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Sample records for neural control task

  1. Neural mechanisms of timing control in a coincident timing task.

    Science.gov (United States)

    Masaki, Hiroaki; Sommer, Werner; Takasawa, Noriyoshi; Yamazaki, Katuo

    2012-04-01

    Many ball sports such as tennis or baseball require precise temporal anticipation of both sensory input and motor output (i.e., receptor anticipation and effector anticipation, respectively) and close performance monitoring. We investigated the neural mechanisms underlying timing control and performance monitoring in a coincident timing task involving both types of anticipations. Peak force for two time-to-peak force (TTP) conditions-recorded with a force-sensitive key-was required to coincide with a specific position of a stimulus rotating either slow or fast on a clock face while the contingent negative variation (CNV) and the motor-elicited negativity were recorded. Absolute timing error was generally smaller for short TTP (high velocity) conditions. CNV amplitudes increased with both faster stimulus velocity and longer TTPs possibly reflecting increased motor programming efforts. In addition, the motor-elicited negativity was largest in the slow stimulus/short TTP condition, probably representing some forms of performance monitoring as well as shorter response duration. Our findings indicate that the coincident timing task is a good model for real-life situations of tool use.

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

    Science.gov (United States)

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

    2014-12-01

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

  3. Inhibitory control and trait aggression: neural and behavioral insights using the emotional stop signal task.

    Science.gov (United States)

    Pawliczek, Christina M; Derntl, Birgit; Kellermann, Thilo; Kohn, Nils; Gur, Ruben C; Habel, Ute

    2013-10-01

    Deficits in response inhibition and heightened impulsivity have been linked to psychiatric disorders and aggression. They have been investigated in clinical groups as well as individuals with trait characteristics, yielding insights into the underlying neural and behavioral mechanisms of response inhibition and impulsivity. The motor inhibition tasks employed in most studies, however, have lacked an emotional component, which is crucial given that both response inhibition and impulsivity attain salience within a socio-emotional context. For this fMRI study, we selected a group with high trait aggression (HA, n=17) and one with low trait aggression (LA, n=16) from 550 males who had completed an Aggression Questionnaire. Neural activation was compared to an emotional version (including angry and neutral faces) of the stop signal task. Behavioral results revealed impaired response inhibition in HA, associated with higher motor impulsivity. This was accompanied by attenuated activation in brain regions involved in response inhibition, including the pre-supplementary motor area (SMA) and motor cortex. Together, these findings offer evidence that a reduced inhibition capacity is present in HA. Notably, response inhibition improved during anger trials in both groups, suggesting a facilitation effect through heightened activation in the related brain regions. In both groups, inclusion of the anger stimuli enhanced the activation of the motor and somatosensory areas, which modulate executive control, and of limbic regions including the amygdala. In summary, the investigation of response inhibition in individuals with high and low trait characteristics affords useful insights into the underlying distinct processing mechanisms. It can contribute to the investigation of trait markers in a clinical context without having to deal with the complex mechanisms of a clinical disorder itself. In contrast, the mechanisms of emotional response inhibition did not differ between groups

  4. Individual differences in aging and cognitive control modulate the neural indexes of context updating and maintenance during task switching.

    Science.gov (United States)

    Adrover-Roig, Daniel; Barceló, Francisco

    2010-04-01

    This study aimed to explore the combined influence of age and cognitive control on the behavioural and electrophysiological indicators of local, restart and mixing costs. Two groups of middle-aged (49-60 y.o., N=40) and older (61-80 y.o., N=40) adults were split according to their overall z-score in a composite of six neuropsychological measures of executive function. All participants performed a task-cueing version of the Wisconsin Card Sorting Test (WCST) adapted for measuring event-related potentials, whereby tonal cues instructed to switch or repeat the task rule. A single-task condition with identical sensory and motor response demands was used to aid interpretation of behavioural and brain responses to cues and target events. Working memory updating of stimulus-response mappings, as putatively indexed by local switch costs and cue-locked P3 activity (350-460 msec post-cue onset), was preserved in both older and low control adults. In turn, low control adults showed larger restart costs and enhanced cue-locked P2 amplitudes (190-250 msec) in the task-switching condition only, suggesting lesser preparatory control in the presence of interference. Low control adults showed comparatively larger mixing costs and smaller cue-locked fronto-central slow negativities (500-700 msec), suggesting an inefficient online maintenance of task-set information over time. In contrast, target-locked brain responses were mostly sensitive to age-related effects, with older adults showing two well-known effects: (1) an "anterior shift" in target P3 activity (350-460 msec), and (2) an attenuation of fronto-central slow negativities in single-task and task-switching conditions, respectively. The additive association found between age and cognitive control for different behavioural indexes of task-switch costs suggests a differential influence of these factors upon two successive information processing stages: individual differences in cognitive control mainly influenced the neural

  5. SOME QUESTIONS OF THE GRID AND NEURAL NETWORK MODELING OF AIRPORT AVIATION SECURITY CONTROL TASKS

    Directory of Open Access Journals (Sweden)

    N. Elisov Lev

    2017-01-01

    Full Text Available The authors’ original problem-solution-approach concerning aviation security management in civil aviation apply- ing parallel calculation processes method and the usage of neural computers is considered in this work. The statement of secure environment modeling problems for grid models and with the use of neural networks is presented. The research sub- ject area of this article is airport activity in the field of civil aviation, considered in the context of aviation security, defined as the state of aviation security against unlawful interference with the aviation field. The key issue in this subject area is aviation safety provision at an acceptable level. In this case, airport security level management becomes one of the main objectives of aviation security. Aviation security management is organizational-regulation in modern systems that can no longer correspond to changing requirements, increasingly getting complex and determined by external and internal envi- ronment factors, associated with a set of potential threats to airport activity. Optimal control requires the most accurate identification of management parameters and their quantitative assessment. The authors examine the possibility of applica- tion of mathematical methods for the modeling of security management processes and procedures in their latest works. Par- allel computing methods and network neurocomputing for modeling of airport security control processes are examined in this work. It is shown that the methods’ practical application of the methods is possible along with the decision support system, where the decision maker plays the leading role.

  6. Neural Substrates of Visual Spatial Coding and Visual Feedback Control for Hand Movements in Allocentric and Target-Directed Tasks

    Science.gov (United States)

    Thaler, Lore; Goodale, Melvyn A.

    2011-01-01

    Neuropsychological evidence suggests that different brain areas may be involved in movements that are directed at visual targets (e.g., pointing or reaching), and movements that are based on allocentric visual information (e.g., drawing or copying). Here we used fMRI to investigate the neural correlates of these two types of movements in healthy volunteers. Subjects (n = 14) performed right hand movements in either a target-directed task (moving a cursor to a target dot) or an allocentric task (moving a cursor to reproduce the distance and direction between two distal target dots) with or without visual feedback about their hand movement. Movements were monitored with an MR compatible touch panel. A whole brain analysis revealed that movements in allocentric conditions led to an increase in activity in the fundus of the left intra-parietal sulcus (IPS), in posterior IPS, in bilateral dorsal premotor cortex (PMd), and in the lateral occipital complex (LOC). Visual feedback in both target-directed and allocentric conditions led to an increase in activity in area MT+, superior parietal–occipital cortex (SPOC), and posterior IPS (all bilateral). In addition, we found that visual feedback affected brain activity differently in target-directed as compared to allocentric conditions, particularly in the pre-supplementary motor area, PMd, IPS, and parieto-occipital cortex. Our results, in combination with previous findings, suggest that the LOC is essential for allocentric visual coding and that SPOC is involved in visual feedback control. The differences in brain activity between target-directed and allocentric visual feedback conditions may be related to behavioral differences in visual feedback control. Our results advance the understanding of the visual coordinate frame used by the LOC. In addition, because of the nature of the allocentric task, our results have relevance for the understanding of neural substrates of magnitude estimation and vector coding of

  7. Neurometaplasticity: Glucoallostasis control of plasticity of the neural networks of error commission, detection, and correction modulates neuroplasticity to influence task precision

    Science.gov (United States)

    Welcome, Menizibeya O.; Dane, Şenol; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

    2017-12-01

    The term "metaplasticity" is a recent one, which means plasticity of synaptic plasticity. Correspondingly, neurometaplasticity simply means plasticity of neuroplasticity, indicating that a previous plastic event determines the current plasticity of neurons. Emerging studies suggest that neurometaplasticity underlie many neural activities and neurobehavioral disorders. In our previous work, we indicated that glucoallostasis is essential for the control of plasticity of the neural network that control error commission, detection and correction. Here we review recent works, which suggest that task precision depends on the modulatory effects of neuroplasticity on the neural networks of error commission, detection, and correction. Furthermore, we discuss neurometaplasticity and its role in error commission, detection, and correction.

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

    Science.gov (United States)

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

    2015-05-01

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

  9. Assembling old tricks for new tasks: a neural model of instructional learning and control.

    Science.gov (United States)

    Huang, Tsung-Ren; Hazy, Thomas E; Herd, Seth A; O'Reilly, Randall C

    2013-06-01

    We can learn from the wisdom of others to maximize success. However, it is unclear how humans take advice to flexibly adapt behavior. On the basis of data from neuroanatomy, neurophysiology, and neuroimaging, a biologically plausible model is developed to illustrate the neural mechanisms of learning from instructions. The model consists of two complementary learning pathways. The slow-learning parietal pathway carries out simple or habitual stimulus-response (S-R) mappings, whereas the fast-learning hippocampal pathway implements novel S-R rules. Specifically, the hippocampus can rapidly encode arbitrary S-R associations, and stimulus-cued responses are later recalled into the basal ganglia-gated pFC to bias response selection in the premotor and motor cortices. The interactions between the two model learning pathways explain how instructions can override habits and how automaticity can be achieved through motor consolidation.

  10. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

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

    Science.gov (United States)

    Jimura, Koji; Cazalis, Fabienne; Stover, Elena R S; Poldrack, Russell A

    2014-01-01

    Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task vs. a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading) and a highly practiced one (plain word reading), allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by 2 weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror vs. plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

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

    Directory of Open Access Journals (Sweden)

    Koji eJimura

    2014-05-01

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

  13. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  14. Neural correlates for task switching in the macaque superior colliculus.

    Science.gov (United States)

    Chan, Jason L; Koval, Michael J; Johnston, Kevin; Everling, Stefan

    2017-10-01

    Successful task switching requires a network of brain areas to select, maintain, implement, and execute the appropriate task. Although frontoparietal brain areas are thought to play a critical role in task switching by selecting and encoding task rules and exerting top-down control, how brain areas closer to the execution of tasks participate in task switching is unclear. The superior colliculus (SC) integrates information from various brain areas to generate saccades and is likely influenced by task switching. Here, we investigated switch costs in nonhuman primates and their neural correlates in the activity of SC saccade-related neurons in monkeys performing cued, randomly interleaved pro- and anti-saccade trials. We predicted that behavioral switch costs would be associated with differential modulations of SC activity in trials on which the task was switched vs. repeated, with activity on the current trial resembling that associated with the task set of the previous trial when a switch occurred. We observed both error rate and reaction time switch costs and changes in the discharge rate and timing of activity in SC neurons between switch and repeat trials. These changes were present later in the task only after fixation on the cue stimuli but before saccade onset. These results further establish switch costs in macaque monkeys and suggest that SC activity is modulated by task-switching processes in a manner inconsistent with the concept of task set inertia.NEW & NOTEWORTHY Task-switching behavior and superior colliculus (SC) activity were investigated in nonhuman primates performing randomly interleaved pro- and anti-saccade tasks. Here, we report error rate and reaction time switch costs in macaque monkeys and associated differences in stimulus-related activity of saccade-related neurons in the SC. These results provide a neural correlate for task switching and suggest that the SC is modulated by task-switching processes and may reflect the completion of task

  15. Neural Multi-task Learning in Automated Assessment

    OpenAIRE

    Cummins, Ronan; Rei, Marek

    2018-01-01

    Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to infor...

  16. Speech versus nonspeech: different tasks, different neural organization.

    Science.gov (United States)

    Bunton, Kate

    2008-11-01

    This article reviews the extant studies of the relation of oromotor nonspeech activities to speech production. The relevancy of nonspeech oral motor behaviors to speech motor performance in assessment and treatment is challenged on several grounds. First, contemporary motor theory suggests that movement control is task specific. In other words, it is tied to the unique goals, sources of information, and characteristics of varying motor acts. Documented differences in movement characteristics for speech production versus nonspeech oral motor tasks support this claim. Second, advantages of training nonspeech oral motor tasks versus training speech production are not supported by current principles of motor learning and neural plasticity. Empirical data supports experience-specific training. Finally, functional imaging studies document differences in activation patterns for speech compared with nonspeech oral motor tasks in neurologically healthy individuals.

  17. A neural network approach to dynamic task assignment of multirobots.

    Science.gov (United States)

    Zhu, Anmin; Yang, Simon X

    2006-09-01

    In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

  18. Speech versus Nonspeech: Different Tasks, Different Neural Organization

    Science.gov (United States)

    Bunton, Kate

    2009-01-01

    This article reviews the extant studies of the relation of oromotor nonspeech activities to speech production. The relevancy of nonspeech oral motor behaviors to speech motor performance in assessment and treatment is challenged on several grounds. First, contemporary motor theory suggests that movement control is task-specific; in other words, tied to the unique goals, sources of information and characteristics of varying motor acts. Documented differences in movement characteristics for speech production versus nonspeech oral motor tasks support this claim. Second, advantages of training nonspeech oral motor tasks versus training speech production are not supported by current principles of motor learning and neural plasticity. Empirical data supports experience-specific training. Finally, functional imaging studies document differences in activation patterns for speech compared to nonspeech oral motor tasks in neurologically healthy individuals. PMID:19058113

  19. Multi-layer neural networks for robot control

    Science.gov (United States)

    Pourboghrat, Farzad

    1989-01-01

    Two neural learning controller designs for manipulators are considered. The first design is based on a neural inverse-dynamics system. The second is the combination of the first one with a neural adaptive state feedback system. Both types of controllers enable the manipulator to perform any given task very well after a period of training and to do other untrained tasks satisfactorily. The second design also enables the manipulator to compensate for unpredictable perturbations.

  20. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  1. Neural efficiency as a function of task demands☆

    Science.gov (United States)

    Dunst, Beate; Benedek, Mathias; Jauk, Emanuel; Bergner, Sabine; Koschutnig, Karl; Sommer, Markus; Ischebeck, Anja; Spinath, Birgit; Arendasy, Martin; Bühner, Markus; Freudenthaler, Heribert; Neubauer, Aljoscha C.

    2014-01-01

    The neural efficiency hypothesis describes the phenomenon that brighter individuals show lower brain activation than less bright individuals when working on the same cognitive tasks. The present study investigated whether the brain activation–intelligence relationship still applies when more versus less intelligent individuals perform tasks with a comparable person-specific task difficulty. In an fMRI-study, 58 persons with lower (n = 28) or respectively higher (n = 30) intelligence worked on simple and difficult inductive reasoning tasks having the same person-specific task difficulty. Consequently, less bright individuals received sample-based easy and medium tasks, whereas bright subjects received sample-based medium and difficult tasks. This design also allowed a comparison of lower versus higher intelligent individuals when working on the same tasks (i.e. sample-based medium task difficulty). In line with expectations, differences in task performance and in brain activation were only found for the subset of tasks with the same sample-based task difficulty, but not when comparing tasks with the same person-specific task difficulty. These results suggest that neural efficiency reflects an (ability-dependent) adaption of brain activation to the respective task demands. PMID:24489416

  2. The neural correlates of cognitive control in bipolar I disorder: an fMRI study of medial frontal cortex activation during a Go/No-go task.

    Science.gov (United States)

    Welander-Vatn, Audun; Jensen, Jimmy; Otnaess, Mona K; Agartz, Ingrid; Server, Andres; Melle, Ingrid; Andreassen, Ole A

    2013-08-09

    In addition to dysregulation of mood, bipolar I disorder (BD I) is characterized by abnormalities in the execution of cognitive control. Hypoactivation of a specific sub-region in the cognitive control network, located in the medial frontal cortex, has been described among BD I patients. The aim of this study was to investigate whether patients with BD I showed decreased activation in this brain region as compared to healthy controls when performing a cognitive control task. Twenty-four BD I patients and 24 healthy controls performed a Go/No-go task during a functional magnetic resonance imaging (fMRI) session. Performance and response times were recorded. The BD I subjects had significantly slower response times and more patients made errors of omission compared to the healthy controls during the task. Both BD I subjects and healthy controls demonstrated activations in the brain region of interest during the task, but analyses revealed no statistically significant differences between groups. Although the patients display some deviances in behavioural measures, this study reveals no significant differences between BD I subjects and healthy controls in recruitment of the medial frontal cortex during a Go/No-go task. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  3. Anomalous neural circuit function in schizophrenia during a virtual Morris water task.

    Science.gov (United States)

    Folley, Bradley S; Astur, Robert; Jagannathan, Kanchana; Calhoun, Vince D; Pearlson, Godfrey D

    2010-02-15

    Previous studies have reported learning and navigation impairments in schizophrenia patients during virtual reality allocentric learning tasks. The neural bases of these deficits have not been explored using functional MRI despite well-explored anatomic characterization of these paradigms in non-human animals. Our objective was to characterize the differential distributed neural circuits involved in virtual Morris water task performance using independent component analysis (ICA) in schizophrenia patients and controls. Additionally, we present behavioral data in order to derive relationships between brain function and performance, and we have included a general linear model-based analysis in order to exemplify the incremental and differential results afforded by ICA. Thirty-four individuals with schizophrenia and twenty-eight healthy controls underwent fMRI scanning during a block design virtual Morris water task using hidden and visible platform conditions. Independent components analysis was used to deconstruct neural contributions to hidden and visible platform conditions for patients and controls. We also examined performance variables, voxel-based morphometry and hippocampal subparcellation, and regional BOLD signal variation. Independent component analysis identified five neural circuits. Mesial temporal lobe regions, including the hippocampus, were consistently task-related across conditions and groups. Frontal, striatal, and parietal circuits were recruited preferentially during the visible condition for patients, while frontal and temporal lobe regions were more saliently recruited by controls during the hidden platform condition. Gray matter concentrations and BOLD signal in hippocampal subregions were associated with task performance in controls but not patients. Patients exhibited impaired performance on the hidden and visible conditions of the task, related to negative symptom severity. While controls showed coupling between neural circuits, regional

  4. Uniform and Non-uniform Perturbations in Brain-Machine Interface Task Elicit Similar Neural Strategies.

    Science.gov (United States)

    Armenta Salas, Michelle; Helms Tillery, Stephen I

    2016-01-01

    The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions.

  5. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  6. Identifying the neural substrates of intrinsic motivation during task performance.

    Science.gov (United States)

    Lee, Woogul; Reeve, Johnmarshall

    2017-10-01

    Intrinsic motivation is the inherent tendency to seek out novelty and challenge, to explore and investigate, and to stretch and extend one's capacities. When people imagine performing intrinsically motivating tasks, they show heightened anterior insular cortex (AIC) activity. To fully explain the neural system of intrinsic motivation, however, requires assessing neural activity while people actually perform intrinsically motivating tasks (i.e., while answering curiosity-inducing questions or solving competence-enabling anagrams). Using event-related functional magnetic resonance imaging, we found that the neural system of intrinsic motivation involves not only AIC activity, but also striatum activity and, further, AIC-striatum functional interactions. These findings suggest that subjective feelings of intrinsic satisfaction (associated with AIC activations), reward processing (associated with striatum activations), and their interactions underlie the actual experience of intrinsic motivation. These neural findings are consistent with the conceptualization of intrinsic motivation as the pursuit and satisfaction of subjective feelings (interest and enjoyment) as intrinsic rewards.

  7. Time- and task-dependent non-neural effects of real and sham TMS.

    Directory of Open Access Journals (Sweden)

    Felix Duecker

    Full Text Available Transcranial magnetic stimulation (TMS is widely used in experimental brain research to manipulate brain activity in humans. Next to the intended neural effects, every TMS pulse produces a distinct clicking sound and sensation on the head which can also influence task performance. This necessitates careful consideration of control conditions in order to ensure that behavioral effects of interest can be attributed to the neural consequences of TMS and not to non-neural effects of a TMS pulse. Surprisingly, even though these non-neural effects of TMS are largely unknown, they are often assumed to be unspecific, i.e. not dependent on TMS parameters. This assumption is inherent to many control strategies in TMS research but has recently been challenged on empirical grounds. Here, we further develop the empirical basis of control strategies in TMS research. We investigated the time-dependence and task-dependence of the non-neural effects of TMS and compared real and sham TMS over vertex. Critically, we show that non-neural TMS effects depend on a complex interplay of these factors. Although TMS had no direct neural effects, both pre- and post-stimulus TMS time windows modulated task performance on both a sensory detection task and a cognitive angle judgment task. For the most part, these effects were quantitatively similar across tasks but effect sizes were clearly different. Moreover, the effects of real and sham TMS were almost identical with interesting exceptions that shed light on the relative contribution of auditory and somato-sensory aspects of a TMS pulse. Knowledge of such effects is of critical importance for the interpretation of TMS experiments and helps deciding what constitutes an appropriate control condition. Our results broaden the empirical basis of control strategies in TMS research and point at potential pitfalls that should be avoided.

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

    DEFF Research Database (Denmark)

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

    2011-01-01

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

  9. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  10. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...

  11. Deep Convolutional Neural Networks for large-scale speech tasks.

    Science.gov (United States)

    Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana

    2015-04-01

    Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

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

  13. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...... tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups....

  14. Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

    Science.gov (United States)

    Miconi, Thomas

    2017-02-23

    Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

  15. Neural Control of the Circulation

    Science.gov (United States)

    Thomas, Gail D.

    2011-01-01

    The purpose of this brief review is to highlight key concepts about the neural control of the circulation that graduate and medical students should be expected to incorporate into their general knowledge of human physiology. The focus is largely on the sympathetic nerves, which have a dominant role in cardiovascular control due to their effects to…

  16. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

  17. Changes in the spinal neural circuits are dependent on the movement speed of the visuomotor task

    Directory of Open Access Journals (Sweden)

    Shinji eKubota

    2015-12-01

    Full Text Available Previous studies have shown that spinal neural circuits are modulated by motor skill training. However, the effects of task movement speed on changes in spinal neural circuits have not been clarified. The aim of this research was to investigate whether spinal neural circuits were affected by task movement speed. Thirty-eight healthy subjects participated in this study. In experiment 1, the effects of task movement speed on the spinal neural circuits were examined. 18 subjects performed a visuomotor task involving ankle muscle slow (9 subjects or fast (9 subjects movement speed. Another 9 subjects performed a non-visuomotor task (controls in fast movement speed. The motor task training lasted for 20 min. The amounts of D1 inhibition and reciprocal Ia inhibition were measured using H-relfex condition-test paradigm and recorded before, and at 5, 15, and 30 min after the training session. In experiment 2, using transcranial magnetic stimulation (TMS, the effects of corticospinal descending inputs on the presynaptic inhibitory pathway were examined before and after performing either a visuomotor (8 subjects or a control task (8 subjects. All measurements were taken under resting conditions. The amount of D1 inhibition increased after the visuomotor task irrespective of movement speed (P < 0.01. The amount of reciprocal Ia inhibition increased with fast movement speed conditioning (P < 0.01, but was unchanged by slow movement speed conditioning. These changes lasted up to 15 min in D1 inhibition and 5 min in reciprocal Ia inhibition after the training session. The control task did not induce changes in D1 inhibition and reciprocal Ia inhibition. The TMS conditioned inhibitory effects of presynaptic inhibitory pathways decreased following visuomotor tasks (P < 0.01. The size of test H-reflex was almost the same size throughout experiments. The results suggest that supraspinal descending inputs for controlling joint movement are responsible for changes

  18. Are muscle synergies useful for neural control ?

    Directory of Open Access Journals (Sweden)

    Aymar ede Rugy

    2013-03-01

    Full Text Available The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an EMG-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions. Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals, but with higher aiming errors. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behaviour depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to

  19. Neural Manifolds for the Control of Movement.

    Science.gov (United States)

    Gallego, Juan A; Perich, Matthew G; Miller, Lee E; Solla, Sara A

    2017-06-07

    The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Motor skill experience modulates executive control for task switching.

