannon; Lenroot, Rhoshel; Catts, Stanley V; Vercammen, Ans; White, Christopher; Gur, Raquel E; Weickert, Thomas W

    2015-06-01

    Growing evidence suggests that testosterone may play a role in the pathophysiology of schizophrenia given that testosterone has been linked to cognition and negative symptoms in schizophrenia. Here, we determine the extent to which serum testosterone levels are related to neural activity in affective processing circuitry in men with schizophrenia. Functional magnetic resonance imaging was used to measure blood-oxygen-level-dependent signal changes as 32 healthy controls and 26 people with schizophrenia performed a facial emotion identification task. Whole brain analyses were performed to determine regions of differential activity between groups during processing of angry versus non-threatening faces. A follow-up ROI analysis using a regression model in a subset of 16 healthy men and 16 men with schizophrenia was used to determine the extent to which serum testosterone levels were related to neural activity. Healthy controls displayed significantly greater activation than people with schizophrenia in the left inferior frontal gyrus (IFG). There was no significant difference in circulating testosterone levels between healthy men and men with schizophrenia. Regression analyses between activation in the IFG and circulating testosterone levels revealed a significant positive correlation in men with schizophrenia (r=.63, p=.01) and no significant relationship in healthy men. This study provides the first evidence that circulating serum testosterone levels are related to IFG activation during emotion face processing in men with schizophrenia but not in healthy men, which suggests that testosterone levels modulate neural processes relevant to facial emotion processing that may interfere with social functioning in men with schizophrenia.

  14. Artificial Neural Network and Rough Set for HV Bushings Condition Monitoring

    CERN Document Server

    Mpanza, LJ

    2011-01-01

    Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.

  15. Artificial Neural Network Algorithm for Condition Monitoring of DC-link Capacitors Based on Capacitance Estimation

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Wang, Huai; Gadalla, Brwene Salah Abdelkarim

    2015-01-01

    In power electronic converters, reliability of DC-link capacitors is one of the critical issues. The estimation of their health status as an application of condition monitoring have been an attractive subject for industrial field and hence for the academic research filed as well. More reliable...... solutions are required to be adopted by the industry applications in which usage of extra hardware, increased cost, and low estimation accuracy are the main challenges. Therefore, development of new condition monitoring methods based on software solutions could be the new era that covers the aforementioned...

  16. Neural-Net Based Optical NDE Method for Structural Health Monitoring

    Science.gov (United States)

    Decker, Arthur J.; Weiland, Kenneth E.

    2003-01-01

    This paper answers some performance and calibration questions about a non-destructive-evaluation (NDE) procedure that uses artificial neural networks to detect structural damage or other changes from sub-sampled characteristic patterns. The method shows increasing sensitivity as the number of sub-samples increases from 108 to 6912. The sensitivity of this robust NDE method is not affected by noisy excitations of the first vibration mode. A calibration procedure is proposed and demonstrated where the output of a trained net can be correlated with the outputs of the point sensors used for vibration testing. The calibration procedure is based on controlled changes of fastener torques. A heterodyne interferometer is used as a displacement sensor for a demonstration of the challenges to be handled in using standard point sensors for calibration.

  17. Persistent neural activity in the prefrontal cortex: a mechanism by which BDNF regulates working memory?

    Science.gov (United States)

    Galloway, Evan M; Woo, Newton H; Lu, Bai

    2008-01-01

    Working memory is the ability to maintain representations of task-relevant information for short periods of time to guide subsequent actions or make decisions. Neurons of the prefrontal cortex exhibit persistent firing during the delay period of working memory tasks. Despite extensive studies, the mechanisms underlying this persistent neural activity remain largely obscure. The neurotransmitter systems of dopamine, NMDA, and GABA have been implicated, but further investigations are necessary to establish their precise roles and relationships. Recent research has suggested a new component: brain-derived neurotrophic factor (BDNF) and its high-affinity receptor, TrkB. We review the research on persistent activity and suggest that BDNF/TrkB signaling in a distinct class of interneurons plays an important role in organizing persistent neural activity at the single-neuron and network levels.

  18. Video-based convolutional neural networks for activity recognition from robot-centric videos

    Science.gov (United States)

    Ryoo, M. S.; Matthies, Larry

    2016-05-01

    In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

  19. Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management

    Directory of Open Access Journals (Sweden)

    Nguyen Kim Quoc

    2015-08-01

    Full Text Available The bottleneck control by active queue management mechanisms at network nodes is essential. In recent years, some researchers have used fuzzy argument to improve the active queue management mechanisms to enhance the network performance. However, the projects using the fuzzy controller depend heavily on professionals and their parameters cannot be updated according to changes in the network, so the effectiveness of this mechanism is not high. Therefore, we propose a model combining the fuzzy controller with neural network (FNN to overcome the limitations above. Results of the training of the neural networks will find the optimal parameters for the adaptive fuzzy controller well to changes of the network. This improves the operational efficiency of the active queue management mechanisms at network nodes.

  20. Sociocultural patterning of neural activity during self-reflection

    DEFF Research Database (Denmark)

    Ma, Yina; Bang, Dan; Wang, Chenbo

    2014-01-01

    ) in Chinese than in Danish participants. Moreover, the group difference in TPJ activity was mediated by a measure of a cultural value (i.e., interdependence of self-construal). Our findings suggest that individuals in different sociocultural contexts may learn and/or adopt distinct strategies for self...

  1. Concurrent multitasking : From neural activity to human cognition

    NARCIS (Netherlands)

    Nijboer, Menno

    2016-01-01

    Multitasking has become an important part of our daily lives. This delicate juggling act between several activities occurs when people drive, when they are working, and even when they should be paying attention in the classroom. While multitasking is typically considered as something to avoid, there

  2. Neural activities during affective processing in people with Alzheimer's disease

    NARCIS (Netherlands)

    Lee, Tatia M. C.; Sun, Delin; Leung, Mei-Kei; Chu, Leung-Wing; Keysers, Christian

    2013-01-01

    This study examined brain activities in people with Alzheimer's disease when viewing happy, sad, and fearful facial expressions of others. A functional magnetic resonance imaging and a voxel-based morphometry methodology together with a passive viewing of emotional faces paradigm were employed to co

  3. Dampened neural activity and abolition of epileptic-like activity in cortical slices by active ingredients of spices.

    Science.gov (United States)

    Pezzoli, Maurizio; Elhamdani, Abdeladim; Camacho, Susana; Meystre, Julie; González, Stephanie Michlig; le Coutre, Johannes; Markram, Henry

    2014-10-31

    Active ingredients of spices (AIS) modulate neural response in the peripheral nervous system, mainly through interaction with TRP channel/receptors. The present study explores how different AIS modulate neural response in layer 5 pyramidal neurons of S1 neocortex. The AIS tested are agonists of TRPV1/3, TRPM8 or TRPA1. Our results demonstrate that capsaicin, eugenol, menthol, icilin and cinnamaldehyde, but not AITC dampen the generation of APs in a voltage- and time-dependent manner. This effect was further tested for the TRPM8 ligands in the presence of a TRPM8 blocker (BCTC) and on TRPM8 KO mice. The observable effect was still present. Finally, the influence of the selected AIS was tested on in vitro gabazine-induced seizures. Results coincide with the above observations: except for cinnamaldehyde, the same AIS were able to reduce the number, duration of the AP bursts and increase the concentration of gabazine needed to elicit them. In conclusion, our data suggests that some of these AIS can modulate glutamatergic neurons in the brain through a TRP-independent pathway, regardless of whether the neurons are stimulated intracellularly or by hyperactive microcircuitry.

  4. Continuous gravity monitoring of geothermal activity; Renzoku juryoku sokutei ni yoru chinetsu katsudo no monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Sugihara, M. [Geological Survey of Japan, Tsukuba (Japan)

    1997-05-27

    To clarify the geothermal activity in the geothermal fields in New Zealand, gravity monitoring was conducted using SCINTREX automatic gravimeter. The measurements were conducted between the end of January and the beginning of March, 1996. Firstly, continuous monitoring was conducted at the standard point for about ten days, and the tidal components were estimated from the records. After that, continuous monitoring was conducted at Waimangu area for several days. Continuous monitoring was repeated at the standard point, again. At the Waimangu area, three times of changes in the pulse-shape amplitude of 0.01 mgal having a width of several hours were observed. For the SCINTREX gravimeter, the inclination of gravimeter is also recorded in addition to the change of gravity. During the monitoring, the gravimeter was also inclined with the changes of gravity. This inclination was useful not only for the correction of gravity measured, but also for evaluating the ground fluctuation due to the underground pressure source. It is likely that the continuous gravity monitoring is the relatively conventional technique which is effective for prospecting the change of geothermal reservoir. 2 figs.

  5. The affective impact of financial skewness on neural activity and choice.

    Directory of Open Access Journals (Sweden)

    Charlene C Wu

    Full Text Available Few finance theories consider the influence of "skewness" (or large and asymmetric but unlikely outcomes on financial choice. We investigated the impact of skewed gambles on subjects' neural activity, self-reported affective responses, and subsequent preferences using functional magnetic resonance imaging (FMRI. Neurally, skewed gambles elicited more anterior insula activation than symmetric gambles equated for expected value and variance, and positively skewed gambles also specifically elicited more nucleus accumbens (NAcc activation than negatively skewed gambles. Affectively, positively skewed gambles elicited more positive arousal and negatively skewed gambles elicited more negative arousal than symmetric gambles equated for expected value and variance. Subjects also preferred positively skewed gambles more, but negatively skewed gambles less than symmetric gambles of equal expected value. Individual differences in both NAcc activity and positive arousal predicted preferences for positively skewed gambles. These findings support an anticipatory affect account in which statistical properties of gambles--including skewness--can influence neural activity, affective responses, and ultimately, choice.

  6. Shape perception simultaneously up- and downregulates neural activity in the primary visual cortex.

    Science.gov (United States)

    Kok, Peter; de Lange, Floris P

    2014-07-07

    An essential part of visual perception is the grouping of local elements (such as edges and lines) into coherent shapes. Previous studies have shown that this grouping process modulates neural activity in the primary visual cortex (V1) that is signaling the local elements [1-4]. However, the nature of this modulation is controversial. Some studies find that shape perception reduces neural activity in V1 [2, 5, 6], while others report increased V1 activity during shape perception [1, 3, 4, 7-10]. Neurocomputational theories that cast perception as a generative process [11-13] propose that feedback connections carry predictions (i.e., the generative model), while feedforward connections signal the mismatch between top-down predictions and bottom-up inputs. Within this framework, the effect of feedback on early visual cortex may be either enhancing or suppressive, depending on whether the feedback signal is met by congruent bottom-up input. Here, we tested this hypothesis by quantifying the spatial profile of neural activity in V1 during the perception of illusory shapes using population receptive field mapping. We find that shape perception concurrently increases neural activity in regions of V1 that have a receptive field on the shape but do not receive bottom-up input and suppresses activity in regions of V1 that receive bottom-up input that is predicted by the shape. These effects were not modulated by task requirements. Together, these findings suggest that shape perception changes lower-order sensory representations in a highly specific and automatic manner, in line with theories that cast perception in terms of hierarchical generative models.

  7. EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Quan Liu

    2015-01-01

    Full Text Available In order to build a reliable index to monitor the depth of anesthesia (DOA, many algorithms have been proposed in recent years, one of which is sample entropy (SampEn, a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN model using bispectral index (BIS or expert assessment of conscious level (EACL as the target. To test the performance of the new index’s sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD. The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

  8. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces.

    Science.gov (United States)

    Panzeri, Stefano; Safaai, Houman; De Feo, Vito; Vato, Alessandro

    2016-01-01

    Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  9. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  10. Alternative Sensor System and MLP Neural Network for Vehicle Pedal Activity Estimation

    Directory of Open Access Journals (Sweden)

    Ahmed M. Wefky

    2010-04-01

    Full Text Available It is accepted that the activity of the vehicle pedals (i.e., throttle, brake, clutch reflects the driver’s behavior, which is at least partially related to the fuel consumption and vehicle pollutant emissions. This paper presents a solution to estimate the driver activity regardless of the type, model, and year of fabrication of the vehicle. The solution is based on an alternative sensor system (regime engine, vehicle speed, frontal inclination and linear acceleration that reflects the activity of the pedals in an indirect way, to estimate that activity by means of a multilayer perceptron neural network with a single hidden layer.

  11. Optimal Recognition Method of Human Activities Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Oniga Stefan

    2015-12-01

    Full Text Available The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

  12. Environmental layout complexity affects neural activity during navigation in humans.

    Science.gov (United States)

    Slone, Edward; Burles, Ford; Iaria, Giuseppe

    2016-05-01

    Navigating large-scale surroundings is a fundamental ability. In humans, it is commonly assumed that navigational performance is affected by individual differences, such as age, sex, and cognitive strategies adopted for orientation. We recently showed that the layout of the environment itself also influences how well people are able to find their way within it, yet it remains unclear whether differences in environmental complexity are associated with changes in brain activity during navigation. We used functional magnetic resonance imaging to investigate how the brain responds to a change in environmental complexity by asking participants to perform a navigation task in two large-scale virtual environments that differed solely in interconnection density, a measure of complexity defined as the average number of directional choices at decision points. The results showed that navigation in the simpler, less interconnected environment was faster and more accurate relative to the complex environment, and such performance was associated with increased activity in a number of brain areas (i.e. precuneus, retrosplenial cortex, and hippocampus) known to be involved in mental imagery, navigation, and memory. These findings provide novel evidence that environmental complexity not only affects navigational behaviour, but also modulates activity in brain regions that are important for successful orientation and navigation.

  13. Neural-activity mapping of memory-based dominance in the crow: neural networks integrating individual discrimination and social behaviour control.

    Science.gov (United States)

    Nishizawa, K; Izawa, E-I; Watanabe, S

    2011-12-01

    Large-billed crows (Corvus macrorhynchos), highly social birds, form stable dominance relationships based on the memory of win/loss outcomes of first encounters and on individual discrimination. This socio-cognitive behaviour predicts the existence of neural mechanisms for integration of social behaviour control and individual discrimination. This study aimed to elucidate the neural substrates of memory-based dominance in crows. First, the formation of dominance relationships was confirmed between males in a dyadic encounter paradigm. Next, we examined whether neural activities in 22 focal nuclei of pallium and subpallium were correlated with social behaviour and stimulus familiarity after exposure to dominant/subordinate familiar individuals and unfamiliar conspecifics. Neural activity was determined by measuring expression level of the immediate-early-gene (IEG) protein Zenk. Crows displayed aggressive and/or submissive behaviour to opponents less frequently but more discriminatively in subsequent encounters, suggesting stable dominance based on memory, including win/loss outcomes of the first encounters and individual discrimination. Neural correlates of aggressive and submissive behaviour were found in limbic subpallium including septum, bed nucleus of the striae terminalis (BST), and nucleus taeniae of amygdala (TnA), but also those to familiarity factor in BST and TnA. Contrastingly, correlates of social behaviour were little in pallium and those of familiarity with exposed individuals were identified in hippocampus, medial meso-/nidopallium, and ventro-caudal nidopallium. Given the anatomical connection and neural response patterns of the focal nuclei, neural networks connecting pallium and limbic subpallium via hippocampus could be involved in the integration of individual discrimination and social behaviour control in memory-based dominance in the crow.

  14. Embedded Triboelectric Active Sensors for Real-Time Pneumatic Monitoring.

    Science.gov (United States)

    Fu, Xian Peng; Bu, Tian Zhao; Xi, Feng Ben; Cheng, Ting Hai; Zhang, Chi; Wang, Zhong Lin

    2017-09-20

    Pneumatic monitoring sensors have great demands for power supply in cylinder systems. Here, we present an embedded sliding triboelectric nanogenerator (TENG) in air cylinder as active sensors for position and velocity monitoring. The embedded TENG is composed of a circular poly(tetrafluoroethylene) polymer and a triangular copper electrode. The working mechanism as triboelectric active sensors and electric output performance are systematically investigated. By integrating into the pneumatic system, the embedded triboelectric active sensors have been used for real-time air pressure/flow monitoring and energy storage. Air pressures are measured from 0.04 to 0.12 MPa at a step of 0.02 MPa with a sensitivity of 49.235 V/MPa, as well as airflow from 50 to 250 L/min at a step of 50 L/min with a sensitivity of 0.002 μA·min/L. This work has first demonstrated triboelectric active sensors for pneumatic monitoring and may promote the development of TENG in intelligent pneumatic system.

  15. Applied research of environmental monitoring using instrumental neutron activation analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Young Sam; Moon, Jong Hwa; Chung, Young Ju

    1997-08-01

    This technical report is written as a guide book for applied research of environmental monitoring using Instrumental Neutron Activation Analysis. The contents are as followings; sampling and sample preparation as a airborne particulate matter, analytical methodologies, data evaluation and interpretation, basic statistical methods of data analysis applied in environmental pollution studies. (author). 23 refs., 7 tabs., 9 figs.

  16. Leisure Time Activities, Parental Monitoring and Drunkenness in Adolescents

    NARCIS (Netherlands)

    Tomcikova, Zuzana; Veselska, Zuzana; Geckova, Andrea Madarasova; van Dijk, Jitse P.; Reijneveld, Sijmen A.

    2013-01-01

    Background: The aim of this cross-sectional study was to explore the association between adolescent drunkenness and participation in risky leisure time activities and parental monitoring. Methods: A sample of 3,694 Slovak elementary school students (mean age 14.5 years; 49.0% males) was assessed for

  17. Leisure time activities, parental monitoring and drunkenness in adolescents

    NARCIS (Netherlands)

    Tomcikova, Z.; Veselska, Z.; Madarasova Geckova, A.; van Dijk, J.P.; Reijneveld, S.A.

    2012-01-01

    Background: The aim of this cross-sectional study was to explore the association between adolescent drunkenness and participation in risky leisure time activities and parental monitoring. Methods: A sample of 3,694 Slovak elementary school students (mean age 14.5 years; 49.0% males) was assessed for

  18. Permanent Infrasound Monitoring of Active Volcanoes in Ecuador

    Science.gov (United States)

    Ruiz, M. C.; Yepes, H. A.; Steele, A.; Segovia, M.; Vaca, S.; Cordova, A.; Enriquez, W.; Vaca, M.; Ramos, C.; Arrais, S.; Tapa, I.; Mejia, F.; Macias, C.

    2013-12-01

    Since 2006, infrasound monitoring has become a permanent tool for observing, analyzing and understanding volcanic activity in Ecuador. Within the framework of a cooperative project between the Japanese International Cooperation Agency (JICA) and the Instituto Geofísico to enhance volcano monitoring capabilities within the country, 10 infrasound sensors were deployed in conjunction with broadband seismic stations at Cotopaxi and Tungurahua volcanoes. Each station comprises 1 ACO microphone (model 7144) and an amplifier with a flat response down to 0.1 Hz. At Tungurahua, between July 2006 and July 2013, the network recorded more than 5,500 explosion events with peak-to-peak pressure amplitudes larger than 45 Pa at station Mason (BMAS) which is located ~ 5.5 km from the active crater. This includes 3 explosions with pressure amplitudes larger than 1,000 Pa and which all have exhibited clear shock wave components. Two seismic and infrasound arrays were also installed in 2006 under the Acoustic Surveillance for Hazardous Eruptions (ASHE) project, used in volcano monitoring at Tungurahua, Sangay, and Reventador. This venture was led by the Geological Survey of Canada and the University of Hawaii. Through the SENESCYT-IGEPN project, the Instituto Geofísico is currently installing a regional network of MB2005 microbarometers with the aim to enhance monitoring of active and potentially active volcanoes that include Reventador, Guagua Pichincha, Chimborazo, Antisana, Sangay, and Volcán Chico in the Galapagos Islands. Through the infrasound monitoring station at Volcán Chico it is also possible to extend observations to any activity initiated from Sierra Negra, Fernandina, Cerro Azul, and Alcedo volcanoes. During the past decade, a series of temporary acoustic arrays have also been deployed around Ecuador's most active volcanoes, helping to aid in short term volcanic monitoring and/or used in a series of research projects aimed at better understanding volcanic systems

  19. Primary and secondary rewards differentially modulate neural activity dynamics during working memory.

    Directory of Open Access Journals (Sweden)

    Stefanie M Beck

    Full Text Available BACKGROUND: Cognitive control and working memory processes have been found to be influenced by changes in motivational state. Nevertheless, the impact of different motivational variables on behavior and brain activity remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: The current study examined the impact of incentive category by varying on a within-subjects basis whether performance during a working memory task was reinforced with either secondary (monetary or primary (liquid rewards. The temporal dynamics of motivation-cognition interactions were investigated by employing an experimental design that enabled isolation of sustained and transient effects. Performance was dramatically and equivalently enhanced in each incentive condition, whereas neural activity dynamics differed between incentive categories. The monetary reward condition was associated with a tonic activation increase in primarily right-lateralized cognitive control regions including anterior prefrontal cortex (PFC, dorsolateral PFC, and parietal cortex. In the liquid condition, the identical regions instead showed a shift in transient activation from a reactive control pattern (primary probe-based activation during no-incentive trials to proactive control (primary cue-based activation during rewarded trials. Additionally, liquid-specific tonic activation increases were found in subcortical regions (amygdala, dorsal striatum, nucleus accumbens, indicating an anatomical double dissociation in the locus of sustained activation. CONCLUSIONS/SIGNIFICANCE: These different activation patterns suggest that primary and secondary rewards may produce similar behavioral changes through distinct neural mechanisms of reinforcement. Further, our results provide new evidence for the flexibility of cognitive control, in terms of the temporal dynamics of activation.

  20. Social Status-Dependent Shift in Neural Circuit Activation Affects Decision Making.

    Science.gov (United States)

    Miller, Thomas H; Clements, Katie; Ahn, Sungwoo; Park, Choongseok; Hye Ji, Eoon; Issa, Fadi A

    2017-02-22

    In a social group, animals make behavioral decisions that fit their social ranks. These behavioral choices are dependent on the various social cues experienced during social interactions. In vertebrates, little is known of how social status affects the underlying neural mechanisms regulating decision-making circuits that drive competing behaviors. Here, we demonstrate that social status in zebrafish (Danio rerio) influences behavioral decisions by shifting the balance in neural circuit activation between two competing networks (escape and swim). We show that socially dominant animals enhance activation of the swim circuit. Conversely, social subordinates display a decreased activation of the swim circuit, but an enhanced activation of the escape circuit. In an effort to understand how social status mediates these effects, we constructed a neurocomputational model of the escape and swim circuits. The model replicates our findings and suggests that social status-related shift in circuit dynamics could be mediated by changes in the relative excitability of the escape and swim networks. Together, our results reveal that changes in the excitabilities of the Mauthner command neuron for escape and the inhibitory interneurons that regulate swimming provide a cellular mechanism for the nervous system to adapt to changes in social conditions by permitting the animal to select a socially appropriate behavioral response.SIGNIFICANCE STATEMENT Understanding how social factors influence nervous system function is of great importance. Using zebrafish as a model system, we demonstrate how social experience affects decision making to enable animals to produce socially appropriate behavior. Based on experimental evidence and computational modeling, we show that behavioral decisions reflect the interplay between competing neural circuits whose activation thresholds shift in accordance with social status. We demonstrate this through analysis of the behavior and neural circuit

  1. Relation of obesity to neural activation in response to food commercials.

    Science.gov (United States)

    Gearhardt, Ashley N; Yokum, Sonja; Stice, Eric; Harris, Jennifer L; Brownell, Kelly D

    2014-07-01

    Adolescents view thousands of food commercials annually, but the neural response to food advertising and its association with obesity is largely unknown. This study is the first to examine how neural response to food commercials differs from other stimuli (e.g. non-food commercials and television show) and to explore how this response may differ by weight status. The blood oxygen level-dependent functional magnetic resonance imaging activation was measured in 30 adolescents ranging from lean to obese in response to food and non-food commercials imbedded in a television show. Adolescents exhibited greater activation in regions implicated in visual processing (e.g. occipital gyrus), attention (e.g. parietal lobes), cognition (e.g. temporal gyrus and posterior cerebellar lobe), movement (e.g. anterior cerebellar cortex), somatosensory response (e.g. postcentral gyrus) and reward [e.g. orbitofrontal cortex and anterior cingulate cortex (ACC)] during food commercials. Obese participants exhibited less activation during food relative to non-food commercials in neural regions implicated in visual processing (e.g. cuneus), attention (e.g. posterior cerebellar lobe), reward (e.g. ventromedial prefrontal cortex and ACC) and salience detection (e.g. precuneus). Obese participants did exhibit greater activation in a region implicated in semantic control (e.g. medial temporal gyrus). These findings may inform current policy debates regarding the impact of food advertising to minors.