    Science.gov (United States)

    Yu, Qiuhua; Chan, Chetwyn C H; Chau, Bolton; Fu, Amy S N

    2017-10-01

    This study aimed to investigate the effect of types of motor skills, including open and closed skills on enhancing proactive and reactive controls for task switching. Thirty-six athletes in open (n=18) or closed (n=18) sports and a control group (n=18) completed the task-switching paradigm and the simple reaction task. The task-switching paradigm drew on the proactive and reactive control of executive functions, whereas the simple reaction task assessed the processing speed. Significant Validity×Group effect revealed that the participants with open skills had a lower switch cost of response time compared to the other two groups when the task cue was 100% valid; whereas the participants regardless of motor skills had a lower switch cost of response time compared to the control group when the task cue was 50% valid. Hierarchical stepwise regression analysis further confirmed these findings. For the simple reaction task, there were no differences found among the three groups. These findings suggest that experience in open skills has benefits of promoting both proactive and reactive controls for task switching, which corresponds to the activity context exposed by the participants. In contrast, experience in closed skills appears to only benefit development of reactive control for task switching. The neural mechanisms for the proactive and reactive controls of executive functions between experts with open and closed skills call for future study. Copyright © 2017. Published by Elsevier B.V.

  1. Simplified LQG Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

  2. Neural circulatory control in vasovagal syncope

    NARCIS (Netherlands)

    van Lieshout, J. J.; Wieling, W.; Karemaker, J. M.

    1997-01-01

    The orthostatic volume displacement associated with the upright position necessitates effective neural cardiovascular modulation. Neural control of cardiac chronotropy and inotropy, and vasomotor tone aims at maintaining venous return, thus opposing gravitational pooling of blood in the lower part

  3. Recurrent Neural Network for Text Classification with Multi-Task Learning

    OpenAIRE

    Liu, Pengfei; Qiu, Xipeng; Huang, Xuanjing

    2016-01-01

    Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and...

  4. Neural Network Reorganization Analysis During an Auditory Oddball Task in Schizophrenia Using Wavelet Entropy

    Directory of Open Access Journals (Sweden)

    Javier Gomez-Pilar

    2015-07-01

    Full Text Available The aim of the present study was to characterize the neural network reorganization during a cognitive task in schizophrenia (SCH by means of wavelet entropy (WE. Previous studies suggest that the cognitive impairment in patients with SCH could be related to the disrupted integrative functions of neural circuits. Nevertheless, further characterization of this effect is needed, especially in the time-frequency domain. This characterization is sensitive to fast neuronal dynamics and their synchronization that may be an important component of distributed neuronal interactions; especially in light of the disconnection hypothesis for SCH and its electrophysiological correlates. In this work, the irregularity dynamics elicited by an auditory oddball paradigm were analyzed through synchronized-averaging (SA and single-trial (ST analyses. They provide complementary information on the spatial patterns involved in the neural network reorganization. Our results from 20 healthy controls and 20 SCH patients showed a WE decrease from baseline to response both in controls and SCH subjects. These changes were significantly more pronounced for healthy controls after ST analysis, mainly in central and frontopolar areas. On the other hand, SA analysis showed more widespread spatial differences than ST results. These findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks. Furthermore, the less pronounced changes in WE from baseline to response for SCH patients suggest an impaired ability to reorganize neural dynamics during an oddball task.

  5. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  6. Analysis of neural interaction in motor cortex during reach-to-grasp task based on Dynamic Bayesian Networks.

    Science.gov (United States)

    Sang, Dong; Lv, Bin; He, Huiguang; He, Jiping; Wang, Feiyue

    2010-01-01

    In this work, we took the analysis of neural interaction based on the data recorded from the motor cortex of a monkey, when it was trained to complete multi-targets reach-to-grasp tasks. As a recently proved effective tool, Dynamic Bayesian Network (DBN) was applied to model and infer interactions of dependence between neurons. In the results, the gained networks of neural interactions, which correspond to different tasks with different directions and orientations, indicated that the target information was not encoded in simple ways by neuronal networks. We also explored the difference of neural interactions between delayed period and peri-movement period during reach-to-grasp task. We found that the motor control process always led to relatively more complex neural interaction networks than the plan thinking process.

  7. A neural network model of individual differences in task switching abilities.

    Science.gov (United States)

    Herd, Seth A; O'Reilly, Randall C; Hazy, Tom E; Chatham, Christopher H; Brant, Angela M; Friedman, Naomi P

    2014-09-01

    We use a biologically grounded neural network model to investigate the brain mechanisms underlying individual differences specific to the selection and instantiation of representations that exert cognitive control in task switching. Existing computational models of task switching do not focus on individual differences and so cannot explain why task switching abilities are separable from other executive function (EF) abilities (such as response inhibition). We explore hypotheses regarding neural mechanisms underlying the "Shifting-Specific" and "Common EF" components of EF proposed in the Unity/Diversity model (Miyake & Friedman, 2012) and similar components in related theoretical frameworks. We do so by adapting a well-developed neural network model of working memory (Prefrontal cortex, Basal ganglia Working Memory or PBWM; Hazy, Frank, & O'Reilly, 2007) to task switching and the Stroop task, and comparing its behavior on those tasks under a variety of individual difference manipulations. Results are consistent with the hypotheses that variation specific to task switching (i.e., Shifting-Specific) may be related to uncontrolled, automatic persistence of goal representations, whereas variation general to multiple EFs (i.e., Common EF) may be related to the strength of PFC representations and their effect on processing in the remainder of the cognitive system. Moreover, increasing signal to noise ratio in PFC, theoretically tied to levels of tonic dopamine and a genetic polymorphism in the COMT gene, reduced Stroop interference but increased switch costs. This stability-flexibility tradeoff provides an explanation for why these two EF components sometimes show opposing correlations with other variables such as attention problems and self-restraint. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Cognitive Control Signals for Neural Prosthetics

    National Research Council Canada - National Science Library

    S. Musallam; B. D. Corneil; B. Greger; H. Scherberger; R. A. Andersen

    2004-01-01

    Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices...

  9. Decentralized neural control application to robotics

    CERN Document Server

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

    2017-01-01

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

  10. Neural Correlates of Single- and Dual-Task Walking in the Real World

    Directory of Open Access Journals (Sweden)

    Sara Pizzamiglio

    2017-09-01

    Full Text Available Recent developments in mobile brain-body imaging (MoBI technologies have enabled studies of human locomotion where subjects are able to move freely in more ecologically valid scenarios. In this study, MoBI was employed to describe the behavioral and neurophysiological aspects of three different commonly occurring walking conditions in healthy adults. The experimental conditions were self-paced walking, walking while conversing with a friend and lastly walking while texting with a smartphone. We hypothesized that gait performance would decrease with increased cognitive demands and that condition-specific neural activation would involve condition-specific brain areas. Gait kinematics and high density electroencephalography (EEG were recorded whilst walking around a university campus. Conditions with dual tasks were accompanied by decreased gait performance. Walking while conversing was associated with an increase of theta (θ and beta (β neural power in electrodes located over left-frontal and right parietal regions, whereas walking while texting was associated with a decrease of β neural power in a cluster of electrodes over the frontal-premotor and sensorimotor cortices when compared to walking whilst conversing. In conclusion, the behavioral “signatures” of common real-life activities performed outside the laboratory environment were accompanied by differing frequency-specific neural “biomarkers”. The current findings encourage the study of the neural biomarkers of disrupted gait control in neurologically impaired patients.

  11. Task-modulated coactivation of vergence neural substrates.

    Science.gov (United States)

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

    2014-10-01

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

  12. Neural correlates of reconfiguration failure reveal the time course of task-set reconfiguration.

    Science.gov (United States)

    Steinhauser, Marco; Maier, Martin E; Ernst, Benjamin

    2017-11-01

    The ability to actively prepare for new tasks is crucial for achieving goal-directed behavior. The task-switching paradigm is frequently used to investigate this task-set reconfiguration. In the present study, we adopted a novel approach to identify a neural signature of reconfiguration in event-related potentials. Our method was to isolate neural correlates of reconfiguration failure and to use these correlates to reveal the time course of reconfiguration in task switches and task repetitions. We employed a task-switching paradigm in which two types of errors could be distinguished: task errors (the incorrect task was applied) and response errors (an incorrect response for the correct task was provided). Because differential activity between both error types distinguishes successful and failed reconfiguration, this activity could be used as a neural signature of the reconfiguration process. We found that, whereas reconfiguration takes place on task repetitions and task switches, it occurred earlier in the former than in the latter. Single-trial analysis revealed that the same activity predicted the amplitude of error-related brain activity, providing further support that this preparatory activity reflects reconfiguration. Our results implicate that reconfiguration is not switch-specific but that task switches and task repetitions differ with respect to the time course of reconfiguration. Furthermore, this study demonstrates that considering neural correlates of failure is a promising approach to link cognitive mechanisms to specific neural processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Flexible body control using neural networks

    Science.gov (United States)

    Mccullough, Claire L.

    1992-01-01

    Progress is reported on the control of Control Structures Interaction suitcase demonstrator (a flexible structure) using neural networks and fuzzy logic. It is concluded that while control by neural nets alone (i.e., allowing the net to design a controller with no human intervention) has yielded less than optimal results, the neural net trained to emulate the existing fuzzy logic controller does produce acceptible system responses for the initial conditions examined. Also, a neural net was found to be very successful in performing the emulation step necessary for the anticipatory fuzzy controller for the CSI suitcase demonstrator. The fuzzy neural hybrid, which exhibits good robustness and noise rejection properties, shows promise as a controller for practical flexible systems, and should be further evaluated.

  14. Neural Networks for Modeling and Control of Particle Accelerators

    Science.gov (United States)

    Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

    2016-04-01

    Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  15. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

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

  16. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  17. A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

    Science.gov (United States)

    von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

    2016-10-26

    The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

  18. Neural correlates of decision-making during a Bayesian choice task.

    Science.gov (United States)

    Poudel, Govinda R; Bhattarai, Anjan; Dickinson, David L; Drummond, Sean P A

    2017-03-01

    Many critical decisions require evaluation of accumulated previous information and/or newly acquired evidence. Although neural correlates of belief updating have been investigated, how these neural processes guide decisions involving Bayesian choice is less clear. Here, we used functional MRI to investigate neural activity during a Bayesian choice task involving two sources of information: base rate odds ('odds') and sample evidence ('evidence'). Thirty-seven healthy control individuals performed the Bayesian choice task in which they had to make probability judgements. Average functional MRI activity during the trials where choice was consistent with use of Odds, use of Evidence, and use of Both was compared. Decision-making consistent with odds, evidence and both each strongly activated the bilateral executive network encompassing the bilateral frontal, cingulate, posterior parietal and occipital cortices. The Evidence consistent, compared with Odds consistent, decisions showed greater activity in the bilateral middle and inferior frontal and right lateral occipital cortices. Decisions consistent with the use of Both strategies were associated with increased activity in the bilateral middle frontal and superior frontal cortices. These findings support the conclusion that both overlapping and distinct brain regions within the frontoparietal network underlie the incorporation of different types of information into a Bayesian decision.

  19. Functional Roles of Neural Preparatory Processes in a Cued Stroop Task Revealed by Linking Electrophysiology with Behavioral Performance.

    Science.gov (United States)

    Wang, Chao; Ding, Mingzhou; Kluger, Benzi M

    2015-01-01

    It is well established that cuing facilitates behavioral performance and that different aspects of instructional cues evoke specific neural preparatory processes in cued task-switching paradigms. To deduce the functional role of these neural preparatory processes the majority of studies vary aspects of the experimental paradigm and describe how these variations alter markers of neural preparatory processes. Although these studies provide important insights, they also have notable limitations, particularly in terms of understanding the causal or functional relationship of neural markers to cognitive and behavioral processes. In this study, we sought to address these limitations and uncover the functional roles of neural processes by examining how variability in the amplitude of neural preparatory processes predicts behavioral performance to subsequent stimuli. To achieve this objective 16 young adults were recruited to perform a cued Stroop task while their brain activity was measured using high-density electroencephalography. Four temporally overlapping but functionally and topographically distinct cue-triggered event related potentials (ERPs) were identified: 1) A left-frontotemporal negativity (250-700 ms) that was positively associated with word-reading performance; 2) a midline-frontal negativity (450-800 ms) that was positively associated with color-naming and incongruent performance; 3) a left-frontal negativity (450-800 ms) that was positively associated with switch trial performance; and 4) a centroparietal positivity (450-800 ms) that was positively associated with performance for almost all trial types. These results suggest that at least four dissociable cognitive processes are evoked by instructional cues in the present task, including: 1) domain-specific task facilitation; 2) switch-specific task-set reconfiguration; 3) preparation for response conflict; and 4) proactive attentional control. Examining the relationship between ERPs and behavioral

  20. The neural control of singing

    Directory of Open Access Journals (Sweden)

    Jean Mary eZarate

    2013-06-01

    Full Text Available Singing provides a unique opportunity to examine music performance—the musical instrument is contained wholly within the body, thus eliminating the need for creating artificial instruments or tasks in neuroimaging experiments. Here, more than two decades of voice and singing research will be reviewed to give an overview of the sensory-motor control of the singing voice, starting from the vocal tract and leading up to the brain regions involved in singing. Additionally, to demonstrate how sensory feedback is integrated with vocal motor control, recent functional magnetic resonance imaging (fMRI research on somatosensory and auditory feedback processing during singing will be presented. The relationship between the brain and singing behavior will be explored also by examining: 1 neuroplasticity as a function of various lengths and types of training, 2 vocal amusia due to a compromised singing network, and 3 singing performance in individuals with congenital amusia. Finally, the auditory-motor control network for singing will be considered alongside dual-stream models of auditory processing in music and speech to refine both these theoretical models and the singing network itself.

  1. The neural control of singing

    Science.gov (United States)

    Zarate, Jean Mary

    2013-01-01

    Singing provides a unique opportunity to examine music performance—the musical instrument is contained wholly within the body, thus eliminating the need for creating artificial instruments or tasks in neuroimaging experiments. Here, more than two decades of voice and singing research will be reviewed to give an overview of the sensory-motor control of the singing voice, starting from the vocal tract and leading up to the brain regions involved in singing. Additionally, to demonstrate how sensory feedback is integrated with vocal motor control, recent functional magnetic resonance imaging (fMRI) research on somatosensory and auditory feedback processing during singing will be presented. The relationship between the brain and singing behavior will be explored also by examining: (1) neuroplasticity as a function of various lengths and types of training, (2) vocal amusia due to a compromised singing network, and (3) singing performance in individuals with congenital amusia. Finally, the auditory-motor control network for singing will be considered alongside dual-stream models of auditory processing in music and speech to refine both these theoretical models and the singing network itself. PMID:23761746

  2. Control of autonomous robot using neural networks

    Science.gov (United States)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  3. Neural network topology design for nonlinear control

    Science.gov (United States)

    Haecker, Jens; Rudolph, Stephan

    2001-03-01

    Neural networks, especially in nonlinear system identification and control applications, are typically considered to be black-boxes which are difficult to analyze and understand mathematically. Due to this reason, an in- depth mathematical analysis offering insight into the different neural network transformation layers based on a theoretical transformation scheme is desired, but up to now neither available nor known. In previous works it has been shown how proven engineering methods such as dimensional analysis and the Laplace transform may be used to construct a neural controller topology for time-invariant systems. Using the knowledge of neural correspondences of these two classical methods, the internal nodes of the network could also be successfully interpreted after training. As further extension to these works, the paper describes the latest of a theoretical interpretation framework describing the neural network transformation sequences in nonlinear system identification and control. This can be achieved By incorporation of the method of exact input-output linearization in the above mentioned two transform sequences of dimensional analysis and the Laplace transformation. Based on these three theoretical considerations neural network topologies may be designed in special situations by pure translation in the sense of a structural compilation of the known classical solutions into their correspondent neural topology. Based on known exemplary results, the paper synthesizes the proposed approach into the visionary goals of a structural compiler for neural networks. This structural compiler for neural networks is intended to automatically convert classical control formulations into their equivalent neural network structure based on the principles of equivalence between formula and operator, and operator and structure which are discussed in detail in this work.

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

    Directory of Open Access Journals (Sweden)

    Nora M. Raschle

    2017-09-01

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

  5. Investigating the Neural Correlates of Emotion-Cognition Interaction Using an Affective Stroop Task.

    Science.gov (United States)

    Raschle, Nora M; Fehlbaum, Lynn V; Menks, Willeke M; Euler, Felix; Sterzer, Philipp; Stadler, Christina

    2017-01-01

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

  6. Income, neural executive processes, and preschool children's executive control.

    Science.gov (United States)

    Ruberry, Erika J; Lengua, Liliana J; Crocker, Leanna Harris; Bruce, Jacqueline; Upshaw, Michaela B; Sommerville, Jessica A

    2017-02-01

    This study aimed to specify the neural mechanisms underlying the link between low household income and diminished executive control in the preschool period. Specifically, we examined whether individual differences in the neural processes associated with executive attention and inhibitory control accounted for income differences observed in performance on a neuropsychological battery of executive control tasks. The study utilized a sample of preschool-aged children (N = 118) whose families represented the full range of income, with 32% of families at/near poverty, 32% lower income, and 36% middle to upper income. Children completed a neuropsychological battery of executive control tasks and then completed two computerized executive control tasks while EEG data were collected. We predicted that differences in the event-related potential (ERP) correlates of executive attention and inhibitory control would account for income differences observed on the executive control battery. Income and ERP measures were related to performance on the executive control battery. However, income was unrelated to ERP measures. The findings suggest that income differences observed in executive control during the preschool period might relate to processes other than executive attention and inhibitory control.

  7. Neural correlates of task switching in paternal 15q11-q13 deletion Prader-Willi syndrome.

    Science.gov (United States)

    Woodcock, Kate A; Humphreys, Glyn W; Oliver, Chris; Hansen, Peter C

    2010-12-02

    We report a first study of brain activity linked to task switching in individuals with Prader-Willi syndrome (PWS). PWS individuals show a specific cognitive deficit in task switching which may be associated with the display of temper outbursts and repetitive questioning. The performance of participants with PWS and typically developing controls was matched in a cued task switching procedure, and brain activity was contrasted on switching and non-switching blocks using fMRI. Individuals with PWS did not show the typical frontal-parietal pattern of neural activity associated with switching blocks, with significantly reduced activation in regions of the posterior parietal and ventromedial prefrontal cortices. We suggest that this is linked to a difficulty in PWS in setting appropriate attentional weights to enable task-set reconfiguration. In addition to this, PWS individuals did not show the typical pattern of deactivation, with significantly less deactivation in an anterior region of the ventromedial prefrontal cortex. One plausible explanation for this is that individuals with PWS show dysfunction within the default mode network, which has been linked to attentional control. The data point to functional changes in the neural circuitry supporting task switching in PWS even when behavioural performance is matched to controls and thus highlight neural mechanisms that may be involved in a specific pathway between genes, cognition and behaviour. Copyright © 2010 Elsevier B.V. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Mattia Rigotti

    2010-10-01

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

  9. Emotion regulation in social anxiety disorder: behavioral and neural responses to three socio-emotional tasks

    Science.gov (United States)

    2013-01-01

    Background Social anxiety disorder (SAD) is thought to involve deficits in emotion regulation, and more specifically, deficits in cognitive reappraisal. However, evidence for such deficits is mixed. Methods Using functional magnetic resonance imaging (fMRI) of blood oxygen-level dependent (BOLD) signal, we examined reappraisal-related behavioral and neural responses in 27 participants with generalized SAD and 27 healthy controls (HC) during three socio-emotional tasks: (1) looming harsh faces (Faces); (2) videotaped actors delivering social criticism (Criticism); and (3) written autobiographical negative self-beliefs (Beliefs). Results Behaviorally, compared to HC, participants with SAD had lesser reappraisal-related reduction in negative emotion in the Beliefs task. Neurally, compared to HC, participants with SAD had lesser BOLD responses in reappraisal-related brain regions when reappraising faces, in visual and attention related regions when reappraising criticism, and in the left superior temporal gyrus when reappraising beliefs. Examination of the temporal dynamics of BOLD responses revealed late reappraisal-related increased responses in HC, compared to SAD. In addition, the dorsomedial prefrontal cortex (DMPFC), which showed reappraisal-related increased activity in both groups, had similar temporal dynamics in SAD and HC during the Faces and Criticism tasks, but greater late response increases in HC, compared to SAD, during the Beliefs task. Reappraisal-related greater late DMPFC responses were associated with greater percent reduction in negative emotion ratings in SAD patients. Conclusions These results suggest a dysfunction of cognitive reappraisal in SAD patients, with overall reduced late brain responses in prefrontal regions, particularly when reappraising faces. Decreased late activity in the DMPFC might be associated with deficient reappraisal and greater negative reactivity. Trial registration ClinicalTrials.gov identifier: NCT00380731 PMID

  10. Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

    Science.gov (United States)

    Collins, Anne G. E.; Frank, Michael J.

    2013-01-01

    Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

  11. Distracted in a Demanding Task : A Classification Study with Artificial Neural Networks

    NARCIS (Netherlands)

    Huijser, Stefan; Taatgen, Niels; van Vugt, Marieke; Verheij, Bart; Wiering, Marco

    2017-01-01

    An important issue in cognitive science research is to know what your subjects are thinking about. In this paper, we trained multiple artificial Neural Network (ANN) classifiers to predict whether subjects’ thoughts were focused on the task (i.e., on-task) or if they were distracted (i.e.,

  12. Application of neural models as controllers in mobile robot velocity control loop

    Science.gov (United States)

    Cerkala, Jakub; Jadlovska, Anna

    2017-01-01

    This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

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

    Science.gov (United States)

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

    2013-01-09

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

  14. A High-Performance Neural Prosthesis Enabled by Control Algorithm Design

    Science.gov (United States)

    Gilja, Vikash; Nuyujukian, Paul; Chestek, Cindy A.; Cunningham, John P.; Yu, Byron M.; Fan, Joline M.; Churchland, Mark M.; Kaufman, Matthew T.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2012-01-01

    Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer disabled patients greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower with less accurate control than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF), that incorporates assumptions about the nature of closed loop neural prosthetic control. When tested with rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperforms existing neural prostheses in all measured domains and halves acquisition time. This control algorithm permits sustained uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation across two monkeys, thereby increasing the clinical viability of neural prostheses. PMID:23160043

  15. Neural Correlates of Decision Making on a Gambling Task

    Science.gov (United States)

    Carlson, Stephanie M.; Zayas, Vivian; Guthormsen, Amy

    2009-01-01

    Individual differences in affective decision making were examined by recording event-related potentials (ERPs) while 74 typically developing 8-year-olds (38 boys, 36 girls) completed a 4-choice gambling task (Hungry Donkey Task; E. A. Crone & M. W. van der Molen, 2004). ERP results indicated: (a) a robust P300 component in response to feedback…

  16. Neural predictive control for active buffet alleviation

    Science.gov (United States)

    Pado, Lawrence E.; Lichtenwalner, Peter F.; Liguore, Salvatore L.; Drouin, Donald

    1998-06-01

    The adaptive neural control of aeroelastic response (ANCAR) and the affordable loads and dynamics independent research and development (IRAD) programs at the Boeing Company jointly examined using neural network based active control technology for alleviating undesirable vibration and aeroelastic response in a scale model aircraft vertical tail. The potential benefits of adaptive control includes reducing aeroelastic response associated with buffet and atmospheric turbulence, increasing flutter margins, and reducing response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and thus loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Wind tunnel tests were undertaken on a rigid 15% scale aircraft in Boeing's mini-speed wind tunnel, which is used for testing at very low air speeds up to 80 mph. The model included a dynamically scaled flexible fail consisting of an aluminum spar with balsa wood cross sections with a hydraulically powered rudder. Neural predictive control was used to actuate the vertical tail rudder in response to strain gauge feedback to alleviate buffeting effects. First mode RMS strain reduction of 50% was achieved. The neural predictive control system was developed and implemented by the Boeing Company to provide an intelligent, adaptive control architecture for smart structures applications with automated synthesis, self-optimization, real-time adaptation, nonlinear control, and fault tolerance capabilities. It is designed to solve complex control problems though a process of automated synthesis, eliminating costly control design and surpassing it in many instances by accounting for real world non-linearities.

  17. Spatio-temporal analysis reveals active control of both task-relevant and task-irrelevant variables.

    Science.gov (United States)

    Rácz, Kornelius; Valero-Cuevas, Francisco J

    2013-01-01

    The Uncontrolled Manifold (UCM) hypothesis and Minimal Intervention principle propose that the observed differential variability across task relevant (i.e., task goals) vs. irrelevant (i.e., in the null space of those goals) variables is evidence of a separation of task variables for efficient neural control, ranked by their respective variabilities (sometimes referred to as hierarchy of control). Support for this comes from spatial domain analyses (i.e., structure of) of kinematic, kinetic, and EMG variability. While proponents admit the possibility of preferential as opposed to strictly uncontrolled variables, such distinctions have only begun to be quantified or considered in the temporal domain when inferring control action. Here we extend the study of task variability during tripod static grasp to the temporal domain by applying diffusion analysis. We show that both task-relevant and task-irrelevant parameters show corrective action at some time scales; and conversely, that task-relevant parameters do not show corrective action at other time scales. That is, the spatial fluctuations of fingertip forces show, as expected, greater ranges of variability in task-irrelevant variables (>98% associated with changes in total grasp force; vs. only acceleration of the object). But at some time scales, however, temporal fluctuations of task-irrelevant variables exhibit negative correlations clearly indicative of corrective action (scaling exponents spatial and temporal features of all task variables when inferring control action and understanding how the CNS confronts task redundancy. Instead of a dichotomy of presence vs. absence of control, we should speak of a continuum of weaker to stronger-and potentially different-control strategies in specific spatiotemporal domains, indicated here by the magnitude of deviation from the 0.5 scaling exponent. Moreover, these results are counter examples to the UCM hypothesis and the Minimal Intervention principle, and the similar

  18. Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

    OpenAIRE

    Lehký, David; Slowik, Ondřej; Novák, Drahomír

    2014-01-01

    Part 7: Genetic Algorithms; International audience; The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficie...

  19. Neural model for learning-to-learn of novel task sets in the motor domain.

    Science.gov (United States)

    Pitti, Alexandre; Braud, Raphaël; Mahé, Sylvain; Quoy, Mathias; Gaussier, Philippe

    2013-01-01

    During development, infants learn to differentiate their motor behaviors relative to various contexts by exploring and identifying the correct structures of causes and effects that they can perform; these structures of actions are called task sets or internal models. The ability to detect the structure of new actions, to learn them and to select on the fly the proper one given the current task set is one great leap in infants cognition. This behavior is an important component of the child's ability of learning-to-learn, a mechanism akin to the one of intrinsic motivation that is argued to drive cognitive development. Accordingly, we propose to model a dual system based on (1) the learning of new task sets and on (2) their evaluation relative to their uncertainty and prediction error. The architecture is designed as a two-level-based neural system for context-dependent behavior (the first system) and task exploration and exploitation (the second system). In our model, the task sets are learned separately by reinforcement learning in the first network after their evaluation and selection in the second one. We perform two different experimental setups to show the sensorimotor mapping and switching between tasks, a first one in a neural simulation for modeling cognitive tasks and a second one with an arm-robot for motor task learning and switching. We show that the interplay of several intrinsic mechanisms drive the rapid formation of the neural populations with respect to novel task sets.

  20. Abnormal Task Modulation of Oscillatory Neural Activity in Schizophrenia

    Directory of Open Access Journals (Sweden)

    Elisa C Dias

    2013-08-01

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

  1. Neural Correlates of Changes in a Visual Search Task due to Cognitive Training in Seniors

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    Nele Wild-Wall

    2012-01-01

    Full Text Available This study aimed to elucidate the underlying neural sources of near transfer after a multidomain cognitive training in older participants in a visual search task. Participants were randomly assigned to a social control, a no-contact control and a training group, receiving a 4-month paper-pencil and PC-based trainer guided cognitive intervention. All participants were tested in a before and after session with a conjunction visual search task. Performance and event-related potentials (ERPs suggest that the cognitive training improved feature processing of the stimuli which was expressed in an increased rate of target detection compared to the control groups. This was paralleled by enhanced amplitudes of the frontal P2 in the ERP and by higher activation in lingual and parahippocampal brain areas which are discussed to support visual feature processing. Enhanced N1 and N2 potentials in the ERP for nontarget stimuli after cognitive training additionally suggest improved attention and subsequent processing of arrays which were not immediately recognized as targets. Possible test repetition effects were confined to processes of stimulus categorisation as suggested by the P3b potential. The results show neurocognitive plasticity in aging after a broad cognitive training and allow pinpointing the functional loci of effects induced by cognitive training.

  2. Behavioral activation can normalize neural hypoactivation in subthreshold depression during a monetary incentive delay task.

    Science.gov (United States)

    Mori, Asako; Okamoto, Yasumasa; Okada, Go; Takagaki, Koki; Jinnin, Ran; Takamura, Masahiro; Kobayakawa, Makoto; Yamawaki, Shigeto

    2016-01-01

    Late adolescents are under increased risk of developing depressive symptoms. Behavioral activation is an effective treatment for subthreshold depression, which can prevent the development of subthreshold depression into a major depressive disorder. However, the neural mechanisms underlying the efficacy of behavioral activation have not been clearly understood. We investigated neural responses during reward processing by individuals with subthreshold depression to clarify the neural mechanisms of behavioral activation. Late adolescent university students with subthreshold depression (n=15, age 18-19 years) as indicated by a high score on the Beck's Depression Inventory-ll (BDI-ll) and 15 age-matched controls with a low BDI-ll score participated in functional magnetic resonance imaging scanning conducted during a monetary incentive delay task on two occasions. The Individuals in the subthreshold depression group received five, weekly behavioral activation sessions between the two scanning sessions. Moreover, they did not receive any medication until the study was completed. Behavioral activation significantly reduced depressive symptoms. Moreover, compared to the changes in brain functions in the control group, the behavioral activation group showed functional changes during loss anticipation in brain structures that mediates cognitive and emotional regulation, including the left ventrolateral prefrontal cortex and angular gyrus. Replication of the study with a larger sample size is required to increase the generalizability of these results. Behavioral activation results in improved functioning of the fronto-parietal region during loss anticipation. These results increase our understanding of the mechanisms underlying specific psychotherapies. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Neural mechanisms underlying the cost of task switching: an ERP study.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: When switching from one task to a new one, reaction times are prolonged. This phenomenon is called switch cost (SC. Researchers have recently used several kinds of task-switching paradigms to uncover neural mechanisms underlying the SC. Task-set reconfiguration and passive dissipation of a previously relevant task-set have been reported to contribute to the cost of task switching. METHODOLOGY/PRINCIPAL FINDINGS: An unpredictable cued task-switching paradigm was used, during which subjects were instructed to switch between a color and an orientation discrimination task. Electroencephalography (EEG and behavioral measures were recorded in 14 subjects. Response-stimulus interval (RSI and cue-stimulus interval (CSI were manipulated with short and long intervals, respectively. Switch trials delayed reaction times (RTs and increased error rates compared with repeat trials. The SC of RTs was smaller in the long CSI condition. For cue-locked waveforms, switch trials generated a larger parietal positive event-related potential (ERP, and a larger slow parietal positivity compared with repeat trials in the short and long CSI condition. Neural SC of cue-related ERP positivity was smaller in the long RSI condition. For stimulus-locked waveforms, a larger switch-related central negative ERP component was observed, and the neural SC of the ERP negativity was smaller in the long CSI. Results of standardized low resolution electromagnetic tomography (sLORETA for both ERP positivity and negativity showed that switch trials evoked larger activation than repeat trials in dorsolateral prefrontal cortex (DLPFC and posterior parietal cortex (PPC. CONCLUSIONS/SIGNIFICANCE: The results provide evidence that both RSI and CSI modulate the neural activities in the process of task-switching, but that these have a differential role during task-set reconfiguration and passive dissipation of a previously relevant task-set.

  4. Functional dissociations in top-down control dependent neural repetition priming.

    NARCIS (Netherlands)

    Klaver, P.; Schnaidt, M.; Fell, J.; Ruhlmann, J.; Elger, C.E.; Fernandez, G.

    2007-01-01

    Little is known about the neural mechanisms underlying top-down control of repetition priming. Here, we use functional brain imaging to investigate these mechanisms. Study and repetition tasks used a natural/man-made forced choice task. In the study phase subjects were required to respond to either

  5. Haptic fMRI: accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks.

    Science.gov (United States)

    Menon, Samir; Yu, Michelle; Kay, Kendrick; Khatib, Oussama

    2014-01-01

    Haptics combined with functional magnetic resonance imaging (Haptic fMRI) can non-invasively study how the human brain coordinates movement during complex manipulation tasks, yet avoiding associated fMRI artifacts remains a challenge. Here, we demonstrate confound-free neural activation measurements using Haptic fMRI for an unconstrained three degree-of-freedom motor task that involves planning, reaching, and visually guided trajectory tracking. Our haptic interface tracked subjects' hand motions, velocities, and accelerations (sample-rate, 350Hz), and provided continuous realtime visual feedback. During fMRI acquisition, we achieved uniform response latencies (reaching, 0.7-1.1s; tracking, 0.4-0.65s); minimized hand jitter (neural activation across cortex; unreliable motions and response latencies, which reduce statistical power; and task-correlated head motion, which causes spurious fMRI activation. Haptic fMRI can thus reliably elicit and localize heterogeneous neural activation for different tasks in motor (movement), pre-motor (planning), and somatosensory (limb displacement) cortex, demonstrating that it is feasible to use the technique to study how the brain achieves three dimensional motor control.

  6. Neural network activation during a stop-signal task discriminates cocaine-dependent from non-drug-abusing men.

    Science.gov (United States)

    Elton, Amanda; Young, Jonathan; Smitherman, Sonet; Gross, Robin E; Mletzko, Tanja; Kilts, Clinton D

    2014-05-01

    Cocaine dependence is defined by a loss of inhibitory control over drug-use behaviors, mirrored by measurable impairments in laboratory tasks of inhibitory control. The current study tested the hypothesis that deficits in multiple subprocesses of behavioral control are associated with reliable neural-processing alterations that define cocaine addiction. While undergoing functional magnetic resonance imaging (fMRI), 38 cocaine-dependent men and 27 healthy control men performed a stop-signal task of motor inhibition. An independent component analysis on fMRI time courses identified task-related neural networks attributed to motor, visual, cognitive and affective processes. The statistical associations of these components with five different stop-signal task conditions were selected for use in a linear discriminant analysis to define a classifier for cocaine addiction from a subsample of 26 cocaine-dependent men and 18 controls. Leave-one-out cross-validation accurately classified 89.5% (39/44; chance accuracy = 26/44 = 59.1%) of subjects with 84.6% (22/26) sensitivity and 94.4% (17/18) specificity. The remaining 12 cocaine-dependent and 9 control men formed an independent test sample, for which accuracy of the classifier was 81.9% (17/21; chance accuracy = 12/21 = 57.1%) with 75% (9/12) sensitivity and 88.9% (8/9) specificity. The cocaine addiction classification score was significantly correlated with a measure of impulsiveness as well as the duration of cocaine use for cocaine-dependent men. The results of this study support the ability of a pattern of multiple neural network alterations associated with inhibitory motor control to define a binary classifier for cocaine addiction. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

  7. Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

    Science.gov (United States)

    Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

    2017-10-01

    This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

  8. Neural correlates of cognitive style and flexible cognitive control.

    Science.gov (United States)

    Shin, Gyeonghee; Kim, Chobok

    2015-06-01

    Human abilities of flexible cognitive control are associated with appropriately regulating the amount of cognitive control required in response to contextual demands. In the context of conflicting situations, for instance, the amount of cognitive control increases according to the level of previously experienced conflict, resulting in optimized performance. We explored whether the amount of cognitive control in conflict resolution was related to individual differences in cognitive style that were determined with the Object-Spatial-Verbal cognitive style questionnaire. In this functional magnetic resonance imaging (fMRI) study, a version of the color-word Stroop task, which evokes conflict between color and verbal components, was employed to explore whether individual preferences for distracting information were related to the increases in neural conflict adaptation in cognitive control network regions. The behavioral data revealed that the more the verbal style was preferred, the greater the conflict adaptation effect was observed, especially when the current trial type was congruent. Consistent with the behavioral data, the imaging results demonstrated increased neural conflict adaptation effects in task-relevant network regions, including the left dorsolateral prefrontal cortex, left fusiform gyrus, and left precuneus, as the preference for verbal style increased. These results provide new evidence that flexible cognitive control is closely associated with individuals' preference of cognitive style. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Differential neural activation for camouflage detection task in Field ...

    Indian Academy of Sciences (India)

    2015-11-28

    Nov 28, 2015 ... and neurological/psychotic disorder/cognitive impairment. They also showed no cortical infarctions on T2-weighted MR images. The local ethics committee ..... match or mismatch on a communication task. J. Educ. Psychol. 74 23–31. Goodal MA and Milner AD 1992 Separate pathways for perception.

  10. The Adaptive Neural Network Control of Quadrotor Helicopter

    Directory of Open Access Journals (Sweden)

    A. S. Yushenko

    2017-01-01

    Full Text Available The current steady-rising interest in using the unmanned multi-rotor aerial vehicles (UMAV designed to solve a wide range of tasks is, mainly, due to their simple design and high weight-carrying capacity as compared to classical helicopter options. Unfortunately, to solve a problem of multi-copter control is complicated because of essential nonlinearity and environmental perturbations. The most widely spread PID controllers and linear-quadratic regulators do not quite well cope with this task. The need arises for the prompt adjustment of PID controller coefficients in the course of operation or their complete re-tuning in cases of changing parameters of the control object.One of the control methods under changing conditions is the use of the sliding mode. This technology enables us to reach the stabilization and proper operation of the controlled system even under accidental external exposures and when there is a lack of the reasonably accurate mathematical model of the control object. The sliding principle is to ensure the system motion in the immediate vicinity of the sliding surface in the phase space. On the other hand, the sliding mode has some essential disadvantages. The most significant one is the high-frequency jitter of the system near the sliding surface. The sliding mode also implies the complete knowledge of the system dynamics. Various methods have been proposed to eliminate these drawbacks. For example, A.G. Aissaoui’s, H. Abid’s and M. Abid’s paper describes the application of fuzzy logic to control a drive and in Lon-Chen Hung’s and Hung-Yuan Chung’s paper an artificial neural network is used for the manipulator control.This paper presents a method of the quad-copter control with the aid of a neural network controller. This method enables us to control the system without a priori information on parameters of the dynamic model of the controlled object. The main neural network is a MIMO (“Multiple Input Multiple

  11. Neural Control of the Lower Urinary Tract

    Science.gov (United States)

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  12. Neural compensation in adulthood following very preterm birth demonstrated during a visual paired associates learning task.

    Science.gov (United States)

    Brittain, Philip J; Froudist Walsh, Sean; Nam, Kie-Woo; Giampietro, Vincent; Karolis, Vyacheslav; Murray, Robin M; Bhattacharyya, Sagnik; Kalpakidou, Anastasia; Nosarti, Chiara

    2014-01-01

    Very preterm birth (VPT; brain insult; however, adaptive neuroplastic processes may subsequently occur in the developing preterm brain which ameliorate, to an extent, the potential sequelae of altered neurophysiology. Here, we used functional magnetic resonance imaging (fMRI) to compare neuronal activation in 24 VPT individuals and 22 controls (CT) in young adulthood during a learning task consisting of the encoding and subsequent recognition of repeated visual paired associates. Structural MRI data were also collected and analysed in order to explore possible structure-function associations. Whilst the two groups did not differ in their learning ability, as demonstrated by their capacity to recognize previously-seen and previously-unseen visual pairs, between-group differences in linear patterns of Blood Oxygenation Level Dependant (BOLD) activity were observed across the four repeated blocks of the task for both the encoding and recognition conditions, suggesting that the way learning takes place differs between the two groups. During encoding, significant between-group differences in patterns of BOLD activity were seen in clusters centred on the cerebellum, the anterior cingulate gyrus, the midbrain/substantia nigra, medial temporal (including parahippocampal) gyrus and inferior and superior frontal gyri. During the recognition condition, significant between-group differences in patterns of BOLD activity were seen in clusters centred on the claustrum and the posterior cerebellum. Structural analysis revealed smaller grey matter volume in right middle temporal gyrus in VPT individuals compared to controls, however volume in this region was not significantly associated with functional activation. These results demonstrate that although cognitive task performance between VPT individuals and controls may be comparable on certain measures, differences in BOLD signal may also be evident, some of which could represent compensatory neural processes following VPT

  13. Eye Movements Reveal Dynamics of Task Control

    Science.gov (United States)

    Mayr, Ulrich; Kuhns, David; Rieter, Miranda

    2013-01-01

    With the goal to determine the cognitive architecture that underlies flexible changes of control settings, we assessed within-trial and across-trial dynamics of attentional selection by tracking of eye movements in the context of a cued task-switching paradigm. Within-trial dynamics revealed a switch-induced, discrete delay in onset of…

  14. Neural Correlates of a Perspective-taking Task Using in a Realistic Three-dimmensional Environment Based Task: A Pilot Functional Magnetic Resonance Imaging Study.

    Science.gov (United States)

    Agarwal, Sri Mahavir; Shivakumar, Venkataram; Kalmady, Sunil V; Danivas, Vijay; Amaresha, Anekal C; Bose, Anushree; Narayanaswamy, Janardhanan C; Amorim, Michel-Ange; Venkatasubramanian, Ganesan

    2017-08-31

    Perspective-taking ability is an essential spatial faculty that is of much interest in both health and neuropsychiatric disorders. There is limited data on the neural correlates of perspective taking in the context of a realistic three-dimensional environment. We report the results of a pilot study exploring the same in eight healthy volunteers. Subjects underwent two runs of an experiment in a 3 Tesla magnetic resonance imaging (MRI) involving alternate blocks of a first-person perspective based allocentric object location memory task (OLMT), a third-person perspective based egocentric visual perspective taking task (VPRT), and a table task (TT) that served as a control. Difference in blood oxygen level dependant response during task performance was analyzed using Statistical Parametric Mapping software, version 12. Activations were considered significant if they survived family-wise error correction at the cluster level using a height threshold of p<0.001, uncorrected at the voxel level. A significant difference in accuracy and reaction time based on task type was found. Subjects had significantly lower accuracy in VPRT compared to TT. Accuracy in the two active tasks was not significantly different. Subjects took significantly longer in the VPRT in comparison to TT. Reaction time in the two active tasks was not significantly different. Functional MRI revealed significantly higher activation in the bilateral visual cortex and left temporoparietal junction (TPJ) in VPRT compared to OLMT. The results underscore the importance of TPJ in egocentric manipulation in healthy controls in the context of reality-based spatial tasks.

  15. Control and Interference in Task Switching--A Review

    Science.gov (United States)

    Kiesel, Andrea; Steinhauser, Marco; Wendt, Mike; Falkenstein, Michael; Jost, Kerstin; Philipp, Andrea M.; Koch, Iring

    2010-01-01

    The task-switching paradigm offers enormous possibilities to study cognitive control as well as task interference. The current review provides an overview of recent research on both topics. First, we review different experimental approaches to task switching, such as comparing mixed-task blocks with single-task blocks, predictable task-switching…

  16. The neural basis of the psychomotor vigilance task.

    Science.gov (United States)

    Drummond, Sean P A; Bischoff-Grethe, Amanda; Dinges, David F; Ayalon, Liat; Mednick, Sara C; Meloy, M J

    2005-09-01

    To identify brain regions underlying the fastest and slowest reaction times on the Psychomotor Vigilance task (PVT) under well-rested conditions, as well as brain regions related to particularly poor performance after sleep deprivation. Subjects took the PVT twice while undergoing functional magnetic resonance imaging: once 12 hours after waking from a normal night of sleep and once after 36 hours of total sleep deprivation (TSD). Session order was counterbalanced. UCSD J. Christian Gillin Laboratory for Sleep and Chronobiology (the sleep core of the General Clinical Research Center) and UCSD Magnetic Resonance Institute. Twenty right-handed healthy adults (8 women; age = 27.4 +/- 6.7 years; education = 15.6 +/- 1.5 years). After a normal night of sleep, optimal performance was related to greater cerebral responses within a cortical sustained attention network and the cortical and subcortical motor systems. Slow reaction times, particularly after TSD, were associated with greater activity in the "default mode network" consisting of frontal and posterior midline regions. Optimal performance on the PVT appears to rely on activation both within the sustained attention system and within the motor system. Poor performance following TSD may result from a disengagement from the task and related inattention, and brain regions responsible for this localize within midline structures shown to be involved in the brain's "default mode." Finally, particularly poor performance after TSD may elicit a subsequent attentional recovery that manifests as greater activation within the same regions normally responsible for fast reaction times.

  17. Folic Acid for the Prevention of Neural Tube Defects : US Preventive Services Task Force Recommendation Statement

    NARCIS (Netherlands)

    Calonge, Ned; Petitti, Diana B.; DeWitt, Thomas G.; Dietrich, Allen J.; Gregory, Kimberly D.; Grossman, David; Isham, George; LeFevre, Michael L.; Leipzig, Rosanne M.; Marion, Lucy N.; Melnyk, Bernadette; Moyer, Virginia A.; Ockene, Judith K.; Sawaya, George F.; Schwartz, J. Sanford; Wilt, Timothy

    2009-01-01

    Description: In 1996, the U. S. Preventive Services Task Force (USPSTF) recommended that all women planning or capable of pregnancy take a multivitamin supplement containing folic acid for the prevention of neural tube defects. This recommendation is an update of the 1996 USPSTF recommendation.

  18. neural network based load frequency control for restructuring power

    African Journals Online (AJOL)

    2012-03-01

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

  19. 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.

  20. Neural network controller for underwater work ROV. Suichu sagyoyo ROV no neural network controller

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Y.; Kidoshi, H.; Arahata, M.; Shoji, K.; Takahashi, Y. (Ishikawajima-Harima Heavy Industries, Co. Ltd., Tokyo (Japan))

    1993-07-01

    The previous underwater work ROV (remotely operated vehicle) has been controlled manually because its dynamic properties are changeable underwater. Ishikawajima-Harima Heavy Industries (IHI) has applied a neural network to an adaptive controller for the ROV. This paper describes objectives of the research, design of control logic, and tank experiments on a model ROV. For the neural network, manual operation was used to provide the initial learning data for the neural network in order to initialize control parameters for optimization. The model ROV was designed to achieve and maintain constant depth in normal operation. As a consequence of the tank experiments, it was demonstrated that the controller can acquire skill of operators, can further improve the acquired skill of operators, and can construct an automatic control system autonomically even if any dynamic properties are not known. 6 refs., 8 figs.

  1. 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.

  2. A neural network multi-task learning approach to biomedical named entity recognition.

    Science.gov (United States)

    Crichton, Gamal; Pyysalo, Sampo; Chiu, Billy; Korhonen, Anna

    2017-08-15

    Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model's performance increased compared to the single-task model's. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. Our

  3. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

  4. Effects of task demands on the early neural processing of fearful and happy facial expressions.

    Science.gov (United States)

    Itier, Roxane J; Neath-Tavares, Karly N

    2017-05-15

    Task demands shape how we process environmental stimuli but their impact on the early neural processing of facial expressions remains unclear. In a within-subject design, ERPs were recorded to the same fearful, happy and neutral facial expressions presented during a gender discrimination, an explicit emotion discrimination and an oddball detection tasks, the most studied tasks in the field. Using an eye tracker, fixation on the face nose was enforced using a gaze-contingent presentation. Task demands modulated amplitudes from 200 to 350ms at occipito-temporal sites spanning the EPN component. Amplitudes were more negative for fearful than neutral expressions starting on N170 from 150 to 350ms, with a temporo-occipital distribution, whereas no clear effect of happy expressions was seen. Task and emotion effects never interacted in any time window or for the ERP components analyzed (P1, N170, EPN). Thus, whether emotion is explicitly discriminated or irrelevant for the task at hand, neural correlates of fearful and happy facial expressions seem immune to these task demands during the first 350ms of visual processing. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Neural Network Control of Asymmetrical Multilevel Converters

    Directory of Open Access Journals (Sweden)

    Patrice WIRA

    2009-12-01

    Full Text Available This paper proposes a neural implementation of a harmonic eliminationstrategy (HES to control a Uniform Step Asymmetrical Multilevel Inverter(USAMI. The mapping between the modulation rate and the requiredswitching angles is learned and approximated with a Multi-Layer Perceptron(MLP neural network. After learning, appropriate switching angles can bedetermined with the neural network leading to a low-computational-costneural controller which is well suited for real-time applications. Thistechnique can be applied to multilevel inverters with any number of levels. Asan example, a nine-level inverter and an eleven-level inverter are consideredand the optimum switching angles are calculated on-line. Comparisons to thewell-known sinusoidal pulse-width modulation (SPWM have been carriedout in order to evaluate the performance of the proposed approach. Simulationresults demonstrate the technical advantages of the proposed neuralimplementation over the conventional method (SPWM in eliminatingharmonics while controlling a nine-level and eleven-level USAMI. Thisneural approach is applied for the supply of an asynchronous machine andresults show that it ensures a highest quality torque by efficiently cancelingthe harmonics generated by the inverters.