  2. Escargot and Scratch regulate neural commitment by antagonizing Notch activity in Drosophila sensory organs.

    Science.gov (United States)

    Ramat, Anne; Audibert, Agnès; Louvet-Vallée, Sophie; Simon, Françoise; Fichelson, Pierre; Gho, Michel

    2016-08-15

    During Notch (N)-mediated binary cell fate decisions, cells adopt two different fates according to the levels of N pathway activation: an Noff-dependent or an Non-dependent fate. How cells maintain these N activity levels over time remains largely unknown. We address this question in the cell lineage that gives rise to the Drosophila mechanosensory organs. In this lineage a primary precursor cell undergoes a stereotyped sequence of oriented asymmetric cell divisions and transits through two neural precursor states before acquiring a neuron identity. Using a combination of genetic and cell biology strategies, we show that Escargot and Scratch, two transcription factors belonging to the Snail superfamily, maintain Noff neural commitment by directly blocking the transcription of N target genes. We propose that Snail factors act by displacing proneural transcription activators from DNA binding sites. As such, Snail factors maintain the Noff state in neural precursor cells by buffering any ectopic variation in the level of N activity. Since Escargot and Scratch orthologs are present in other precursor cells, our findings are fundamental for understanding precursor cell fate acquisition in other systems.

  3. Model for a flexible motor memory based on a self-active recurrent neural network.

    Science.gov (United States)

    Boström, Kim Joris; Wagner, Heiko; Prieske, Markus; de Lussanet, Marc

    2013-10-01

    Using recent recurrent network architecture based on the reservoir computing approach, we propose and numerically simulate a model that is focused on the aspects of a flexible motor memory for the storage of elementary movement patterns into the synaptic weights of a neural network, so that the patterns can be retrieved at any time by simple static commands. The resulting motor memory is flexible in that it is capable to continuously modulate the stored patterns. The modulation consists in an approximately linear inter- and extrapolation, generating a large space of possible movements that have not been learned before. A recurrent network of thousand neurons is trained in a manner that corresponds to a realistic exercising scenario, with experimentally measured muscular activations and with kinetic data representing proprioceptive feedback. The network is "self-active" in that it maintains recurrent flow of activation even in the absence of input, a feature that resembles the "resting-state activity" found in the human and animal brain. The model involves the concept of "neural outsourcing" which amounts to the permanent shifting of computational load from higher to lower-level neural structures, which might help to explain why humans are able to execute learned skills in a fluent and flexible manner without the need for attention to the details of the movement.

  4. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

    Science.gov (United States)

    Grandjean, Bernard; Maier, Marc A

    2017-02-01

    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  5. Category-based induction from similarity of neural activation.

    Science.gov (United States)

    Weber, Matthew J; Osherson, Daniel

    2014-03-01

    The idea that similarity might be an engine of inductive inference dates back at least as far as David Hume. However, Hume's thesis is difficult to test without begging the question, since judgments of similarity may be infected by inferential processes. We present a one-parameter model of category-based induction that generates predictions about arbitrary statements of conditional probability over a predicate and a set of items. The prediction is based on the unconditional probabilities and similarities that characterize that predicate and those items. To test Hume's thesis, we collected brain activation from various regions of the ventral visual stream during a categorization task that did not invite comparison of categories. We then calculated the similarity of those activation patterns using a simple measure of vectorwise similarity and supplied those similarities to the model. The model's outputs correlated well with subjects' judgments of conditional probability. Our results represent a promising first step toward confirming Hume's thesis; similarity, assessed without reference to induction, may well drive inductive inference.

  6. Data Fusion Using Different Activation Functions in Artificial Neural Networks for Vehicular Navigation

    Directory of Open Access Journals (Sweden)

    MALLESWARAN M,

    2010-12-01

    Full Text Available Global positioning System (GPS and Inertial Navigation System (INS data can be integrated together to provide a reliable navigation. GPS/INS data integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and increase in position errors with time for INS. This paper aims to provide GPS/INS data integration utilizing Artificial Neural Network (ANN architecture. This architecture is based on Feed Forward Neural Networks, which generally includes Radial Basis Function (RBF neural network and Back Propagation neural network (BPN. These are systematic methods for training multi-layer artificial networks. The BPN-ANN and RBF-ANN modules are trained to predict the INS position error and provide accurate positioning of the moving vehicle. This paper also compares performance of theGPS/INS data integration system by using different activation function like Bipolar Sigmoidal Function (BPSF, Binary Sigmoidal Function (BISF, Hyperbolic Tangential Function (HTF and Gaussian Function (GF in BPN-ANN and using Gaussian function in RBF-ANN.

  7. Artificial neural networks based controller for glucose monitoring during clamp test.

    Directory of Open Access Journals (Sweden)

    Merav Catalogna

    Full Text Available Insulin resistance (IR is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.

  8. Abnormal Task Modulation of Oscillatory Neural Activity in Schizophrenia

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    Elisa C Dias

    2013-08-01

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

  9. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

    Institute of Scientific and Technical Information of China (English)

    JI Li; WANG XiaoDong; LUO Si; QIN Liang; YANG XvShu; LIU ShuShen; WANG LianSheng

    2008-01-01

    Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a powerful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endocrine disruptors, in this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estrogens (training set). Only nine descriptors calculated solely from the molecular structures of compounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The obtained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

  10. Small-World Connections to Induce Firing Activity and Phase Synchronization in Neural Networks

    Institute of Scientific and Technical Information of China (English)

    QIN Ying-Hua; LUO Xiao-Shu

    2009-01-01

    We investigate how the firing activity and the subsequent phase synchronization of neural networks with small-world topological connections depend on the probability p of adding-links. Network elements are described by two-dimensional map neurons (2DMNs) in a quiescent original state. Neurons burst for a given coupling strength when the topological randomness p increases, which is absent in a regular-lattice neural network. The bursting activity becomes frequent and synchronization of neurons emerges as topological randomness further increases.The maximal firing frequency and phase synchronization appear at a particular value of p. However, if the randomness p further increases, the firing frequency decreases and synchronization is apparently destroyed.

  11. Mutual information and self-control of a fully-connected low-activity neural network

    Science.gov (United States)

    Bollé, D.; Carreta, D. Dominguez

    2000-11-01

    A self-control mechanism for the dynamics of a three-state fully connected neural network is studied through the introduction of a time-dependent threshold. The self-adapting threshold is a function of both the neural and the pattern activity in the network. The time evolution of the order parameters is obtained on the basis of a recently developed dynamical recursive scheme. In the limit of low activity the mutual information is shown to be the relevant parameter in order to determine the retrieval quality. Due to self-control an improvement of this mutual information content as well as an increase of the storage capacity and an enlargement of the basins of attraction are found. These results are compared with numerical simulations.

  12. Individual differences in sensitivity to reward and punishment and neural activity during reward and avoidance learning.

    Science.gov (United States)

    Kim, Sang Hee; Yoon, HeungSik; Kim, Hackjin; Hamann, Stephan

    2015-09-01

    In this functional neuroimaging study, we investigated neural activations during the process of learning to gain monetary rewards and to avoid monetary loss, and how these activations are modulated by individual differences in reward and punishment sensitivity. Healthy young volunteers performed a reinforcement learning task where they chose one of two fractal stimuli associated with monetary gain (reward trials) or avoidance of monetary loss (avoidance trials). Trait sensitivity to reward and punishment was assessed using the behavioral inhibition/activation scales (BIS/BAS). Functional neuroimaging results showed activation of the striatum during the anticipation and reception periods of reward trials. During avoidance trials, activation of the dorsal striatum and prefrontal regions was found. As expected, individual differences in reward sensitivity were positively associated with activation in the left and right ventral striatum during reward reception. Individual differences in sensitivity to punishment were negatively associated with activation in the left dorsal striatum during avoidance anticipation and also with activation in the right lateral orbitofrontal cortex during receiving monetary loss. These results suggest that learning to attain reward and learning to avoid loss are dependent on separable sets of neural regions whose activity is modulated by trait sensitivity to reward or punishment.

  13. Therapeutic Drug Monitoring in the Treatment of Active Tuberculosis

    Directory of Open Access Journals (Sweden)

    Aylin Babalik

    2011-01-01

    Full Text Available Therapeutic drug monitoring ensures optimal dosing while aiming to reduce toxicity. However, due to the high costs and complexity of testing, therapeutic drug monitoring is not routinely used in the treatment of individuals with active tuberculosis, despite the efficacy demonstrated in several randomized trials. This study reviewed data spanning five years regarding the frequency of finding low drug levels in patients with tuberculosis, the dosing adjustments that were required to achieve adequate levels and the factors associated with low drug levels.

  14. Research on image retrieval using deep convolutional neural network combining L1 regularization and PRelu activation function

    Science.gov (United States)

    QingJie, Wei; WenBin, Wang

    2017-06-01

    In this paper, the image retrieval using deep convolutional neural network combined with regularization and PRelu activation function is studied, and improves image retrieval accuracy. Deep convolutional neural network can not only simulate the process of human brain to receive and transmit information, but also contains a convolution operation, which is very suitable for processing images. Using deep convolutional neural network is better than direct extraction of image visual features for image retrieval. However, the structure of deep convolutional neural network is complex, and it is easy to over-fitting and reduces the accuracy of image retrieval. In this paper, we combine L1 regularization and PRelu activation function to construct a deep convolutional neural network to prevent over-fitting of the network and improve the accuracy of image retrieval

  15. Chaotic oscillations in a map-based model of neural activity.

    Science.gov (United States)

    Courbage, M; Nekorkin, V I; Vdovin, L V

    2007-12-01

    We propose a discrete time dynamical system (a map) as a phenomenological model of excitable and spiking-bursting neurons. The model is a discontinuous two-dimensional map. We find conditions under which this map has an invariant region on the phase plane, containing a chaotic attractor. This attractor creates chaotic spiking-bursting oscillations of the model. We also show various regimes of other neural activities (subthreshold oscillations, phasic spiking, etc.) derived from the proposed model.

  16. Human facial neural activities and gesture recognition for machine-interfacing applications

    Directory of Open Access Journals (Sweden)

    Hamedi M

    2011-12-01

    Full Text Available M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transportation Research Alliance, 3Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia (UTM, Johor Bahru, Malaysia; 4College of Innovative Management, Valaya Alongkorn Rajabhat University, Pathum Thani, 5Nanoscale Science and Engineering Research Alliance (N'SERA, Advanced Research Center for Photonics, Faculty of Science, King Mongkut's Institute of Technology Ladkrabang, Bangkok, ThailandAbstract: The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy

  17. A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

    Science.gov (United States)

    Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

    2017-03-01

    The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

  18. Pigeon NCL and NFL neuronal activity represents neural correlates of the sample.

    Science.gov (United States)

    Johnston, Melissa; Anderson, Catrona; Colombo, Michael

    2017-06-01

    Four birds were trained on a delayed matching-to-sample task with common outcomes where correct responses during both red and green trials yielded reward. We recorded neuronal activity from the avian nidopallium caudolaterale, the avian equivalent of the mammalian prefrontal cortex, and the avian nidopallium frontolaterale, a higher-order visual processing region. In both regions we found sustained activity during the delay period of both red and green trials. These findings provide the first evidence that delay activity in the pigeon's nidopallium caudolaterale and nidopallium frontolaterale represent a neural correlate for the to-be-remembered sample stimulus. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  19. Altered spontaneous neural activity in the occipital face area reflects behavioral deficits in developmental prosopagnosia.

    Science.gov (United States)

    Zhao, Yuanfang; Li, Jingguang; Liu, Xiqin; Song, Yiying; Wang, Ruosi; Yang, Zetian; Liu, Jia

    2016-08-01

    Individuals with developmental prosopagnosia (DP) exhibit severe difficulties in recognizing faces and to a lesser extent, also exhibit difficulties in recognizing non-face objects. We used fMRI to investigate whether these behavioral deficits could be accounted for by altered spontaneous neural activity. Two aspects of spontaneous neural activity were measured: the intensity of neural activity in a voxel indexed by the fractional amplitude of spontaneous low-frequency fluctuations (fALFF), and the connectivity of a voxel to neighboring voxels indexed by regional homogeneity (ReHo). Compared with normal adults, both the fALFF and ReHo values within the right occipital face area (rOFA) were significantly reduced in DP subjects. Follow-up studies on the normal adults revealed that these two measures indicated further functional division of labor within the rOFA. The fALFF in the rOFA was positively correlated with behavioral performance in recognition of non-face objects, whereas ReHo in the rOFA was positively correlated with processing of faces. When considered together, the altered fALFF and ReHo within the same region (rOFA) may account for the comorbid deficits in both face and object recognition in DPs, whereas the functional division of labor in these two measures helps to explain the relative independency of deficits in face recognition and object recognition in DP.

  20. A multichannel integrated circuit for electrical recording of neural activity, with independent channel programmability.

    Science.gov (United States)

    Mora Lopez, Carolina; Prodanov, Dimiter; Braeken, Dries; Gligorijevic, Ivan; Eberle, Wolfgang; Bartic, Carmen; Puers, Robert; Gielen, Georges

    2012-04-01

    Since a few decades, micro-fabricated neural probes are being used, together with microelectronic interfaces, to get more insight in the activity of neuronal networks. The need for higher temporal and spatial recording resolutions imposes new challenges on the design of integrated neural interfaces with respect to power consumption, data handling and versatility. In this paper, we present an integrated acquisition system for in vitro and in vivo recording of neural activity. The ASIC consists of 16 low-noise, fully-differential input channels with independent programmability of its amplification (from 100 to 6000 V/V) and filtering (1-6000 Hz range) capabilities. Each channel is AC-coupled and implements a fourth-order band-pass filter in order to steeply attenuate out-of-band noise and DC input offsets. The system achieves an input-referred noise density of 37 nV/√Hz, a NEF of 5.1, a CMRR > 60 dB, a THD < 1% and a sampling rate of 30 kS/s per channel, while consuming a maximum of 70 μA per channel from a single 3.3 V. The ASIC was implemented in a 0.35 μm CMOS technology and has a total area of 5.6 × 4.5 mm². The recording system was successfully validated in in vitro and in vivo experiments, achieving simultaneous multichannel recordings of cell activity with satisfactory signal-to-noise ratios.

  1. Suppressed expression of mitogen-activated protein kinases in hyperthermia induced defective neural tube.

    Science.gov (United States)

    Zhang, Tianliang; Leng, Zhaoting; Liu, Wenjing; Wang, Xia; Yan, Xue; Yu, Li

    2015-05-06

    Neural tube defects (NTDs) are common congenital malformations. Mitogen-activated protein kinases (MAPKs) pathway is involved in many physiological processes. HMGB1 has been showed closely associated with neurulation and NTDs induced by hyperthermia and could activate MAPKs pathway. Since hyperthermia caused increased activation of MAPKs in many systems, the present study aims to investigate whether HMGB1 contributes to hyperthermia induced NTDs through MAPKs pathway. The mRNA levels of MAPKs and HMGB1 between embryonic day 8.5 and 10 (E8.5-10) in hyperthermia induced defective neural tube were detected by real-time quantitative polymerase chain reaction (qPCR). By immunofluorescence and western blotting, the expressions of HMGB1 and phosphorylated MAPKs (ERK1/2, JNK and p38) in neural tubes after hyperthermia were studied. The mRNA levels of MAPKs and HMGB1, as well as the expressions of HMGB1 along with phosphorylated JNK, p38 and ERK, were downregulated in NTDs groups induced by hyperthermia compared with control. The findings suggested that HMGB1 may contribute to hyperthermia induced NTDs formation through decreased cell proliferation due to inhibited phosphorylated ERK1/2 MAPK.

  2. Dynamics of modularity of neural activity in the brain during development

    Science.gov (United States)

    Deem, Michael; Chen, Man

    2014-03-01

    Theory suggests that more modular systems can have better response functions at short times. This theory suggests that greater cognitive performance may be achieved for more modular neural activity, and that modularity of neural activity may, therefore, likely increase with development in children. We study the relationship between age and modularity of brain neural activity in developing children. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. We interpret these results as evidence of selection for plasticity in the cognitive function of the human brain. We present a model to illustrate how modularity can provide greater cognitive performance at short times and enhance fast, low-level, automatic cognitive processes. Conversely, high-level, effortful, conscious cognitive processes may not benefit from modularity. We use quasispecies theory to predict how the average modularity evolves with age, given a fitness function extracted from the model. We suggest further experiments exploring the effect of modularity on cognitive performance and suggest that modularity may be a potential biomarker for injury, rehabilitation, or disease.

  3. Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available Functional magnetic resonance imaging (fMRI is recently developed and applied to measure the hemodynamic response related to neural activity. The fMRI can not only noninvasively record brain signals without risks of ionising radiation inherent in other scanning methods, such as CT or PET scans, but also record signal from all regions of the brain, unlike EEG/MEG which are biased towards the cortical surface. This paper introduces the fundamental principles and summarizes the research progress of the last year for imaging neural activity in the human brain. Aims of functional analysis of neural activity from fMRI include biological findings, functional connectivity, vision and hearing research, emotional research, neurosurgical planning, pain management, and many others. Besides formulations and basic processing methods, models and strategies of processing technology are introduced, including general linear model, nonlinear model, generative model, spatial pattern analysis, statistical analysis, correlation analysis, and multimodal combination. This paper provides readers the most recent representative contributions in the area.

  4. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model.

    Directory of Open Access Journals (Sweden)

    Jan Tønnesen

    Full Text Available Intrastriatal grafts of stem cell-derived dopamine (DA neurons induce behavioral recovery in animal models of Parkinson's disease (PD, but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral mesencephalon of tyrosine hydroxylase-GFP transgenic mice were expanded as neurospheres and transplanted into organotypic cultures of wild type mouse striatum. Differentiated GFP-labeled DA neurons in the grafts exhibited mature neuronal properties, including spontaneous firing of action potentials, presence of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation or inhibition of grafted cells and host neurons using channelrhodopsin-2 (ChR2 and halorhodopsin (NpHR, respectively, revealed complex, bi-directional synaptic interactions between grafted cells and host neurons and extensive synaptic connectivity within the graft. Our data demonstrate for the first time using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying behavioral recovery as well as adverse effects following stem cell-based DA cell replacement strategies in PD.

  5. Functional integration of grafted neural stem cell-derived dopaminergic neurons monitored by optogenetics in an in vitro Parkinson model.

    Science.gov (United States)

    Tønnesen, Jan; Parish, Clare L; Sørensen, Andreas T; Andersson, Angelica; Lundberg, Cecilia; Deisseroth, Karl; Arenas, Ernest; Lindvall, Olle; Kokaia, Merab

    2011-03-04

    Intrastriatal grafts of stem cell-derived dopamine (DA) neurons induce behavioral recovery in animal models of Parkinson's disease (PD), but how they functionally integrate in host neural circuitries is poorly understood. Here, Wnt5a-overexpressing neural stem cells derived from embryonic ventral mesencephalon of tyrosine hydroxylase-GFP transgenic mice were expanded as neurospheres and transplanted into organotypic cultures of wild type mouse striatum. Differentiated GFP-labeled DA neurons in the grafts exhibited mature neuronal properties, including spontaneous firing of action potentials, presence of post-synaptic currents, and functional expression of DA D₂ autoreceptors. These properties resembled those recorded from identical cells in acute slices of intrastriatal grafts in the 6-hydroxy-DA-induced mouse PD model and from DA neurons in intact substantia nigra. Optogenetic activation or inhibition of grafted cells and host neurons using channelrhodopsin-2 (ChR2) and halorhodopsin (NpHR), respectively, revealed complex, bi-directional synaptic interactions between grafted cells and host neurons and extensive synaptic connectivity within the graft. Our data demonstrate for the first time using optogenetics that ectopically grafted stem cell-derived DA neurons become functionally integrated in the DA-denervated striatum. Further optogenetic dissection of the synaptic wiring between grafted and host neurons will be crucial to clarify the cellular and synaptic mechanisms underlying behavioral recovery as well as adverse effects following stem cell-based DA cell replacement strategies in PD.

  6. On-line Monitoring and Active Control for Transformer Noise

    Science.gov (United States)

    Liang, Jiabi; Zhao, Tong; Tian, Chun; Wang, Xia; He, Zhenhua; Duan, Lunfeng

    This paper introduces the system for on-line monitoring and active noise control towards the transformer noise based on LabVIEW and the hardware equipment including the hardware and software. For the hardware part, it is mainly focused on the composition and the role of hardware devices, as well as the mounting location in the active noise control experiment. And the software part introduces the software flow chats, the measurement and analysis module for the sound pressure level including A, B, C weighting methods, the 1/n octave spectrum and the power spectrum, active noise control module and noise data access module.

  7. Remote monitoring of biodynamic activity using electric potential sensors

    Energy Technology Data Exchange (ETDEWEB)

    Harl, C J; Prance, R J; Prance, H [Centre for Physical Electronics and Quantum Technology, Department of Engineering and Design, School of Science and Technology, University of Sussex, Brighton, BN1 9QT (United Kingdom)], E-mail: c.j.harland@sussex.ac.uk

    2008-12-01

    Previous work in applying the electric potential sensor to the monitoring of body electrophysiological signals has shown that it is now possible to monitor these signals without needing to make any electrical contact with the body. Conventional electrophysiology makes use of electrodes which are placed in direct electrical contact with the skin. The electric potential sensor requires no cutaneous electrical contact, it operates by sensing the displacement current using a capacitive coupling. When high resolution body electrophysiology is required a strong (capacitive) coupling is used to maximise the collected signal. However, in remote applications where there is typically an air-gap between the body and the sensor only a weak coupling can be achieved. In this paper we demonstrate that the electric potential sensor can be successfully used for the remote sensing and monitoring of bioelectric activity. We show examples of heart-rate measurements taken from a seated subject using sensors mounted in the chair. We also show that it is possible to monitor body movements on the opposite side of a wall to the sensor. These sensing techniques have biomedical applications for non-contact monitoring of electrophysiological conditions and can be applied to passive through-the-wall surveillance systems for security applications.

  8. Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets.