  6. What can the monetary incentive delay task tell us about the neural processing of reward and punishment?

    Directory of Open Access Journals (Sweden)

    Lutz K

    2014-04-01

    Full Text Available Kai Lutz,1–3 Mario Widmer1,2,41Department of Neurology, University Hospital Zürich, Zürich, 2Cereneo, Center for Neurology and Rehabilitation, Vitznau, 3Division of Neuropsychology, Institute of Psychology, University of Zürich, Zürich, 4Neural Control of Movement Lab, ETH Zürich, Zürich, SwitzerlandAbstract: Since its introduction in 2000, the monetary incentive delay (MID task has been used extensively to investigate changes in neural activity in response to the processing of reward and punishment in healthy, but also in clinical populations. Typically, the MID task requires an individual to react to a target stimulus presented after an incentive cue to win or to avoid losing the indicated reward. In doing so, this paradigm allows the detailed examination of different stages of reward processing like reward prediction, anticipation, outcome processing, and consumption as well as the processing of tasks under different reward conditions. This review gives an overview of different utilizations of the MID task by outlining the neuronal processes involved in distinct aspects of human reward processing, such as anticipation versus consumption, reward versus punishment, and, with a special focus, reward-based learning processes. Furthermore, literature on specific influences on reward processing like behavioral, clinical and developmental influences, is reviewed, describing current findings and possible future directions.Keywords: reward, punishment, dopamine, reward system

  7. Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

    Directory of Open Access Journals (Sweden)

    Markus A Wenzel

    Full Text Available Brain-computer interfaces (BCIs that are based on event-related potentials (ERPs can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG. Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI, because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG.The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

  8. The neural substrates of response inhibition to negative information across explicit and implicit tasks in GAD patients: Electrophysiological evidence from an ERP study

    Directory of Open Access Journals (Sweden)

    Fengqiong eYu

    2015-03-01

    Full Text Available Background: It has been established that the inability to inhibit a response to negative stimuli is the genesis of anxiety. However, the neural substrates of response inhibition to sad faces across explicit and implicit tasks in general anxiety disorder (GAD patients remain unclear.Methods: Electrophysiological data were recorded when subjects performed two modified emotional go/no-go tasks in which neutral and sad faces were presented: one task was explicit (emotion categorization, and the other task was implicit (gender categorization.Results: In the explicit task, electrophysiological evidence showed decreased amplitudes of no-go/go difference waves at the N2 interval in the GAD group compared to the control group. However, in the implicit task, the amplitudes of no-go/go difference waves at the N2 interval showed a reversed trend. Source localization analysis on no-go/N2 components revealed a decreased current source density (CSD in the right dorsal lateral prefrontal cortex in GAD individuals relative to controls. In the implicit task, the left superior temporal gyrus and the left inferior parietal lobe showed enhanced activation in GAD individuals and may compensate for the dysfunction of the right dorsal lateral prefrontal cortex.Conclusions: These findings indicated that the processing of response inhibition to socially sad faces in GAD individuals was interrupted in the explicit task. However, this processing was preserved in the implicit task. The neural substrates of response inhibition to sad faces were dissociated between implicit and explicit tasks.

  9. Fault diagnosis in satellite attitude control systems using artificial neural networkk

    Science.gov (United States)

    Ayodele I., Olanipekun

    The nonlinear behavior exhibited by altitude control system processes and also the presence of external constraints on the operating conditions causes hitch in the dynamics of system processes. This research work proposes a fault detection/tolerant prediction in an altitude control system. This is done through the artificial neural network fault detection by deploying the neural network approach. A fault detection and isolation module is developed in the actuator system of the Altitude Control System, thereby achieving the goal of this thesis. This can be done by two basic classification stages: Neural Residual Generator (Neural Observer)- This stage is responsible for generating residual errors that can reflect the real behavior of the entire process as against its normal conditions. Adaptive Neural Classifier - This stage is responsible for managing the isolation task of the fault detected by evaluating the generated residual errors from the neural estimator which gives detailed information about faults detected e.g., fault location and time. These two stages can be implemented by executing the tasks listed below: 1. Study and develop a generic three axis stabilized altitude control model based on the reaction wheels. This is established with three separate PD controllers designed for each reaction wheel of the satellite axis using the Matlab - SIMULINK. 2. Develop a dynamic neural network residual generator based on Dynamic Multilayer Perceptron Network (DMLP) which is then applied to the reaction wheel model designed commonly called the actuator in the altitude control system of a satellite 3. Develop a neural network adaptive classifier based on the Learning Vector Quantization (LVQ) model which is used for the isolation concept. The advantages of the proposed dynamic neural network and neural adaptive classifier approach are showcased.

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

    OpenAIRE

    Zhang, Sheng; Li, Chiang-shan Ray

    2009-01-01

    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 ...

  11. Robust Adaptive Control via Neural Linearization and Compensation

    Directory of Open Access Journals (Sweden)

    Roberto Carmona Rodríguez

    2012-01-01

    Full Text Available We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.

  12. Sticky Plans: Inhibition and Binding during Serial-Task Control

    Science.gov (United States)

    Mayr, Ulrich

    2009-01-01

    Recent evidence suggests substantial response-time costs associated with lag-2 repetitions of tasks within explicitly controlled task sequences [Koch, I., Philipp, A. M., Gade, M. (2006). Chunking in task sequences modulates task inhibition. "Psychological Science," 17, 346-350; Schneider, D. W. (2007). Task-set inhibition in chunked task…

  13. A biologically inspired neural network controller for ballistic arm movements

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2007-09-01

    Full Text Available Abstract Background In humans, the implementation of multijoint tasks of the arm implies a highly complex integration of sensory information, sensorimotor transformations and motor planning. Computational models can be profitably used to better understand the mechanisms sub-serving motor control, thus providing useful perspectives and investigating different control hypotheses. To this purpose, the use of Artificial Neural Networks has been proposed to represent and interpret the movement of upper limb. In this paper, a neural network approach to the modelling of the motor control of a human arm during planar ballistic movements is presented. Methods The developed system is composed of three main computational blocks: 1 a parallel distributed learning scheme that aims at simulating the internal inverse model in the trajectory formation process; 2 a pulse generator, which is responsible for the creation of muscular synergies; and 3 a limb model based on two joints (two degrees of freedom and six muscle-like actuators, that can accommodate for the biomechanical parameters of the arm. The learning paradigm of the neural controller is based on a pure exploration of the working space with no feedback signal. Kinematics provided by the system have been compared with those obtained in literature from experimental data of humans. Results The model reproduces kinematics of arm movements, with bell-shaped wrist velocity profiles and approximately straight trajectories, and gives rise to the generation of synergies for the execution of movements. The model allows achieving amplitude and direction errors of respectively 0.52 cm and 0.2 radians. Curvature values are similar to those encountered in experimental measures with humans. The neural controller also manages environmental modifications such as the insertion of different force fields acting on the end-effector. Conclusion The proposed system has been shown to properly simulate the development of

  14. Neural responses during social and self-knowledge tasks in bulimia nervosa

    Directory of Open Access Journals (Sweden)

    Carrie J Mcadams

    2013-09-01

    Full Text Available Self-evaluation closely dependent upon body shape and weight is one of the defining criteria for bulimia nervosa. We studied 53 adult women, 17 with bulimia nervosa, 18 with a recent history of anorexia nervosa, and 18 healthy comparison women, using three different fMRI tasks that required thinking about self-knowledge and social interactions: the Social Identity task, the Physical Identity task, and the Social Attribution task. Previously, we identified regions of interest (ROI in the same tasks using whole brain voxel-wise comparisons of the healthy comparison women and women with a recent history of anorexia nervosa. Here, we report on the neural activations in those ROIs in subjects with bulimia nervosa. In the Social Attribution task, we examined activity in the right temporoparietal junction, an area frequently associated with mentalization. In the Social Identity task, we examined activity in the precuneus and dorsal anterior cingulate. In the Physical Identity task, we examined activity in a ventral region of the dorsal anterior cingulate. Interestingly, in all tested regions, the average activation in subjects with bulimia was more than the average activation levels seen in the subjects with a history of anorexia but less than that seen in healthy subjects. In three regions, the right temporoparietal junction, the precuneus, and the dorsal anterior cingulate, group responses in the subjects with bulimia were significantly different from healthy subjects but not subjects with anorexia. The neural activations of people with bulimia nervosa performing fMRI tasks engaging social processing are more similar to people with anorexia nervosa than healthy people. This suggests biological measures of social processes may be helpful in characterizing individuals with eating disorders.

  15. Neural Networks for Modeling and Control of Particle Accelerators

    CERN Document Server

    Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.

    2016-01-01

    We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

  16. Neural compensation in adulthood following very preterm birth demonstrated during a visual paired associates learning task

    Directory of Open Access Journals (Sweden)

    Philip J. Brittain

    2014-01-01

    was not significantly associated with functional activation. These results demonstrate that although cognitive task performance between VPT individuals and controls may be comparable on certain measures, differences in BOLD signal may also be evident, some of which could represent compensatory neural processes following VPT-related brain insult.

  17. Fusion Control of Flexible Logic Control and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihua Fu

    2014-01-01

    Full Text Available Based on the basic physical meaning of error E and error variety EC, this paper analyzes the logical relationship between them and uses Universal Combinatorial Operation Model in Universal Logic to describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model of Zero-level Universal Combinatorial Operation in Universal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.

  18. An Integrated Model of Cognitive Control in Task Switching

    Science.gov (United States)

    Altmann, Erik M.; Gray, Wayne D.

    2008-01-01

    A model of cognitive control in task switching is developed in which controlled performance depends on the system maintaining access to a code in episodic memory representing the most recently cued task. The main constraint on access to the current task code is proactive interference from old task codes. This interference and the mechanisms that…

  19. Exploring Possible Neural Mechanisms of Intelligence Differences Using Processing Speed and Working Memory Tasks: An fMRI Study

    Science.gov (United States)

    Waiter, Gordon D.; Deary, Ian J.; Staff, Roger T.; Murray, Alison D.; Fox, Helen C.; Starr, John M.; Whalley, Lawrence J.

    2009-01-01

    To explore the possible neural foundations of individual differences in intelligence test scores, we examined the associations between Raven's Matrices scores and two tasks that were administered in a functional magnetic resonance imaging (fMRI) setting. The two tasks were an n-back working memory (N = 37) task and inspection time (N = 47). The…

  20. Neural Bases of Unconscious Error Detection in a Chinese Anagram Solution Task: Evidence from ERP Study.

    Directory of Open Access Journals (Sweden)

    Hua-Zhan Yin

    Full Text Available In everyday life, error monitoring and processing are important for improving ongoing performance in response to a changing environment. However, detecting an error is not always a conscious process. The temporal activation patterns of brain areas related to cognitive control in the absence of conscious awareness of an error remain unknown. In the present study, event-related potentials (ERPs in the brain were used to explore the neural bases of unconscious error detection when subjects solved a Chinese anagram task. Our ERP data showed that the unconscious error detection (UED response elicited a more negative ERP component (N2 than did no error (NE and detect error (DE responses in the 300-400-ms time window, and the DE elicited a greater late positive component (LPC than did the UED and NE in the 900-1200-ms time window after the onset of the anagram stimuli. Taken together with the results of dipole source analysis, the N2 (anterior cingulate cortex might reflect unconscious/automatic conflict monitoring, and the LPC (superior/medial frontal gyrus might reflect conscious error recognition.

  1. 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.

  2. Neural PID Control Strategy for Networked Process Control

    Directory of Open Access Journals (Sweden)

    Jianhua Zhang

    2013-01-01

    Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

  3. The IAEA prepares for its control tasks

    Energy Technology Data Exchange (ETDEWEB)

    Haunschild, Hans-Hilger [Federal Ministry for Education and Research, Bonn (Germany)

    2015-04-15

    As expected, the 15{sup th} general conference of the IAEA in Vienna focused on safety control. They were handled objectively and without any polemic and will be the main tasks of the IAEA in the future. In addition technical support will be the second main task. The Federal Republic of Germany, which is currently already part of the countries with the highest contribution, is ready for greater involvement. The treaty on Non-Proliferation of Nuclear Weapons (NPT) came into force with the ratification of 40 countries on 5. March 1970 and has been signed in the meantime by 98 states of which 66 already ratified it. Due to the deadlines laid down by the treaty around 50 countries need to conclude agreements on safety controls as provided in the treaty until the end of February 1972. Thus it was to be expected, that the XV General Conference of the International Atomic Energy Agency (IAEA Vienna, 21. to 27. September 1971) will reflect on supervision measures according to the NPT-treaty.

  4. Improving prediction of neural networks: a study of tow financial prediction tasks

    Directory of Open Access Journals (Sweden)

    Tarun K. Sen

    2004-01-01

    Full Text Available Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.

  5. Neural time course of emotional conflict control: an ERP study.

    Science.gov (United States)

    Shen, Yimo; Xue, Song; Wang, Kangcheng; Qiu, Jiang

    2013-04-29

    Previous imaging studies have revealed brain mechanisms associated with emotional conflict control. However, the neural time course remains largely unknown. Therefore, in the present study a face-word Stroop task was used to explore the electrophysiological correlates of emotional conflict control by using event-related potentials (ERPs). Behavioral data indicated that response time of congruent condition was faster than incongruent condition, while the accuracy rates of congruent condition was higher than incongruent condition, which showed a robust emotional conflict effect. ERP revealed N350-550 and P700-800 components in the incongruent minus congruent condition. N350-550 might be related to conflict resolution and response selection; P700-800 might be related to post-response monitoring. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  6. Neural Network for Optimization of Existing Control Systems

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1995-01-01

    The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....

  7. Identification and Position Control of Marine Helm using Artificial Neural Network Neural Network

    Directory of Open Access Journals (Sweden)

    Hui ZHU

    2008-02-01

    Full Text Available If nonlinearities such as saturation of the amplifier gain and motor torque, gear backlash, and shaft compliances- just to name a few - are considered in the position control system of marine helm, traditional control methods are no longer sufficient to be used to improve the performance of the system. In this paper an alternative approach to traditional control methods - a neural network reference controller - is proposed to establish an adaptive control of the position of the marine helm to achieve the controlled variable at the command position. This neural network controller comprises of two neural networks. One is the plant model network used to identify the nonlinear system and the other the controller network used to control the output to follow the reference model. The experimental results demonstrate that this adaptive neural network reference controller has much better control performance than is obtained with traditional controllers.

  8. Error awareness and salience processing in the oddball task: Shared neural mechanisms.

    Directory of Open Access Journals (Sweden)

    Helga A Harsay

    2012-08-01

    Full Text Available A body of work suggests that there are similarities in the way we become aware of an error and process motivationally salient events. Yet, evidence for a shared neural mechanism has not been provided. A within-subject investigation of the brain regions involved in error awareness and salience processing has not been reported. While the neural response to motivationally salient events is classically studied during target detection after longer target-to-target intervals in an oddball task and engages a widespread insula-thalamo-cortical brain network, error awareness has recently been linked to, most prominently, anterior insula cortex. Here we explore whether the anterior insula activation for error awareness is related to salience processing, by testing for activation overlap in subjects undergoing two different task settings. Using a within-subjects design, we show activation overlap in six major brain areas during aware errors in an antisaccade task and during target detection (which were associated with longer target-to-target interval conditions in an oddball task: anterior insula, anterior cingulate, supplementary motor area, thalamus, brainstem and parietal lobe. Within subject analyses shows that the insula is engaged in both error awareness and the processing of salience, and that the anterior insula is more involved in both processes than the posterior insula. The results of a fine-grained spatial pattern overlap analysis between active clusters in the same subjects indicated that even if the anterior insula is activated for both error awareness and salience processing, the two types of processes might tend to activate non-identical neural ensembles on a finer-grained spatial level. Together, these outcomes suggest a similar functional phenomenon in the two different task settings. Error awareness and salience processing share a functional anatomy, with a tendency towards subregional dorsal and ventral specialization within the

  9. Error awareness and salience processing in the oddball task: shared neural mechanisms.

    Science.gov (United States)

    Harsay, Helga A; Spaan, Marcus; Wijnen, Jasper G; Ridderinkhof, K Richard

    2012-01-01

    A body of work suggests similarities in the way we become aware of an error and process motivationally salient events. Yet, evidence for a shared neural mechanism has not been provided. A within subject investigation of the brain regions involved in error awareness and salience processing has not been reported. While the neural response to motivationally salient events is classically studied during target detection after longer target-to-target intervals in an oddball task and engages a widespread insula-thalamo-cortical brain network, error awareness has recently been linked to, most prominently, anterior insula cortex. Here we explore whether the anterior insula activation for error awareness is related to salience processing, by testing for activation overlap in subjects undergoing two different task settings. Using a within subjects design, we show activation overlap in six major brain areas during aware errors in an antisaccade task and during target detection after longer target-to-target intervals in an oddball task: anterior insula, anterior cingulate, supplementary motor area, thalamus, brainstem, and parietal lobe. Within subject analyses shows that the insula is engaged in both error awareness and the processing of salience, and that the anterior insula is more involved in both processes than the posterior insula. The results of a fine-grained spatial pattern overlap analysis between active clusters in the same subjects indicates that even if the anterior insula is activated for both error awareness and salience processing, the two types of processes might tend to activate non-identical neural ensembles on a finer-grained spatial level. Together, these outcomes suggest a similar functional phenomenon in the two different task settings. Error awareness and salience processing share a functional anatomy, with a tendency toward subregional dorsal and ventral specialization within the anterior insula.

  10. Experimental Studies of Neural Network Control for One-Wheel Mobile Robot

    Directory of Open Access Journals (Sweden)

    P. K. Kim

    2012-01-01

    Full Text Available This paper presents development and control of a disc-typed one-wheel mobile robot, called GYROBO. Several models of the one-wheel mobile robot are designed, developed, and controlled. The current version of GYROBO is successfully balanced and controlled to follow the straight line. GYROBO has three actuators to balance and move. Two actuators are used for balancing control by virtue of gyro effect and one actuator for driving movements. Since the space is limited and weight balance is an important factor for the successful balancing control, careful mechanical design is considered. To compensate for uncertainties in robot dynamics, a neural network is added to the nonmodel-based PD-controlled system. The reference compensation technique (RCT is used for the neural network controller to help GYROBO to improve balancing and tracking performances. Experimental studies of a self-balancing task and a line tracking task are conducted to demonstrate the control performances of GYROBO.

  11. Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

    Science.gov (United States)

    Yin, Xi; Liu, Xiaoming

    2018-02-01

    This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

  12. Neural Generalized Predictive Control of a non-linear Process

    DEFF Research Database (Denmark)

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

    1998-01-01

    The use of neural network in non-linear control is made difficult by the fact the stability and robustness is not guaranteed and that the implementation in real time is non-trivial. In this paper we introduce a predictive controller based on a neural network model which has promising stability qu...... detail and discuss the implementation difficulties. The neural generalized predictive controller is tested on a pneumatic servo sys-tem....

  13. IDENTIFICATION AND CONTROL OF AN ASYNCHRONOUS MACHINE USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    A ZERGAOUI

    2000-06-01

    Full Text Available In this work, we present the application of artificial neural networks to the identification and control of the asynchronous motor, which is a complex nonlinear system with variable internal dynamics.  We show that neural networks can be applied to control the stator currents of the induction motor.  The results of the different simulations are presented to evaluate the performance of the neural controller proposed.

  14. The neural correlates of impaired inhibitory control in anxiety.

    Science.gov (United States)

    Ansari, Tahereh L; Derakshan, Nazanin

    2011-04-01

    According to Attentional Control Theory (Eysenck et al., 2007) anxiety impairs the inhibition function of working memory by increasing the influence of stimulus-driven processes over efficient top-down control. We investigated the neural correlates of impaired inhibitory control in anxiety using an antisaccade task. Low- and high-anxious participants performed anti- and prosaccade tasks and electrophysiological activity was recorded. Consistent with previous research high-anxious individuals had longer antisaccade latencies in response to the to-be-inhibited target, compared with low-anxious individuals. Central to our predictions, high-anxious individuals showed lower ERP activity, at frontocentral and central recording sites, than low anxious individuals, in the period immediately prior to onset of the to-be-inhibited target on correct antisaccade trials. Our findings indicate that anxiety interferes with the efficient recruitment of top-down mechanisms required for the suppression of prepotent responses. Implications are discussed within current models of attentional control in anxiety (Bishop, 2009; Eysenck et al., 2007). Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. The Neural Basis of Sustained and Transient Attentional Control in Young Adults with ADHD

    Science.gov (United States)

    Banich, Marie T.; Burgess, Gregory C.; Depue, Brendan E.; Ruzic, Luka; Bidwell, L. Cinnamon; Hitt-Laustsen, Sena; Du, Yiping P.; Willcutt, Erik G.

    2009-01-01

    Differences in neural activation during performance on an attentionally demanding Stroop task were examined between 23 young adults with ADHD carefully selected to not be co-morbid for other psychiatric disorders and 23 matched controls. A hybrid blocked/single-trial design allowed for examination of more sustained vs. more transient aspects of…

  16. Task Switching: Interplay of Reconfiguration and Interference Control

    Science.gov (United States)

    Vandierendonck, Andre; Liefooghe, Baptist; Verbruggen, Frederick

    2010-01-01

    The task-switching paradigm is being increasingly used as a tool for studying cognitive control and task coordination. Different procedural variations have been developed. They have in common that a comparison is made between transitions in which the previous task is repeated and transitions that involve a change toward another task. In general, a…

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

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

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

  18. Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks.

    Science.gov (United States)

    Carpentier, Sarah M; Moreno, Sylvain; McIntosh, Anthony R

    2016-10-01

    Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudinal study of monolingual English-speaking 4- to 6-year-old children randomly selected to receive daily music or French language training, excluding weekends. Children completed passive EEG music note and French vowel auditory oddball detection tasks before and after training. Brain signal complexity was measured on source waveforms at multiple temporal scales as an index of neural information processing and network communication load. Comparing pretraining with posttraining, musical training was associated with increased EEG complexity at coarse temporal scales during the music and French vowel tasks in widely distributed cortical regions. Conversely, very minimal decreases in complexity at fine scales and trends toward coarse-scale increases were displayed after French training during the tasks. Spectral analysis failed to distinguish between training types and found overall theta (3.5-7.5 Hz) power increases after all training forms, with spatially fewer decreases in power at higher frequencies (>10 Hz). These findings demonstrate that musical training increased diversity of brain network states to support domain-specific music skill acquisition and music-to-language transfer effects.

  19. Neural Correlates of Trait Rumination During an Emotion Interference Task in Women With PTSD.

    Science.gov (United States)

    Buchholz, Katherine R; Bruce, Steven E; Koucky, Ellen M; Artime, Tiffany M; Wojtalik, Jessica A; Brown, Wilson J; Sheline, Yvette I

    2016-08-01

    Rumination, defined as repetitive, negative, self-focused thinking, is hypothesized to be a transdiagnostic factor that is associated with depression, anxiety, and posttraumatic stress disorder (PTSD). Theory has suggested that in individuals with PTSD, rumination serves as a cognitive avoidance factor that contributes to the maintenance of symptoms by inhibiting the cognitive and emotional processing of the traumatic event, subsequently interfering with treatment engagement and outcome. Little is known about the neural correlates of rumination in women with PTSD. The current study utilized functional magnetic resonance imaging (fMRI) to examine neural correlates during an emotion interference task of self-reported rumination in women with PTSD. Women with PTSD (39 participants) were recruited at a university-based trauma clinic and completed a clinical evaluation that included measures of PTSD symptoms, rumination, and depressive symptoms, as well as a neuroimaging session in which the participants were administered an emotion interference task. There was a significant relationship between self-reported rumination and activity in the right orbital frontal cortex, BA 11; t(37) = 5.62, p = .004, k = 46 during the task. This finding suggested that women with PTSD, who had higher levels of rumination, may experience greater difficulty inhibiting negative emotional stimuli compared to women with lower levels of rumination. Copyright © 2016 International Society for Traumatic Stress Studies.

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

    African Journals Online (AJOL)

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

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

    DEFF Research Database (Denmark)

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

    1999-01-01

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

  2. Neural responses to incongruency in a blocked-trial Stroop fMRI task in major depressive disorder.

    Science.gov (United States)

    Kikuchi, Toshiaki; Miller, Jeffrey M; Schneck, Noam; Oquendo, Maria A; Mann, J John; Parsey, Ramin V; Keilp, John G

    2012-12-20

    Patients with major depressive disorder (MDD) perform poorly on the Stroop task, which is a measure of the executive control of attention, with impaired interference resolution. The neural correlates of this deficit are not well described. To examine how this deficit relates to pathophysiological abnormalities in MDD, we conducted an fMRI Stroop study comparing MDD subjects to controls. Forty-two unmedicated patients with current MDD and 17 control subjects underwent fMRI scanning with a color-word Stroop task. Subjects assessed font color during alternating color identification (e.g., 'XXXX' in blue) and incongruent color/word blocks (e.g., the word 'red' in blue). We examined neural activation that was greater in incongruent than color identification blocks (Z>2.3 and corrected pincongruent blocks across multiple brain regions, including middle frontal gyrus, paracingulate and posterior cingulate, precuneus, occipital regions, and brain stem. No brain regions were identified in which MDD subjects were more active than controls during incongruent blocks. Not all MDD subjects were antidepressant-naïve. Brain regions related to executive function, visual processing, and semantic processing are less active during processing of incongruent stimuli in MDD subjects as compared to controls. Deficits of attention in MDD may be the product of a failure to maintain activity across a distributed network in a sustained manner, as is required over the sequential trials in this block design. Further studies may clarify whether the abnormalities represent a trait or state deficit. Copyright © 2012 Elsevier B.V. All rights reserved.