    Science.gov (United States)

    Khan, A M; Lee, Y K; Kim, T S

    2008-01-01

    Automatic recognition of human activities is one of the important and challenging research areas in proactive and ubiquitous computing. In this work, we present some preliminary results of recognizing human activities using augmented features extracted from the activity signals measured using a single triaxial accelerometer sensor and artificial neural nets. The features include autoregressive (AR) modeling coefficients of activity signals, signal magnitude areas (SMA), and title angles (TA). We have recognized four human activities using AR coefficients (ARC) only, ARC with SMA, and ARC with SMA and TA. With the last augmented features, we have achieved the recognition rate above 99% for all four activities including lying, standing, walking, and running. With our proposed technique, real time recognition of some human activities is possible.

  9. Amplified induced neural oscillatory activity predicts musicians' benefits in categorical speech perception.

    Science.gov (United States)

    Bidelman, Gavin M

    2017-04-21

    Event-related brain potentials (ERPs) reveal musical experience refines neural encoding and confers stronger categorical perception (CP) and neural organization for speech sounds. In addition to evoked brain activity, the human EEG can be decomposed into induced (non-phase-locked) responses whose various frequency bands reflect different mechanisms of perceptual-cognitive processing. Here, we aimed to clarify which spectral properties of these neural oscillations are most prone to music-related neuroplasticity and which are linked to behavioral benefits in the categorization of speech. We recorded electrical brain activity while musicians and nonmusicians rapidly identified speech tokens from a sound continuum. Time-frequency analysis parsed evoked and induced EEG into alpha- (∼10Hz), beta- (∼20Hz), and gamma- (>30Hz) frequency bands. We found that musicians' enhanced behavioral CP was accompanied by improved evoked speech responses across the frequency spectrum, complementing previously observed enhancements in evoked potential studies (i.e., ERPs). Brain-behavior correlations implied differences in the underlying neural mechanisms supporting speech CP in each group: modulations in induced gamma power predicted the slope of musicians' speech identification functions whereas early evoked alpha activity predicted behavior in nonmusicians. Collectively, findings indicate that musical training tunes speech processing via two complementary mechanisms: (i) strengthening the formation of auditory object representations for speech signals (gamma-band) and (ii) improving network control and/or the matching of sounds to internalized memory templates (alpha/beta-band). Both neurobiological enhancements may be deployed behaviorally and account for musicians' benefits in the perceptual categorization of speech. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  10. Activation of endogenous neural stem cells in experimental intracerebral hemorrhagic rat brains

    Institute of Scientific and Technical Information of China (English)

    唐涛; 黎杏群; 武衡; 罗杰坤; 张花先; 罗团连

    2004-01-01

    Background Many researchers suggest that adult mammalian central nervous system (CNS) is incapable of completing self-repair or regeneration. And there are accumulating lines of evidence which suggest that endogenous neural stem cells (NSCs) are activated in many pathological conditions, including stroke in the past decades, which might partly account for rehabilitation afterwards. In this study, we investigated whether there was endogenous neural stem cell activation in intracerebral hemorrhagic (ICH) rat brains.Methods After ICH induction by stereotactical injection of collagenase type Ⅶ into globus pallidus, 5-Bromo-2 Deoxyuridine (BrdU) was administered intraperitoneally to label newborn cells. Immunohistochemical method was used to detect Nestin, a marker for neural stem cells, and BrdU.Results Nestin-positive or BrdU-Labeled cells were predominantly located at 2 sites: basal ganglion around hemotoma, ependyma and nearby subventricular zone (SVZ). No positive cells for the 2 markers were found in the 2 sites of normal control group and sham group, as well as in non-leisoned parenchyma, both hippocampi and olfactory bulbs in the 4 groups. Nestin+ cells presented 4 types of morphology, and BrdU+ nucleus were polymorphologic. Postive cell counting around hemotoma showed that at day 2, Nestin+ cells were seen around hemotoma in model group , the number of which increased at day 4, day 7(P<0.01), peaked at day 14(P<0.05), and reduced significantly by day 28(P<0.01).Conclusion Endogenous neural stem cells were activated in experimental intracerebral hemorrhagic rat brains.

  11. Rejuvenation of MPTP-induced human neural precursor cell senescence by activating autophagy

    Energy Technology Data Exchange (ETDEWEB)

    Zhu, Liang [East Hospital, Tongji University School of Medicine, Shanghai (China); Dong, Chuanming [East Hospital, Tongji University School of Medicine, Shanghai (China); Department of Anatomy and Neurobiology, The Jiangsu Key Laboratory of Neuroregeneration, Nantong University, Nantong (China); Sun, Chenxi; Ma, Rongjie; Yang, Danjing [East Hospital, Tongji University School of Medicine, Shanghai (China); Zhu, Hongwen, E-mail: hongwen_zhu@hotmail.com [Tianjin Hospital, Tianjin Academy of Integrative Medicine, Tianjin (China); Xu, Jun, E-mail: xunymc2000@yahoo.com [East Hospital, Tongji University School of Medicine, Shanghai (China)

    2015-08-21

    Aging of neural stem cell, which can affect brain homeostasis, may be caused by many cellular mechanisms. Autophagy dysfunction was found in aged and neurodegenerative brains. However, little is known about the relationship between autophagy and human neural stem cell (hNSC) aging. The present study used 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) to treat neural precursor cells (NPCs) derived from human embryonic stem cell (hESC) line H9 and investigate related molecular mechanisms involved in this process. MPTP-treated NPCs were found to undergo premature senescence [determined by increased senescence-associated-β-galactosidase (SA-β-gal) activity, elevated intracellular reactive oxygen species level, and decreased proliferation] and were associated with impaired autophagy. Additionally, the cellular senescence phenotypes were manifested at the molecular level by a significant increase in p21 and p53 expression, a decrease in SOD2 expression, and a decrease in expression of some key autophagy-related genes such as Atg5, Atg7, Atg12, and Beclin 1. Furthermore, we found that the senescence-like phenotype of MPTP-treated hNPCs was rejuvenated through treatment with a well-known autophagy enhancer rapamycin, which was blocked by suppression of essential autophagy gene Beclin 1. Taken together, these findings reveal the critical role of autophagy in the process of hNSC aging, and this process can be reversed by activating autophagy. - Highlights: • We successfully establish hESC-derived neural precursor cells. • MPTP treatment induced senescence-like state in hESC-derived NPCs. • MPTP treatment induced impaired autophagy of hESC-derived NPCs. • MPTP-induced hESC-derived NPC senescence was rejuvenated by activating autophagy.

  12. Sentinel-1 Contribution to Monitoring Maritime Activity in the Arctic

    Science.gov (United States)

    Santamaria, Carlos; Greidanus, Harm; Fournier, Melanie; Eriksen, Torkild; Vespe, Michele; Alvarez, Marlene; Arguedas, Virginia Fernandez; Delaney, Conor; Argentieri, Pietro

    2016-08-01

    This paper presents results on the use of Sentinel-1 combined with satellite AIS to monitor maritime activity in the Arctic. Such activities are expected to increase, even if not uniformly across the Arctic, as the ice cover in the region retreats due to changes in climate. The objectives of monitoring efforts in the region can vary from country to country, but are generally related to increasing awareness on non- cooperative, small and cruise ships, fisheries, safety at sea, and Search and Rescue. A ship monitoring study has been conducted, involving more than 2,000 Sentinel-1 images acquired during one year in the central Arctic, where the ship densities are high. The main challenges to SAR-based monitoring in this area are described, solutions for some of them are proposed, and analyses of the results are shown. With the high detection thresholds needed to prevent false alarms from sea ice, 16% of the ships detected overall in the Sentinel-1 images have not been correlated to AIS- transmitting ships, and 48% of the AIS-transmitting ships are not correlated to ships detected in the images.

  13. Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks

    Directory of Open Access Journals (Sweden)

    Jose M. Lopez- Higuera

    2008-10-01

    Full Text Available A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.

  14. Multiple-color optical activation, silencing, and desynchronization of neural activity, with single-spike temporal resolution.

    Directory of Open Access Journals (Sweden)

    Xue Han

    Full Text Available The quest to determine how precise neural activity patterns mediate computation, behavior, and pathology would be greatly aided by a set of tools for reliably activating and inactivating genetically targeted neurons, in a temporally precise and rapidly reversible fashion. Having earlier adapted a light-activated cation channel, channelrhodopsin-2 (ChR2, for allowing neurons to be stimulated by blue light, we searched for a complementary tool that would enable optical neuronal inhibition, driven by light of a second color. Here we report that targeting the codon-optimized form of the light-driven chloride pump halorhodopsin from the archaebacterium Natronomas pharaonis (hereafter abbreviated Halo to genetically-specified neurons enables them to be silenced reliably, and reversibly, by millisecond-timescale pulses of yellow light. We show that trains of yellow and blue light pulses can drive high-fidelity sequences of hyperpolarizations and depolarizations in neurons simultaneously expressing yellow light-driven Halo and blue light-driven ChR2, allowing for the first time manipulations of neural synchrony without perturbation of other parameters such as spiking rates. The Halo/ChR2 system thus constitutes a powerful toolbox for multichannel photoinhibition and photostimulation of virally or transgenically targeted neural circuits without need for exogenous chemicals, enabling systematic analysis and engineering of the brain, and quantitative bioengineering of excitable cells.

  15. Long-range neural activity evoked by premotor cortex stimulation: a TMS/EEG co-registration study

    Directory of Open Access Journals (Sweden)

    Marco eZanon

    2013-11-01

    Full Text Available The premotor cortex is one of the fundamental structures composing the neural networks of the human brain. It is implicated in many behaviors and cognitive tasks, ranging from movement to attention and eye-related activity. Therefore, neural circuits that are related to premotor cortex have been studied to clarify their connectivity and/or role in different tasks. In the present work, we aimed to investigate the propagation of the neural activity evoked in the dorsal premotor cortex using transcranial magnetic stimulation/electroencephalography (TMS/EEG. Towards this end, interest was focused on the neural dynamics elicited in long-ranging temporal and spatial networks. Twelve healthy volunteers underwent a single-pulse TMS protocol in a resting condition with eyes closed, and the evoked activity, measured by EEG, was compared to a sham condition in a time window ranging from 45 msec to about 200 msec after TMS. Spatial and temporal investigations were carried out with sLORETA. TMS was found to induce propagation of neural activity mainly in the contralateral sensorimotor and frontal cortices, at about 130 msec after delivery of the stimulus. Different types of analyses showed propagated activity also in posterior, mainly visual, regions, in a time window between 70 and 130 msec. Finally, a likely rebounding activation of the sensorimotor and frontal regions, was observed in various time ranges. Taken together, the present findings further characterize the neural circuits that are driven by dorsal premotor cortex activation in healthy humans.

  16. Differential brain activation to angry faces by elite warfighters: neural processing evidence for enhanced threat detection.

    Directory of Open Access Journals (Sweden)

    Martin P Paulus

    Full Text Available BACKGROUND: Little is known about the neural basis of elite performers and their optimal performance in extreme environments. The purpose of this study was to examine brain processing differences between elite warfighters and comparison subjects in brain structures that are important for emotion processing and interoception. METHODOLOGY/PRINCIPAL FINDINGS: Navy Sea, Air, and Land Forces (SEALs while off duty (n = 11 were compared with n = 23 healthy male volunteers while performing a simple emotion face-processing task during functional magnetic resonance imaging. Irrespective of the target emotion, elite warfighters relative to comparison subjects showed relatively greater right-sided insula, but attenuated left-sided insula, activation. Navy SEALs showed selectively greater activation to angry target faces relative to fearful or happy target faces bilaterally in the insula. This was not accounted for by contrasting positive versus negative emotions. Finally, these individuals also showed slower response latencies to fearful and happy target faces than did comparison subjects. CONCLUSIONS/SIGNIFICANCE: These findings support the hypothesis that elite warfighters deploy greater processing resources toward potential threat-related facial expressions and reduced processing resources to non-threat-related facial expressions. Moreover, rather than expending more effort in general, elite warfighters show more focused neural and performance tuning. In other words, greater neural processing resources are directed toward threat stimuli and processing resources are conserved when facing a nonthreat stimulus situation.

  17. Adaptive neural networks control for camera stabilization with active suspension system

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-08-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  18. A current model of neural circuitry active in forming mental images.

    Science.gov (United States)

    Brodziak, Andrzej

    2013-12-12

    My aim here is to formulate a compact, intuitively understandable model of neural circuits active in imagination that would be consistent with the current state of knowledge, but that would be simple enough to be able to use for teaching. I argue that such a model should be based on the recent idea of "concept neurons" and circuits of 2 separate loops necessary for recalling mental images and consolidation of memory traces of long-term memory. This paper discusses the role of the hippocampus and temporal lobe, emphasizing the essential importance of recurrent pathways and oscillations occurring in the upper layers of hierarchical neural structures, as well as oscillations in thalamo-cortical loops. The elaborated model helps explain specific processes such as imagining future situations, novel objects, and anticipated action, as well as imagination concerning oneself, which is indispensable for the sense of identity and self-awareness. I attempt to present this compact, simple model of neural circuitry active in imagination by using some intuitive, demonstrative figures.

  19. Neural regions that underlie reinforcement learning are also active for social expectancy violations.

    Science.gov (United States)

    Harris, Lasana T; Fiske, Susan T

    2010-01-01

    Prediction error, the difference between an expected and an actual outcome, serves as a learning signal that interacts with reward and punishment value to direct future behavior during reinforcement learning. We hypothesized that similar learning and valuation signals may underlie social expectancy violations. Here, we explore the neural correlates of social expectancy violation signals along the universal person-perception dimensions trait warmth and competence. In this context, social learning may result from expectancy violations that occur when a target is inconsistent with an a priori schema. Expectancy violation may activate neural regions normally implicated in prediction error and valuation during appetitive and aversive conditioning. Using fMRI, we first gave perceivers high warmth or competence behavioral information that led to dispositional or situational attributions for the behavior. Participants then saw pictures of people responsible for the behavior; they represented social groups either inconsistent (rated low on either warmth or competence) or consistent (rated high on either warmth or competence) with the behavior information. Warmth and competence expectancy violations activate striatal regions that represent evaluative and prediction error signals. Social cognition regions underlie consistent expectations. These findings suggest that regions underlying reinforcement learning may work in concert with social cognition regions in warmth and competence social expectancy. This study illustrates the neural overlap between neuroeconomics and social neuroscience.

  20. QA/QC activities and ecological monitoring in the Acid Deposition Monitoring Network in East Asia (EANET

    Directory of Open Access Journals (Sweden)

    Ueda H

    2009-01-01

    Full Text Available An overview is presented of Quality assurance/Quality control QA/QC activities and current features of the ecological monitoring in the frame of the Acid Deposition Monitoring Network in East Asia EANET. It is stressed that standardization of the methodologies applicable for new topics, such as the catchment analysis and ozone impacts, should be investigated for future monitoring.

  1. Activities on PNS neural interfaces for the control of hand prostheses.

    Science.gov (United States)

    Carpaneto, J; Cutrone, A; Bossi, S; Sergi, P; Citi, L; Rigosa, J; Rossini, P M; Micera, S

    2011-01-01

    The development of interfaces linking the human nervous system with artificial devices is an important area of research. Several groups are working on the development of devices able to restore sensory-motor function in subjects affected by neurological disorders, injuries or amputations. Neural electrodes implanted in peripheral nervous system, and in particular intrafascicular electrodes, seem to be a promising approach for the control of hand prosthesis thanks to the possibility to selectively access motor and sensory fibers for decoding motor commands and delivering sensory feedback. In this paper, activities on the use of PNS interfaces for the control of hand prosthesis are presented. In particular, the design and feasibility study of a self-opening neural interface is presented together with the decoding of ENG signals in one amputee to control a dexterous hand prosthesis.

  2. Active vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks

    Science.gov (United States)

    Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.

    2016-02-01

    The study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.

  3. Reversal of rocuronium-induced neuromuscular blockade by sugammadex allows for optimization of neural monitoring of the recurrent laryngeal nerve.

    Science.gov (United States)

    Lu, I-Cheng; Wu, Che-Wei; Chang, Pi-Ying; Chen, Hsiu-Ya; Tseng, Kuang-Yi; Randolph, Gregory W; Cheng, Kuang-I; Chiang, Feng-Yu

    2016-04-01

    The use of neuromuscular blocking agent may effect intraoperative neuromonitoring (IONM) during thyroid surgery. An enhanced neuromuscular-blockade (NMB) recovery protocol was investigated in a porcine model and subsequently clinically applied during human thyroid neural monitoring surgery. Prospective animal and retrospective clinical study. In the animal experiment, 12 piglets were injected with rocuronium 0.6 mg/kg and randomly allocated to receive normal saline, sugammadex 2 mg/kg, or sugammadex 4 mg/kg to compare the recovery of laryngeal electromyography (EMG). In a subsequent clinical application study, 50 patients who underwent thyroidectomy with IONM followed an enhanced NMB recovery protocol-rocuronium 0.6 mg/kg at anesthesia induction and sugammadex 2 mg/kg at the operation start. The train-of-four (TOF) ratio was used for continuous quantitative monitoring of neuromuscular transmission. In our porcine model, it took 49 ± 15, 13.2 ± 5.6, and 4.2 ± 1.5 minutes for the 80% recovery of laryngeal EMG after injection of saline, sugammadex 2 mg/kg, and sugammadex 4 mg/kg, respectively. In subsequent clinical human application, the TOF ratio recovered from 0 to >0.9 within 5 minutes after administration of sugammadex 2 mg/kg at the operation start. All patients had positive and high EMG amplitude at the early stage of the operation, and intubation was without difficulty in 96% of patients. Both porcine modeling and clinical human application demonstrated that sugammadex 2 mg/kg allows effective and rapid restoration of neuromuscular function suppressed by rocuronium. Implementation of this enhanced NMB recovery protocol assures optimal conditions for tracheal intubation as well as IONM in thyroid surgery. NA. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

  4. The BDNF Val66Met Polymorphism Influences Reading Ability and Patterns of Neural Activation in Children.

    Science.gov (United States)

    Jasińska, Kaja K; Molfese, Peter J; Kornilov, Sergey A; Mencl, W Einar; Frost, Stephen J; Lee, Maria; Pugh, Kenneth R; Grigorenko, Elena L; Landi, Nicole

    2016-01-01

    Understanding how genes impact the brain's functional activation for learning and cognition during development remains limited. We asked whether a common genetic variant in the BDNF gene (the Val66Met polymorphism) modulates neural activation in the young brain during a critical period for the emergence and maturation of the neural circuitry for reading. In animal models, the bdnf variation has been shown to be associated with the structure and function of the developing brain and in humans it has been associated with multiple aspects of cognition, particularly memory, which are relevant for the development of skilled reading. Yet, little is known about the impact of the Val66Met polymorphism on functional brain activation in development, either in animal models or in humans. Here, we examined whether the BDNF Val66Met polymorphism (dbSNP rs6265) is associated with children's (age 6-10) neural activation patterns during a reading task (n = 81) using functional magnetic resonance imaging (fMRI), genotyping, and standardized behavioral assessments of cognitive and reading development. Children homozygous for the Val allele at the SNP rs6265 of the BDNF gene outperformed Met allele carriers on reading comprehension and phonological memory, tasks that have a strong memory component. Consistent with these behavioral findings, Met allele carriers showed greater activation in reading-related brain regions including the fusiform gyrus, the left inferior frontal gyrus and left superior temporal gyrus as well as greater activation in the hippocampus during a word and pseudoword reading task. Increased engagement of memory and spoken language regions for Met allele carriers relative to Val/Val homozygotes during reading suggests that Met carriers have to exert greater effort required to retrieve phonological codes.

  5. Active Geophysical Monitoring in Oil and Gas Industry

    Science.gov (United States)

    Bakulin, A.; Calvert, R.

    2005-12-01

    Effective reservoir management is a Holy Grail of the oil and gas industry. Quest for new technologies is never ending but most often they increase effectiveness and decrease the costs. None of the newcomers proved to be a silver bullet in such a key metric of the industry as average oil recovery factor. This factor is still around 30 %, meaning that 70 % of hydrocarbon reserves are left in the ground in places where we already have expensive infrastructure (platforms, wells) to extract them. Main reason for this inefficiency is our inability to address realistic reservoir complexity. Most of the time we fail to properly characterize our reservoirs before production. As a matter of fact, one of the most important parameters -- permeability -- can not be mapped from remote geophysical methods. Therefore we always start production blind even though reservoir state before production is the simplest one. Once first oil is produced, we greatly complicate the things and quickly become unable to estimate the state and condition of the reservoir (fluid, pressures, faults etc) or oilfield hardware (wells, platforms, pumps) to make a sound next decision in the chain of reservoir management. Our modeling capabilities are such that if we know true state of the things - we can make incredibly accurate predictions and make extremely efficient decisions. Thus the bottleneck is our inability to properly describe the state of the reservoirs in real time. Industry is starting to recognize active monitoring as an answer to this critical issue. We will highlight industry strides in active geophysical monitoring from well to reservoir scale. It is worth noting that when one says ``monitoring" production technologists think of measuring pressures at the wellhead or at the pump, reservoir engineers think of measuring extracted volumes and pressures, while geophysicist may think of change in elastic properties. We prefer to think of monitoring as to measuring those parameters of the

  6. A Gas Sensor Array For Environmental Air Monitoring: A Study Case Of Application Of Artificial Neural Networks

    Science.gov (United States)

    Penza, Michele; Suriano, Domenico; Cassano, Gennaro; Rossi, Riccardo; Alvisi, Marco; Pfister, Valerio; Trizio, Livia; Brattoli, Magda; De Gennaro, Gianluigi

    2011-09-01

    An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air-pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal-modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi-layer feed-forward network based on error back-propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron-based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2 characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (NMSE) in the test set as low as 6%.

  7. A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming.

    Science.gov (United States)

    Liu, Q; Wang, J

    2008-04-01

    In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.

  8. Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

    Science.gov (United States)

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

    2012-08-01

    Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills.

  9. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    Science.gov (United States)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  10. Integrated active sensor system for real time vibration monitoring.

    Science.gov (United States)

    Liang, Qijie; Yan, Xiaoqin; Liao, Xinqin; Cao, Shiyao; Lu, Shengnan; Zheng, Xin; Zhang, Yue

    2015-11-05

    We report a self-powered, lightweight and cost-effective active sensor system for vibration monitoring with multiplexed operation based on contact electrification between sensor and detected objects. The as-fabricated sensor matrix is capable of monitoring and mapping the vibration state of large amounts of units. The monitoring contents include: on-off state, vibration frequency and vibration amplitude of each unit. The active sensor system delivers a detection range of 0-60 Hz, high accuracy (relative error below 0.42%), long-term stability (10000 cycles). On the time dimension, the sensor can provide the vibration process memory by recording the outputs of the sensor system in an extend period of time. Besides, the developed sensor system can realize detection under contact mode and non-contact mode. Its high performance is not sensitive to the shape or the conductivity of the detected object. With these features, the active sensor system has great potential in automatic control, remote operation, surveillance and security systems.

  11. Enhanced food anticipatory activity associated with enhanced activation of extrahypothalamic neural pathways in serotonin2C receptor null mutant mice.

    Directory of Open Access Journals (Sweden)

    Jennifer L Hsu

    Full Text Available The ability to entrain circadian rhythms to food availability is important for survival. Food-entrained circadian rhythms are characterized by increased locomotor activity in anticipation of food availability (food anticipatory activity. However, the molecular components and neural circuitry underlying the regulation of food anticipatory activity remain unclear. Here we show that serotonin(2C receptor (5-HT2CR null mutant mice subjected to a daytime restricted feeding schedule exhibit enhanced food anticipatory activity compared to wild-type littermates, without phenotypic differences in the impact of restricted feeding on food consumption, body weight loss, or blood glucose levels. Moreover, we show that the enhanced food anticipatory activity in 5-HT2CR null mutant mice develops independent of external light cues and persists during two days of total food deprivation, indicating that food anticipatory activity in 5-HT2CR null mutant mice reflects the locomotor output of a food-entrainable oscillator. Whereas restricted feeding induces c-fos expression to a similar extent in hypothalamic nuclei of wild-type and null mutant animals, it produces enhanced expression in the nucleus accumbens and other extrahypothalamic regions of null mutant mice relative to wild-type subjects. These data suggest that 5-HT2CRs gate food anticipatory activity through mechanisms involving extrahypothalamic neural pathways.

  12. Elman神经网络在变形预报中的应用研究%Applied Research in the Deformation Monitoring Based on Elman Neural Network Method

    Institute of Scientific and Technical Information of China (English)

    白雪武; 梁东伟; 马友利

    2012-01-01

    As a rapid development of nonlinear science in dealing with some background unclear and extremely complex information, neural network will show its unique superiority. This article applies Elman neural network to deformation monitoring of landslide to set up the forecasting model and Matlab neural network toolbox of MATLAB program design is applied to concrete examples. Through the model of prediction accuracy, the Elman neural network model to landslide monitoring and forecasting of feasibility is verified.%神经网络作为一门快速发展起来的非线性科学,在处理一些背景不清楚而且极其复杂信息的时候,就会显示出其独特的优越性。本文通过Elman神经网络应用到滑坡变形监测中,建立预报模型,并以Matlab神经网络工具箱进行程序设计,最后运用到具体实例中,通过模型的预报精度,来验证Elman神经网络模型在滑坡监测预报中的可行性。

  13. Selective neural activation in a histologically derived model of peripheral nerve

    Science.gov (United States)

    Butson, Christopher R.; Miller, Ian O.; Normann, Richard A.; Clark, Gregory A.

    2011-06-01

    Functional electrical stimulation (FES) is a general term for therapeutic methods that use electrical stimulation to aid or replace lost ability. For FES systems that communicate with the nervous system, one critical component is the electrode interface through which the machine-body information transfer must occur. In this paper, we examine the influence of inhomogeneous tissue conductivities and positions of nodes of Ranvier on activation of myelinated axons for neuromuscular control as a function of electrode configuration. To evaluate these effects, we developed a high-resolution bioelectric model of a fascicle from a stained cross-section of cat sciatic nerve. The model was constructed by digitizing a fixed specimen of peripheral nerve, extruding the image along the axis of the nerve, and assigning each anatomical component to one of several different tissue types. Electrodes were represented by current sources in monopolar, transverse bipolar, and longitudinal bipolar configurations; neural activation was determined using coupled field-neuron simulations with myelinated axon cable models. We found that the use of an isotropic tissue medium overestimated neural activation thresholds compared with the use of physiologically based, inhomogeneous tissue medium, even after controlling for mean impedance levels. Additionally, the positions of the cathodic sources relative to the nodes of Ranvier had substantial effects on activation, and these effects were modulated by the electrode configuration. Our results indicate that physiologically based tissue properties cause considerable variability in the neural response, and the inclusion of these properties is an important component in accurately predicting activation. The results are used to suggest new electrode designs to enable selective stimulation of small diameter fibers.

  14. Thinking about the thoughts of others; temporal and spatial neural activation during false belief reasoning.

    Science.gov (United States)

    Mossad, Sarah I; AuCoin-Power, Michelle; Urbain, Charline; Smith, Mary Lou; Pang, Elizabeth W; Taylor, Margot J

    2016-07-01

    Theory of Mind (ToM) is the ability to understand the perspectives, mental states and beliefs of others in order to anticipate their behaviour and is therefore crucial to social interactions. Although fMRI has been widely used to establish the neural networks implicated in ToM, little is known about the timing of ToM-related brain activity. We used magnetoencephalography (MEG) to measure the neural processes underlying ToM, as MEG provides very accurate timing and excellent spatial localization of brain processes. We recorded MEG activity during a false belief task, a reliable measure of ToM, in twenty young adults (10 females). MEG data were recorded in a 151 sensor CTF system (MISL, Coquitlam, BC) and data were co-registered to each participant's MRI (Siemens 3T) for source reconstruction. We found stronger right temporoparietal junction (rTPJ) activations in the false belief condition from 150ms to 225ms, in the right precuneus from 275ms to 375ms, in the right inferior frontal gyrus from 200ms to 300ms and the superior frontal gyrus from 300ms to 400ms. Our findings extend the literature by demonstrating the timing and duration of neural activity in the main regions involved in the "mentalizing" network, showing that activations related to false belief in adults are predominantly right lateralized and onset around 100ms. The sensitivity of MEG will allow us to determine spatial and temporal differences in the brain processes in ToM in younger populations or those who demonstrate deficits in this ability. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Human psychophysiological activity monitoring methods using fiber optic sensors

    Science.gov (United States)

    Zyczkowski, M.; Uzieblo-Zyczkowska, B.

    2010-10-01

    The paper presents the concept of fiber optic sensor system for human psycho-physical activity detection. A fiber optic sensor that utilizes optical phase interferometry or intensity in modalmetric to monitor a patient's vital signs such as respiration cardiac activity, blood pressure and body's physical movements. The sensor, which is non-invasive, comprises an optical fiber interferometer that includes an optical fiber proximately situated to the patient so that time varying acusto-mechanical signals from the patient are coupled into the optical fiber. The system can be implemented in embodiments ranging form a low cost in-home to a high end product for in hospital use.

  16. Limited Activity Monitoring in Toddlers with Autism Spectrum Disorder

    OpenAIRE

    Shic, Frederick; Bradshaw, Jessica; Klin, Ami; Scassellati, Brian; Chawarska, Katarzyna

    2010-01-01

    This study used eye-tracking to examine how 20-month old toddlers with autism spectrum disorder (ASD) (N=28), typical development (TD) (N=34), and non-autistic developmental delays (DD) (N=16) monitored the activities occurring in a context of an adult-child play interaction. Toddlers with ASD, in comparison to control groups, showed less attention to the activities of others and focused more on background objects (e.g. toys). In addition, while all groups spent the same time overall looking ...

  17. Interactivity and reward-related neural activation during a serious videogame.

    Directory of Open Access Journals (Sweden)

    Steven W Cole

    Full Text Available This study sought to determine whether playing a "serious" interactive digital game (IDG--the Re-Mission videogame for cancer patients--activates mesolimbic neural circuits associated with incentive motivation, and if so, whether such effects stem from the participatory aspects of interactive gameplay, or from the complex sensory/perceptual engagement generated by its dynamic event-stream. Healthy undergraduates were randomized to groups in which they were scanned with functional magnetic resonance imaging (FMRI as they either actively played Re-Mission or as they passively observed a gameplay audio-visual stream generated by a yoked active group subject. Onset of interactive game play robustly activated mesolimbic projection regions including the caudate nucleus and nucleus accumbens, as well as a subregion of the parahippocampal gyrus. During interactive gameplay, subjects showed extended activation of the thalamus, anterior insula, putamen, and motor-related regions, accompanied by decreased activation in parietal and medial prefrontal cortex. Offset of interactive gameplay activated the anterior insula and anterior cingulate. Between-group comparisons of within-subject contrasts confirmed that mesolimbic activation was significantly more pronounced in the active playgroup than in the passive exposure control group. Individual difference analyses also found the magnitude of parahippocampal activation following gameplay onset to correlate with positive attitudes toward chemotherapy assessed both at the end of the scanning session and at an unannounced one-month follow-up. These findings suggest that IDG-induced activation of reward-related mesolimbic neural circuits stems primarily from participatory engagement in gameplay (interactivity, rather than from the effects of vivid and dynamic sensory stimulation.

  18. Interactivity and reward-related neural activation during a serious videogame.

    Science.gov (United States)

    Cole, Steven W; Yoo, Daniel J; Knutson, Brian

    2012-01-01

    This study sought to determine whether playing a "serious" interactive digital game (IDG)--the Re-Mission videogame for cancer patients--activates mesolimbic neural circuits associated with incentive motivation, and if so, whether such effects stem from the participatory aspects of interactive gameplay, or from the complex sensory/perceptual engagement generated by its dynamic event-stream. Healthy undergraduates were randomized to groups in which they were scanned with functional magnetic resonance imaging (FMRI) as they either actively played Re-Mission or as they passively observed a gameplay audio-visual stream generated by a yoked active group subject. Onset of interactive game play robustly activated mesolimbic projection regions including the caudate nucleus and nucleus accumbens, as well as a subregion of the parahippocampal gyrus. During interactive gameplay, subjects showed extended activation of the thalamus, anterior insula, putamen, and motor-related regions, accompanied by decreased activation in parietal and medial prefrontal cortex. Offset of interactive gameplay activated the anterior insula and anterior cingulate. Between-group comparisons of within-subject contrasts confirmed that mesolimbic activation was significantly more pronounced in the active playgroup than in the passive exposure control group. Individual difference analyses also found the magnitude of parahippocampal activation following gameplay onset to correlate with positive attitudes toward chemotherapy assessed both at the end of the scanning session and at an unannounced one-month follow-up. These findings suggest that IDG-induced activation of reward-related mesolimbic neural circuits stems primarily from participatory engagement in gameplay (interactivity), rather than from the effects of vivid and dynamic sensory stimulation.

  19. A Neural Mechanism for Nonconscious Activation of Conditioned Placebo and Nocebo Responses

    Science.gov (United States)

    Jensen, Karin B.; Kaptchuk, Ted J.; Chen, Xiaoyan; Kirsch, Irving; Ingvar, Martin; Gollub, Randy L.; Kong, Jian

    2015-01-01

    Fundamental aspects of human behavior operate outside of conscious awareness. Yet, theories of conditioned responses in humans, such as placebo and nocebo effects on pain, have a strong emphasis on conscious recognition of contextual cues that trigger the response. Here, we investigated the neural pathways involved in nonconscious activation of conditioned pain responses, using functional magnetic resonance imaging in healthy participants. Nonconscious compared with conscious activation of conditioned placebo analgesia was associated with increased activation of the orbitofrontal cortex, a structure with direct connections to affective brain regions and basic reward processing. During nonconscious nocebo, there was increased activation of the thalamus, amygdala, and hippocampus. In contrast to previous assumptions about conditioning in humans, our results show that conditioned pain responses can be elicited independently of conscious awareness and our results suggest a hierarchical activation of neural pathways for nonconscious and conscious conditioned responses. Demonstrating that the human brain has a nonconscious mechanism for responding to conditioned cues has major implications for the role of associative learning in behavioral medicine and psychiatry. Our results may also open up for novel approaches to translational animal-to-human research since human consciousness and animal cognition is an inherent paradox in all behavioral science. PMID:25452576

  20. Neural activity related to cognitive and emotional empathy in post-traumatic stress disorder.

    Science.gov (United States)

    Mazza, Monica; Tempesta, Daniela; Pino, Maria Chiara; Nigri, Anna; Catalucci, Alessia; Guadagni, Veronica; Gallucci, Massimo; Iaria, Giuseppe; Ferrara, Michele

    2015-04-01

    The aim of this study is to evaluate the empathic ability and its functional brain correlates in post-traumatic stress disorder subjects (PTSD). Seven PTSD subjects and ten healthy controls, all present in the L'Aquila area during the earthquake of the April 2009, underwent fMRI during which they performed a modified version of the Multifaceted Empathy Test. PTSD patients showed impairments in implicit and explicit emotional empathy, but not in cognitive empathy. Brain responses during cognitive empathy showed an increased activation in patients compared to controls in the right medial frontal gyrus and the left inferior frontal gyrus. During implicit emotional empathy responses patients with PTSD, compared to controls, exhibited greater neural activity in the left pallidum and right insula; instead the control group showed an increased activation in right inferior frontal gyrus. Finally, in the explicit emotional empathy responses the PTSD group showed a reduced neural activity in the left insula and the left inferior frontal gyrus. The behavioral deficit limited to the emotional empathy dimension, accompanied by different patterns of activation in empathy related brain structures, represent a first piece of evidence of a dissociation between emotional and cognitive empathy in PTSD patients. The present findings support the idea that empathy is a multidimensional process, with different facets depending on distinct anatomical substrates. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Science.gov (United States)

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  2. Neural interaction of speech and gesture: differential activations of metaphoric co-verbal gestures.

    Science.gov (United States)

    Kircher, Tilo; Straube, Benjamin; Leube, Dirk; Weis, Susanne; Sachs, Olga; Willmes, Klaus; Konrad, Kerstin; Green, Antonia

    2009-01-01

    Gestures are an important part of human communication. However, little is known about the neural correlates of gestures accompanying speech comprehension. The goal of this study is to investigate the neural basis of speech-gesture interaction as reflected in activation increase and decrease during observation of natural communication. Fourteen German participants watched video clips of 5 s duration depicting an actor who performed metaphoric gestures to illustrate the abstract content of spoken sentences. Furthermore, video clips of isolated gestures (without speech), isolated spoken sentences (without gestures) and gestures in the context of an unknown language (Russian) were additionally presented while functional magnetic resonance imaging (fMRI) data were acquired. Bimodal speech and gesture processing led to left hemispheric activation increases of the posterior middle temporal gyrus, the premotor cortex, the inferior frontal gyrus, and the right superior temporal sulcus. Activation reductions during the bimodal condition were located in the left superior temporal gyrus and the left posterior insula. Gesture related activation increases and decreases were dependent on language semantics and were not found in the unknown-language condition. Our results suggest that semantic integration processes for bimodal speech plus gesture comprehension are reflected in activation increases in the classical left hemispheric language areas. Speech related gestures seem to enhance language comprehension during the face-to-face communication.

  3. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    Directory of Open Access Journals (Sweden)

    Francisco Javier Ordóñez

    2016-01-01

    Full Text Available Human activity recognition (HAR tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i is suitable for multimodal wearable sensors; (ii can perform sensor fusion naturally; (iii does not require expert knowledge in designing features; and (iv explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

  4. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

    Science.gov (United States)

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-18

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

  5. Advanced Performance Modeling with Combined Passive and Active Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Dovrolis, Constantine [Georgia Inst. of Technology, Atlanta, GA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-04-15

    To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performance information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.

  6. Neural activation during processing of aversive faces predicts treatment outcome in alcoholism.

    Science.gov (United States)

    Charlet, Katrin; Schlagenhauf, Florian; Richter, Anne; Naundorf, Karina; Dornhof, Lina; Weinfurtner, Christopher E J; König, Friederike; Walaszek, Bernadeta; Schubert, Florian; Müller, Christian A; Gutwinski, Stefan; Seissinger, Annette; Schmitz, Lioba; Walter, Henrik; Beck, Anne; Gallinat, Jürgen; Kiefer, Falk; Heinz, Andreas

    2014-05-01

    Neuropsychological studies reported decoding deficits of emotional facial expressions in alcohol-dependent patients, and imaging studies revealed reduced prefrontal and limbic activation during emotional face processing. However, it remains unclear whether this reduced neural activation is mediated by alcohol-associated volume reductions and whether it interacts with treatment outcome. We combined analyses of neural activation during an aversive face-cue-comparison task and local gray matter volumes (GM) using Biological Parametric Mapping in 33 detoxified alcohol-dependent patients and 33 matched healthy controls. Alcoholics displayed reduced activation toward aversive faces-neutral shapes in bilateral fusiform gyrus [FG; Brodmann areas (BA) 18/19], right middle frontal gyrus (BA46/47), right inferior parietal gyrus (BA7) and left cerebellum compared with controls, which were explained by GM differences (except for cerebellum). Enhanced functional activation in patients versus controls was found in left rostral anterior cingulate cortex (ACC) and medial frontal gyrus (BA10/11), even after GM reduction control. Increased ACC activation correlated significantly with less (previous) lifetime alcohol intake [Lifetime Drinking History (LDH)], longer abstinence and less subsequent binge drinking in patients. High LDH appear to impair treatment outcome via its neurotoxicity on ACC integrity. Thus, high activation of the rostral ACC elicited by affective faces appears to be a resilience factor predicting better treatment outcome. Although no group differences were found, increased FG activation correlated with patients' higher LDH. Because high LDH correlated with worse task performance for facial stimuli in patients, elevated activation in the fusiform 'face' area may reflect inefficient compensatory activation. Therapeutic interventions (e.g. emotion evaluation training) may enable patients to cope with social stress and to decrease relapses after detoxification.

  7. Reiterative AP2a activity controls sequential steps in the neural crest gene regulatory network.

    Science.gov (United States)

    de Crozé, Noémie; Maczkowiak, Frédérique; Monsoro-Burq, Anne H

    2011-01-04

    The neural crest (NC) emerges from combinatorial inductive events occurring within its progenitor domain, the neural border (NB). Several transcription factors act early at the NB, but the initiating molecular events remain elusive. Recent data from basal vertebrates suggest that ap2 might have been critical for NC emergence; however, the role of AP2 factors at the NB remains unclear. We show here that AP2a initiates NB patterning and is sufficient to elicit a NB-like pattern in neuralized ectoderm. In contrast, the other early regulators do not participate in ap2a initiation at the NB, but cooperate to further establish a robust NB pattern. The NC regulatory network uses a multistep cascade of secreted inducers and transcription factors, first at the NB and then within the NC progenitors. Here we report that AP2a acts at two distinct steps of this cascade. As the earliest known NB specifier, AP2a mediates Wnt signals to initiate the NB and activate pax3; as a NC specifier, AP2a regulates further NC development independent of and downstream of NB patterning. Our findings reconcile conflicting observations from various vertebrate organisms. AP2a provides a paradigm for the reiterated use of multifunctional molecules, thereby facilitating emergence of the NC in vertebrates.

  8. Mild blast events alter anxiety, memory, and neural activity patterns in the anterior cingulate cortex.

    Science.gov (United States)

    Xie, Kun; Kuang, Hui; Tsien, Joe Z

    2013-01-01

    There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our in vivo neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder.

  9. Mild blast events alter anxiety, memory, and neural activity patterns in the anterior cingulate cortex.

    Directory of Open Access Journals (Sweden)

    Kun Xie

    Full Text Available There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our in vivo neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder.

  10. Biomimetics Micro Robot with Active Hardware Neural Networks Locomotion Control and Insect-Like Switching Behaviour

    Directory of Open Access Journals (Sweden)

    Ken Saito

    2012-11-01

    Full Text Available In this paper, we presented the 4.0, 2.7, 2.5 mm, width, length, height size biomimetics micro robot system which was inspired by insects. The micro robot system was made from silicon wafer fabricated by micro electro mechanical systems (MEMS technology. The mechanical system of the robot was equipped with small size rotary type actuators, link mechanisms and six legs to realize the insect‐like switching behaviour. In addition, we constructed the active hardware neural networks (HNN by analogue CMOS circuits as a locomotion controlling system. The HNN utilized the pulse‐type hardware neuron model (P‐HNM as a basic component. The HNN outputs the driving pulses using synchronization phenomena such as biological neural networks. The driving pulses can operate the actuators of the biomimetics micro robot directly. Therefore, the HNN realized the robot control without using any software programs or A/D converters. The micro robot emulated the locomotion method and the neural networks of an insect with rotary type actuators, link mechanisms and HNN. The micro robot performed forward and backward locomotion, and also changed direction by inputting an external trigger pulse. The locomotion speed was 26.4 mm/min when the step width was 0.88 mm.

  11. The Effects of Dynamical Synapses on Firing Rate Activity: A Spiking Neural Network Model.

    Science.gov (United States)

    Khalil, Radwa; Moftah, Marie Z; Moustafa, Ahmed A

    2017-09-18

    Accumulating evidence relates the fine-tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABAA signaling and a lateral spread length between neighboring neurons (i.e. local connectivity). Furthermore, a number of studies consider Short-Term synaptic Plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson Input Frequency (IF). Nevertheless, evaluating the firing activity in response to the dynamical interaction between STP (triggered by ranges of IF), and these key parameters in vitro remains elusive. Therefore, we designed a Spiking Neural Network (SNN) model in which we incorporated the following parameters: local density of arbor essences and a lateral spread length between neighboring neurons. We also created several network scenarios based on these key parameters. Then, we implemented two classes of STP: (1) Short-Term synaptic Depression (STD), and (2) Short-Term synaptic Facilitation (STF). Each class has two differential forms based on the parametric value of its synaptic time constant (either for depressing or facilitating synapses). Lastly, we compared the neural firing responses before and after the treatment with STP. We found that dynamical synapses(STP) have a critical differential role on evaluating, and modulating the firing rate activity in each network scenario. Moreover, we investigated the impact of changing the balance between excitation (E) / inhibition (I) on stabilizing this firing activity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  12. AAL Middleware Infrastructure for Green Bed Activity Monitoring

    Directory of Open Access Journals (Sweden)

    Filippo Palumbo

    2013-01-01

    Full Text Available This paper describes a service-oriented middleware platform for ambient assisted living and its use in two different bed activity services: bedsore prevention and sleeping monitoring. A detailed description of the middleware platform, its elements and interfaces, as well as a service that is able to classify some typical user's positions in the bed is presented. Wireless sensor networks are supposed to be widely deployed in indoor settings and on people's bodies in tomorrow's pervasive computing environments. The key idea of this work is to leverage their presence by collecting the received signal strength measured among fixed general-purpose wireless sensor devices, deployed in the environment, and wearable ones. The RSS measurements are used to classify a set of user's positions in the bed, monitoring the activities of the user, and thus supporting the bedsores and the sleep monitoring issues. Moreover, the proposed services are able to decrease the energy consumption by exploiting the context information coming from the proposed middleware.

  13. Dissociable neural activity to self- vs. externally administered thermal hyperalgesia: a parametric fMRI study.

    Science.gov (United States)

    Mohr, C; Leyendecker, S; Helmchen, C

    2008-02-01

    Little is known regarding how cognitive strategies help to modulate neural responses of the human brain in ongoing pain syndromes to alleviate pain. Under pathological pain conditions, any self-elicited contact with usually non-painful stimuli may become painful. We examined whether the human brain is capable of dissociating self-controlled from externally administered thermal hyperalgesia in the experimental capsaicin model. Using functional magnetic resonance imaging, 17 male subjects were investigated in a parametric design with heat stimuli at topically capsaicin-sensitized skin. In contrast to external stimulation, self-administered pain was controllable. For both conditions application trials without noticeable thermal stimulation were introduced and used as high-level baseline (HLB) to account for the capsaicin-induced ongoing pain and other covariables. Following subtraction of the HLB, the anterior insula and the anterior cingulate cortex (ACC) but not the somatosensory cortices maintained parametric neural responses to thermal hyperalgesia. A stronger pain-related activity increase during self-administered stimuli was observed in the posterior insula. In contrast, prefrontal cortex showed stronger increases to uncontrollable external heat stimuli. In the state of ongoing pain (capsaicin), pain-intensity-encoding regions (anterior insula, ACC) but not those with sensory discriminative functions (SI, SII) showed graded, pain-intensity-related neural responses in thermal hyperalgesia. Some areas were able to dissociate between self- and externally administered stimuli in thermal hyperalgesia, which might be related to differences in perceived controllability. Thus, neural mechanisms maintain the ability to dissociate external from self-generated states of injury in thermal hyperalgesia. This may help to understand how cognitive strategies potentially alleviate chronic pain syndromes.