  3. Neural correlate of vernier acuity tasks assessed by functional MRI (FMRI).

    Science.gov (United States)

    Sheth, Kevin N; Walker, B Michael; Modestino, Edward J; Miki, Atsushi; Terhune, Kyla P; Francis, Ellie L; Haselgrove, John C; Liu, Grant T

    2007-01-01

    Vernier acuity refers to the ability to discern a small offset within a line. However, while Vernier acuity has been extensively studied psychophysically, its neural correlates are uncertain. Based upon previous psychophysical and electrophysiologic data, we hypothesized that extrastriate areas of the brain would be involved in Vernier acuity tasks, so we designed event-related functional MRI (fMRI) paradigms to identify cortical regions of the brain involved in this behavior. Normal subjects identified suprathreshold and subthreshold Vernier offsets. The results suggest a cortical network including frontal, parietal, occipital, and cerebellar regions subserves the observation, processing, interpretation, and acknowledgment of briefly presented Vernier offsets.

  4. Accelerator diagnosis and control by Neural Nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-01-01

    Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

  5. Task-specific codes for face recognition: how they shape the neural representation of features for detection and individuation.

    Science.gov (United States)

    Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

    2008-01-01

    The variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural processing. Our study appeals to a computational framework characterizing the features representing object categories as sets of overlapping image fragments. Within this framework, we assess the extent to which task-relevant information differs across image fragments. Based on objective differences we find among task-specific representations, we test the sensitivity of the human visual system to these different face descriptions independently of one another. Both behavior and functional magnetic resonance imaging reveal effects elicited by objective task-specific levels of information. Behaviorally, recognition performance with image fragments improves with increasing task-specific information carried by different face fragments. Neurally, this sensitivity to the two tasks manifests as differential localization of neural responses across the ventral visual pathway. Fragments diagnostic for detection evoke larger neural responses than non-diagnostic ones in the right posterior fusiform gyrus and bilaterally in the inferior occipital gyrus. In contrast, fragments diagnostic for individuation evoke larger responses than non-diagnostic ones in the anterior inferior temporal gyrus. Finally, for individuation only, pattern analysis reveals sensitivity to task-specific information within the right "fusiform face area". OUR RESULTS DEMONSTRATE: 1) information diagnostic for face detection and individuation is roughly separable; 2) the human visual system is independently sensitive to both types of information; 3) neural responses differ according to the type of task-relevant information

  6. Measuring time with different neural chronometers during a synchronization-continuation task.

    Science.gov (United States)

    Merchant, Hugo; Zarco, Wilbert; Pérez, Oswaldo; Prado, Luis; Bartolo, Ramón

    2011-12-06

    Temporal information processing is critical for many complex behaviors including speech and music cognition, yet its neural substrate remains elusive. We examined the neurophysiological properties of medial premotor cortex (MPC) of two Rhesus monkeys during the execution of a synchronization-continuation tapping task that includes the basic sensorimotor components of a variety of rhythmic behaviors. We show that time-keeping in the MPC is governed by separate cell populations. One group encoded the time remaining for an action, showing activity whose duration changed as a function of interval duration, reaching a peak at similar magnitudes and times with respect to the movement. The other cell group showed a response that increased in duration or magnitude as a function of the elapsed time from the last movement. Hence, the sensorimotor loops engaged during the task may depend on the cyclic interplay between different neuronal chronometers that quantify the time passed and the remaining time for an action.

  7. Neural control of finger movement via intracortical brain–machine interface

    Science.gov (United States)

    Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

    2017-12-01

    Objective. Intracortical brain–machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s‑1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We

  8. Neural control of finger movement via intracortical brain-machine interface.

    Science.gov (United States)

    Irwin, Z T; Schroeder, K E; Vu, P P; Bullard, A J; Tat, D M; Nu, C S; Vaskov, A; Nason, S R; Thompson, D E; Bentley, J N; Patil, P G; Chestek, C A

    2017-12-01

    Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys' ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s -1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe that these results represent an important step

  9. Neural activation of swallowing and swallowing-related tasks in healthy young adults: an attempt to separate the components of deglutition.

    Science.gov (United States)

    Malandraki, Georgia A; Sutton, Bradley P; Perlman, Adrienne L; Karampinos, Dimitrios C; Conway, Charles

    2009-10-01

    Understanding the underlying neural pathways that govern the highly complex neuromuscular action of swallowing is considered crucial in the process of correctly identifying and treating swallowing disorders. The aim of the present investigation was to identify the neural activations of the different components of deglutition in healthy young adults using functional magnetic resonance imaging (fMRI). Ten right-handed young healthy individuals were scanned in a 3-Tesla Siemens Allegra MRI scanner. Participants were visually cued for both a "Swallow" task and for component/control tasks ("Prepare to swallow", "Tap your tongue", and "Clear your throat") in a randomized order (event-related design). Behavioral interleaved gradient (BIG) methodology was used to address movement-related artifacts. Areas activated during each of the three component tasks enabled a partial differentiation of the neural localization for various components of the swallow. Areas that were more activated during throat clearing than other components included the posterior insula and small portions of the post- and pre-central gyri bilaterally. Tongue tapping showed higher activation in portions of the primary sensorimotor and premotor cortices and the parietal lobules. Planning did not show any areas that were more activated than in the other component tasks. When swallowing was compared with all other tasks, there was significantly more activation in the cerebellum, thalamus, cingulate gyrus, and all areas of the primary sensorimotor cortex bilaterally.

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

    Science.gov (United States)

    Hermer-Vazquez, Linda

    2008-04-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  13. Conditioned task-set competition: Neural mechanisms of emotional interference in depression.

    Science.gov (United States)

    Stolicyn, Aleks; Steele, J Douglas; Seriès, Peggy

    2017-04-01

    Depression has been associated with increased response times at the incongruent-, neutral-, and negative-word trials of the classical and emotional Stroop tasks (Epp et al., Clinical Psychology Review, 32, 316-328, 2012). Response-time slowdown effects at incongruent- and negative-word trials of the Stroop tasks were reported to correlate with depressive severity, indicating strong relevance of the effects to the symptomatology. This study proposes a novel integrative computational model of neural mechanisms of both the classical and emotional Stroop effects, drawing on the previous prominent theoretical explanations of performance at the classical Stroop task (Cohen, Dunbar, & McClelland, Psychological Review, 97, 332-361, 1990; Herd, Banich, & O'Reilly, Journal of Cognitive Neuroscience, 18, 22-32, 2006), and in addition suggesting that negative emotional words represent conditioned stimuli for future negative outcomes. The model is shown to explain the classical Stroop effect and the slow (between-trial) emotional Stroop effect with biologically plausible mechanisms, providing an advantage over the previous theoretical accounts (Matthews & Harley, Cognition & Emotion, 10, 561-600, 1996; Wyble, Sharma, & Bowman, Cognition & Emotion, 22, 1019-1051, 2008). Simulation results suggested a candidate mechanism responsible for the pattern of depressive performance at the classical and the emotional Stroop tasks. Hyperactivity of the amygdala, together with increased inhibitory influence of the amygdala over dopaminergic neurotransmission, could be at the origin of the performance deficits.

  14. Adolescent neural response to reward is related to participant sex and task motivation.

    Science.gov (United States)

    Alarcón, Gabriela; Cservenka, Anita; Nagel, Bonnie J

    2017-02-01

    Risky decision making is prominent during adolescence, perhaps contributed to by heightened sensation seeking and ongoing maturation of reward and dopamine systems in the brain, which are, in part, modulated by sex hormones. In this study, we examined sex differences in the neural substrates of reward sensitivity during a risky decision-making task and hypothesized that compared with girls, boys would show heightened brain activation in reward-relevant regions, particularly the nucleus accumbens, during reward receipt. Further, we hypothesized that testosterone and estradiol levels would mediate this sex difference. Moreover, we predicted boys would make more risky choices on the task. While boys showed increased nucleus accumbens blood oxygen level-dependent (BOLD) response relative to girls, sex hormones did not mediate this effect. As predicted, boys made a higher percentage of risky decisions during the task. Interestingly, boys also self-reported more motivation to perform well and earn money on the task, while girls self-reported higher state anxiety prior to the scan session. Motivation to earn money partially mediated the effect of sex on nucleus accumbens activity during reward. Previous research shows that increased motivation and salience of reinforcers is linked with more robust striatal BOLD response, therefore psychosocial factors, in addition to sex, may play an important role in reward sensitivity. Elucidating neurobiological mechanisms that support adolescent sex differences in risky decision making has important implications for understanding individual differences that lead to advantageous and adverse behaviors that affect health outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2017-12-01

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

  16. Investigation of Timing to Switch Control Mode in Powered Knee Prostheses during Task Transitions.

    Directory of Open Access Journals (Sweden)

    Fan Zhang

    Full Text Available Current powered prosthetic legs require switching control modes according to the task the user is performing (e.g. level-ground walking, stair climbing, walking on slopes, etc.. To allow prosthesis users safely and seamlessly transition between tasks, it is critical to determine when to switch the prosthesis control mode during task transitions. Our previous study defined critical timings for different types of task transitions in ambulation; however, it is unknown whether it is the unique timing that allows safe and seamless transitions. The goals of this study were to (1 systematically investigate the effects of mode switch timing on the prosthesis user's performance in task transitions, and (2 identify appropriate timing to switch the prosthesis control mode so that the users can seamlessly transition between different locomotion tasks. Five able-bodied (AB and two transfemoral (TF amputee subjects were tested as they wore a powered knee prosthesis. The prosthesis control mode was switched manually at various times while the subjects performed different types of task transitions. The subjects' task transition performances were evaluated by their walking balance and success in performing seamless task transitions. The results demonstrated that there existed a time window within which switching the prosthesis control mode neither interrupted the subjects' task transitions nor disturbed their walking balance. Therefore, the results suggested the control mode switching of a lower limb prosthesis can be triggered within an appropriate time window instead of a specific timing or an individual phase. In addition, a generalized criterion to determine the appropriate mode switch timing was proposed. The outcomes of this study could provide important guidance for future designs of neurally controlled powered knee prostheses that are safe and reliable to use.

  17. Towards a Personalized Task Selection Model with Shared Instructional Control

    Science.gov (United States)

    Corbalan, Gemma; Kester, Liesbeth; Van Merrienboer, Jeroen J. G.

    2006-01-01

    Modern education emphasizes the need to flexibly personalize learning tasks to individual learners. This article discusses a personalized task-selection model with shared instructional control based on two current tendencies for the dynamic sequencing of learning tasks: (1) personalization by an instructional agent which makes sequencing decisions…

  18. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

    Science.gov (United States)

    Wang, Xiao-Jing

    2016-01-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

  19. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    Directory of Open Access Journals (Sweden)

    H Francis Song

    2016-02-01

    Full Text Available The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle, which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural

  20. Rethinking neural efficiency : Effects of controlling for strategy use

    NARCIS (Netherlands)

    Toffanin, Paolo; Johnson, Addie; de Jong, Ritske; Martens, Sander

    2007-01-01

    A sentence verification task (SVT) was used to test whether differences in neural activation patterns that have been attributed to IQ may actually depend on differential strategy use between IQ groups. Electroencephalograms were recorded from 14 low (89

  1. The Correlation among Neural Dynamic Processing of Conflict Control, Testosterone and Cortisol Levels in 10-Year-Old Children

    Directory of Open Access Journals (Sweden)

    Fangfang Shangguan

    2017-06-01

    Full Text Available Cognitive control is related to goal-directed self-regulation abilities, which is fundamental for human development. Conflict control includes the neural processes of conflict monitoring and conflict resolution. Testosterone and cortisol are essential hormones for the development of cognitive functions. However, there are no studies that have investigated the correlation of these two hormones with conflict control in preadolescents. In this study, we aimed to explore whether testosterone, cortisol, and testosterone/cortisol ratio worked differently for preadolescent’s conflict control processes in varied conflict control tasks. Thirty-two 10-year-old children (16 boys and 16 girls were enrolled. They were instructed to accomplish three conflict control tasks with different conflict dimensions, including the Flanker, Simon, and Stroop tasks, and electrophysiological signals were recorded. Salivary samples were collected from each child. The testosterone and cortisol levels were determined by enzyme-linked immunosorbent assay. The electrophysiological results showed that the incongruent trials induced greater N2/N450 and P3/SP responses than the congruent trials during neural processes of conflict monitoring and conflict resolution in the Flanker and Stroop tasks. The hormonal findings showed that (1 the testosterone/cortisol ratio was correlated with conflict control accuracy and conflict resolution in the Flanker task; (2 the testosterone level was associated with conflict control performance and neural processing of conflict resolution in the Stroop task; (3 the cortisol level was correlated with conflict control performance and neural processing of conflict monitoring in the Simon task. In conclusion, in 10-year-old children, the fewer processes a task needs, the more likely there is an association between the T/C ratios and the behavioral and brain response, and the dual-hormone effects on conflict resolution may be testosterone-driven in

  2. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System...... Identification, Prediction, Simulation and Control of a dynamic, non-linear and noisy process. Further, the difficulties to control a practical non-linear laboratory process in a satisfactory way by using a traditional controller are overcomed by using a trained neural network to perform non-linear System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...

  3. When predictions take control: The effect of task predictions on task switching performance

    Directory of Open Access Journals (Sweden)

    Wout eDuthoo

    2012-08-01

    Full Text Available In this paper, we aimed to investigate the role of self-generated predictions in the flexible control of behaviour. Therefore, we ran a task switching experiment in which participants were asked to try to predict the upcoming task in three conditions varying in switch rate (30%, 50% and 70%. Irrespective of their predictions, the colour of the target indicated which task participants had to perform. In line with previous studies (Mayr, 2006; Monsell & Mizon, 2006, the switch cost was attenuated as the switch rate increased. Importantly, a clear task repetition bias was found in all conditions, yet the task repetition prediction rate dropped from 78% over 66% to 49% with increasing switch probability in the three conditions. Irrespective of condition, the switch cost was strongly reduced in expectation of a task alternation compared to the cost of an unexpected task alternation following repetition predictions. Hence, our data suggest that the reduction in the switch cost with increasing switch probability is caused by a diminished expectancy for the task to repeat. Taken together, this paper highlights the importance of predictions in the flexible control of behaviour, and suggests a crucial role for task repetition expectancy in the context-sensitive adjusting of task switching performance.

  4. Neural control of choroidal blood flow.

    Science.gov (United States)

    Reiner, Anton; Fitzgerald, Malinda E C; Del Mar, Nobel; Li, Chunyan

    2017-12-08

    The choroid is richly innervated by parasympathetic, sympathetic and trigeminal sensory nerve fibers that regulate choroidal blood flow in birds and mammals, and presumably other vertebrate classes as well. The parasympathetic innervation has been shown to vasodilate and increase choroidal blood flow, the sympathetic input has been shown to vasoconstrict and decrease choroidal blood flow, and the sensory input has been shown to both convey pain and thermal information centrally and act locally to vasodilate and increase choroidal blood flow. As the choroid lies behind the retina and cannot respond readily to retinal metabolic signals, its innervation is important for adjustments in flow required by either retinal activity, by fluctuations in the systemic blood pressure driving choroidal perfusion, and possibly by retinal temperature. The former two appear to be mediated by the sympathetic and parasympathetic nervous systems, via central circuits responsive to retinal activity and systemic blood pressure, but adjustments for ocular perfusion pressure also appear to be influenced by local autoregulatory myogenic mechanisms. Adaptive choroidal responses to temperature may be mediated by trigeminal sensory fibers. Impairments in the neural control of choroidal blood flow occur with aging, and various ocular or systemic diseases such as glaucoma, age-related macular degeneration (AMD), hypertension, and diabetes, and may contribute to retinal pathology and dysfunction in these conditions, or in the case of AMD be a precondition. The present manuscript reviews findings in birds and mammals that contribute to the above-summarized understanding of the roles of the autonomic and sensory innervation of the choroid in controlling choroidal blood flow, and in the importance of such regulation for maintaining retinal health. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  5. Neural-Network Control Of Prosthetic And Robotic Hands

    Science.gov (United States)

    Buckley, Theresa M.

    1991-01-01

    Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

  6. Plasticity of Executive Control through Task Switching Training in Adolescents.

    Science.gov (United States)

    Zinke, Katharina; Einert, Manuela; Pfennig, Lydia; Kliegel, Matthias

    2012-01-01

    Research has shown that cognitive training can enhance performance in executive control tasks. The current study was designed to explore if executive control, specifically task switching, can be trained in adolescents, what particular aspects of executive control may underlie training and transfer effects, and if acute bouts of exercise directly prior to cognitive training enhance training effects. For that purpose, a task switching training was employed that has been shown to be effective in other age groups. A group of adolescents (10-14 years, n = 20) that received a three-session task switching training was compared to a group (n = 20) that received the same task switching training but who exercised on a stationary bike before each training session. Additionally, a no-contact and an exercise only control group were included (both ns = 20). Analyses indicated that both training groups significantly reduced their switching costs over the course of the training sessions for reaction times and error rates, respectively. Analyses indicated transfer to mixing costs in a task switching task that was similar to the one used in training. Far transfer was limited to a choice reaction time task and a tendency for faster reaction times in an updating task. Analyses revealed no additional effects of the exercise intervention. Findings thus indicate that executive control can be enhanced in adolescents through training and that updating may be of particular relevance for the effects of task switching training.

  7. The neurodynamics underlying attentional control in set shifting tasks.

    Science.gov (United States)

    Stemme, Anja; Deco, Gustavo; Busch, Astrid

    2007-09-01

    In this work we address key phenomena observed with classical set shifting tasks as the "Wisconsin Card Sorting Test" or the "Stroop" task: Different types of errors and increased response times reflecting decreased attention. A component of major importance in these tasks is referred to as the "attentional control" thought to be implemented by the prefrontal cortex which acts primarily by an amplification of task relevant information. This mode of operation is illustrated by a neurodynamical model developed for a new kind of set shifting experiment: The Wisconsin-Delayed-Match-to-Sample task combines uninstructed shifts as investigated in Wisconsin-like tasks with a Delayed-Match-to-Sample paradigm. These newly developed WDMS experiments in conjunction with the neurodynamical simulations are able to explain the reason for decreased attention in set shifting experiments as well the different consequences of decreased attention in tasks requiring bivalent yes/no responses compared to tasks requiring multivalent responses.

  8. Discrimination task reveals differences in neural bases of tinnitus and hearing impairment.

    Directory of Open Access Journals (Sweden)

    Fatima T Husain

    Full Text Available We investigated auditory perception and cognitive processing in individuals with chronic tinnitus or hearing loss using functional magnetic resonance imaging (fMRI. Our participants belonged to one of three groups: bilateral hearing loss and tinnitus (TIN, bilateral hearing loss without tinnitus (HL, and normal hearing without tinnitus (NH. We employed pure tones and frequency-modulated sweeps as stimuli in two tasks: passive listening and active discrimination. All subjects had normal hearing through 2 kHz and all stimuli were low-pass filtered at 2 kHz so that all participants could hear them equally well. Performance was similar among all three groups for the discrimination task. In all participants, a distributed set of brain regions including the primary and non-primary auditory cortices showed greater response for both tasks compared to rest. Comparing the groups directly, we found decreased activation in the parietal and frontal lobes in the participants with tinnitus compared to the HL group and decreased response in the frontal lobes relative to the NH group. Additionally, the HL subjects exhibited increased response in the anterior cingulate relative to the NH group. Our results suggest that a differential engagement of a putative auditory attention and short-term memory network, comprising regions in the frontal, parietal and temporal cortices and the anterior cingulate, may represent a key difference in the neural bases of chronic tinnitus accompanied by hearing loss relative to hearing loss alone.

  9. Self-control assessments of capuchin monkeys with the rotating tray task and the accumulation task.

    Science.gov (United States)

    Beran, Michael J; Perdue, Bonnie M; Rossettie, Mattea S; James, Brielle T; Whitham, Will; Walker, Bradlyn; Futch, Sara E; Parrish, Audrey E

    2016-08-01

    Recent studies of delay of gratification in capuchin monkeys using a rotating tray (RT) task have shown improved self-control performance in these animals in comparison to the accumulation (AC) task. In this study, we investigated whether this improvement resulted from the difference in methods between the rotating tray task and previous tests, or whether it was the result of greater overall experience with delay of gratification tasks. Experiment 1 produced similar performance levels by capuchins monkeys in the RT and AC tasks when identical reward and temporal parameters were used. Experiment 2 demonstrated a similar result using reward amounts that were more similar to previous AC experiments with these monkeys. In Experiment 3, monkeys performed multiple versions of the AC task with varied reward and temporal parameters. Their self-control behavior was found to be dependent on the overall delay to reward consumption, rather than the overall reward amount ultimately consumed. These findings indicate that these capuchin monkeys' self-control capacities were more likely to have improved across studies because of the greater experience they had with delay of gratification tasks. Experiment 4 and Experiment 5 tested new, task-naïve monkeys on both tasks, finding more limited evidence of self-control, and no evidence that one task was more beneficial than the other in promoting self-control. The results of this study suggest that future testing of this kind should focus on temporal parameters and reward magnitude parameters to establish accurate measures of delay of gratification capacity and development in this species and perhaps others. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Neural Processing of Auditory Signals and Modular Neural Control for Sound Tropism of Walking Machines

    Directory of Open Access Journals (Sweden)

    Hubert Roth

    2008-11-01

    Full Text Available The specialized hairs and slit sensillae of spiders (Cupiennius salei can sense the airflow and auditory signals in a low-frequency range. They provide the sensor information for reactive behavior, like e.g. capturing a prey. In analogy, in this paper a setup is described where two microphones and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right. The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it.

  11. Stability of a neural predictive controller scheme on a neural model

    DEFF Research Database (Denmark)

    Luther, Jim Benjamin; Sørensen, Paul Haase

    2009-01-01

    In previous works presenting various forms of neural-network-based predictive controllers, the main emphasis has been on the implementation aspects, i.e. the development of a robust optimization algorithm for the controller, which will be able to perform in real time. However, the stability issue....... The resulting controller is tested on a nonlinear pneumatic servo system....

  12. An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

    Directory of Open Access Journals (Sweden)

    Wei He

    2013-01-01

    Full Text Available Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

  13. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  14. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    Science.gov (United States)

    Masri Husam Fayiz, Al

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

  15. Lung nodule malignancy prediction using multi-task convolutional neural network

    Science.gov (United States)

    Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

    2017-03-01

    In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

  16. Control of force during rapid visuomotor force-matching tasks can be described by discrete time PID control algorithms.

    Science.gov (United States)

    Dideriksen, Jakob Lund; Feeney, Daniel F; Almuklass, Awad M; Enoka, Roger M

    2017-08-01

    Force trajectories during isometric force-matching tasks involving isometric contractions vary substantially across individuals. In this study, we investigated if this variability can be explained by discrete time proportional, integral, derivative (PID) control algorithms with varying model parameters. To this end, we analyzed the pinch force trajectories of 24 subjects performing two rapid force-matching tasks with visual feedback. Both tasks involved isometric contractions to a target force of 10% maximal voluntary contraction. One task involved a single action (pinch) and the other required a double action (concurrent pinch and wrist extension). 50,000 force trajectories were simulated with a computational neuromuscular model whose input was determined by a PID controller with different PID gains and frequencies at which the controller adjusted muscle commands. The goal was to find the best match between each experimental force trajectory and all simulated trajectories. It was possible to identify one realization of the PID controller that matched the experimental force produced during each task for most subjects (average index of similarity: 0.87 ± 0.12; 1 = perfect similarity). The similarities for both tasks were significantly greater than that would be expected by chance (single action: p = 0.01; double action: p = 0.04). Furthermore, the identified control frequencies in the simulated PID controller with the greatest similarities decreased as task difficulty increased (single action: 4.0 ± 1.8 Hz; double action: 3.1 ± 1.3 Hz). Overall, the results indicate that discrete time PID controllers are realistic models for the neural control of force in rapid force-matching tasks involving isometric contractions.