  14. Phase locked neural activity in the human brainstem predicts preference for musical consonance.

    Science.gov (United States)

    Bones, Oliver; Hopkins, Kathryn; Krishnan, Ananthanarayan; Plack, Christopher J

    2014-05-01

    When musical notes are combined to make a chord, the closeness of fit of the combined spectrum to a single harmonic series (the 'harmonicity' of the chord) predicts the perceived consonance (how pleasant and stable the chord sounds; McDermott, Lehr, & Oxenham, 2010). The distinction between consonance and dissonance is central to Western musical form. Harmonicity is represented in the temporal firing patterns of populations of brainstem neurons. The current study investigates the role of brainstem temporal coding of harmonicity in the perception of consonance. Individual preference for consonant over dissonant chords was measured using a rating scale for pairs of simultaneous notes. In order to investigate the effects of cochlear interactions, notes were presented in two ways: both notes to both ears or each note to different ears. The electrophysiological frequency following response (FFR), reflecting sustained neural activity in the brainstem synchronised to the stimulus, was also measured. When both notes were presented to both ears the perceptual distinction between consonant and dissonant chords was stronger than when the notes were presented to different ears. In the condition in which both notes were presented to the both ears additional low-frequency components, corresponding to difference tones resulting from nonlinear cochlear processing, were observable in the FFR effectively enhancing the neural harmonicity of consonant chords but not dissonant chords. Suppressing the cochlear envelope component of the FFR also suppressed the additional frequency components. This suggests that, in the case of consonant chords, difference tones generated by interactions between notes in the cochlea enhance the perception of consonance. Furthermore, individuals with a greater distinction between consonant and dissonant chords in the FFR to individual harmonics had a stronger preference for consonant over dissonant chords. Overall, the results provide compelling evidence

  15. Neural activation in speech production and reading aloud in native and non-native languages.

    Science.gov (United States)

    Berken, Jonathan A; Gracco, Vincent L; Chen, Jen-Kai; Soles, Jennika; Watkins, Kate E; Baum, Shari; Callahan, Megan; Klein, Denise

    2015-05-15

    We used fMRI to investigate neural activation in reading aloud in bilinguals differing in age of acquisition. Three groups were compared: French-English bilinguals who acquired two languages from birth (simultaneous), French-English bilinguals who learned their L2 after the age of 5 years (sequential), and English-speaking monolinguals. While the bilingual groups contrasted in age of acquisition, they were matched for language proficiency, although sequential bilinguals produced speech with a less native-like accent in their L2 than in their L1. Simultaneous bilinguals activated similar brain regions to an equivalent degree when reading in their two languages. In contrast, sequential bilinguals more strongly activated areas related to speech-motor control and orthographic to phonological mapping, the left inferior frontal gyrus, left premotor cortex, and left fusiform gyrus, when reading aloud in L2 compared to L1. In addition, the activity in these regions showed a significant positive correlation with age of acquisition. The results provide evidence for the engagement of overlapping neural substrates for processing two languages when acquired in native context from birth. However, it appears that the maturation of certain brain regions for both speech production and phonological encoding is limited by a sensitive period for L2 acquisition regardless of language proficiency.

  16. Right hemisphere neural activations in the recall of waking fantasies and of dreams.

    Science.gov (United States)

    Benedetti, Francesco; Poletti, Sara; Radaelli, Daniele; Ranieri, Rebecca; Genduso, Valeria; Cavallotti, Simone; Castelnovo, Anna; Smeraldi, Enrico; Scarone, Silvio; D'Agostino, Armando

    2015-10-01

    The story-like organization of dreams is characterized by a pervasive bizarreness of events and actions that resembles psychotic thought, and largely exceeds that observed in normal waking fantasies. Little is known about the neural correlates of the confabulatory narrative construction of dreams. In this study, dreams, fantasies elicited by ambiguous pictorial stimuli, and non-imaginative first- and third-person narratives from healthy participants were recorded, and were then studied for brain blood oxygen level-dependent functional magnetic resonance imaging on a 3.0-Tesla scanner while listening to their own narrative reports and attempting a retrieval of the corresponding experience. In respect to non-bizarre reports of daytime activities, the script-driven recall of dreams and fantasies differentially activated a right hemisphere network including areas in the inferior frontal gyrus, and superior and middle temporal gyrus. Neural responses were significantly greater for fantasies than for dreams in all regions, and inversely proportional to the degree of bizarreness observed in narrative reports. The inferior frontal gyrus, superior and middle temporal gyrus have been implicated in the semantic activation, integration and selection needed to build a coherent story representation and to resolve semantic ambiguities; in deductive and inferential reasoning; in self- and other-perspective taking, theory of mind, moral and autobiographical reasoning. Their degree of activation could parallel the level of logical robustness or inconsistency experienced when integrating information and mental representations in the process of building fantasy and dream narratives.

  17. Overlapping patterns of neural activity for different forms of novelty in fMRI

    Directory of Open Access Journals (Sweden)

    Colin Shaun Hawco

    2014-09-01

    Full Text Available When stimuli are presented multiple times, the neural response to repeated stimuli is reduced relative to novel stimuli (repetition suppression. Responses to different types of novelty were examined. Stimulus novelty was examined by contrasting first vs. second presentation of triads of objects during memory encoding. Semantic novelty was contrasted by comparing unrelated (semantically novel triads of objects to triads in which all three objects were related (e.g. all objects were tools. In recognition, associative novelty was examined by contrasting rearranged triads (previously seen objects in a new association with intact triads. Activity was observed in posterior regions (occipital and fusiform, with the largest extent of activity for stimulus novelty and smallest for associational novelty. Frontal activity was also observed in stimulus and semantic novelty. Additional analysis indicated that the hemodynamic response in voxels identified in the stimulus and semantic novelty contrasts was modulated by reaction time on a trial-by-trial basis. That is, the duration of the hemodynamic response was driven by reaction time. This was not the case for associative novelty. The high level of overlap across different forms of novelty suggests a similar mechanism for reduced neural activity, which may be related to reduced visual processing time. This is consistent with a facilitation model of repetition suppression, which posits a reduced peak and duration of neuronal firing for repeated stimuli.

  18. Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

    Science.gov (United States)

    Li, Xiumin; Small, Michael

    2012-06-01

    Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

  19. An Active Stereo Vision System Based on Neural Pathways of Human Binocular Motor System

    Institute of Scientific and Technical Information of China (English)

    Yu-zhang Gu; Makoto Sato; Xiao-lin Zhang

    2007-01-01

    An active stereo vision system based on a model of neural pathways of human binocular motor system is proposed. With this model, it is guaranteed that the two cameras of the active stereo vision system can keep their lines of sight fixed on the same target object during smooth pursuit. This feature is very important for active stereo vision systems, since not only 3D reconstruction needs the two cameras have an overlapping field of vision, but also it can facilitate the 3D reconstruction algorithm. To evaluate the effectiveness of the proposed method, some software simulations are done to demonstrate the same target tracking characteristic in a virtual environment apt to mistracking easily. Here, mistracking means two eyes track two different objects separately. Then the proposed method is implemented in our active stereo vision system to perform real tracking task in a laboratory scene where several persons walk self-determining. Before the proposed model is implemented in the system, mistracking occurred frequently. After it is enabled, mistracking never occurred. The result shows that the vision system based on neural pathways of human binocular motor system can reliably avoid mistracking.

  20. LANDSLIDE ACTIVITY MONITORING WITH THE HELP OF UNMANNED AERIAL VEHICLE

    Directory of Open Access Journals (Sweden)

    V. Peterman

    2015-08-01

    Full Text Available This paper presents a practical example of a landslide monitoring through the use of a UAV - tracking and monitoring the movements of the Potoska Planina landslide located above the village of Koroska Bela in the western Karavanke Mountains in north-western Slovenia. Past geological research in this area indicated slope landmass movement of more than 10 cm per year. However, much larger movements have been detected since - significant enough to be observed photogrammetrically with the help of a UAV. With the intention to assess the dynamics of the landslide we have established a system of periodic observations carried out twice per year – in mid-spring and mid-autumn. This paper offers an activity summary along with the presentation of data acquisition, data processing and results.

  1. Landslide Activity Monitoring with the Help of Unmanned Aerial Vehicle

    Science.gov (United States)

    Peterman, V.

    2015-08-01

    This paper presents a practical example of a landslide monitoring through the use of a UAV - tracking and monitoring the movements of the Potoska Planina landslide located above the village of Koroska Bela in the western Karavanke Mountains in north-western Slovenia. Past geological research in this area indicated slope landmass movement of more than 10 cm per year. However, much larger movements have been detected since - significant enough to be observed photogrammetrically with the help of a UAV. With the intention to assess the dynamics of the landslide we have established a system of periodic observations carried out twice per year - in mid-spring and mid-autumn. This paper offers an activity summary along with the presentation of data acquisition, data processing and results.

  2. Ambulation monitoring of transtibial amputation subjects with patient activity monitor versus pedometer.

    Science.gov (United States)

    Dudek, Nancy L; Khan, Omar D; Lemaire, Edward D; Marks, Meridith B; Saville, Leyana

    2008-01-01

    Our study aimed to compare the accuracy of step count and ambulation distance determined with the Yamax Digi-Walker SW-700 pedometer (DW) and the Ossur patient activity monitor (PAM) in 20 transtibial amputation subjects who were functioning at the K3 Medicare Functional Classification Level. Subjects completed four simulated household tasks in an apartment setup and a gymnasium walking course designed to simulate outdoor walking without the presence of environmental barriers or varied terrain. The mean step count accuracy of the DW and the PAM was equivalent for both the household activity (75.3% vs 70.6%) and the walking course (93.8% vs 94.0%). The mean distance measurement accuracy was better with the DW than with the PAM (household activity: 72.8% vs 0%, walking course: 92.5% vs 86.3%; p < 0.05). With acceptable step count accuracy, both devices are appropriate for assessing relatively continuous ambulation. The DW may be preferred for its more accurate distance measurements. Neither device is ideal for monitoring in-home ambulation.

  3. Forecast and restoration of geomagnetic activity indices by using the software-computational neural network complex

    Science.gov (United States)

    Barkhatov, Nikolay; Revunov, Sergey

    2010-05-01

    It is known that currently used indices of geomagnetic activity to some extent reflect the physical processes occurring in the interaction of the perturbed solar wind with Earth's magnetosphere. Therefore, they are connected to each other and with the parameters of near-Earth space. The establishment of such nonlinear connections is interest. For such purposes when the physical problem is complex or has many parameters the technology of artificial neural networks is applied. Such approach for development of the automated forecast and restoration method of geomagnetic activity indices with the establishment of creative software-computational neural network complex is used. Each neural network experiments were carried out at this complex aims to search for a specific nonlinear relation between the analyzed indices and parameters. At the core of the algorithm work program a complex scheme of the functioning of artificial neural networks (ANN) of different types is contained: back propagation Elman network, feed forward network, fuzzy logic network and Kohonen layer classification network. Tools of the main window of the complex (the application) the settings used by neural networks allow you to change: the number of hidden layers, the number of neurons in the layer, the input and target data, the number of cycles of training. Process and the quality of training the ANN is a dynamic plot of changing training error. Plot of comparison of network response with the test sequence is result of the network training. The last-trained neural network with established nonlinear connection for repeated numerical experiments can be run. At the same time additional training is not executed and the previously trained network as a filter input parameters get through and output parameters with the test event are compared. At statement of the large number of different experiments provided the ability to run the program in a "batch" mode is stipulated. For this purpose the user a

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

    Directory of Open Access Journals (Sweden)

    André Aleman

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

  5. Social status alters defeat-induced neural activation in Syrian hamsters.

    Science.gov (United States)

    Morrison, K E; Curry, D W; Cooper, M A

    2012-05-17

    Although exposure to social stress leads to increased depression-like and anxiety-like behavior, some individuals are more vulnerable than others to these stress-induced changes in behavior. Prior social experience is one factor that can modulate how individuals respond to stressful events. In this study, we investigated whether experience-dependent resistance to the behavioral consequences of social defeat was associated with a specific pattern of neural activation. We paired weight-matched male Syrian hamsters in daily aggressive encounters for 2 weeks, during which they formed a stable dominance relationship. We also included control animals that were exposed to an empty cage each day for 2 weeks. Twenty-four hours after the final pairing or empty cage exposure, half of the subjects were socially defeated in 3, 5-min encounters, whereas the others were not socially defeated. Twenty-four hours after social defeat, animals were tested for conditioned defeat in a 5-min social interaction test with a non-aggressive intruder. We collected brains after social defeat and processed the tissue for c-Fos immunoreactivity. We found that dominants were more likely than subordinates to counter-attack the resident aggressor during social defeat, and they showed less submissive and defensive behavior at conditioned defeat testing compared with subordinates. Also, social status was associated with distinct patterns of defeat-induced neural activation in select brain regions, including the amygdala, prefrontal cortex, hypothalamus, and lateral septum. Our results indicate that social status is an important form of prior experience that predicts both initial coping style and the degree of resistance to social defeat. Further, the differences in defeat-induced neural activation suggest possible brain regions that may control resistance to conditioned defeat in dominant individuals.

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

    Science.gov (United States)

    Aleman, André; Swart, Marte

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Ida Wessing

    2015-06-01

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

  8. Long term continuous radon monitoring in a seismically active area

    CERN Document Server

    Piersanti, A; Galli, G

    2015-01-01

    We present the results of a long term, continuous radon monitoring experiment started in April 2010 in a seismically active area, affected during the 2010-2013 data acquisition time window by an intense micro seismic activity and by several small seismic events. We employed both correlation and cross-correlation analyses in order to investigate possible relationship existing between the collected radon data, seismic events and meteorological parameters. Our results do not support the feasibility of a robust one-to-one association between the small magnitude earthquakes characterizing the local seismic activity and single radon measurement anomalies, but evidence significant correlation patterns between the spatio-temporal variations of seismic moment release and soil radon emanations, the latter being anyway dominantly modulated by meteorological parameters variations.

  9. Application of an artificial neural network for evaluation of activity concentration exemption limits in NORM industry.

    Science.gov (United States)

    Wiedner, Hannah; Peyrés, Virginia; Crespo, Teresa; Mejuto, Marcos; García-Toraño, Eduardo; Maringer, Franz Josef

    2016-12-27

    NORM emits many different gamma energies that have to be analysed by an expert. Alternatively, artificial neural networks (ANNs) can be used. These mathematical software tools can generalize "knowledge" gained from training datasets, applying it to new problems. No expert knowledge of gamma-ray spectrometry is needed by the end-user. In this work an ANN was created that is able to decide from the raw gamma-ray spectrum if the activity concentrations in a sample are above or below the exemption limits.

  10. Ferroelectric thin-film active sensors for structural health monitoring

    Science.gov (United States)

    Lin, Bin; Giurgiutiu, Victor; Yuan, Zheng; Liu, Jian; Chen, Chonglin; Jiang, Jiechao; Bhalla, Amar S.; Guo, Ruyan

    2007-04-01

    Piezoelectric wafer active sensors (PWAS) have been proven a valuable tool in structural health monitoring. Piezoelectric wafer active sensors are able to send and receive guided Lamb/Rayleigh waves that scan the structure and detect the presence of incipient cracks and structural damage. In-situ thin-film active sensor deposition can eliminate the bonding layer to improve the durability issue and reduce the acoustic impedance mismatch. Ferroelectric thin films have been shown to have piezoelectric properties that are close to those of single-crystal ferroelectrics but the fabrication of ferroelectric thin films on structural materials (steel, aluminum, titanium, etc.) has not been yet attempted. In this work, in-situ fabrication method of piezoelectric thin-film active sensors arrays was developed using the nano technology approach. Specification for the piezoelectric thin-film active sensors arrays was based on electro-mechanical-acoustical model. Ferroelectric BaTiO3 (BTO) thin films were successfully deposited on Ni tapes by pulsed laser deposition under the optimal synthesis conditions. Microstructural studies by X-ray diffractometer and transmission electron microscopy reveal that the as-grown BTO thin films have the nanopillar structures with an average size of approximately 80 nm in diameter and the good interface structures with no inter-diffusion or reaction. The dielectric and ferroelectric property measurements exhibit that the BTO films have a relatively large dielectric constant, a small dielectric loss, and an extremely large piezoelectric response with a symmetric hysteresis loop. The research objective is to develop the fabrication and optimum design of thin-film active sensor arrays for structural health monitoring applications. The short wavelengths of the micro phased arrays will permit the phased-array imaging of smaller parts and smaller damage than is currently not possible with existing technology.

  11. Lidocaine alters the input resistance and evokes neural activity in crayfish sensory neurons.

    Science.gov (United States)

    Keceli, M B; Purali, N

    2007-03-01

    Lidocaine, a use-dependent Na(+) channel blocker, paradoxically evokes neural activation in the slowly adapting stretch receptor organ of crayfish at 5-10 mmol/l concentration. For elucidating the underlying mechanisms of this paradoxical effect, a series of conventional electrophysiological experiments were performed in the stretch receptor neurons of crayfish. In the presence of tetrodotoxin, lidocaine did not evoke impulse activity, however, a slowly developing and dose-dependent depolarization occurred in both the rapidly and slowly adapting stretch receptors. Similar effects were observed by perfusion of equivalent concentrations of benzocaine but not of procaine or prilocaine. Lidocaine did not evoke neural activity in the rapidly adapting neuron which fires action potential(s) in response to rapid changes in membrane potential. Slowly developing mode of the depolarization indicated the reason why only depolarization but not action potential responses were observed in the rapidly adapting neuron. The depolarizing effect of lidocaine was independent from any ionic channel or exchanger system. However, lidocaine and benzocaine but not procaine and prilocaine evoked a dose-dependent alteration in the input resistance of the neuron. It was proposed that the principal mechanism of the effect could stem from a change in the physical properties of the neuronal membrane.

  12. Neural Activities Underlying the Feedback Express Salience Prediction Errors for Appetitive and Aversive Stimuli

    Science.gov (United States)

    Gu, Yan; Hu, Xueping; Pan, Weigang; Yang, Chun; Wang, Lijun; Li, Yiyuan; Chen, Antao

    2016-01-01

    Feedback information is essential for us to adapt appropriately to the environment. The feedback-related negativity (FRN), a frontocentral negative deflection after the delivery of feedback, has been found to be larger for outcomes that are worse than expected, and it reflects a reward prediction error derived from the midbrain dopaminergic projections to the anterior cingulate cortex (ACC), as stated in reinforcement learning theory. In contrast, the prediction of response-outcome (PRO) model claims that the neural activity in the mediofrontal cortex (mPFC), especially the ACC, is sensitive to the violation of expectancy, irrespective of the valence of feedback. Additionally, increasing evidence has demonstrated significant activities in the striatum, anterior insula and occipital lobe for unexpected outcomes independently of their valence. Thus, the neural mechanism of the feedback remains under dispute. Here, we investigated the feedback with monetary reward and electrical pain shock in one task via functional magnetic resonance imaging. The results revealed significant prediction-error-related activities in the bilateral fusiform gyrus, right middle frontal gyrus and left cingulate gyrus for both money and pain. This implies that some regions underlying the feedback may signal a salience prediction error rather than a reward prediction error. PMID:27694920

  13. Passive and Active Sensing Technologies for Structural Health Monitoring

    Science.gov (United States)

    Do, Richard

    A combination of passive and active sensing technologies is proposed as a structural health monitoring solution for several applications. Passive sensing is differentiated from active sensing in that with the former, no energy is intentionally imparted into the structure under test; sensors are deployed in a pure detection mode for collecting data mined for structural health monitoring purposes. In this thesis, passive sensing using embedded fiber Bragg grating optical strain gages was used to detect varying degrees of impact damage using two different classes of features drawn from traditional spectral analysis and auto-regressive time series modeling. The two feature classes were compared in detail through receiver operating curve performance analysis. The passive detection problem was then augmented with an active sensing system using ultrasonic guided waves (UGWs). This thesis considered two main challenges associated with UGW SHM including in-situ wave propagation property determination and thermal corruption of data. Regarding determination of wave propagation properties, of which dispersion characteristics are the most important, a new dispersion curve extraction method called sparse wavenumber analysis (SWA) was experimentally validated. Also, because UGWs are extremely sensitive to ambient temperature changes on the structure, it significantly affects the wave propagation properties by causing large errors in the residual error in the processing of the UGWs from an array. This thesis presented a novel method that compensates for uniform temperature change by considering the magnitude and phase of the signal separately and applying a scalable transformation.

  14. Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

    Science.gov (United States)

    Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

    2010-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  15. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations.

    Science.gov (United States)

    Wang, Sheng-Jun; Hilgetag, Claus C; Zhou, Changsong

    2011-01-01

    Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

  16. Automated monitoring of activated sludge using image analysis

    OpenAIRE

    Motta, Maurício da; M. N. Pons; Roche, N; A.L. Amaral; Ferreira, E. C.; Alves, M.M.; Mota, M.; Vivier, H.

    2000-01-01

    An automated procedure for the characterisation by image analysis of the morphology of activated sludge has been used to monitor in a systematic manner the biomass in wastewater treatment plants. Over a period of one year, variations in terms mainly of the fractal dimension of flocs and of the amount of filamentous bacteria could be related to rain events affecting the plant influent flow rate and composition. Grand Nancy Council. Météo-France. Brasil. Ministério da Ciênc...

  17. Anoxic Activated Sludge Monitoring with Combined Nitrate and Titrimetric Measurements

    DEFF Research Database (Denmark)

    Petersen, B.; Gernaey, Krist; Vanrolleghem, P.A.

    2002-01-01

    An experimental procedure for anoxic activated sludge monitoring with combined nitrate and titrimetric measurements is proposed and evaluated successfully with two known carbon sources, (-)acetate and dextrose. For nitrate measurements an ion-selective nitrate electrode is applied to allow...... was with the carbon source in excess, since excess nitrate provoked nitrite build-up thereby complicating the data interpretation. A conceptual model could quantitatively describe the experimental observations and thus link the experimentally measured proton production with the consumption of electron acceptor...... and carbon source during denitrification....

  18. Noncontact monitoring of cardiorespiratory activity by electromagnetic coupling.

    Science.gov (United States)

    Teichmann, Daniel; Foussier, Jérôme; Jia, Jing; Leonhardt, Steffen; Walter, Marian

    2013-08-01

    In this paper, the method of noncontact monitoring of cardiorespiratory activity by electromagnetic coupling with human tissue is investigated. Two measurement modalities were joined: an inductive coupling sensor based on magnetic eddy current induction and a capacitive coupling sensor based on displacement current induction. The system's sensitivity to electric tissue properties and its dependence on motion are analyzed theoretically as well as experimentally for the inductive and capacitive coupling path. The potential of both coupling methods to assess respiration and pulse without contact and a minimum of thoracic wall motion was verified by laboratory experiments. The demonstrator was embedded in a chair to enable recording from the back part of the thorax.

  19. Monitoring rice farming activities in the Mekong Delta region

    Science.gov (United States)

    Nguyen, S. T.; Chen, C. F.; Chen, C. R.; Chiang, S. H.; Chang, L. Y.; Khin, L. V.

    2015-12-01

    Half of the world's population depends on rice for survival. Rice agriculture thus plays an important role in the developing world's economy. Vietnam is one of the largest rice producers and suppliers on earth and more than 80% of the exported rice was produced from the Mekong Delta region, which is situated in the southwestern Vietnam and encompasses approximately 40,000 km2. Changes in climate conditions could likely trigger the increase of insect populations and rice diseases, causing the potential loss of rice yields. Monitoring rice-farming activities through crop phenology detection can provide policymakers with timely strategies to mitigate possible impacts on the potential yield as well as rice grain exports to ensure food security for the region. The main objective of this study is to develop a logistic-based algorithm to investigate rice sowing and harvesting activities from the multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS)-Landsat fusion data. We processed the data for two main cropping seasons (i.e., winter-spring and summer-autumn seasons) through a three-step procedure: (1) MODIS-Landsat data fusion, (2) construction of the time-series enhanced vegetation index 2 (EVI2) data, (3) rice crop phenology detection. The EVI2 data derived from the fusion results between MODIS and Landsat data were compared with that of Landsat data indicated close correlation between the two datasets (R2 = 0.93). The time-series EVI2 data were processed using the double logistic method to detect the progress of sowing and harvesting activities in the region. The comparisons between the estimated sowing and harvesting dates and the field survey data revealed the root mean squared error (RMSE) values of 8.4 and 5.5 days for the winter-spring crop and 9.4 and 12.8 days for the summer-autumn crop, respectively. This study demonstrates the effectiveness of the double logistic-based algorithm for rice crop monitoring from temporal MODIS-Landsat fusion data

  20. Monitoring Active Volcanos Using Aerial Images and the Orthoview Tool

    Directory of Open Access Journals (Sweden)

    Maria Marsella

    2014-12-01

    Full Text Available In volcanic areas, where it can be difficult to perform direct surveys, digital photogrammetry techniques are rarely adopted for routine volcano monitoring. Nevertheless, they have remarkable potentialities for observing active volcanic features (e.g., fissures, lava flows and the connected deformation processes. The ability to obtain accurate quantitative data of definite accuracy in short time spans makes digital photogrammetry a suitable method for controlling the evolution of rapidly changing large-area volcanic phenomena. The systematic acquisition of airborne photogrammetric datasets can be adopted for implementing a more effective procedure aimed at long-term volcano monitoring and hazard assessment. In addition, during the volcanic crisis, the frequent acquisition of oblique digital images from helicopter allows for quasi-real-time monitoring to support mitigation actions by civil protection. These images are commonly used to update existing maps through a photo-interpretation approach that provide data of unknown accuracy. This work presents a scientific tool (Orthoview that implements a straightforward photogrammetric approach to generate digital orthophotos from single-view oblique images provided that at least four Ground Control Points (GCP and current Digital Elevation Models (DEM are available. The influence of the view geometry, of sparse and not-signalized GCP and DEM inaccuracies is analyzed for evaluating the performance of the developed tool in comparison with other remote sensing techniques. Results obtained with datasets from Etna and Stromboli volcanoes demonstrate that 2D features measured on the produced orthophotos can reach sub-meter-level accuracy.

  1. Validity of physical activity monitors in adults participating in free-living activities

    DEFF Research Database (Denmark)

    Berntsen, S; Hageberg, R; Aandstad, A

    2010-01-01

    Background For a given subject, time in moderate to very vigorous intensity physical activity (MVPA) varies substantially among physical activity monitors. Objective In the present study, the primary objective, whether time in MVPA recorded with SenseWear Pro(2) Armband (Armband; BodyMedia...

  2. Stress monitoring versus microseismic ruptures in an active deep mine

    Science.gov (United States)

    Tonnellier, Alice; Bouffier, Christian; Bigarré, Pascal; Nyström, Anders; Österberg, Anders; Fjellström, Peter

    2015-04-01

    monitoring data coming from the mine in quasi-real time and facilitates information exchanges and decision making for experts and stakeholders. On the basis of these data acquisition and sharing, preliminary analysis has been started to highlight whether stress variations and seismic sources behaviour might be directly bound with mine working evolution and could improve the knowledge on the equilibrium states inside the mine. Knowing such parameters indeed will be a potential solution to understand better the response of deep mining activities to the exploitation solicitations and to develop, if possible, methods to prevent from major hazards such as rock bursts and other ground failure phenomena.

  3. Cognitive-affective neural plasticity following active-controlled mindfulness intervention.

    Science.gov (United States)

    Allen, Micah; Dietz, Martin; Blair, Karina S; van Beek, Martijn; Rees, Geraint; Vestergaard-Poulsen, Peter; Lutz, Antoine; Roepstorff, Andreas

    2012-10-31

    Mindfulness meditation is a set of attention-based, regulatory, and self-inquiry training regimes. Although the impact of mindfulness training (MT) on self-regulation is well established, the neural mechanisms supporting such plasticity are poorly understood. MT is thought to act through interoceptive salience and attentional control mechanisms, but until now conflicting evidence from behavioral and neural measures renders difficult distinguishing their respective roles. To resolve this question we conducted a fully randomized 6 week longitudinal trial of MT, explicitly controlling for cognitive and treatment effects with an active-control group. We measured behavioral metacognition and whole-brain blood oxygenation level-dependent (BOLD) signals using functional MRI during an affective Stroop task before and after intervention in healthy human subjects. Although both groups improved significantly on a response-inhibition task, only the MT group showed reduced affective Stroop conflict. Moreover, the MT group displayed greater dorsolateral prefrontal cortex responses during executive processing, consistent with increased recruitment of top-down mechanisms to resolve conflict. In contrast, we did not observe overall group-by-time interactions on negative affect-related reaction times or BOLD responses. However, only participants with the greatest amount of MT practice showed improvements in response inhibition and increased recruitment of dorsal anterior cingulate cortex, medial prefrontal cortex, and right anterior insula during negative valence processing. Our findings highlight the importance of active control in MT research, indicate unique neural mechanisms for progressive stages of mindfulness training, and suggest that optimal application of MT may differ depending on context, contrary to a one-size-fits-all approach.

  4. Neural network controlled three-phase four-wire shunt active power filter

    Energy Technology Data Exchange (ETDEWEB)

    Elmitwally, A.; Abdelkader, S. [Mansura University (Egypt); El-Kateb, M. [Bath University (United Kingdom). Dept. of Electronic and Electrical Engineering

    2000-03-01

    A three-phase four-wire shunt active power filter for harmonic mitigation and reactive power compensation in power systems supplying nonlinear loads is presented. Three adaptive linear neurons are used to tackle the desired three-phase filter current templates. Another feedforward three layer neural network is adopted to control the output filter compensating currents online. This is accomplished by producing the appropriate switching patterns of the converter's legs IGBTs. Adequate tracking of the filter current references is obtained by this method. The active filter inject the current required to compensate for the harmonic and reactive components of the line currents. Simulation results of the proposed active filter indicate a remarkable improvement in the source current waveforms. This is reflected in the enhancement of the unified power quality index defined. Also, the filter has exhibited quite a high dynamic response for step variations in the load current assuring its potential for real-time applications. (author)

  5. Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks.

    Science.gov (United States)

    Villas-Boas, Mariana D; Olivera, Francisco; de Azevedo, Jose Paulo S

    2017-09-01

    Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation tools to provide efficient water quality monitoring. For this purpose, a nonlinear principal component analysis (NLPCA) based on an autoassociative neural network was performed to assess the redundancy of the parameters and monitoring locations of the water quality network in the Piabanha River watershed. Oftentimes, a small number of variables contain the most relevant information, while the others add little or no interpretation to the variability of water quality. Principal component analysis (PCA) is widely used for this purpose. However, conventional PCA is not able to capture the nonlinearities of water quality data, while neural networks can represent those nonlinear relationships. The results presented in this work demonstrate that NLPCA performs better than PCA in the reconstruction of the water quality data of Piabanha watershed, explaining most of data variance. From the results of NLPCA, the most relevant water quality parameter is fecal coliforms (FCs) and the least relevant is chemical oxygen demand (COD). Regarding the monitoring locations, the most relevant is Poço Tarzan (PT) and the least is Parque Petrópolis (PP).

  6. The relation of ongoing brain activity, evoked neural responses, and cognition

    Directory of Open Access Journals (Sweden)

    Sepideh Sadaghiani

    2010-06-01

    Full Text Available Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a ‘segregationist’ view on ongoing activity, both in time and space, countering this view with an emphasis on integration within a hierarchical spatiotemporal organization of intrinsic activity. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function - provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behaviour.

  7. Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

    Directory of Open Access Journals (Sweden)

    Sheng-Jun Wang

    2011-06-01

    Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

  8. Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network.

    Science.gov (United States)

    Pani, Ajaya Kumar; Mohanta, Hare Krishna

    2015-05-01

    Particle size soft sensing in cement mills will be largely helpful in maintaining desired cement fineness or Blaine. Despite the growing use of vertical roller mills (VRM) for clinker grinding, very few research work is available on VRM modeling. This article reports the design of three types of feed forward neural network models and least square support vector regression (LS-SVR) model of a VRM for online monitoring of cement fineness based on mill data collected from a cement plant. In the data pre-processing step, a comparative study of the various outlier detection algorithms has been performed. Subsequently, for model development, the advantage of algorithm based data splitting over random selection is presented. The training data set obtained by use of Kennard-Stone maximal intra distance criterion (CADEX algorithm) was used for development of LS-SVR, back propagation neural network, radial basis function neural network and generalized regression neural network models. Simulation results show that resilient back propagation model performs better than RBF network, regression network and LS-SVR model. Model implementation has been done in SIMULINK platform showing the online detection of abnormal data and real time estimation of cement Blaine from the knowledge of the input variables. Finally, closed loop study shows how the model can be effectively utilized for maintaining cement fineness at desired value.

  9. Constitutively active Notch1 converts cranial neural crest-derived frontonasal mesenchyme to perivascular cells in vivo

    Directory of Open Access Journals (Sweden)

    Sophie R. Miller

    2017-03-01

    Full Text Available Perivascular/mural cells originate from either the mesoderm or the cranial neural crest. Regardless of their origin, Notch signalling is necessary for their formation. Furthermore, in both chicken and mouse, constitutive Notch1 activation (via expression of the Notch1 intracellular domain is sufficient in vivo to convert trunk mesoderm-derived somite cells to perivascular cells, at the expense of skeletal muscle. In experiments originally designed to investigate the effect of premature Notch1 activation on the development of neural crest-derived olfactory ensheathing glial cells (OECs, we used in ovo electroporation to insert a tetracycline-inducible NotchΔE construct (encoding a constitutively active mutant of mouse Notch1 into the genome of chicken cranial neural crest cell precursors, and activated NotchΔE expression by doxycycline injection at embryonic day 4. NotchΔE-targeted cells formed perivascular cells within the frontonasal mesenchyme, and expressed a perivascular marker on the olfactory nerve. Hence, constitutively activating Notch1 is sufficient in vivo to drive not only somite cells, but also neural crest-derived frontonasal mesenchyme and perhaps developing OECs, to a perivascular cell fate. These results also highlight the plasticity of neural crest-derived mesenchyme and glia.

  10. Adaptive Neural-Sliding Mode Control of Active Suspension System for Camera Stabilization

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    2015-01-01

    Full Text Available The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

  11. Human facial neural activities and gesture recognition for machine-interfacing applications.

    Science.gov (United States)

    Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

    2011-01-01

    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

  12. QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a pow- erful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endo- crine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estro- gens (training set). Only nine descriptors calculated solely from the molecular structures of com- pounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The ob- tained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

  13. Inca: a novel p21-activated kinase-associated protein required for cranial neural crest development.

    Science.gov (United States)

    Luo, Ting; Xu, Yanhua; Hoffman, Trevor L; Zhang, Tailin; Schilling, Thomas; Sargent, Thomas D

    2007-04-01

    Inca (induced in neural crest by AP2) is a novel protein discovered in a microarray screen for genes that are upregulated in Xenopus embryos by the transcriptional activator protein Tfap2a. It has no significant similarity to any known protein, but is conserved among vertebrates. In Xenopus, zebrafish and mouse embryos, Inca is expressed predominantly in the premigratory and migrating neural crest (NC). Knockdown experiments in frog and fish using antisense morpholinos reveal essential functions for Inca in a subset of NC cells that form craniofacial cartilage. Cells lacking Inca migrate successfully but fail to condense into skeletal primordia. Overexpression of Inca disrupts cortical actin and prevents formation of actin "purse strings", which are required for wound healing in Xenopus embryos. We show that Inca physically interacts with p21-activated kinase 5 (PAK5), a known regulator of the actin cytoskeleton that is co-expressed with Inca in embryonic ectoderm, including in the NC. These results suggest that Inca and PAK5 cooperate in restructuring cytoskeletal organization and in the regulation of cell adhesion in the early embryo and in NC cells during craniofacial development.

  14. Bradykinin promotes neuron-generating division of neural progenitor cells through ERK activation.

    Science.gov (United States)

    Pillat, Micheli M; Lameu, Claudiana; Trujillo, Cleber A; Glaser, Talita; Cappellari, Angélica R; Negraes, Priscilla D; Battastini, Ana M O; Schwindt, Telma T; Muotri, Alysson R; Ulrich, Henning

    2016-09-15

    During brain development, cells proliferate, migrate and differentiate in highly accurate patterns. In this context, published results indicate that bradykinin functions in neural fate determination, favoring neurogenesis and migration. However, mechanisms underlying bradykinin function are yet to be explored. Our findings indicate a previously unidentified role for bradykinin action in inducing neuron-generating division in vitro and in vivo, given that bradykinin lengthened the G1-phase of the neural progenitor cells (NPC) cycle and increased TIS21 (also known as PC3 and BTG2) expression in hippocampus from newborn mice. This role, triggered by activation of the kinin-B2 receptor, was conditioned by ERK1/2 activation. Moreover, immunohistochemistry analysis of hippocampal dentate gyrus showed that the percentage of Ki67(+) cells markedly increased in bradykinin-treated mice, and ERK1/2 inhibition affected this neurogenic response. The progress of neurogenesis depended on sustained ERK phosphorylation and resulted in ERK1/2 translocation to the nucleus in NPCs and PC12 cells, changing expression of genes such as Hes1 and Ngn2 (also known as Neurog2). In agreement with the function of ERK in integrating signaling pathways, effects of bradykinin in stimulating neurogenesis were reversed following removal of protein kinase C (PKC)-mediated sustained phosphorylation.

  15. Artificial neural network prediction of the psychometric activities of phenylalkylamines using DFT-calculated molecular descriptors

    Directory of Open Access Journals (Sweden)

    MINA HAGHDADI

    2010-10-01

    Full Text Available In the present work, a quantitative structure–activity relationship (QSAR method was used to predict the psychometric activity values (as mescaline unit, log MU of 48 phenylalkylamine derivatives from their density functional theory (DFT calculated molecular descriptors and an artificial neural network (ANN. In the first step, the molecular descriptors were obtained by DFT calculation at the 6-311G level of theory. Then the stepwise multiple linear regression method was employed to screen the descriptor spaces. In the next step, an artificial neural network and multiple linear regressions (MLR models were developed to construct nonlinear and linear QSAR models, respectively. The standard errors in the prediction of log MU by the MLR model were 0.398, 0.443 and 0.427 for training, internal and external test sets, respectively, while these values for the ANN model were 0.132, 0.197 and 0.202, respectively. The obtained results show the applicability of QSAR approaches by using ANN techniques in prediction of log MU of phenylalkylamine derivatives from their DFT-calculated molecular descriptors.

  16. Perceived moral traits of others differentiate the neural activation that underlies inequity-aversion

    Science.gov (United States)

    Nakatani, Hironori; Ogawa, Akitoshi; Suzuki, Chisato; Asamizuya, Takeshi; Ueno, Kenichi; Cheng, Kang; Okanoya, Kazuo

    2017-01-01

    We have a social preference to reduce inequity in the outcomes between oneself and others. Such a preference varies according to others. We performed functional magnetic resonance imaging during an economic game to investigate how the perceived moral traits of others modulate the neural activities that underlie inequity-aversion. The participants unilaterally allocated money to three partners (good, neutral, and bad). During presentation of the good and neutral partners, the anterior region of the rostral medial frontal cortex (arMFC) showed increased functional connectivity with the caudate head and the anterior insula, respectively. Following this, participants allocated more money to the good partner, and less to the bad partner, compared with the neutral partner. The caudate head and anterior insula showed greater activation during fair allocation to the good and unfair allocation to the neutral partners, respectively. However, these regions were silent during allocations to the bad partner. Therefore, the arMFC-caudate/insula circuit encompasses distinct neural processes that underlie inequity-aversion in monetary allocations that the different moral traits of others can modulate. PMID:28230155

  17. Trait self-esteem and neural activities related to self-evaluation and social feedback.

    Science.gov (United States)

    Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

    2016-02-04

    Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback.

  18. Analgesic Neural Circuits Are Activated by Electroacupuncture at Two Sets of Acupoints

    Directory of Open Access Journals (Sweden)

    Man-Li Hu

    2016-01-01

    Full Text Available To investigate analgesic neural circuits activated by electroacupuncture (EA at different sets of acupoints in the brain, goats were stimulated by EA at set of Baihui-Santai acupoints or set of Housanli acupoints for 30 min. The pain threshold was measured using the potassium iontophoresis method. The levels of c-Fos were determined with Streptavidin-Biotin Complex immunohistochemistry. The results showed pain threshold induced by EA at set of Baihui-Santai acupoints was 44.74%±4.56% higher than that by EA at set of Housanli acupoints (32.64%±5.04%. Compared with blank control, EA at two sets of acupoints increased c-Fos expression in the medial septal nucleus (MSN, the arcuate nucleus (ARC, the nucleus amygdala basalis (AB, the lateral habenula nucleus (HL, the ventrolateral periaqueductal grey (vlPAG, the locus coeruleus (LC, the nucleus raphe magnus (NRM, the pituitary gland, and spinal cord dorsal horn (SDH. Compared with EA at set of Housanli points, EA at set of Baihui-Santai points induced increased c-Fos expression in AB but decrease in MSN, the paraventricular nucleus of the hypothalamus, HL, and SDH. It suggests that ARC-PAG-NRM/LC-SDH and the hypothalamus-pituitary may be the common activated neural pathways taking part in EA-induced analgesia at the two sets of acupoints.

  19. Drosophila Grainyhead specifies late programmes of neural proliferation by regulating the mitotic activity and Hox-dependent apoptosis of neuroblasts.

    Science.gov (United States)

    Cenci, Caterina; Gould, Alex P

    2005-09-01

    The Drosophila central nervous system is generated by stem-cell-like progenitors called neuroblasts. Early in development, neuroblasts switch through a temporal series of transcription factors modulating neuronal fate according to the time of birth. At later stages, it is known that neuroblasts switch on expression of Grainyhead (Grh) and maintain it through many subsequent divisions. We report that the function of this conserved transcription factor is to specify the regionalised patterns of neurogenesis that are characteristic of postembryonic stages. In the thorax, Grh prolongs neural proliferation by maintaining a mitotically active neuroblast. In the abdomen, Grh terminates neural proliferation by regulating the competence of neuroblasts to undergo apoptosis in response to Abdominal-A expression. This study shows how a factor specific to late-stage neural progenitors can regulate the time at which neural proliferation stops, and identifies mechanisms linking it to the Hox axial patterning system.

  20. GABAA receptors in visual and auditory cortex and neural activity changes during basic visual stimulation

    Directory of Open Access Journals (Sweden)

    Pengmin eQin

    2012-12-01

    Full Text Available Recent imaging studies have demonstrated that levels of resting GABA in the visual cortex predict the degree of stimulus-induced activity in the same region. These studies have used the presentation of discrete visual stimulus; the change from closed eyes to open also represents a simple visual stimulus, however, and has been shown to induce changes in local brain activity and in functional connectivity between regions. We thus aimed to investigate the role of the GABA system, specifically GABAA receptors, in the changes in brain activity between the eyes closed (EC and eyes open (EO state in order to provide detail at the receptor level to complement previous studies of GABA concentrations. We conducted an fMRI study involving two different modes of the change from EC to EO: An EO and EC block design, allowing the modelling of the haemodynamic response, followed by longer periods of EC and EO to allow the measuring of functional connectivity. The same subjects also underwent [18F]Flumazenil PET measure GABAA receptor binding potentials. It was demonstrated that the local-to-global ratio of GABAA receptor binding potential in the visual cortex predicted the degree of changes in neural activity from EC to EO. This same relationship was also shown in the auditory cortex. Furthermore, the local-to-global ratio of GABAA receptor binding potential in the visual cortex also predicts the change of functional connectivity between visual and auditory cortex from EC to EO. These findings contribute to our understanding of the role of GABAA receptors in stimulus-induced neural activity in local regions and in inter-regional functional connectivity.

  1. Striatal Activity and Reward Relativity: Neural Signals Encoding Dynamic Outcome Valuation.

    Science.gov (United States)

    Webber, Emily S; Mankin, David E; Cromwell, Howard C

    2016-01-01

    The striatum is a key brain region involved in reward processing. Striatal activity has been linked to encoding reward magnitude and integrating diverse reward outcome information. Recent work has supported the involvement of striatum in the valuation of outcomes. The present work extends this idea by examining striatal activity during dynamic shifts in value that include different levels and directions of magnitude disparity. A novel task was used to produce diverse relative reward effects on a chain of instrumental action. Rats (Rattus norvegicus) were trained to respond to cues associated with specific outcomes varying by food pellet magnitude. Animals were exposed to single-outcome sessions followed by mixed-outcome sessions, and neural activity was compared among identical outcome trials from the different behavioral contexts. Results recording striatal activity show that neural responses to different task elements reflect incentive contrast as well as other relative effects that involve generalization between outcomes or possible influences of outcome variety. The activity that was most prevalent was linked to food consumption and post-food consumption periods. Relative encoding was sensitive to magnitude disparity. A within-session analysis showed strong contrast effects that were dependent upon the outcome received in the immediately preceding trial. Significantly higher numbers of responses were found in ventral striatum linked to relative outcome effects. Our results support the idea that relative value can incorporate diverse relationships, including comparisons from specific individual outcomes to general behavioral contexts. The striatum contains these diverse relative processes, possibly enabling both a higher information yield concerning value shifts and a greater behavioral flexibility.

  2. Rapid fluctuations in extracellular brain glucose levels induced by natural arousing stimuli and intravenous cocaine: fueling the brain during neural activation

    Science.gov (United States)

    Lenoir, Magalie

    2012-01-01

    Glucose, a primary energetic substrate for neural activity, is continuously influenced by two opposing forces that tend to either decrease its extracellular levels due to enhanced utilization in neural cells or increase its levels due to entry from peripheral circulation via enhanced cerebral blood flow. How this balance is maintained under physiological conditions and changed during neural activation remains unclear. To clarify this issue, enzyme-based glucose sensors coupled with high-speed amperometry were used in freely moving rats to evaluate fluctuations in extracellular glucose levels induced by brief audio stimulus, tail pinch (TP), social interaction with another rat (SI), and intravenous cocaine (1 mg/kg). Measurements were performed in nucleus accumbens (NAcc) and substantia nigra pars reticulata (SNr), which drastically differ in neuronal activity. In NAcc, where most cells are powerfully excited after salient stimulation, glucose levels rapidly (latency 2–6 s) increased (30–70 μM or 6–14% over baseline) by all stimuli; the increase differed in magnitude and duration for each stimulus. In SNr, where most cells are transiently inhibited by salient stimuli, TP, SI, and cocaine induced a biphasic glucose response, with the initial decrease (−20–40 μM or 5–10% below baseline) followed by a reboundlike increase. The critical role of neuronal activity in mediating the initial glucose response was confirmed by monitoring glucose currents after local microinjections of glutamate (GLU) or procaine (PRO). While intra-NAcc injection of GLU transiently increased glucose levels in this structure, intra-SNr PRO injection resulted in rapid, transient decreases in SNr glucose. Therefore, extracellular glucose levels in the brain change very rapidly after physiological and pharmacological stimulation, the response is structure specific, and the pattern of neuronal activity appears to be a critical factor determining direction and magnitude of physiological

  3. Active Volcano Monitoring using a Space-based Hyperspectral Imager

    Science.gov (United States)

    Cipar, J. J.; Dunn, R.; Cooley, T.