  17. Robust adaptive fuzzy neural tracking control for a class of unknown ...

    Indian Academy of Sciences (India)

    In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

  18. Intelligent fuzzy-neural pattern generation and control of a quadrupedal bionic inspection robot

    Science.gov (United States)

    Sayfeddine, D.; Bulgakov, A. G.

    2017-02-01

    This paper represents a case study on ‘single leg single step’ pattern generation and control of quadrupedal bionic robot movement using intelligent fuzzy-neural approaches. The aim is to set up a flip-flop mechanical configuration allowing the robot to move one step forward. The same algorithm can be integrated to develop a full trajectory pattern as an interconnected task of global path planning for autonomous quadrupedal robots.

  19. 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.

  20. Task-space sensory feedback control of robot manipulators

    CERN Document Server

    Cheah, Chien Chern

    2015-01-01

    This book presents recent advances in robot control theory on task space sensory feedback control of robot manipulators. By using sensory feedback information, the robot control systems are robust to various uncertainties in modelling and calibration errors of the sensors. Several sensory task space control methods that do not require exact knowledge of either kinematics or dynamics of robots, are presented. Some useful methods such as approximate Jacobian control, adaptive Jacobian control, region control and multiple task space regional feedback are included. These formulations and methods give robots a high degree of flexibility in dealing with unforeseen changes and uncertainties in its kinematics and dynamics, which is similar to human reaching movements and tool manipulation. It also leads to the solution of several long-standing problems and open issues in robot control, such as force control with constraint uncertainty, control of multi-fingered robot hand with uncertain contact points, singularity i...

  1. Specific Interference between a Cognitive Task and Sensory Organization for Stance Balance Control in Healthy Young Adults: Visuospatial Effects

    Science.gov (United States)

    Chong, Raymond K. Y.; Mills, Bradley; Dailey, Leanna; Lane, Elizabeth; Smith, Sarah; Lee, Kyoung-Hyun

    2010-01-01

    We tested the hypothesis that a computational overload results when two activities, one motor and the other cognitive that draw on the same neural processing pathways, are performed concurrently. Healthy young adult subjects carried out two seemingly distinct tasks of maintaining standing balance control under conditions of low (eyes closed),…

  2. A key role for experimental task performance: effects of math talent, gender and performance on the neural correlates of mental rotation.

    Science.gov (United States)

    Hoppe, Christian; Fliessbach, Klaus; Stausberg, Sven; Stojanovic, Jelena; Trautner, Peter; Elger, Christian E; Weber, Bernd

    2012-02-01

    The neurophysiological mechanisms underlying superior cognitive performance are a research area of high interest. The majority of studies on the brain-performance relationship assessed the effects of capability-related group factors (e.g. talent, gender) on task-related brain activations while only few studies examined the effect of the inherent experimental task performance factor. In this functional MRI study, we combined both approaches and simultaneously assessed the effects of three relatively independent factors on the neurofunctional correlates of mental rotation in same-aged adolescents: math talent (gifted/controls: 17/17), gender (male/female: 16/18) and experimental task performance (median split on accuracy; high/low: 17/17). Better experimental task performance of mathematically gifted vs. control subjects and male vs. female subjects validated the selected paradigm. Activation of the inferior parietal lobule (IPL) was identified as a common effect of mathematical giftedness, gender and experimental task performance. However, multiple linear regression analyses (stepwise) indicated experimental task performance as the only predictor of parietal activations. In conclusion, increased activation of the IPL represents a positive neural correlate of mental rotation performance, irrespective of but consistent with the obtained neurocognitive and behavioral effects of math talent and gender. As experimental performance may strongly affect task-related activations this factor needs to be considered in capability-related group comparison studies on the brain-performance relationship. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Towards building hybrid biological/in silico neural networks for motor neuroprosthetic control

    Directory of Open Access Journals (Sweden)

    Mehmet eKocaturk

    2015-08-01

    Full Text Available In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE as a practical platform for the development of novel brain machine interface (BMI controllers which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two target reaching task in one dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN simulations with powerful online data visualization tools and is a low-cost, PC-based and all-in-one solution for developing neurally-inspired BMI controllers. We believe the BNDE is the first implementation which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.

  4. Exploring adolescent cognitive control in a combined interference switching task.

    Science.gov (United States)

    Mennigen, Eva; Rodehacke, Sarah; Müller, Kathrin U; Ripke, Stephan; Goschke, Thomas; Smolka, Michael N

    2014-08-01

    Cognitive control enables individuals to flexibly adapt to environmental challenges. In the present functional magnetic resonance imaging (fMRI) study, we investigated 185 adolescents at the age of 14 with a combined response interference switching task measuring behavioral responses (reaction time, RT and error rate, ER) and brain activity during the task. This task comprises two types of conflict which are co-occurring, namely, task switching and stimulus-response incongruence. Data indicated that already in adolescents an overlapping cognitive control network comprising the dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (DLPFC), pre-supplementary motor area (preSMA) and posterior parietal cortex (PPC) is recruited by conflicts arising from task switching and response incongruence. Furthermore our study revealed higher blood oxygenation level dependent (BOLD) responses elicited by incongruent stimuli in participants with a pronounced incongruence effect, calculated as the RT difference between incongruent and congruent trials. No such correlation was observed for switch costs. Furthermore, increased activation of the default mode network (DMN) was only observed in congruent trials compared to incongruent trials, but not in task repetition relative to task switch trials. These findings suggest that even though the two processes of task switching and response incongruence share a common cognitive control network they might be processed differentially within the cognitive control network. Results are discussed in the context of a novel hypothesis concerning antagonistic relations between the DMN and the cognitive control network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Active controllers and the time duration to learn a task

    Science.gov (United States)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  6. Attractor switching by neural control of chaotic neurodynamics.

    Science.gov (United States)

    Pasemann, F; Stollenwerk, N

    1998-11-01

    Chaotic attractors of discrete-time neural networks include infinitely many unstable periodic orbits, which can be stabilized by small parameter changes in a feedback control. Here we explore the control of unstable periodic orbits in a chaotic neural network with only two neurons. Analytically, a local control algorithm is derived on the basis of least squares minimization of the future deviations between actual system states and the desired orbit. This delayed control allows a consistent neural implementation, i.e. the same types of neurons are used for chaotic and controlling modules. The control signal is realized with one layer of neurons, allowing selective switching between different stabilized periodic orbits. For chaotic modules with noise, random switching between different periodic orbits is observed.

  7. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Directory of Open Access Journals (Sweden)

    Eric A Pohlmeyer

    Full Text Available Brain-machine interface (BMI systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings. These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  8. Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

    Science.gov (United States)

    Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline W; Sanchez, Justin C

    2014-01-01

    Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.

  9. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Black, Michael J.

    2008-12-01

    Computer-mediated connections between human motor cortical neurons and assistive devices promise to improve or restore lost function in people with paralysis. Recently, a pilot clinical study of an intracortical neural interface system demonstrated that a tetraplegic human was able to obtain continuous two-dimensional control of a computer cursor using neural activity recorded from his motor cortex. This control, however, was not sufficiently accurate for reliable use in many common computer control tasks. Here, we studied several central design choices for such a system including the kinematic representation for cursor movement, the decoding method that translates neuronal ensemble spiking activity into a control signal and the cursor control task used during training for optimizing the parameters of the decoding method. In two tetraplegic participants, we found that controlling a cursor's velocity resulted in more accurate closed-loop control than controlling its position directly and that cursor velocity control was achieved more rapidly than position control. Control quality was further improved over conventional linear filters by using a probabilistic method, the Kalman filter, to decode human motor cortical activity. Performance assessment based on standard metrics used for the evaluation of a wide range of pointing devices demonstrated significantly improved cursor control with velocity rather than position decoding. Disclosure. JPD is the Chief Scientific Officer and a director of Cyberkinetics Neurotechnology Systems (CYKN); he holds stock and receives compensation. JDS has been a consultant for CYKN. LRH receives clinical trial support from CYKN.

  10. Neural adaptations associated with interlimb transfer in a ballistic wrist flexion task.

    Directory of Open Access Journals (Sweden)

    Kathy L Ruddy

    2016-05-01

    Full Text Available Cross education is the process whereby training of one limb gives rise to increases in the subsequent performance of its opposite counterpart. The execution of many unilateral tasks is associated with increased excitability of corticospinal projections from primary motor cortex (M1 to the opposite limb. It has been proposed that these effects are causally related. Our aim was to establish whether changes in corticospinal excitability arising from prior training of the opposite limb determine levels of interlimb transfer. We used three vision conditions shown previously to modulate the excitability of corticospinal projections to the inactive (right limb during wrist flexion movements performed by the training (left limb. These were: mirrored visual feedback of the training limb; no visual feedback of either limb; and visual feedback of the inactive limb. Training comprised 300 discrete, ballistic wrist flexion movements executed as rapidly as possible. Performance of the right limb on the same task was assessed prior to, at the mid point of, and following left limb training. There was no evidence that variations in the excitability of corticospinal projections (assessed by transcranial magnetic stimulation (TMS to the inactive limb were associated with, or predictive of, the extent of interlimb transfer that was expressed. There were however associations between alterations in muscle activation dynamics observed for the untrained limb, and the degree of positive transfer that arose from training of the opposite limb. The results suggest that the acute adaptations that mediate the bilateral performance gains realised through unilateral practice of this ballistic wrist flexion task are mediated by neural elements other than those within M1 that are recruited at rest by single-pulse TMS.

  11. Neural Adaptations Associated with Interlimb Transfer in a Ballistic Wrist Flexion Task

    Science.gov (United States)

    Ruddy, Kathy L.; Rudolf, Anne K.; Kalkman, Barbara; King, Maedbh; Daffertshofer, Andreas; Carroll, Timothy J.; Carson, Richard G.

    2016-01-01

    Cross education is the process whereby training of one limb gives rise to increases in the subsequent performance of its opposite counterpart. The execution of many unilateral tasks is associated with increased excitability of corticospinal projections from primary motor cortex (M1) to the opposite limb. It has been proposed that these effects are causally related. Our aim was to establish whether changes in corticospinal excitability (CSE) arising from prior training of the opposite limb determine levels of interlimb transfer. We used three vision conditions shown previously to modulate the excitability of corticospinal projections to the inactive (right) limb during wrist flexion movements performed by the training (left) limb. These were: (1) mirrored visual feedback of the training limb; (2) no visual feedback of either limb; and (3) visual feedback of the inactive limb. Training comprised 300 discrete, ballistic wrist flexion movements executed as rapidly as possible. Performance of the right limb on the same task was assessed prior to, at the mid point of, and following left limb training. There was no evidence that variations in the excitability of corticospinal projections (assessed by transcranial magnetic stimulation (TMS)) to the inactive limb were associated with, or predictive of, the extent of interlimb transfer that was expressed. There were however associations between alterations in muscle activation dynamics observed for the untrained limb, and the degree of positive transfer that arose from training of the opposite limb. The results suggest that the acute adaptations that mediate the bilateral performance gains realized through unilateral practice of this ballistic wrist flexion task are mediated by neural elements other than those within M1 that are recruited at rest by single-pulse TMS. PMID:27199722

  12. Passive stability and active control in a rhythmic task

    NARCIS (Netherlands)

    Wei, Kunlin; Dijkstra, Tjeerd M. H.; Sternad, Dagmar

    2007-01-01

    Rhythmically bouncing a ball with a racket is a task that affords passively stable solutions as demonstrated by stability analyses of a mathematical model of the task. Passive stability implies that no active control is needed as errors die out without requiring corrective actions. Empirical results

  13. Task Delegation Based Access Control Models for Workflow Systems

    Science.gov (United States)

    Gaaloul, Khaled; Charoy, François

    e-Government organisations are facilitated and conducted using workflow management systems. Role-based access control (RBAC) is recognised as an efficient access control model for large organisations. The application of RBAC in workflow systems cannot, however, grant permissions to users dynamically while business processes are being executed. We currently observe a move away from predefined strict workflow modelling towards approaches supporting flexibility on the organisational level. One specific approach is that of task delegation. Task delegation is a mechanism that supports organisational flexibility, and ensures delegation of authority in access control systems. In this paper, we propose a Task-oriented Access Control (TAC) model based on RBAC to address these requirements. We aim to reason about task from organisational perspectives and resources perspectives to analyse and specify authorisation constraints. Moreover, we present a fine grained access control protocol to support delegation based on the TAC model.

  14. Neural Control of Breathing and CO2 Homeostasis.

    Science.gov (United States)

    Guyenet, Patrice G; Bayliss, Douglas A

    2015-09-02

    Recent advances have clarified how the brain detects CO2 to regulate breathing (central respiratory chemoreception). These mechanisms are reviewed and their significance is presented in the general context of CO2/pH homeostasis through breathing. At rest, respiratory chemoreflexes initiated at peripheral and central sites mediate rapid stabilization of arterial PCO2 and pH. Specific brainstem neurons (e.g., retrotrapezoid nucleus, RTN; serotonergic) are activated by PCO2 and stimulate breathing. RTN neurons detect CO2 via intrinsic proton receptors (TASK-2, GPR4), synaptic input from peripheral chemoreceptors and signals from astrocytes. Respiratory chemoreflexes are arousal state dependent whereas chemoreceptor stimulation produces arousal. When abnormal, these interactions lead to sleep-disordered breathing. During exercise, central command and reflexes from exercising muscles produce the breathing stimulation required to maintain arterial PCO2 and pH despite elevated metabolic activity. The neural circuits underlying central command and muscle afferent control of breathing remain elusive and represent a fertile area for future investigation. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. A hyperstable neural network for the modelling and control of ...

    Indian Academy of Sciences (India)

    A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other ...

  16. Neural Control of Energy Balance: Translating Circuits to Therapies

    OpenAIRE

    Gautron, Laurent; Elmquist, Joel K.; Williams, Kevin W.

    2015-01-01

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacother...

  17. When global rule reversal meets local task switching: The neural mechanisms of coordinated behavioral adaptation to instructed multi-level demand changes.

    Science.gov (United States)

    Shi, Yiquan; Wolfensteller, Uta; Schubert, Torsten; Ruge, Hannes

    2018-02-01

    Cognitive flexibility is essential to cope with changing task demands and often it is necessary to adapt to combined changes in a coordinated manner. The present fMRI study examined how the brain implements such multi-level adaptation processes. Specifically, on a "local," hierarchically lower level, switching between two tasks was required across trials while the rules of each task remained unchanged for blocks of trials. On a "global" level regarding blocks of twelve trials, the task rules could reverse or remain the same. The current task was cued at the start of each trial while the current task rules were instructed before the start of a new block. We found that partly overlapping and partly segregated neural networks play different roles when coping with the combination of global rule reversal and local task switching. The fronto-parietal control network (FPN) supported the encoding of reversed rules at the time of explicit rule instruction. The same regions subsequently supported local task switching processes during actual implementation trials, irrespective of rule reversal condition. By contrast, a cortico-striatal network (CSN) including supplementary motor area and putamen was increasingly engaged across implementation trials and more so for rule reversal than for nonreversal blocks, irrespective of task switching condition. Together, these findings suggest that the brain accomplishes the coordinated adaptation to multi-level demand changes by distributing processing resources either across time (FPN for reversed rule encoding and later for task switching) or across regions (CSN for reversed rule implementation and FPN for concurrent task switching). © 2017 Wiley Periodicals, Inc.

  18. Task, muscle and frequency dependent vestibular control of posture

    NARCIS (Netherlands)

    Forbes, P.A.; Siegmund, G.P.; Schouten, A.C.; Blouin, J.S.

    2015-01-01

    The vestibular system is crucial for postural control; however there are considerable differences in the task dependence and frequency response of vestibular reflexes in appendicular and axial muscles. For example, vestibular reflexes are only evoked in appendicular muscles when vestibular

  19. Gender-specific strategy use and neural correlates in a spatial perspective taking task.

    Science.gov (United States)

    Kaiser, Stefan; Walther, Stephan; Nennig, Ernst; Kronmüller, Klaus; Mundt, Christoph; Weisbrod, Matthias; Stippich, Christoph; Vogeley, Kai

    2008-08-01

    In the context of the present study spatial perspective taking refers to the ability to translocate one's own egocentric viewpoint to somebody else's viewpoint in space. We adopted a spatial perspective taking paradigm and performed a functional magnetic resonance imaging study to assess gender differences of neural activity during perspective taking. 24 healthy subjects (12 male/12 female) were asked to systematically either take their own (first-person-perspective, 1PP) or another person's perspective (third-person-perspective, 3PP). Presented stimuli consisted of a virtual scenery with an avatar and red balls around him that had to be counted, if visible, from 1PP or 3PP. Reaction time was increased and correctness scores were decreased during the cognitively more effortful 3PP condition. Correctness scores showed a trend towards a more pronounced decline of performance during 3PP as compared to 1PP in female subjects. Female subjects correctness scores declined by 6.7% from 1PP to 3PP, while in male subjects this performance decline was only 2.7%. Debriefings after the experiment, reaction times depending on angle of rotation and error rates suggest that males are more likely to employ an object-based strategy in contrast to a consistently employed egocentric perspective transformation in females. In the whole group, neural activity was increased in the parieto-occipital, right inferior frontal and supplementary motor areas, confirming previous studies. With respect to gender, male subjects showed stronger activation in the precuneus and the right inferior frontal gyrus than female subjects in a region-of-interest approach. In a subgroup of male subjects whose strategy reports suggest object-based strategies these differences seem to be more pronounced. In conclusion, the differential recruitment of brain regions most likely reflects different strategies in solving this spatial perspective taking task.

  20. Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

    Science.gov (United States)

    Marathe, Amar R; Lawhern, Vernon J; Wu, Dongrui; Slayback, David; Lance, Brent J

    2016-03-01

    The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibrated to account for changes in the neural signals due to a variety of factors including changes in human state, the surrounding environment, and task conditions. Novel approaches to reduce calibration time or effort will dramatically improve the usability of BCI systems. Active Learning (AL) is an iterative semi-supervised learning technique for learning in situations in which data may be abundant, but labels for the data are difficult or expensive to obtain. In this paper, we apply AL to a simulated BCI system for target identification using data from a rapid serial visual presentation (RSVP) paradigm to minimize the amount of training samples needed to initially calibrate a neural classifier. Our results show AL can produce similar overall classification accuracy with significantly less labeled data (in some cases less than 20%) when compared to alternative calibration approaches. In fact, AL classification performance matches performance of 10-fold cross-validation (CV) in over 70% of subjects when training with less than 50% of the data. To our knowledge, this is the first work to demonstrate the use of AL for offline electroencephalography (EEG) calibration in a simulated BCI paradigm. While AL itself is not often amenable for use in real-time systems, this work opens the door to alternative AL-like systems that are more amenable for BCI applications and thus enables future efforts for developing highly adaptive BCI systems.

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

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

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

  2. Interface Concepts for Command & Control Tasks

    NARCIS (Netherlands)

    Delft, J.H. van; Passenier, P.O.

    2000-01-01

    This paper addresses new interface concepts for information visualisation and manipulation in Command and Control. These concepts focus on the use of multiple views on the tactical situation to enhance situational awareness and to improve situation assessment. Topics covered include the application

  3. Robot welding process control development task

    Science.gov (United States)

    Romine, Peter L.

    1992-01-01

    The completion of, and improvements made to, the software developed during 1990 for program maintenance on the PC and HEURIKON and transfer to the CYRO, and integration of the Rocketdyne vision software with the CYRO is documented. The new programs were used successfully by NASA, Rocketdyne, and UAH technicians and engineers to create, modify, upload, download, and control CYRO NC programs.

  4. Neural networks for process control and optimization: two industrial applications.

    Science.gov (United States)

    Bloch, Gérard; Denoeux, Thierry

    2003-01-01

    The two most widely used neural models, multilayer perceptron (MLP) and radial basis function network (RBFN), are presented in the framework of system identification and control. The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation. The advantages of neural network models are summarized: universal approximation capabilities, flexibility, and parsimony. Two applications are described in steel industry and water treatment, respectively, the control of alloying process in a hot dipped galvanizing line and the control of a coagulation process in a drinking water treatment plant. These examples highlight the interest of neural techniques, when complex nonlinear phenomena are involved, but the empirical knowledge of control operators can be learned.

  5. Dual adaptive dynamic control of mobile robots using neural networks.

    Science.gov (United States)

    Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato

    2009-02-01

    This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

  6. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  7. Gaze Behavior While Operating a Complex Instrument Control Task.

    Science.gov (United States)

    Kalicinski, Michael; Steinberg, Fabian; Dalecki, Marc; Bock, Otmar

    2016-07-01

    The recent developments of technology in almost all areas of industrial processing, workplace, smart homes, mobility, media, and communication change humans' everyday life environment and behavioral responses in numerous ways. Our main objective in this study was to determine whether subjects' operator performance in a complex sensorimotor task is associated with their gaze behavior. In two experiments subjects operated a complex control task. To this end they watched multiple displays, made strategic decisions, and used multiple actuators to maximize their virtual earnings from operating a virtual power plant. In Experiment 1 we compared gaze behavior during the tasks with respect to operator performance in two different age groups (young vs. old), and in Experiment 2 in two different gravity conditions (normal vs. microgravity). We found gaze pattern changed in older subjects as well as in microgravity. Older adults and subjects in microgravity looked longer at areas that are less relevant for task success. Most importantly, these changes in gaze pattern accounted for the effects of age and microgravity and on total earnings in the instrument-control task. In conclusion, age- and gravity-related changes of gaze behavior show a similar pattern. Gaze behavior seems to play an important role in complex control tasks and might predict alterations of operational performance. Kalicinski M, Steinberg F, Dalecki M, Bock O. Gaze behavior while operating a complex instrument control task. Aerosp Med Hum Perform. 2016; 87(7):646-651.

  8. Dual task and postural control in Alzheimer's and Parkinson's disease

    Directory of Open Access Journals (Sweden)

    Larissa Pires de Andrade

    2014-03-01

    Full Text Available Patients with neurodegenerative diseases are required to use cognitive resources while maintaining postural control. The aim of this study was to investigate the effects of a frontal cognitive task on postural control in patients with Alzheimer, Parkinson and controls. Thirty-eight participants were instructed to stand upright on a force platform in two experimental conditions: single and dual task. Participants with Parkinson's disease presented an increase in the coefficient of variation greater than 100% in the dual task as compared to the single task for center of pressure (COP area and COP path. In addition, patients with Parkinson's and Alzheimer's disease had a higher number of errors during the execution of the cognitive task when compared to the group of elderly without neurodegenerative diseases. The motor cortex, which is engaged in postural control, does not seem to compete with frontal brain regions in the performance of the cognitive task. However, patients with Parkinson's and Alzheimer's disease presented worsened performance in cognitive task.

  9. Stability and synchronization control of stochastic neural networks

    CERN Document Server

    Zhou, Wuneng; Zhou, Liuwei; Tong, Dongbing

    2016-01-01

    This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

  10. Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots.

    Science.gov (United States)

    Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan

    2017-06-26

    Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

  11. A hugh marketing research task: birth control.

    Science.gov (United States)

    Simon, J L

    1968-02-01

    Research in underdeveloped countries to sell family planning is discussed. The article also aims at pinpointing other possible research areas. Census reports were actually the earliest work relevant to birth control. Later came the research on psychosocial factors affecting family size in developed countries. After World War I, client oriented research into family planning began. The history of this type of research is discussed with more emphasis on the surveys of the knowledge, attitude and contraception practices (KAP) in various countries. The author claims the KAP surveys to be the largest worldwide market research job ever done. Propagands campaigns, contraceptive costs, bonuses for contraceptive practices, and effectiveness of persuasion techniques are discussed.

  12. Neural networks for predictive control of the mechanism of ...

    African Journals Online (AJOL)

    In this paper, we are interested in the study of the control of orientation of a wind turbine like means of optimization of his output/input ratio (efficiency). The approach suggested is based on the neural predictive control which is justified by the randomness of the wind on the one hand, and on the other hand by the capacity of ...

  13. Cognitive control over learning: Creating, clustering and generalizing task-set structure

    Science.gov (United States)

    Collins, Anne G.E.; Frank, Michael J.