    2010-12-01

    Active volcanoes occur on every continent, often in close proximity to heavily populated areas. While ground-based studies are essential for scientific research and disaster mitigation, remote sensing from space can provide rapid and continuous monitoring of active and potentially active volcanoes [Ramsey and Flynn, 2004]. In this paper, we report on hyperspectral measurements of Kilauea volcano, Hawaii. Hyperspectral images obtained by the US Air Force TacSat-3/ARTEMIS sensor [Lockwood et al, 2006] are used to obtain estimates of the surface temperatures for the volcano. ARTEMIS measures surface-reflected light in the visible, near-infrared, and short-wave infrared bands (VNIR-SWIR). The SWIR bands are known to be sensitive to thermal radiation [Green, 1996]. For example, images from the NASA Hyperion hyperspectral sensor have shown the extent of wildfires and active volcanoes [Young, 2009]. We employ the methodology described by Dennison et al, (2006) to obtain an estimate of the temperature of the active region of Kilauea. Both day and night-time images were used in the analysis. To improve the estimate, we aggregated neighboring pixels. The active rim of the lava lake is clearly discernable in the temperature image, with a measured temperature exceeding 1100o C. The temperature decreases markedly on the exterior of the summit crater. While a long-wave infrared (LWIR) sensor would be ideal for volcano monitoring, we have shown that the thermal state of an active volcano can be monitored using the SWIR channels of a reflective hyperspectral imager. References: Dennison, Philip E., Kraivut Charoensiri, Dar A. Roberts, Seth H. Peterson, and Robert O. Green (2006). Wildfire temperature and land cover modeling using hyperspectral data, Remote Sens. Environ., vol. 100, pp. 212-222. Green, R. O. (1996). Estimation of biomass fire temperature and areal extent from calibrated AVIRIS spectra, in Summaries of the 6th Annual JPL Airborne Earth Science Workshop, Pasadena, CA

  4. Neural induction and factors that stabilize a neural fate

    OpenAIRE

    Rogers, Crystal; Moody, Sally A.; Casey, Elena

    2009-01-01

    The neural ectoderm of vertebrates forms when the BMP signaling pathway is suppressed. Herein we review the molecules that directly antagonize extracellular BMP and the signaling pathways that further contribute to reduce BMP activity in the neural ectoderm. Downstream of neural induction, a large number of “neural fate stabilizing” (NFS) transcription factors are expressed in the presumptive neural ectoderm, developing neural tube, and ultimately in neural stem cells. Herein we review what i...

  5. An implantable two axis micromanipulator made with a 3D printer for recording neural activity in free-swimming fish.

    Science.gov (United States)

    Rogers, Loranzie S; Van Wert, Jacey C; Mensinger, Allen F

    2017-08-15

    Chronically implanted electrodes allow monitoring neural activity from free moving animals. While a wide variety of implanted headstages, microdrives and electrodes exist for terrestrial animals, few have been developed for use with aquatic animals. A two axis micromanipulator was fabricated with a Formlabs 3D printer for implanting electrodes into free-swimming oyster toadfish (Opsanus tau). The five piece manipulator consisted of a base, body, electrode holder, manual screw drive and locking nut. The manipulator measured approximately 25×20×30mm (l×w×h) and weighed 5.28g after hand assembly. Microwire electrodes were inserted successfully with the manipulator to record high fidelity signals from the anterior lateral line nerve of the toadfish. The micromanipulator allowed the chronically implanted electrodes to be repositioned numerous times to record from multiple sites and extended successful recording time in the toadfish by several days. Three dimensional printing allowed an inexpensive (<$US 5 material), two axis micromanipulator to be printed relatively rapidly (<2h) to successfully record from multiple sites in the anterior lateral line nerve of free-swimming toadfish. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Spatial working memory in neurofibromatosis 1: Altered neural activity and functional connectivity.

    Science.gov (United States)

    Ibrahim, Amira F A; Montojo, Caroline A; Haut, Kristen M; Karlsgodt, Katherine H; Hansen, Laura; Congdon, Eliza; Rosser, Tena; Bilder, Robert M; Silva, Alcino J; Bearden, Carrie E

    2017-01-01

    Neurofibromatosis Type 1 (NF1) is a genetic disorder that disrupts central nervous system development and neuronal function. Cognitively, NF1 is characterized by difficulties with executive control and visuospatial abilities. Little is known about the neural substrates underlying these deficits. The current study utilized Blood-Oxygen-Level-Dependent (BOLD) functional MRI (fMRI) to explore the neural correlates of spatial working memory (WM) deficits in patients with NF1. BOLD images were acquired from 23 adults with NF1 (age M = 32.69; 61% male) and 25 matched healthy controls (age M = 33.08; 64% male) during an in-scanner visuo-spatial WM task. Whole brain functional and psycho-physiological interaction analyses were utilized to investigate neural activity and functional connectivity, respectively, during visuo-spatial WM performance. Participants also completed behavioral measures of spatial reasoning and verbal WM. Relative to healthy controls, participants with NF1 showed reduced recruitment of key components of WM circuitry, the left dorsolateral prefrontal cortex and right parietal cortex. In addition, healthy controls exhibited greater simultaneous deactivation between the posterior cingulate cortex (PCC) and temporal regions than NF1 patients. In contrast, NF1 patients showed greater PCC and bilateral parietal connectivity with visual cortices as well as between the PCC and the cerebellum. In NF1 participants, increased functional coupling of the PCC with frontal and parietal regions was associated with better spatial reasoning and WM performance, respectively; these relationships were not observed in controls. Dysfunctional engagement of WM circuitry, and aberrant functional connectivity of 'task-negative' regions in NF1 patients may underlie spatial WM difficulties characteristic of the disorder.

  7. Maternal hyperglycemia activates an ASK1-FoxO3a-caspase 8 pathway that leads to embryonic neural tube defects.

    Science.gov (United States)

    Yang, Peixin; Li, Xuezheng; Xu, Cheng; Eckert, Richard L; Reece, E Albert; Zielke, Horst Ronald; Wang, Fang

    2013-08-27

    Neural tube defects result from failure to completely close neural tubes during development. Maternal diabetes is a substantial risk factor for neural tube defects, and available evidence suggests that the mechanism that links hyperglycemia to neural tube defects involves oxidative stress and apoptosis. We demonstrated that maternal hyperglycemia correlated with activation of the apoptosis signal-regulating kinase 1 (ASK1) in the developing neural tube, and Ask1 gene deletion was associated with reduced neuroepithelial cell apoptosis and development of neural tube defects. ASK1 activation stimulated the activity of the transcription factor FoxO3a, which increased the abundance of the apoptosis-promoting adaptor protein TRADD, leading to activation of caspase 8. Hyperglycemia-induced apoptosis and the development of neural tube defects were reduced with genetic ablation of either FoxO3a or Casp8 or inhibition of ASK1 by thioredoxin. Examination of human neural tissues affected by neural tube defects revealed increased activation or abundance of ASK1, FoxO3a, TRADD, and caspase 8. Thus, activation of an ASK1-FoxO3a-TRADD-caspase 8 pathway participates in the development of neural tube defects, which could be prevented by inhibiting intermediates in this cascade.

  8. Investigation on the use of artificial neural networks to overcome the effects of environmental and operational changes on guided waves monitoring

    Science.gov (United States)

    El Mountassir, M.; Yaacoubi, S.; Dahmene, F.

    2015-07-01

    Intelligent feature extraction and advanced signal processing techniques are necessary for a better interpretation of ultrasonic guided waves signals either in structural health monitoring (SHM) or in nondestructive testing (NDT). Such signals are characterized by at least multi-modal and dispersive components. In addition, in SHM, these signals are closely vulnerable to environmental and operational conditions (EOCs), and can be severely affected. In this paper we investigate the use of Artificial Neural Network (ANN) to overcome these effects and to provide a reliable damage detection method with a minimal of false indications. An experimental case of study (full scale pipe) is presented. Damages sizes have been increased and their shapes modified in different steps. Various parameters such as the number of inputs and the number of hidden neurons were studied to find the optimal configuration of the neural network.

  9. Dynamic synchronization and chaos in an associative neural network with multiple active memories.

    Science.gov (United States)

    Raffone, Antonino; van Leeuwen, Cees

    2003-09-01

    Associative memory dynamics in neural networks are generally based on attractors. Retrieval based on fixed-point attractors works if only one memory pattern is retrieved at the time, but cannot enable the simultaneous retrieval of more than one pattern. Stable phase-locking of periodic oscillations or limit cycle attractors leads to incorrect feature bindings if the simultaneously retrieved patterns share some of their features. We investigate retrieval dynamics of multiple active patterns in a network of chaotic model neurons. Several memory patterns are kept simultaneously active and separated from each other by a dynamic itinerant synchronization between neurons. Neurons representing shared features alternate their synchronization between patterns, thus multiplexing their binding relationships. Our model includes a mechanism for self-organized readout or decoding of memory pattern coherence in terms of short-term potentiation and short-term depression of synaptic weights.

  10. Rapid, parallel path planning by propagating wavefronts of spiking neural activity

    Directory of Open Access Journals (Sweden)

    Filip Jan Ponulak

    2013-07-01

    Full Text Available Efficient path planning and navigation is critical for animals, robotics, logistics and transportation. We study a model in which spatial navigation problems can rapidly be solved in the brain by parallel mental exploration of alternative routes using propagating waves of neural activity. A wave of spiking activity propagates through a hippocampus-like network, altering the synaptic connectivity. The resulting vector field of synaptic change then guides a simulated animal to the appropriate selected target locations. We demonstrate that the navigation problem can be solved using realistic, local synaptic plasticity rules during a single passage of a wavefront. Our model can find optimal solutions for competing possible targets or learn and navigate in multiple environments. The model provides a hypothesis on the possible computational mechanisms for optimal path planning in the brain, at the same time it is useful for neuromorphic implementations, where the parallelism of information processing proposed here can fully be harnessed in hardware.

  11. Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

    2015-01-01

    is by active truncated models. In these models only the very top part of the system is represented by a physical model whereas the behavior of the part below the truncation is calculated by numerical models and accounted for in the physical model by active actuators applying relevant forces to the physical...... model. Hence, in principal it is possible to achieve reliable experimental data for much larger water depths than what the actual depth of the test basin would suggest. However, since the computations must be faster than real time, as the numerical simulations and the physical experiment run...... simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...

  12. Multistability of complex-valued neural networks with discontinuous activation functions.

    Science.gov (United States)

    Liang, Jinling; Gong, Weiqiang; Huang, Tingwen

    2016-12-01

    In this paper, based on the geometrical properties of the discontinuous activation functions and the Brouwer's fixed point theory, the multistability issue is tackled for the complex-valued neural networks with discontinuous activation functions and time-varying delays. To address the network with discontinuous functions, Filippov solution of the system is defined. Through rigorous analysis, several sufficient criteria are obtained to assure the existence of 25(n) equilibrium points. Among them, 9(n) points are locally stable and 16(n)-9(n) equilibrium points are unstable. Furthermore, to enlarge the attraction basins of the 9(n) equilibrium points, some mild conditions are imposed. Finally, one numerical example is provided to illustrate the effectiveness of the obtained results.

  13. Neural activity associated with semantic versus phonological anomia treatments in aphasia.

    Science.gov (United States)

    van Hees, Sophia; McMahon, Katie; Angwin, Anthony; de Zubicaray, Greig; Copland, David A

    2014-02-01

    Naming impairments in aphasia are typically targeted using semantic and/or phonologically based tasks. However, it is not known whether these treatments have different neural mechanisms. Eight participants with aphasia received twelve treatment sessions using an alternating treatment design, with fMRI scans pre- and post-treatment. Half the sessions employed Phonological Components Analysis (PCA), and half the sessions employed Semantic Feature Analysis (SFA). Pre-treatment activity in the left caudate correlated with greater immediate treatment success for items treated with SFA, whereas recruitment of the left supramarginal gyrus and right precuneus post-treatment correlated with greater immediate treatment success for items treated with PCA. The results support previous studies that have found greater treatment outcome to be associated with activity in predominantly left hemisphere regions, and suggest that different mechanisms may be engaged dependent on the type of treatment employed.

  14. Negative stereotype activation alters interaction between neural correlates of arousal, inhibition and cognitive control.

    Science.gov (United States)

    Forbes, Chad E; Cox, Christine L; Schmader, Toni; Ryan, Lee

    2012-10-01

    Priming negative stereotypes of African Americans can bias perceptions toward novel Black targets, but less is known about how these perceptions ultimately arise. Examining how neural regions involved in arousal, inhibition and control covary when negative stereotypes are activated can provide insight into whether individuals attempt to downregulate biases. Using fMRI, White egalitarian-motivated participants were shown Black and White faces at fast (32 ms) or slow (525 ms) presentation speeds. To create a racially negative stereotypic context, participants listened to violent and misogynistic rap (VMR) in the background. No music (NM) and death metal (DM) were used as control conditions in separate blocks. Fast exposure of Black faces elicited amygdala activation in the NM and VMR conditions (but not DM), that also negatively covaried with activation in prefrontal regions. Only in VMR, however, did amygdala activation for Black faces persist during slow exposure and positively covary with activation in dorsolateral prefrontal cortex while negatively covarying with activation in orbitofrontal cortex. Findings suggest that contexts that prime negative racial stereotypes seem to hinder the downregulation of amygdala activation that typically occurs when egalitarian perceivers are exposed to Black faces.

  15. Persistent neural activity during the maintenance of spatial position in working memory.

    Science.gov (United States)

    Srimal, Riju; Curtis, Clayton E

    2008-01-01

    The mechanism for the short-term maintenance of information involves persistent neural activity during the retention interval, which forms a bridge between the cued memoranda and its later contingent response. Here, we used event-related functional magnetic resonance imaging to identify cortical areas with activity that persists throughout working memory delays with the goal of testing if such activity represents visuospatial attention or prospective saccade goals. We did so by comparing two spatial working memory tasks. During a memory-guided saccade (MGS) task, a location was maintained during a delay after which a saccade was generated to the remembered location. During a spatial item recognition (SIR) task identical to MGS until after the delay, a button press indicated whether a newly cued location matched the remembered location. Activity in frontal and parietal areas persisted above baseline and was greater in the hemisphere contralateral to the cued visual field. However, delay-period activity did not differ between the tasks. Notably, in the putative frontal eye field (FEF), delay period activity did not differ despite that the precise metrics of the memory-guided saccade were known during the MGS delay and saccades were never made in SIR. Persistent FEF activity may therefore represent a prioritized attentional map of space, rather than the metrics for saccades.

  16. Passive and Active Monitoring on a High Performance Research Network.

    Energy Technology Data Exchange (ETDEWEB)

    Matthews, Warren

    2001-05-01

    The bold network challenges described in ''Internet End-to-end Performance Monitoring for the High Energy and Nuclear Physics Community'' presented at PAM 2000 have been tackled by the intrepid administrators and engineers providing the network services. After less than a year, the BaBar collaboration has collected almost 100 million particle collision events in a database approaching 165TB (Tera=10{sup 12}). Around 20TB has been exported via the Internet to the BaBar regional center at IN2P3 in Lyon, France, for processing and around 40 TB of simulated events have been imported to SLAC from Lawrence Livermore National Laboratory (LLNL). An unforseen challenge has arisen due to recent events and highlighted security concerns at DoE funded labs. New rules and regulations suggest it is only a matter of time before many active performance measurements may not be possible between many sites. Yet, at the same time, the importance of understanding every aspect of the network and eradicating packet loss for high throughput data transfers has become apparent. Work at SLAC to employ passive monitoring using netflow and OC3MON is underway and techniques to supplement and possibly replace the active measurements are being considered. This paper will detail the special needs and traffic characterization of a remarkable research project, and how the networking hurdles have been resolved (or not!) to achieve the required high data throughput. Results from active and passive measurements will be compared, and methods for achieving high throughput and the effect on the network will be assessed along with tools that directly measure throughput and applications used to actually transfer data.

  17. Neural Networks: Making Connections about the Brain and about College while Monitoring Student Engagement in Second Graders.

    Science.gov (United States)

    Mead, Kristina S

    2010-01-01

    This article describes a neuroscience outreach program developed by college undergraduates and aimed at second graders. Over a period of four weeks, twenty-five Denison students enrolled in a non-majors course on gender and the brain visited twenty-four second grade classrooms to engage a total of 464 students. We had a mission to both promote college awareness and to specifically bring some brain science into the classroom. The desire to engage students with the brain was in part a wish to celebrate brain awareness week and in part a wish to follow a feminist tenet of bridging theory and practice via activism. The college students chose six activities: a brain puzzle, a sock content guessing game, a jelly bean olfaction and taste test, mystery noises, a message transmission game, and a version of tag. During our outreach with the second graders, my students monitored student engagement and compared engagement between male and female second graders. Engagement was high for nearly all activities but girls were more engaged than boys during the brain puzzle and jelly bean activities. Effect sizes measured as Cohen's "d" statistics were small to large (0.2 to 0.93). The other four activities (mystery socks, mystery noises, message transmission and neuron chain tag) showed no difference in engagement between male and female second graders. Our program benefited the Denison students as well, introducing many to community involvement and awakening in them an interest in teaching or working with kids.

  18. Adolescents' risky decision-making activates neural networks related to social cognition and cognitive control processes.

    Science.gov (United States)

    Rodrigo, María José; Padrón, Iván; de Vega, Manuel; Ferstl, Evelyn C

    2014-01-01

    This study examines by means of functional magnetic resonance imaging the neural mechanisms underlying adolescents' risk decision-making in social contexts. We hypothesize that the social context could engage brain regions associated with social cognition processes and developmental changes are also expected. Sixty participants (adolescents: 17-18, and young adults: 21-22 years old) read narratives describing typical situations of decision-making in the presence of peers. They were asked to make choices in risky situations (e.g., taking or refusing a drug) or ambiguous situations (e.g., eating a hamburger or a hotdog). Risky as compared to ambiguous scenarios activated bilateral temporoparietal junction (TPJ), bilateral middle temporal gyrus (MTG), right medial prefrontal cortex, and the precuneus bilaterally; i.e., brain regions related to social cognition processes, such as self-reflection and theory of mind (ToM). In addition, brain structures related to cognitive control were active [right anterior cingulate cortex (ACC), bilateral dorsolateral prefrontal cortex (DLPFC), bilateral orbitofrontal cortex], whereas no significant clusters were obtained in the reward system (ventral striatum). Choosing the dangerous option involved a further activation of control areas (ACC) and emotional and social cognition areas (temporal pole). Adolescents employed more neural resources than young adults in the right DLPFC and the right TPJ in risk situations. When choosing the dangerous option, young adults showed a further engagement in ToM related regions (bilateral MTG) and in motor control regions related to the planning of actions (pre-supplementary motor area). Finally, the right insula and the right superior temporal gyrus were more activated in women than in men, suggesting more emotional involvement and more intensive modeling of the others' perspective in the risky conditions. These findings call for more comprehensive developmental accounts of decision-making in

  19. Adolescents’ risky decision-making activates neural networks related to social cognition and cognitive control processes

    Directory of Open Access Journals (Sweden)

    María José eRodrigo

    2014-02-01

    Full Text Available This study examines by means of fMRI the neural mechanisms underlying adolescents’ risk decision-making in social contexts. We hypothesize that the social context could engage brain regions associated with social cognition processes and developmental changes are also expected. Sixty participants (adolescents: 17-18, and young adults: 21-22 years old read narratives describing typical situations of decision-making in the presence of peers. They were asked to make choices in risky situations (e.g., taking or refusing a drug or ambiguous situations (e.g., eating a hamburger or a hotdog. Risky as compared to ambiguous scenarios activated bilateral temporoparietal junction (TPJ, bilateral middle temporal gyrus (MTG, right medial prefrontal cortex (mPFC, and the precuneus bilaterally; i.e., brain regions related to social cognition processes, such as self-reflection and theory of mind. In addition, brain structures related to cognitive control were active (right ACC, bilateral DLPFC, bilateral OFC, whereas no significant clusters were obtained in the reward system (VS. Choosing the dangerous option involved a further activation of control areas (ACC and emotional and social cognition areas (temporal pole. Adolescents employed more neural resources than young adults in the right DLPFC and the right TPJ in risk situations. When choosing the dangerous option, young adults showed a further engagement in theory of mind related regions (bilateral middle temporal gyrus and in motor control regions related to the planning of actions (pre-supplementary motor area. Finally, the right insula and the right superior temporal gyrus were more activated in women than in men, suggesting more emotional involvement and more intensive modeling of the others’ perspective in the risky conditions. These findings call for more comprehensive developmental accounts of decision-making in social contexts that incorporate the role of emotional and social cognition processes.

  20. Visual avoidance in phobia: particularities in neural activity, autonomic responding, and cognitive risk evaluations

    Directory of Open Access Journals (Sweden)

    Tatjana eAue

    2013-05-01

    Full Text Available We investigated the neural mechanisms and the autonomic and cognitive responses associated with visual avoidance behavior in spider phobia. Spider phobic and control participants imagined visiting different forest locations with the possibility of encountering spiders, snakes, or birds (neutral reference category. In each experimental trial, participants saw a picture of a forest location followed by a picture of a spider, snake, or bird, and then rated their personal risk of encountering these animals in this context, as well as their fear. The greater the visual avoidance of spiders that a phobic participant demonstrated (as measured by eye tracking, the higher were her autonomic arousal and neural activity in the amygdala, orbitofrontal cortex (OFC, anterior cingulate cortex (ACC, and precuneus at picture onset. Visual avoidance of spiders in phobics also went hand in hand with subsequently reduced cognitive risk of encounters. Control participants, in contrast, displayed a positive relationship between gaze duration toward spiders, on the one hand, and autonomic responding, as well as OFC, ACC, and precuneus activity, on the other hand. In addition, they showed reduced encounter risk estimates when they looked longer at the animal pictures. Our data are consistent with the idea that one reason for phobics to avoid phobic information may be grounded in heightened activity in the fear circuit, which signals potential threat. Because of the absence of alternative efficient regulation strategies, visual avoidance may then function to down-regulate cognitive risk evaluations for threatening information about the phobic stimuli. Control participants, in contrast, may be characterized by a different coping style, whereby paying visual attention to potentially threatening information may help them to actively down-regulate cognitive evaluations of risk.

  1. Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia.

    Science.gov (United States)

    Gálvez, Jorge A; Jalali, Ali; Ahumada, Luis; Simpao, Allan F; Rehman, Mohamed A

    2017-08-23

    Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. We retrieved three data sets from patients receiving general anesthesia in 2015 with either mask, laryngeal mask airway or endotracheal tube. Patients underwent myringotomy, tonsillectomy, adenoidectomy or inguinal hernia repair procedures. We retrieved measurements for end-tidal carbon dioxide, tidal volume, and peak inspiratory pressure and calculated statistical features for each data element per patient. We applied machine learning algorithms (decision tree, support vector machine, and neural network) to classify patients into noninvasive or invasive airway device support. We identified 300 patients per group (mask, laryngeal mask airway, and endotracheal tube) for a total of 900 patients. The neural network classifier performed better than the boosted trees and support vector machine classifiers based on the test data sets. The sensitivity, specificity, and accuracy for neural network classification are 97.5%, 96.3%, and 95.8%. In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.

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

    Directory of Open Access Journals (Sweden)

    Irina Popova

    2013-08-01

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

  3. Drought monitoring with soil moisture active passive (SMAP) measurements

    Science.gov (United States)

    Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

    2017-09-01

    Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an

  4. Activity monitor intervention to promote physical activity of physicians-in-training: randomized controlled trial.

    Directory of Open Access Journals (Sweden)

    Anne N Thorndike

    Full Text Available BACKGROUND: Physicians are expected to serve as role models for healthy lifestyles, but long work hours reduce time for healthy behaviors. A hospital-based physical activity intervention could improve physician health and increase counseling about exercise. METHODS: We conducted a two-phase intervention among 104 medical residents at a large hospital in Boston, Massachusetts. Phase 1 was a 6-week randomized controlled trial comparing daily steps of residents assigned to an activity monitor displaying feedback about steps and energy consumed (intervention or to a blinded monitor (control. Phase 2 immediately followed and was a 6-week non-randomized team steps competition in which all participants wore monitors with feedback. Phase 1 outcomes were: 1 median steps/day and 2 proportion of days activity monitor worn. The Phase 2 outcome was mean steps/day on days monitor worn (≥500 steps/day. Physiologic measurements were collected at baseline and study end. Median steps/day were compared using Wilcoxon rank-sum tests. Mean steps were compared using repeated measures regression analyses. RESULTS: In Phase 1, intervention and control groups had similar activity (6369 vs. 6063 steps/day, p = 0.16 and compliance with wearing the monitor (77% vs. 77% of days, p = 0.73. In Phase 2 (team competition, residents recorded more steps/day than during Phase 1 (CONTROL: 7,971 vs. 7,567, p = 0.002; INTERVENTION: 7,832 vs. 7,739, p = 0.13. Mean compliance with wearing the activity monitor decreased for both groups during Phase 2 compared to Phase 1 (60% vs. 77%, p<0.001. Mean systolic blood pressure decreased (p = 0.004 and HDL cholesterol increased (p<0.001 among all participants at end of study compared to baseline. CONCLUSIONS: Although the activity monitor intervention did not have a major impact on activity or health, the high participation rates of busy residents and modest changes in steps, blood pressure, and HDL suggest that more

  5. A basic study of activity type detection and energy expenditure estimation for children and youth in daily life using 3-axis accelerometer and 3-stage cascaded artificial neural network.

    Science.gov (United States)

    Yongwon Jang; Yoonseon Song; Hyung Wook Noh; Seunghwan Kim

    2015-08-01

    It is important to prevent obesity in childhood given that many obese adults have been obese since childhood. An activity monitor could provide an effective aid in preventing obesity if it records not only the calorie assessment but also activity detection to check how active a child is in daily life. The current study is for activity monitoring algorithm and we designed 3-stage cascaded artificial neural network. To develop the algorithm, we recruited 76 participants, made 3-axis accelerometer for them, and acquired activity data and calorie consumption data through them. Finally, we designed 3-stage cascaded network to classify the activities and to assess energy consumption. The 3-stage network classifies 4 activities of walking, running, stairs moving, and jumping rope with overall accuracy of 94.70%, and predicts calorie consumption with average accuracy of 81.91%, which is better than the results of the 2-stage network. Future work would include the enhancement of the network performance.

  6. Electrophysiological monitoring and identification of neural roots during somatic-autonomic reflex pathway procedure for neurogenic bladder

    Institute of Scientific and Technical Information of China (English)

    DAI Cheng-fu; XIAO Chuan-guo

    2005-01-01

    Objective: To identify and separate the ventral root from dorsal root, which is the key for success of the artificial somatic-autonomic reflex pathway procedure for neurogenic bladder after spinal cord injury (SCI). Here we report the results of intra-operating room monitoring with 10 paralyzed patients.Methods: Ten male volunteers with complete suprasacral SCI underwent the artificial somatic-autonomic procedure under general anesthesia. Vastus medialis, tibialis anticus and gastrocnemius medialis of the left lower limb were monitored for electromyogram (EMG) activities resulted from L4, L5, and S1 stimulation respectively to differentiate the ventral root from dorsal root. A Laborie Urodynamics system was connected with a three channel urodynamic catheter inserted into the bladder. The L2 and L3 roots were stimulated separately while the intravesical pressure was monitored to evaluate the function of each root.Results: The thresholds of stimulation on ventral root were 0.02 ms duration, 0.2-0.4 mA, (mean 0.3 mA±0.07 mA), compared with 0.2-0.4 ms duration, 1.5-3 mA (mean 2.3 mA±0.5 mA)for dorsal root (P<0.01) to cause revoked potentials and EMG. Electrical stimulation on L4 roots resulted in the EMG being recorded mainly on vastus medialis, while stimulation on L5 or S1 roots caused electrical activities of tibialis anticus or gastrocnemius medialis respectively. The continuous stimulation for about 3-5 seconds on S2 or S3 ventral root (0.02 ms, 20 Hz, and 0.4 mA) could resulted in bladder detrusor contraction, but the strongest bladder contraction over 50 cm H2O was usually caused by stimulation on S3 ventral root in 7 of the 10 patients.Conclusions: Intra-operating room electrophysiological monitoring is of great help to identify and separate ventral root from dorsal root, and to select the appropriate sacral ventral root for best bladder reinnervation. Different parameters and thresholds on different roots are the most important factors to keep in mind to

  7. Detection of micro solder balls using active thermography and probabilistic neural network

    Science.gov (United States)

    He, Zhenzhi; Wei, Li; Shao, Minghui; Lu, Xingning

    2017-03-01

    Micro solder ball/bump has been widely used in electronic packaging. It has been challenging to inspect these structures as the solder balls/bumps are often embedded between the component and substrates, especially in flip-chip packaging. In this paper, a detection method for micro solder ball/bump based on the active thermography and the probabilistic neural network is investigated. A VH680 infrared imager is used to capture the thermal image of the test vehicle, SFA10 packages. The temperature curves are processed using moving average technique to remove the peak noise. And the principal component analysis (PCA) is adopted to reconstruct the thermal images. The missed solder balls can be recognized explicitly in the second principal component image. Probabilistic neural network (PNN) is then established to identify the defective bump intelligently. The hot spots corresponding to the solder balls are segmented from the PCA reconstructed image, and statistic parameters are calculated. To characterize the thermal properties of solder bump quantitatively, three representative features are selected and used as the input vector in PNN clustering. The results show that the actual outputs and the expected outputs are consistent in identification of the missed solder balls, and all the bumps were recognized accurately, which demonstrates the viability of the PNN in effective defect inspection in high-density microelectronic packaging.

  8. Effect of external auditory pacing on the neural activity of stuttering speakers.

    Science.gov (United States)

    Toyomura, Akira; Fujii, Tetsunoshin; Kuriki, Shinya

    2011-08-15

    External auditory pacing, such as metronome sound and speaking in unison with others, has a fluency-enhancing effect in stuttering speakers. The present study investigated the neural mechanism of the fluency-enhancing effect by using functional magnetic resonance imaging (fMRI). 12 stuttering speakers and 12 nonstuttering controls were scanned while performing metronome-timed speech, choral speech, and normal speech. Compared to nonstuttering controls, stuttering speakers showed a significantly greater increase in activation in the superior temporal gyrus under both metronome-timed and choral speech conditions relative to a normal speech condition. The caudate, globus pallidus, and putamen of the basal ganglia showed clearly different patterns of signal change from rest among the different conditions and between stuttering and nonstuttering speakers. The signal change of stuttering speakers was significantly lower than that of nonstuttering controls under the normal speech condition but was raised to the level of the controls, with no intergroup difference, in metronome-timed speech. In contrast, under the chorus condition the signal change of stuttering speakers remained lower than that of the controls. Correlation analysis further showed that the signal change of the basal ganglia and motor areas was negatively correlated with stuttering severity, but it was not significantly correlated with the stuttering rate during MRI scanning. These findings shed light on the specific neural processing of stuttering speakers when they time their speech to auditory stimuli, and provide additional evidence of the efficacy of external auditory pacing. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Effects of vibratory stimulation-induced kinesthetic illusions on the neural activities of patients with stroke.

    Science.gov (United States)

    Kodama, Takayuki; Nakano, Hideki; Ohsugi, Hironori; Murata, Shin

    2016-01-01

    [Purpose] This study evaluated the influence of vibratory stimulation-induced kinesthetic illusion on brain function after stroke. [Subjects] Twelve healthy individuals and 13 stroke patients without motor or sensory loss participated. [Methods] Electroencephalograms were taken at rest and during vibratory stimulation. As a neurophysiological index of brain function, we measured the μ-rhythm, which is present mainly in the kinesthetic cortex and is attenuated by movement or motor imagery and compared the data using source localization analyses in the Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA) program. [Results] At rest, μ-rhythms appeared in the sensorimotor and supplementary motor cortices in both healthy controls and stroke patients. Under vibratory stimulation, no μ-rhythm appeared in the sensorimotor cortex of either group. Moreover, in the supplementary motor area, which stores the motor imagery required for kinesthetic illusions, the μ-rhythms of patients were significantly stronger than those of the controls, although the μ-rhythms of both groups were reduced. Thus, differences in neural activity in the supplementary motor area were apparent between the subject groups. [Conclusion] Kinesthetic illusions do occur in patients with motor deficits due to stroke. The neural basis of the supplementary motor area in stroke patients may be functionally different from that found in healthy controls.

  10. Differential neural activation of vascular alpha-adrenoceptors in oral tissues of cats.

    Science.gov (United States)

    Koss, Michael C

    2002-04-05

    The aim of this study was to determine the relative contribution of alpha(1)- and alpha(2)-adrenoceptors involved in sympathetic-evoked vasoconstrictor responses in tissues perfused by the lingual arterial circulation in pentobarbital anesthetized cats. Blood flow in the lingual artery was measured by ultrasonic flowmetry. Laser-Doppler flowmetry was utilized to measure oral tissue vasoconstrictor responses in the maxillary gingiva and from the surface of the tongue. Electrical stimulation of the preganglionic superior cervical sympathetic nerve resulted in frequency-dependent blood flow decreases at all three sites. These responses were stable over time and were uniformly antagonized by administration of phentolamine (0.3 - 3.0 mg kg(-1)). The selective alpha(1)-adrenoceptor antagonist, prazosin (10 - 300 microg kg(-1)), attenuated vasoconstriction in the lingual artery and gingiva, but was ineffective in blocking vasoconstriction in the tongue. Subsequent administration of rauwolscine (300 microg kg(-1)) antagonized remaining vasoconstrictor responses. In contrast, rauwolscine (10 - 300 microg kg(-1)), given alone, blocked evoked vasoconstriction in the tongue, and was without effect on gingival or lingual artery vasoconstrictor responses. Subsequent administration of prazosin (300 microg kg(-1)) largely antagonized remaining neurally elicited responses. These results suggest that neural vasoconstrictor responses in some regional vascular beds in the cat oral cavity are mediated by both alpha(1)- and alpha(2)-adrenoceptors. In contrast, tongue surface vasoconstrictor responses to sympathetic nerve activation appear to be mediated primarily by alpha(2)-adrenoceptors.

  11. Neural correlates of envy: Regional homogeneity of resting-state brain activity predicts dispositional envy.

    Science.gov (United States)

    Xiang, Yanhui; Kong, Feng; Wen, Xue; Wu, Qihan; Mo, Lei

    2016-11-15

    Envy differs from common negative emotions across cultures. Although previous studies have explored the neural basis of episodic envy via functional magnetic resonance imaging (fMRI), little is known about the neural processes associated with dispositional envy. In the present study, we used regional homogeneity (ReHo) as an index in resting-state fMRI (rs-fMRI) to identify brain regions involved in individual differences in dispositional envy, as measured by the Dispositional Envy Scale (DES). Results showed that ReHo in the inferior/middle frontal gyrus (IFG/MFG) and dorsomedial prefrontal cortex (DMPFC) positively predicted dispositional envy. Moreover, of all the personality traits measured by the Revised NEO Personality Inventory (NEO-PI-R), only neuroticism was significantly associated with dispositional envy. Furthermore, neuroticism mediated the underlying association between the ReHo of the IFG/MFG and dispositional envy. Hence, to the best of our knowledge, this study provides the first evidence that spontaneous brain activity in multiple regions related to self-evaluation, social perception, and social emotion contributes to dispositional envy. In addition, our findings reveal that neuroticism may play an important role in the cognitive processing of dispositional envy.

  12. Cutaneous retinal activation and neural entrainment in transcranial alternating current stimulation: A systematic review.

    Science.gov (United States)

    Schutter, Dennis J L G

    2016-10-15

    Transcranial alternating current stimulation (tACS) applies exogenous oscillatory electric field potentials to entrain neural rhythms and is used to investigate brain-function relationships and its potential to enhance perceptual and cognitive performance. However, due to current spread tACS can cause cutaneous activation of the retina and phosphenes. Several lines of evidence suggest that retinal phosphenes are capable of inducing neural entrainment, making the contributions of central and peripheral stimulation to the effects in the brain difficult to disentangle. In this literature review, the importance of this issue is further illustrated by the fact that photic stimulation can have a direct impact on perceptual and cognitive performance. This leaves open the possibility that peripheral photic stimulation can at least in part explain the central effects that are attributed to tACS. The extent to which phosphene perception contributes to the effects of exogenous oscillatory electric fields in the brain and influence perception and cognitive performance needs to be examined to understand the working mechanisms of tACS in neurophysiology and behaviour.

  13. Nanosensors for a Monitoring System in Intelligent and Active Packaging

    Directory of Open Access Journals (Sweden)

    Guillermo Fuertes

    2016-01-01

    Full Text Available A theoretical wireless nanosensor network (WNSN system that gives information about the food packaging condition is proposed. The protection effectiveness is estimated by measuring many factors, such as the existence of microorganisms, bacteria, gases, and contaminants. This study is focused on the detection of an antimicrobial agent (AA attached on a polymer forming an active integrated package. All monitoring technologies for food conservation are analyzed. Nanobiosensor nanomachine (NM, which converts biological or chemical signals into electrical signals, is used. A mathematical model, which describes the constituent’s emigration from the package to food, is programmed in MatLab software. The results show three nanobiosensors forming a WNSN. The nanobiosensors are able to carry out the average concentration for different spots in the package. This monitoring system shows reading percentages in three degrees and different colors: excellent (green, good (cyan, and lacking (red. To confirm the utility of the model, different simulations are performed. Using the WNSNs, results of AA existing in food package (FP through time were successfully obtained.

  14. Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays.

    Science.gov (United States)

    Wang, Dongshu; Huang, Lihong

    2014-03-01

    In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results.

  15. Performance Assessment and Active System Monitoring for Refrigeration Systems

    DEFF Research Database (Denmark)

    Green, Torben

    for algorithms that ensures or improves the performance of the system. A supermarket refrigeration system is usually a complex and distributed control system, and it can therefore be difficult to assess the performance without a formal method. The main interest for a supermarket, with respect...... of the refrigeration system has been addressed in the project. The proposed methods for improvement relies on a minimum of detailed knowledge about the refrigeration system. In addition, since a refrigeration system often operates in steady state an active system monitoring setup has been proposed, to enable...... a method for assessing the operational performance at a plan-wide level and is therefore providing a tool for improving the plant-wide performance. The performance function has been used in dierent setups to improve the performance of the refrigeration system. Static and the dynamic performance...

  16. The MAGNUM (Multicolor Active Galactic NUclei Monitoring) Project

    Science.gov (United States)

    Yoshii, Y.; Kobayashi, Y.; Minezaki, T.

    2003-05-01

    The MAGNUM Project is designed to carry out long-term monitoring observations of hundreds of active galactic nuclei in the visible and near-infrared wavelength regions. In order to obtain these observations, we built a new 2m optical-infrared telescope, and located it near the Haleakala summit at a height of 3050m within the area of the University of Hawaii's Haleakala Observatory on the Hawaiian Island of Maui. The Project was funded in 1995 and preliminary observations were started early in 2001. We are working toward the realization of an unmanned, automated observatory which is suitable to relatively simple and stable observations over many years. We present an overview of the Project and its current status.

  17. Automated observatory for multicolor active galactic nuclei monitoring (MAGNUM)

    Science.gov (United States)

    Kobayashi, Yukiyasu; Yoshii, Yuzuru; Minezaki, Takeo; Enya, Keigo; Aoki, Tsutomu; Suganuma, Masahiro; Tomita, Hiroyuki; Doi, Mamoru; Motohara, Kentaro; Peterson, Bruce A.; Smith, Craig H.; Little, John K.; Greene, Ben

    2003-02-01

    We present the outline and the current status of the MAGNUM automated observation system. The operational objective of the MAGNUM Project is to carry out long-term multi-color monitoring observations of active galactic nuclei in the visible and near-infrared wavelength regions. In order to obtain these observations, we built a new 2 m optical-infrared telescope, and sited it at the University of Hawaii's Haleakala Observatory on the Hawaiian Island of Maui. Preliminary observations were started early in 2001. We are working toward the final form of the MAGNUM observation system, which is an unmanned, automated observatory. This system requirement was set by considering that the observation procedures are relatively simple, and the targets must be observed consistently over many years.

  18. Seasonal prediction of tropical cyclone activity over the north Indian Ocean using three artificial neural networks

    Science.gov (United States)

    Nath, Sankar; Kotal, S. D.; Kundu, P. K.

    2016-12-01

    Three artificial neural network (ANN) methods, namely, multilayer perceptron (MLP), radial basis function (RBF) and generalized regression neural network (GRNN) are utilized to predict the seasonal tropical cyclone (TC) activity over the north Indian Ocean (NIO) during the post-monsoon season (October, November, December). The frequency of TC and large-scale climate variables derived from NCEP/NCAR reanalysis dataset of resolution 2.5° × 2.5° were analyzed for the period 1971-2013. Data for the years 1971-2002 were used for the development of the models, which were tested with independent sample data for the year 2003-2013. Using the correlation analysis, the five large-scale climate variables, namely, geopotential height at 500 hPa, relative humidity at 500 hPa, sea-level pressure, zonal wind at 700 hPa and 200 hPa for the preceding month September, are selected as potential predictors of the post-monsoon season TC activity. The result reveals that all the three different ANN methods are able to provide satisfactory forecast in terms of the various metrics, such as root mean-square error (RMSE), standard deviation (SD), correlation coefficient ( r), and bias and index of agreement ( d). Additionally, leave-one-out cross validation (LOOCV) method is also performed and the forecast skill is evaluated. The results show that the MLP model is found to be superior to the other two models (RBF, GRNN). The (MLP) is expected to be very useful to operational forecasters for prediction of TC activity.

  19. DNA methyltransferase activity is required for memory-related neural plasticity in the lateral amygdala.

    Science.gov (United States)

    Maddox, Stephanie A; Watts, Casey S; Schafe, Glenn E

    2014-01-01

    We have previously shown that auditory Pavlovian fear conditioning is associated with an increase in DNA methyltransferase (DNMT) expression in the lateral amygdala (LA) and that intra-LA infusion or bath application of an inhibitor of DNMT activity impairs the consolidation of an auditory fear memory and long-term potentiation (LTP) at thalamic and cortical inputs to the LA, in vitro. In the present study, we use awake behaving neurophysiological techniques to examine the role of DNMT activity in memory-related neurophysiological changes accompanying fear memory consolidation and reconsolidation in the LA, in vivo. We show that auditory fear conditioning results in a training-related enhancement in the amplitude of short-latency auditory-evoked field potentials (AEFPs) in the LA. Intra-LA infusion of a DNMT inhibitor impairs both fear memory consolidation and, in parallel, the consolidation of training-related neural plasticity in the LA; that is, short-term memory (STM) and short-term training-related increases in AEFP amplitude in the LA are intact, while long-term memory (LTM) and long-term retention of training-related increases in AEFP amplitudes are impaired. In separate experiments, we show that intra-LA infusion of a DNMT inhibitor following retrieval of an auditory fear memory has no effect on post-retrieval STM or short-term retention of training-related changes in AEFP amplitude in the LA, but significantly impairs both post-retrieval LTM and long-term retention of AEFP amplitude changes in the LA. These findings are the first to demonstrate the necessity of DNMT activity in the consolidation and reconsolidation of memory-associated neural plasticity, in vivo.

  20. Effects of aripiprazole on caffeine-induced hyperlocomotion and neural activation in the striatum.

    Science.gov (United States)

    Batista, Luara A; Viana, Thércia G; Silveira, Vívian T; Aguiar, Daniele C; Moreira, Fabrício A

    2016-01-01

    Aripiprazole is an antipsychotic that acts as a partial agonist at dopamine D2 receptors. In addition to its antipsychotic activity, this compound blocks the effects of some psychostimulant drugs. It has not been verified, however, if aripiprazole interferes with the effects of caffeine. Hence, this study tested the hypothesis that aripiprazole prevents caffeine-induced hyperlocomotion and investigated the effects of these drugs on neural activity in the striatum. Male Swiss mice received injections of vehicle or antipsychotic drugs followed by vehicle or caffeine. Locomotion was analyzed in a circular arena and c-Fos protein expression was quantified in the dorsolateral, dorsomedial, and ventrolateral striatum, and in the core and shell regions of nucleus accumbens. Aripiprazole (0.1, 1, and 10 mg/kg) prevented caffeine (10 mg/kg)-induced hyperlocomotion at doses that do not change basal locomotion. Haloperidol (0.01, 0.03, and 0.1 mg/kg) also decreased caffeine-induced hyperlocomotion at all doses, although at the two higher doses, this compound reduced basal locomotion. Immunohistochemistry analysis showed that aripiprazole increases c-Fos protein expression in all regions studied, whereas caffeine did not alter c-Fos protein expression. Combined treatment of aripiprazole and caffeine resulted in a decrease in the number of c-Fos positive cells as compared to the group receiving aripiprazole alone. In conclusion, aripiprazole prevents caffeine-induced hyperlocomotion and increases neural activation in the striatum. This latter effect is reduced by subsequent administration of caffeine. These results advance our understanding on the pharmacological profile of aripiprazole.

  1. Aerial monitoring in active mud volcano by UAV technique

    Science.gov (United States)

    Pisciotta, Antonino; Capasso, Giorgio; Madonia, Paolo

    2016-04-01

    UAV photogrammetry opens various new applications in the close range domain, combining aerial and terrestrial photogrammetry, but also introduces low-cost alternatives to the classical manned aerial photogrammetry. Between 2014 and 2015 tree aerial surveys have been carried out. Using a quadrotor drone, equipped with a compact camera, it was possible to generate high resolution elevation models and orthoimages of The "Salinelle", an active mud volcanoes area, located in territory of Paternò (South Italy). The main risks are related to the damages produced by paroxysmal events. Mud volcanoes show different cyclic phases of activity, including catastrophic events and periods of relative quiescence characterized by moderate activity. Ejected materials often are a mud slurry of fine solids suspended in liquids which may include water and hydrocarbon fluids, the bulk of released gases are carbon dioxide, with some methane and nitrogen, usually pond-shaped of variable dimension (from centimeters to meters in diameter). The scope of the presented work is the performance evaluation of a UAV system that was built to rapidly and autonomously acquire mobile three-dimensional (3D) mapping data in a volcanic monitoring scenario.

  2. Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

    Science.gov (United States)

    Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

    2015-12-01

    Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be

  3. Hedgehog Controls Quiescence and Activation of Neural Stem Cells in the Adult Ventricular-Subventricular Zone

    Directory of Open Access Journals (Sweden)