    2013-01-01

    Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID

  14. The application of neural network PID controller to control the light gasoline etherification

    Science.gov (United States)

    Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

    2017-06-01

    Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

  15. Neural characterization of the speed-accuracy tradeoff in a perceptual decision-making task.

    Science.gov (United States)

    Wenzlaff, Hermine; Bauer, Markus; Maess, Burkhard; Heekeren, Hauke R

    2011-01-26

    Decisions often necessitate a tradeoff between speed and accuracy (SAT), that is, fast decisions are more error prone while careful decisions take longer. Sequential sampling models assume that evidence for different response alternatives is accumulated over time and suggest that SAT modulates the decision system by setting a lower threshold (boundary) on required accumulated evidence to commit a response under time pressure. We investigated how such a speed accuracy tradeoff is implemented neurally under different levels of sensory evidence. Using magnetoencephalography (MEG) and a face-house categorization task, we show that the later decision- and motor-related systems rather than the early sensory system are modulated by SAT. Source analysis revealed that the bilateral supplementary motor areas (SMAs) and the medial precuneus were more activated under the speed instruction and correlated negatively (right SMA) with the boundary parameter, whereas the left dorsolateral prefrontal cortex (DLPFC) was more activated under the accuracy instruction and showed a positive correlation with the boundary. The findings are interpreted in the sense that SMA activity dynamically facilitates fast responses during stimulus processing, potentially by disinhibiting thalamo-striatal loops, whereas DLPFC reflects accumulated evidence before response execution.

  16. Neural correlates of spatial working memory manipulation in a sequential Vernier discrimination task.

    Science.gov (United States)

    Gutiérrez-Garralda, Juan M; Hernandez-Castillo, Carlos R; Barrios, Fernando A; Pasaye, Erick H; Fernandez-Ruiz, Juan

    2014-12-17

    Visuospatial working memory refers to the short-term storage and manipulation of visuospatial information. To study the neural bases of these processes, 17 participants took part in a modified sequential Vernier task while they were being scanned using an event-related functional MRI protocol. During each trial, participants retained the spatial position of a line during a delay period to later evaluate if it was presented aligned to a second line. This design allowed testing the manipulation of the spatial information from memory. During encoding, there was a larger parietal and cingulate activation under the experimental condition, whereas the opposite was true for the occipital cortex. Throughout the delay period of the experimental condition there was significant bilateral activation in the caudal superior frontal sulcus/middle frontal gyrus, as well as the insular and superior parietal lobes, which confirms the findings from previous studies. During manipulation of spatial memory, the analysis showed higher activation in the lingual gyrus. This increase of activity in visual areas during the manipulation phase fits with the hypothesis that information stored in sensory cortices becomes reactivated once the information is needed to be utilized.

  17. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

    2017-12-01

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

  18. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

    Science.gov (United States)

    Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Cha, Kenny; Richter, Caleb

    2017-10-16

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aims of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With IRB approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2,242 views with 2,454 masses (1,057 malignant, 1,397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p=0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited. © 2017 Institute of Physics and Engineering in Medicine.

  19. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...

  20. Steam turbine stress control using NARX neural network

    Science.gov (United States)

    Dominiczak, Krzysztof; Rzadkowski, Romuald; Radulski, Wojciech

    2015-11-01

    Considered here is concept of steam turbine stress control, which is based on Nonlinear AutoRegressive neural networks with eXogenous inputs. Using NARX neural networks,whichwere trained based on experimentally validated FE model allows to control stresses in protected thickwalled steam turbine element with FE model quality. Additionally NARX neural network, which were trained base on FE model, includes: nonlinearity of steam expansion in turbine steam path during transients, nonlinearity of heat exchange inside the turbine during transients and nonlinearity of material properties during transients. In this article NARX neural networks stress controls is shown as an example of HP rotor of 18K390 turbine. HP part thermodynamic model as well as heat exchange model in vicinity of HP rotor,whichwere used in FE model of the HP rotor and the HP rotor FE model itself were validated based on experimental data for real turbine transient events. In such a way it is ensured that NARX neural network behave as real HP rotor during steam turbine transient events.

  1. Impaired Attentional Control in Pedophiles in a Sexual Distractor Task

    Science.gov (United States)

    Jordan, Kirsten; Fromberger, Peter; von Herder, Jakob; Steinkrauss, Henrike; Nemetschek, Rebekka; Witzel, Joachim; Müller, Jürgen L.

    2016-01-01

    Pedophilic disorder, a subtype of paraphilia, is defined as a recurrent sexual interest in prepubescent children, which is characterized by persistent thoughts, fantasies, urges, sexual arousal, or behavior. Besides a deviant sexual preference, sexual preoccupation was found to be a dynamic risk factor for reoffending. Thus, it is conceivable that sex offenders and especially sex offenders against children have difficulties to control their responses to sexual stimuli. In the current study pedophiles, forensic and non-forensic control subjects had to solve a cognitive task, while sexual distractors were presented simultaneously. This kind of task also requires control functions. Therefore, data were analyzed with respect to attentional control while comparing eye movements toward sexual distractors and toward the cognitive task. We were mainly interested in how early (fixation latency) and late (relative fixation time) attentional processes were allocated to both, the cognitive target stimuli and the sexual distractors. Pedophiles demonstrated significantly lower attentional control in the sexual distractor task than both control groups (non-pedophiles). They showed a shorter fixation latency and longer fixation time for sexual distractors than non-pedophiles. Furthermore, pedophiles demonstrated a longer fixation latency and shorter fixation time for cognitive target stimuli. For classification analyses, an attentional control index (ACI) was built, i.e., the difference between eye movements on cognitive target stimuli and sexual distractors. For the ACI of early attentional processes, i.e., fixation latency, a good classification between pedophiles and non-pedophiles was found. We assumed that the measured attentional control represents inhibitory executive functions, specifically interference control. Further studies should examine if low attentional control in pedophiles is due to low motivation to solve the task or rather to a lack of ability to control

  2. Impaired attentional control in pedophiles in a sexual distractor task

    Directory of Open Access Journals (Sweden)

    Kirsten Jordan

    2016-12-01

    Full Text Available Pedophilic disorder, a subtype of paraphilia, is defined as a recurrent sexual interest in prepubescent children, which is characterized by persistent thoughts, fantasies, urges, sexual arousal or behavior. Besides a deviant sexual preference, sexual preoccupation was found to be a dynamic risk factor for reoffending. Thus, it is conceivable that sex offenders and especially sex offenders against children have difficulties to control their responses to sexual stimuli. In the current study pedophiles, forensic and non-forensic control subjects had to solve a cognitive task while sexual distractors were presented simultaneously. This kind of task also requires control functions. Therefore, data was analyzed with respect to attentional control while comparing eye movements towards sexual distractors and towards the cognitive task. We were mainly interested in how early (fixation latency and late (relative fixation time attentional processes were allocated to both, the cognitive target stimuli and the sexual distractors. Pedophiles demonstrated significantly lower attentional control in the sexual distractor task than both control groups (non-pedophiles. They showed a shorter fixation latency and longer fixation time for sexual distractors than non-pedophiles. Furthermore, pedophiles demonstrated a longer fixation latency and shorter fixation time for cognitive target stimuli. For classification analyses, an attention control index (ACI was built, i.e. the difference between eye movements on cognitive target stimuli and sexual distractors. For the ACI of early attentional processes, i.e. fixation latency, a good classification between pedophiles and non-pedophiles was found. We assumed that the measured attentional control represents inhibitory executive functions, specifically interference control. Further studies should examine if low attentional control in pedophiles is due to low motivation to solve the task or rather to a lack of ability to

  3. Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

    Directory of Open Access Journals (Sweden)

    Şinasi Arslan

    2013-01-01

    Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.

  4. Effects of aversive odour presentation on inhibitory control in the Stroop colour-word interference task.

    Science.gov (United States)

    Finkelmeyer, Andreas; Kellermann, Thilo; Bude, Daniela; Niessen, Thomas; Schwenzer, Michael; Mathiak, Klaus; Reske, Martina

    2010-10-18

    Due to the unique neural projections of the olfactory system, odours have the ability to directly influence affective processes. Furthermore, it has been shown that emotional states can influence various non-emotional cognitive tasks, such as memory and planning. However, the link between emotional and cognitive processes is still not fully understood. The present study used the olfactory pathway to induce a negative emotional state in humans to investigate its effect on inhibitory control performance in a standard, single-trial manual Stroop colour-word interference task. An unpleasant (H2S) and an emotionally neutral (Eugenol) odorant were presented in two separate experimental runs, both in blocks alternating with ambient air, to 25 healthy volunteers, while they performed the cognitive task. Presentation of the unpleasant odorant reduced Stroop interference by reducing the reaction times for incongruent stimuli, while the presentation of the neutral odorant had no effect on task performance. The odour-induced negative emotional state appears to facilitate cognitive processing in the task used in the present study, possibly by increasing the amount of cognitive control that is being exerted. This stands in contrast to other findings that showed impaired cognitive performance under odour-induced negative emotional states, but is consistent with models of mood-congruent processing.

  5. Effects of aversive odour presentation on inhibitory control in the Stroop colour-word interference task

    Directory of Open Access Journals (Sweden)

    Nießen Thomas

    2010-10-01

    Full Text Available Abstract Background Due to the unique neural projections of the olfactory system, odours have the ability to directly influence affective processes. Furthermore, it has been shown that emotional states can influence various non-emotional cognitive tasks, such as memory and planning. However, the link between emotional and cognitive processes is still not fully understood. The present study used the olfactory pathway to induce a negative emotional state in humans to investigate its effect on inhibitory control performance in a standard, single-trial manual Stroop colour-word interference task. An unpleasant (H2S and an emotionally neutral (Eugenol odorant were presented in two separate experimental runs, both in blocks alternating with ambient air, to 25 healthy volunteers, while they performed the cognitive task. Results Presentation of the unpleasant odorant reduced Stroop interference by reducing the reaction times for incongruent stimuli, while the presentation of the neutral odorant had no effect on task performance. Conclusions The odour-induced negative emotional state appears to facilitate cognitive processing in the task used in the present study, possibly by increasing the amount of cognitive control that is being exerted. This stands in contrast to other findings that showed impaired cognitive performance under odour-induced negative emotional states, but is consistent with models of mood-congruent processing.

  6. Task driven feedback control of robot arms - A step toward intelligent control

    Science.gov (United States)

    Bejczy, A. K.; Tarn, T. J.; Li, Z. F.

    1986-01-01

    The process of connecting task descriptions originating from machine intelligence planning programs to the mechanization of feedback control of robot arms is analyzed. It is shown in this paper that control theories and practices can be extended to a higher level where feedback control of robot arms directly can respond to work space task commands provided that the work space task as a command is given in the form of a closed function of time. A general mathematical procedure using tools from differential geometry is introduced for synthesizing task space motion planning so that the planned motion can be used as a direct input to the robot arm feedback control system to achieve desired robot hand motion. By definition, 'intelligent control' is being manifested through robot performance in the task space relative to task space commands. Thus, the capability of implementing feedback control of robot arms directly driven by appropriate task descriptions in the workspace as commands is a step toward intelligent control.

  7. Neural representation of reward probability: evidence from the illusion of control.

    Science.gov (United States)

    Kool, Wouter; Getz, Sarah J; Botvinick, Matthew M

    2013-06-01

    To support reward-based decision-making, the brain must encode potential outcomes both in terms of their incentive value and their probability of occurrence. Recent research has made it clear that the brain bears multiple representations of reward magnitude, meaning that a single choice option may be represented differently-and even inconsistently-in different brain areas. There are some hints that the same may be true for reward probability. Preliminary evidence hints that, even as systematic distortions of probability are expressed in behavior, these may not always be uniformly reflected at the neural level: Some neural representations of probability may be immune from such distortions. This study provides new evidence consistent with this possibility. Participants in a behavioral experiment displayed a classic "illusion of control," providing higher estimates of reward probability for gambles they had chosen than for identical gambles that were imposed on them. However, an fMRI study of the same task revealed that neural prediction error signals, arising when gamble outcomes were revealed, were unaffected by the illusion of control. The resulting behavioral-neural dissociation reinforces the case for multiple, inconsistent internal representations of reward probability, while also prompting a reinterpretation of the illusion of control effect itself.

  8. Neural correlates of deductive reasoning: An ERP study with the Wason Selection Task.

    Science.gov (United States)

    Cutmore, Tim R H; Halford, Graeme S; Wang, Ya; Ramm, Brentyn J; Spokes, Tara; Shum, David H K

    2015-12-01

    The Wason Selection Task (WST) is a well-known test of reasoning in which one turns over cards to test a rule about the two faces. Modifications were made to the WST to enable more direct and analytical investigation of reasoning processes. The modifications included extensive training to reduce variations in task interpretation, isolation of working memory in the decision phase, a separate rule for each card and variations in the form of the rule (number-letter as well as letter-number), separate scoring for each card, and inclusion of control cards that could be recognized by features without relational processing. The cognitive complexity of each card was also analyzed to enable investigation of this factor. Behavioral and event-related potential data were recorded. Negative cards differed from positive cards and control cards were differentiated from cards involved in inferences. The N2 component differentiated the negative conditions (not-P, not-Q cards) from the positive conditions (P, Q cards). The P3 component was largest for control and P cards (the simpler conditions). The late slow wave tended to show more sustained processing of not-P, not-Q and Q cards and was little influenced by the simpler control and P cards. Effects were interpreted in terms of cognitive complexity. In particular, the negative conditions had a larger N2 response than the positive conditions, reflecting greater cognitive complexity of the former and their sustained processing. Copyright © 2015. Published by Elsevier B.V.

  9. Coactivation of Cognitive Control Networks During Task Switching.

    Science.gov (United States)

    Yin, Shouhang; Deák, Gedeon; Chen, Antao

    2017-12-14

    The ability to flexibly switch between tasks is considered an important component of cognitive control that involves frontal and parietal cortical areas. The present study was designed to characterize network dynamics across multiple brain regions during task switching. Functional magnetic resonance images (fMRI) were captured during a standard rule-switching task to identify switching-related brain regions. Multiregional psychophysiological interaction (PPI) analysis was used to examine effective connectivity between these regions. During switching trials, behavioral performance declined and activation of a generic cognitive control network increased. Concurrently, task-related connectivity increased within and between cingulo-opercular and fronto-parietal cognitive control networks. Notably, the left inferior frontal junction (IFJ) was most consistently coactivated with the 2 cognitive control networks. Furthermore, switching-dependent effective connectivity was negatively correlated with behavioral switch costs. The strength of effective connectivity between left IFJ and other regions in the networks predicted individual differences in switch costs. Task switching was supported by coactivated connections within cognitive control networks, with left IFJ potentially acting as a key hub between the fronto-parietal and cingulo-opercular networks. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  10. Adaptive model predictive process control using neural networks

    Science.gov (United States)

    Buescher, K.L.; Baum, C.C.; Jones, R.D.

    1997-08-19

    A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.

  11. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

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

  12. Neural Control Mechanisms and Body Fluid Homeostasis

    Science.gov (United States)

    Johnson, Alan Kim

    1998-01-01

    The goal of the proposed research was to study the nature of afferent signals to the brain that reflect the status of body fluid balance and to investigate the central neural mechanisms that process this information for the activation of response systems which restore body fluid homeostasis. That is, in the face of loss of fluids from intracellular or extracellular fluid compartments, animals seek and ingest water and ionic solutions (particularly Na(+) solutions) to restore the intracellular and extracellular spaces. Over recent years, our laboratory has generated a substantial body of information indicating that: (1) a fall in systemic arterial pressure facilitates the ingestion of rehydrating solutions and (2) that the actions of brain amine systems (e.g., norepinephrine; serotonin) are critical for precise correction of fluid losses. Because both acute and chronic dehydration are associated with physiological stresses, such as exercise and sustained exposure to microgravity, the present research will aid in achieving a better understanding of how vital information is handled by the nervous system for maintenance of the body's fluid matrix which is critical for health and well-being.

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

    Science.gov (United States)

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

    2014-01-01

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

  14. The development of the neural substrates of cognitive control in adolescents with autism spectrum disorders.

    Science.gov (United States)

    Solomon, Marjorie; Yoon, Jong H; Ragland, J Daniel; Niendam, Tara A; Lesh, Tyler A; Fairbrother, Wonja; Carter, Cameron S

    2014-09-01

    Autism spectrum disorders (ASDs) involve impairments in cognitive control. In typical development (TYP), neural systems underlying cognitive control undergo substantial maturation during adolescence. Development is delayed in adolescents with ASD. Little is known about the neural substrates of this delay. We used event-related functional magnetic resonance imaging and a cognitive control task involving overcoming a prepotent response tendency to examine the development of cognitive control in young (ages 12-15; n = 13 with ASD and n = 13 with TYP) and older (ages 16-18; n = 14 with ASD and n = 14 with TYP) adolescents with whole-brain voxelwise univariate and task-related functional connectivity analyses. Older ASD and TYP showed reduced activation in sensory and premotor areas relative to younger ones. The older ASD group showed reduced left parietal activation relative to TYP. Functional connectivity analyses showed a significant age by group interaction with the older ASD group exhibiting increased functional connectivity strength between the ventrolateral prefrontal cortex and the anterior cingulate cortex, bilaterally. This functional connectivity strength was related to task performance in ASD, whereas that between dorsolateral prefrontal cortex and parietal cortex (Brodmann areas 9 and 40) was related to task performance in TYP. Adolescents with ASD rely more on reactive cognitive control, involving last-minute conflict detection and control implementation by the anterior cingulate cortex and ventrolateral prefrontal cortex, versus proactive cognitive control requiring processing by dorsolateral prefrontal cortex and parietal cortex. Findings await replication in larger longitudinal studies that examine their functional consequences and amenability to intervention. © 2013 Society of Biological Psychiatry Published by Society of Biological Psychiatry All rights reserved.

  15. Is conflict monitoring supramodal? Spatiotemporal dynamics of cognitive control processes in an auditory Stroop task

    Science.gov (United States)

    Donohue, Sarah E.; Liotti, Mario; Perez, Rick; Woldorff, Marty G.

    2011-01-01

    The electrophysiological correlates of conflict processing and cognitive control have been well characterized for the visual modality in paradigms such as the Stroop task. Much less is known about corresponding processes in the auditory modality. Here, electroencephalographic recordings of brain activity were measured during an auditory Stroop task, using three different forms of behavioral response (Overt verbal, Covert verbal, and Manual), that closely paralleled our previous visual-Stroop study. As expected, behavioral responses were slower and less accurate for incongruent compared to congruent trials. Neurally, incongruent trials showed an enhanced fronto-central negative-polarity wave (Ninc), similar to the N450 in visual-Stroop tasks, with similar variations as a function of behavioral response mode, but peaking ~150 ms earlier, followed by an enhanced positive posterior wave. In addition, sequential behavioral and neural effects were observed that supported the conflict-monitoring and cognitive-adjustment hypothesis. Thus, while some aspects of the conflict detection processes, such as timing, may be modality-dependent, the general mechanisms would appear to be supramodal. PMID:21964643

  16. Taboo: Working memory and mental control in an interactive task

    Science.gov (United States)

    Hansen, Whitney A.; Goldinger, Stephen D.

    2014-01-01

    Individual differences in working memory (WM) predict principled variation in tasks of reasoning, response time, memory, and other abilities. Theoretically, a central function of WM is keeping task-relevant information easily accessible while suppressing irrelevant information. The present experiment was a novel study of mental control, using performance in the game Taboo as a measure. We tested effects of WM capacity on several indices, including perseveration errors (repeating previous guesses or clues) and taboo errors (saying at least part of a taboo or target word). By most measures, high-span participants were superior to low-span participants: High-spans were better at guessing answers, better at encouraging correct guesses from teammates, and less likely to either repeat themselves or produce taboo clues. Differences in taboo errors occurred only in an easy control condition. The results suggest that WM capacity predicts behavior in tasks requiring mental control, extending this finding to an interactive group setting. PMID:19827699

  17. An architecture for designing fuzzy logic controllers using neural networks

    Science.gov (United States)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

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

    Directory of Open Access Journals (Sweden)

    Stefania Tronci

    2017-01-01

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

  19. Discriminative training of self-structuring hidden control neural models

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

    1995-01-01

    This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...

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

    Directory of Open Access Journals (Sweden)

    Ji-fang eCui

    2013-11-01

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

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

    Science.gov (United States)

    Cui, Ji-Fang; Chen, Ying-He; Wang, Ya; Shum, David H K; Chan, Raymond C K

    2013-01-01

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

  2. Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D.; Hochberg, Leigh R.; Donoghue, John P.; Friehs, Gerhard M.; Black, Michael J.

    2012-01-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain–computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (click 2-D cursor control of a personal computer. PMID:21278024

  3. Neural control of energy balance: translating circuits to therapies.

    Science.gov (United States)

    Gautron, Laurent; Elmquist, Joel K; Williams, Kevin W

    2015-03-26

    Recent insights into the neural circuits controlling energy balance and glucose homeostasis have rekindled the hope for development of novel treatments for obesity and diabetes. However, many therapies contribute relatively modest beneficial gains with accompanying side effects, and the mechanisms of action for other interventions remain undefined. This Review summarizes current knowledge linking the neural circuits regulating energy and glucose balance with current and potential pharmacotherapeutic and surgical interventions for the treatment of obesity and diabetes. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. The Reference Ability Neural Network Study: Life-time stability of reference-ability neural networks derived from task maps of young adults.

    Science.gov (United States)

    Habeck, C; Gazes, Y; Razlighi, Q; Steffener, J; Brickman, A; Barulli, D; Salthouse, T; Stern, Y

    2016-01-15

    Analyses of large test batteries administered to individuals ranging from young to old have consistently yielded a set of latent variables representing reference abilities (RAs) that capture the majority of the variance in age-related cognitive change: Episodic Memory, Fluid Reasoning, Perceptual Processing Speed, and Vocabulary. In a previous paper (Stern et al., 2014), we introduced the Reference Ability Neural Network Study, which administers 12 cognitive neuroimaging tasks (3 for each RA) to healthy adults age 20-80 in order to derive unique neural networks underlying these 4 RAs and investigate how these networks may be affected by aging. We used a multivariate approach, linear indicator regression, to derive a unique covariance pattern or Reference Ability Neural Network (RANN) for each of the 4 RAs. The RANNs were derived from the neural task data of 64 younger adults of age 30 and below. We then prospectively applied the RANNs to fMRI data from the remaining sample of 227 adults of age 31 and above in order to classify each subject-task map into one of the 4 possible reference domains. Overall classification accuracy across subjects in the sample age 31 and above was 0.80±0.18. Classification accuracy by RA domain was also good, but variable; memory: 0.72±0.32; reasoning: 0.75±0.35; speed: 0.79±0.31; vocabulary: 0.94±0.16. Classification accuracy was not associated with cross-sectional age, suggesting that these networks, and their specificity to the respective reference domain, might remain intact throughout the age range. Higher mean brain volume was correlated with increased overall classification accuracy; better overall performance on the tasks in the scanner was also associated with classification accuracy. For the RANN network scores, we observed for each RANN that a higher score was associated with a higher corresponding classification accuracy for that reference ability. Despite the absence of behavioral performance information in the

  5. Adaptive nonlinear control of missiles using neural networks

    Science.gov (United States)

    McFarland, Michael Bryan

    Research has shown that neural networks can be used to improve upon approximate dynamic inversion for control of uncertain nonlinear systems. In one architecture, the neural network adaptively cancels inversion errors through on-line learning. Such learning is accomplished by a simple weight update rule derived from Lyapunov theory, thus assuring stability of the closed-loop system. In this research, previous results using linear-in-parameters neural networks were reformulated in the context of a more general class of composite nonlinear systems, and the control scheme was shown to possess important similarities and major differences with established methods of adaptive control. The neural-adaptive nonlinear control methodology in question has been used to design an autopilot for an anti-air missile with enhanced agile maneuvering capability, and simulation results indicate that this approach is a feasible one. There are, however, certain difficulties associated with choosing the proper network architecture which make it difficult to achieve the rapid learning required in this application. Accordingly, this technique has been further extended to incorporate the important class of feedforward neural networks with a single hidden layer. These neural networks feature well-known approximation capabilities and provide an effective, although nonlinear, parameterization of the adaptive control problem. Numerical results from a six-degree-of-freedom nonlinear agile anti-air missile simulation demonstrate the effectiveness of the autopilot design based on multilayer networks. Previous work in this area has implicitly assumed precise knowledge of the plant order, and made no allowances for unmodeled dynamics. This thesis describes an approach to the problem of controlling a class of nonlinear systems in the face of both unknown nonlinearities and unmodeled dynamics. The proposed methodology is similar to robust adaptive control techniques derived for control of linear

  6. Neural mechanisms underlying cognitive control of men with lifelong antisocial behavior.

    Science.gov (United States)

    Schiffer, Boris; Pawliczek, Christina; Mu Ller, Bernhard; Forsting, Michael; Gizewski, Elke; Leygraf, Norbert; Hodgins, Sheilagh

    2014-04-30

    Results of meta-analyses suggested subtle deficits in cognitive control among antisocial individuals. Because almost all studies focused on children with conduct problems or adult psychopaths, however, little is known about cognitive control mechanisms among the majority of persistent violent offenders who present an antisocial personality disorder (ASPD). The present study aimed to determine whether offenders with ASPD, relative to non-offenders, display dysfunction in the neural mechanisms underlying cognitive control and to assess the extent to which these dysfunctions are associated with psychopathic traits and trait impulsivity. Participants comprised 21 violent offenders and 23 non-offenders who underwent event-related functional magnetic resonance imaging while performing a non-verbal Stroop task. The offenders, relative to the non-offenders, exhibited reduced response time interference and a different pattern of conflict- and error-related activity in brain areas involved in cognitive control, attention, language, and emotion processing, that is, the anterior cingulate, dorsolateral prefrontal, superior temporal and postcentral cortices, putamen, thalamus, and amygdala. Moreover, between-group differences in behavioural and neural responses revealed associations with core features of psychopathy and attentional impulsivity. Thus, the results of the present study confirmed the hypothesis that offenders with ASPD display alterations in the neural mechanisms underlying cognitive control and that those alterations relate, at least in part, to personality characteristics. Copyright © 2014. Published by Elsevier Ireland Ltd.

  7. Contextual and Developmental Differences in the Neural Architecture of Cognitive Control.

    Science.gov (United States)

    Petrican, Raluca; Grady, Cheryl L

    2017-08-09

    Because both development and context impact functional brain architecture, the neural connectivity signature of a cognitive or affective predisposition may similarly vary across different ages and circumstances. To test this hypothesis, we investigated the effects of age and cognitive versus social-affective context on the stable and time-varying neural architecture of inhibition, the putative core cognitive control component, in a subsample (N = 359, 22-36 years, 174 men) of the Human Connectome Project. Among younger individuals, a neural signature of superior inhibition emerged in both stable and dynamic connectivity analyses. Dynamically, a context-free signature emerged as stronger segregation of internal cognition (default mode) and environmentally driven control (salience, cingulo-opercular) systems. A dynamic social-affective context-specific signature was observed most clearly in the visual system. Stable connectivity analyses revealed both context-free (greater default mode segregation) and context-specific (greater frontoparietal segregation for higher cognitive load; greater attentional and environmentally driven control system segregation for greater reward value) signatures of inhibition. Superior inhibition in more mature adulthood was typified by reduced segregation in the default network with increasing reward value and increased ventral attention but reduced cingulo-opercular and subcortical system segregation with increasing cognitive load. Failure to evidence this neural profile after the age of 30 predicted poorer life functioning. Our results suggest that distinguishable neural mechanisms underlie individual differences in cognitive control during different young adult stages and across tasks, thereby underscoring the importance of better understanding the interplay among dispositional, developmental, and contextual factors in shaping adaptive versus maladaptive patterns of thought and behavior.SIGNIFICANCE STATEMENT The brain's functional

  8. Projection learning algorithm for threshold - controlled neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  9. Neural aspects of second language representation and language control.

    Science.gov (United States)

    Abutalebi, Jubin

    2008-07-01

    A basic issue in the neurosciences of language is whether an L2 can be processed through the same neural mechanism underlying L1 acquisition and processing. In the present paper I review data from functional neuroimaging studies focusing on grammatical and lexico-semantic processing in bilinguals. The available evidence indicates that the L2 seems to be acquired through the same neural structures responsible for L1 acquisition. This fact is also observed for grammar acquisition in late L2 learners contrary to what one may expect from critical period accounts. However, neural differences for an L2 may be observed, in terms of more extended activity of the neural system mediating L1 processing. These differences may disappear once a more 'native-like' proficiency is established, reflecting a change in language processing mechanisms: from controlled processing for a weak L2 system (i.e., a less proficient L2) to more automatic processing. The neuroimaging data reviewed in this paper also support the notion that language control is a crucial aspect specific to the bilingual language system. The activity of brain areas related to cognitive control during the processing of a 'weak' L2 may reflect competition and conflict between languages which may be resolved with the intervention of these areas.

  10. Neural systems for preparatory control of imitation

    National Research Council Canada - National Science Library

    Cross, Katy A; Iacoboni, Marco

    2014-01-01

    Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action...

  11. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2017-11-02

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  12. Neural Network Predictive Control for Vanadium Redox Flow Battery

    Directory of Open Access Journals (Sweden)

    Hai-Feng Shen

    2013-01-01

    Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.

  13. Comparative Study between Robust Control of Robotic Manipulators by Static and Dynamic Neural Networks

    OpenAIRE

    Ghrab, Nadya; Kallel, Hichem

    2013-01-01

    A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dyna...

  14. A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution.

    Science.gov (United States)

    Ardid, Salva; Wang, Xiao-Jing

    2013-12-11

    A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a "tweaking hypothesis" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.

  15. Effects of task language and second-language proficiency on the neural correlates of phonemic fluency in native Japanese speakers: a functional near-infrared spectroscopy study.

    Science.gov (United States)

    Wroblewski, Greggory J; Matsuo, Koji; Hirata, Keiko; Matsubara, Toshio; Harada, Kenichiro; Watanabe, Yoshifumi; Shinoda, Koh

    2017-09-27

    Data collected during a phonemic fluency task (or 'FAS test'), a standard component of neuropsychological batteries for assessment of cognitive deficits, may be language-dependent and may differ depending on second-language proficiency. The unique orthographic/phonological system of the task language, and the reported cognitive advantages inherent to bilinguals, may each influence the task's neural correlates. However, language background is not currently assessed in most studies testing phonemic fluency. Here, we used 52-channel functional near-infrared spectroscopy in college-aged native-Japanese subjects to examine functional changes in oxygenated hemoglobin elicited during a phonemic fluency task performed in Japanese and in English. We found activity differences that were related to task language and second-language proficiency. Besides loci activated in the Japanese test, bilateral precentral channels were specifically recruited in the English test. Furthermore, the higher-proficiency group showed almost no increase in oxygenated hemoglobin in either language context, whereas participants with lower proficiency showed widespread increases for both contexts. We interpret precentral increases as the consequence of additional articulatory resource recruitment in a second-language context. As for the lack of such variation in the higher-proficiency group, it may reflect an advantage in nonverbal executive control in this group. Together, our results point to language background and proficiency as confounding variables in neuroimaging studies of phonemic fluency and that the adequacy of such measures in populations with varying language backgrounds needs to be considered in future studies.

  16. Adaptive Task-Space Cooperative Tracking Control of Networked Robotic Manipulators Without Task-Space Velocity Measurements.

    Science.gov (United States)

    Liang, Xinwu; Wang, Hesheng; Liu, Yun-Hui; Chen, Weidong; Hu, Guoqiang; Zhao, Jie

    2016-10-01

    In this paper, the task-space cooperative tracking control problem of networked robotic manipulators without task-space velocity measurements is addressed. To overcome the problem without task-space velocity measurements, a novel task-space position observer is designed to update the estimated task-space position and to simultaneously provide the estimated task-space velocity, based on which an adaptive cooperative tracking controller without task-space velocity measurements is presented by introducing new estimated task-space reference velocity and acceleration. Furthermore, adaptive laws are provided to cope with uncertain kinematics and dynamics and rigorous stability analysis is given to show asymptotical convergence of the task-space tracking and synchronization errors in the presence of communication delays under strongly connected directed graphs. Simulation results are given to demonstrate the performance of the proposed approach.

  17. Earth Station Neural Network Control Methodology and Simulation

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2012-01-01

    Renewable energy resources are inexhaustible, clean as compared with conventional resources. Also, it is used to supply regions with no grid, no telephone lines, and often with difficult accessibility by common transport. Satellite earth stations which located in remote areas are the most important application of renewable energy. Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This paper presents the mathematic...

  18. Computation and control with neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Corneliusen, A.; Terdal, P.; Knight, T.; Spencer, J.

    1989-10-04

    As energies have increased exponentially with time so have the size and complexity of accelerators and control systems. NN may offer the kinds of improvements in computation and control that are needed to maintain acceptable functionality. For control their associative characteristics could provide signal conversion or data translation. Because they can do any computation such as least squares, they can close feedback loops autonomously to provide intelligent control at the point of action rather than at a central location that requires transfers, conversions, hand-shaking and other costly repetitions like input protection. Both computation and control can be integrated on a single chip, printed circuit or an optical equivalent that is also inherently faster through full parallel operation. For such reasons one expects lower costs and better results. Such systems could be optimized by integrating sensor and signal processing functions. Distributed nets of such hardware could communicate and provide global monitoring and multiprocessing in various ways e.g. via token, slotted or parallel rings (or Steiner trees) for compatibility with existing systems. Problems and advantages of this approach such as an optimal, real-time Turing machine are discussed. Simple examples are simulated and hardware implemented using discrete elements that demonstrate some basic characteristics of learning and parallelism. Future microprocessors' are predicted and requested on this basis. 19 refs., 18 figs.

  19. Supervisory control of multiple robots in dynamic tasking environments.

    Science.gov (United States)

    Chen, Jessie Y C; Barnes, Michael J

    2012-01-01

    A military targeting environment was simulated to examine the effects of an intelligent route-planning agent RoboLeader, which could support dynamic robot re-tasking based on battlefield developments, on the performance of robotics operators. We manipulated the level of assistance (LOAs) provided by RoboLeader as well as the presence of a visualisation tool that provided feedback to the participants on their primary task (target encapsulation) performance. Results showed that the participants' primary task benefited from RoboLeader on all LOAs conditions compared to manual performance; however, visualisation had little effect. Frequent video gamers demonstrated significantly better situation awareness of the mission environment than did infrequent gamers. Those participants with higher spatial ability performed better on a secondary target detection task than did those with lower spatial ability. Finally, participants' workload assessments were significantly lower when they were assisted by RoboLeader than when they performed the target entrapment task manually. Practitioner Summary: This study demonstrated the utility of an intelligent agent for enhancing robotics operators' supervisory control performance as well as reducing their workload during a complex urban scenario involving moving targets. The results furthered the understanding of the interplay among level-of-autonomy, multitasking performance and individual differences in military tasking environments.

  20. Prediction of human actions: expertise and task-related effects on neural activation of the action observation network.

    Science.gov (United States)

    Balser, Nils; Lorey, Britta; Pilgramm, Sebastian; Stark, Rudolf; Bischoff, Matthias; Zentgraf, Karen; Williams, Andrew Mark; Munzert, Jörn

    2014-08-01

    The action observation network (AON) is supposed to play a crucial role when athletes anticipate the effect of others' actions in sports such as tennis. We used functional magnetic resonance imaging to explore whether motor expertise leads to a differential activation pattern within the AON during effect anticipation and whether spatial and motor anticipation tasks are associated with a differential activation pattern within the AON depending on participant expertise level. Expert (N=16) and novice (N=16) tennis players observed video clips depicting forehand strokes with the instruction to either indicate the predicted direction of ball flight (spatial anticipation) or to decide on an appropriate response to the observed action (motor anticipation). The experts performed better than novices on both tennis anticipation tasks, with the experts showing stronger neural activation in areas of the AON, namely, the superior parietal lobe, the intraparietal sulcus, the inferior frontal gyrus, and the cerebellum. When novices were contrasted with experts, motor anticipation resulted in stronger activation of the ventral premotor cortex, the supplementary motor area, and the superior parietal lobe than spatial anticipation task did. In experts, the comparison of motor and spatial anticipation revealed no increased activation. We suggest that the stronger activation of areas in the AON during the anticipation of action effects in experts reflects their use of the more fine-tuned motor representations they have acquired and improved during years of training. Furthermore, results suggest that the neural processing of different anticipation tasks depends on the expertise level. Copyright © 2014 Wiley Periodicals, Inc.

  1. Modulations of the executive control network by stimulus onset asynchrony in a Stroop task.

    Science.gov (United States)

    Coderre, Emily L; van Heuven, Walter J B

    2013-07-31

    Manipulating task difficulty is a useful way of elucidating the functional recruitment of the brain's executive control network. In a Stroop task, pre-exposing the irrelevant word using varying stimulus onset asynchronies ('negative' SOAs) modulates the amount of behavioural interference and facilitation, suggesting disparate mechanisms of cognitive processing in each SOA. The current study employed a Stroop task with three SOAs (-400, -200, 0 ms), using functional magnetic resonance imaging to investigate for the first time the neural effects of SOA manipulation. Of specific interest were 1) how SOA affects the neural representation of interference and facilitation; 2) response priming effects in negative SOAs; and 3) attentional effects of blocked SOA presentation. The results revealed three regions of the executive control network that were sensitive to SOA during Stroop interference; the 0 ms SOA elicited the greatest activation of these areas but experienced relatively smaller behavioural interference, suggesting that the enhanced recruitment led to more efficient conflict processing. Response priming effects were localized to the right inferior frontal gyrus, which is consistent with the idea that this region performed response inhibition in incongruent conditions to overcome the incorrectly-primed response, as well as more general action updating and response preparation. Finally, the right superior parietal lobe was sensitive to blocked SOA presentation and was most active for the 0 ms SOA, suggesting that this region is involved in attentional control. SOA exerted both trial-specific and block-wide effects on executive processing, providing a unique paradigm for functional investigations of the cognitive control network.

  2. Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

    Directory of Open Access Journals (Sweden)

    Ahcene Farah

    2002-06-01

    Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

  3. Context-specific control and context selection in conflict tasks

    NARCIS (Netherlands)

    Schouppe, N.; Ridderinkhof, K.R.; Verguts, T.; Notebaert, W.

    2014-01-01

    This study investigated whether participants prefer contexts with relatively little cognitive conflict and whether this preference is related to context-specific control. A conflict selection task was administered in which participants had to choose between two categories that contained different

  4. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    Science.gov (United States)

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  5. Stability analysis of embedded nonlinear predictor neural generalized predictive controller

    Directory of Open Access Journals (Sweden)

    Hesham F. Abdel Ghaffar

    2014-03-01

    Full Text Available Nonlinear Predictor-Neural Generalized Predictive Controller (NGPC is one of the most advanced control techniques that are used with severe nonlinear processes. In this paper, a hybrid solution from NGPC and Internal Model Principle (IMP is implemented to stabilize nonlinear, non-minimum phase, variable dead time processes under high disturbance values over wide range of operation. Also, the superiority of NGPC over linear predictive controllers, like GPC, is proved for severe nonlinear processes over wide range of operation. The necessary conditions required to stabilize NGPC is derived using Lyapunov stability analysis for nonlinear processes. The NGPC stability conditions and improvement in disturbance suppression are verified by both simulation using Duffing’s nonlinear equation and real-time using continuous stirred tank reactor. Up to our knowledge, the paper offers the first hardware embedded Neural GPC which has been utilized to verify NGPC–IMP improvement in realtime.

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Task planning as a part of production control

    OpenAIRE

    Junnonen, Juha-Matti; Seppänen, Olli

    2004-01-01

    The execution of a construction project requires production planning and control to be performed with different levels of accuracy. In master plans, the overall progress of the whole construction project is planned and controlled. For practical implementation, site management requires more detailed plans. This can be achieved with the help of task planning, a method of planning which begins from what should be done. and examines in detail how the time, cost, and quality objectives can be achi...

  8. Task, muscle and frequency dependent vestibular control of posture

    OpenAIRE

    Forbes, Patrick A.; Gunter P Siegmund; Schouten, Alfred C.; Blouin, Jean-Sébastien

    2015-01-01

    The vestibular system is crucial for postural control; however there are considerable differences in the task dependence and frequency response of vestibular reflexes in appendicular and axial muscles. For example, vestibular reflexes are only evoked in appendicular muscles when vestibular information is relevant to postural control, while in neck muscles they are maintained regardless of the requirement to maintain head on trunk balance. Recent investigations have also shown that the bandwid...

  9. Piecewise-linear artificial neural networks for PID controller tuning

    Directory of Open Access Journals (Sweden)

    Petr Doležel

    2012-12-01

    Full Text Available A new algorithm of PID controller tuning is presented in this paper. It is well known that there have been introduced manytechniques for PID controller tuning, both theoretical and experimental ones. However, this algorithm is suitable especially forhighly nonlinear processes. It uses a model of the controlled process in the shape of piecewise-linear neural network which islinearized continuously and resulting linearized model is used for PID controller online tuning. While at the beginning of the paperthe algorithm is described in theory, at the end there are mentioned some practical applications

  10. Neck collar for restraining head and body movements in rats for behavioral task performance and simultaneous neural activity recording.

    Science.gov (United States)

    Tateyama, Yukina; Oyama, Kei; Lo, Cheuk Wa Christopher; Iijima, Toshio; Tsutsui, Ken-Ichiro

    2016-04-01

    Head fixation has been one of the major methods in behavioral neurophysiology because it allows precision in stimulus application and behavioral assessment. Most neural recordings in awake monkeys have been obtained under head fixation, which is nowadays also being used in awake rodents. However, head fixation devices in rats often become unstable within several months, which increases risks for inflammation, infection, and necrosis of the bone and surrounding tissue. In this study we developed a novel non-invasive "neck collar system" for restraining the head and body movements of behaving rats. The attachment of the neck collar for 2-3 months did not affect the animals' health and welfare. Rats under neck-collar fixation could learn a behavioral task (standard delayed licking task) with the same efficiency as those under standard head fixation. They could also learn a more complicated task (delayed pro/anti-licking task) under neck-collar fixation and afterwards transfer their learning to the task under standard head fixation. Furthermore, we were able to record single-unit activity in rats under neck-collar fixation during the performance of the standard delayed licking task. This system consists of economical materials and is easily constructed, and it enables head-restraint without surgery, thus eliminating the risk of inflammation or infection. We consider the neck-collar fixation developed in this study would be useful for restraining the head of a behaving rodent. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning. Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

  12. A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

    Science.gov (United States)

    Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

    2016-01-01

    We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

  13. Neural coding for instruction-based task sets in human frontoparietal and visual cortex

    NARCIS (Netherlands)

    Muhle-Karbe, Paul S.; Duncan, John; De Baene, W.; Mitchell, Daniel; Brass, Marcel

    2017-01-01

    Task preparation has traditionally been thought to rely upon persistent memory representations of instructions that permit their execution after delays. Accumulating evidence suggests, however, that accurate retention of task knowledge can be insufficient for successful performance. Here, we

  14. Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

    Directory of Open Access Journals (Sweden)

    Kazuhiko Hiramoto

    2018-01-01

    Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

  15. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    Science.gov (United States)

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors.

    Directory of Open Access Journals (Sweden)

    Jessica R Andrews-Hanna

    Full Text Available BACKGROUND: Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established. METHODOLOGY/PRINCIPAL FINDINGS: To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution. CONCLUSIONS/SIGNIFICANCE: Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

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

    Directory of Open Access Journals (Sweden)

    Bahita Mohamed

    2011-01-01

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

  18. ER fluid applications to vibration control devices and an adaptive neural-net controller

    Science.gov (United States)

    Morishita, Shin; Ura, Tamaki

    1993-07-01

    Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

  19. A Randomized Controlled ERP Study on the Effects of Multi-Domain Cognitive Training and Task Difficulty on Task Switching Performance in Older Adults.

    Science.gov (United States)

    Küper, Kristina; Gajewski, Patrick D; Frieg, Claudia; Falkenstein, Michael

    2017-01-01

    Executive functions are subject to a marked age-related decline, but have been shown to benefit from cognitive training interventions. As of yet, it is, however, still relatively unclear which neural mechanism can mediate training-related performance gains. In the present electrophysiological study, we examined the effects of multi-domain cognitive training on performance in an untrained cue-based task switch paradigm featuring Stroop color words: participants either had to indicate the word meaning of Stroop stimuli (word task) or perform the more difficult task of color naming (color task). One-hundred and three older adults (>65 years old) were randomly assigned to a training group receiving a 4-month multi-domain cognitive training, a passive no-contact control group or an active (social) control group receiving a 4-month relaxation training. For all groups, we recorded performance and EEG measures before and after the intervention. For the cognitive training group, but not for the two control groups, we observed an increase in response accuracy at posttest, irrespective of task and trial type. No training-related effects on reaction times were found. Cognitive training was also associated with an overall increase in N2 amplitude and a decrease of P2 latency on single trials. Training-related performance gains were thus likely mediated by an enhancement of response selection and improved access to relevant stimulus-response mappings. Additionally, cognitive training was associated with an amplitude decrease in the time window of the target-locked P3 at fronto-central electrodes. An increase in the switch positivity during advance task preparation emerged after both cognitive and relaxation training. Training-related behavioral and event-related potential (ERP) effects were not modulated by task difficulty. The data suggest that cognitive training increased slow negative potentials during target processing which enhanced the N2 and reduced a subsequent P3-like

  20. Prediction and control of neural responses to pulsatile electrical stimulation

    Science.gov (United States)

    Campbell, Luke J.; Sly, David James; O'Leary, Stephen John

    2012-04-01

    This paper aims to predict and control the probability of firing of a neuron in response to pulsatile electrical stimulation of the type delivered by neural prostheses such as the cochlear implant, bionic eye or in deep brain stimulation. Using the cochlear implant as a model, we developed an efficient computational model that predicts the responses of auditory nerve fibers to electrical stimulation and evaluated the model's accuracy by comparing the model output with pooled responses from a group of guinea pig auditory nerve fibers. It was found that the model accurately predicted the changes in neural firing probability over time to constant and variable amplitude electrical pulse trains, including speech-derived signals, delivered at rates up to 889 pulses s-1. A simplified version of the model that did not incorporate adaptation was used to adaptively predict, within its limitations, the pulsatile electrical stimulus required to cause a desired response from neurons up to 250 pulses s-1. Future stimulation strategies for cochlear implants and other neural prostheses may be enhanced using similar models that account for the way that neural responses are altered by previous stimulation.

  1. Noise Control for a Moving Evaluation Point Using Neural Networks

    Science.gov (United States)

    Maeda, Toshiki; Shiraishi, Toshihiko

    2016-09-01

    This paper describes the noise control for a moving evaluation point using neural networks by making the best use of its learning ability. Noise control is a technology which is effective on low-frequency noise. Based on the principle of superposition, a primary sound wave can be cancelled at an evaluation point by emitting a secondary opposite sound wave. To obtain good control performance, it is important to precisely identify the characteristics of all the sound paths. One of the most popular algorithms of noise control is filtered-x LMS algorithm. This algorithm can deliver a good result while all the sound paths do not change. However, the control system becomes uncontrollable while the evaluation point is moving. To solve the problem, the characteristics of all the paths are must be identified at all time. In this paper, we applied neural networks with the learning ability to the noise control system to follow the time-varying paths and verified its control performance by numerical simulations. Then, dropout technique for the networks is also applied. Dropout is a technique that prevent the network from overfitting and enables better control performance. By applying dropout for noise control, it prevents the system from diverging.

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

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

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

  3. Neural Control of Chronic Stress Adaptation

    Directory of Open Access Journals (Sweden)

    James eHerman

    2013-08-01

    Full Text Available Stress initiates adaptive processes that allow the organism to physiologically cope with prolonged or intermittent exposure to real or perceived threats. A major component of this response is repeated activation of glucocorticoid secretion by the hypothalamo-pituitary-adrenocortical (HPA axis, which promotes redistribution of energy in a wide range of organ systems, including the brain. Prolonged or cumulative increases in glucocorticoid secretion can reduce benefits afforded by enhanced stress reactivity and eventually become maladaptive. The long-term impact of stress is kept in check by the process of habituation, which reduces HPA axis responses upon repeated exposure to homotypic stressors and likely limits deleterious actions of prolonged glucocorticoid secretion. Habituation is regulated by limbic stress-regulatory sites, and is at least in part glucocorticoid feedback-dependent. Chronic stress also sensitizes reactivity to new stimuli. While sensitization may be important in maintaining response flexibility in response to new threats, it may also add to the cumulative impact of glucocorticoids on the brain and body. Finally, unpredictable or severe stress exposure may cause long-term and lasting dysregulation of the HPA axis, likely due to altered limbic control of stress effector pathways. Stress-related disorders, such as depression and PTSD, are accompanied by glucocorticoid imbalances and structural/ functional alterations in limbic circuits that resemble those seen following chronic stress, suggesting that inappropriate processing of stressful information may be part of the pathological process.

  4. Diagnostics and control of pressurized reactors using artificial neural networks

    Science.gov (United States)

    Ikonomopoulos, Andreas; Tsoukalas, Lefteri H.; Uhrig, Robert E.

    1992-09-01

    A methodology employing artificial neural networks and fuzzy arithmetic in the diagnosis and control of complex systems such as pressurized water reactors is presented. Fuzzy numbers represent the linguistic values of plant-specific variables, e.g., performance or availability. The notion of a virtual instrument, i.e., a software-based measuring device calibrated to the idiosyncrasies of a specific system is used. Neural networks perform a mapping of physically measurable parameters to fuzzy numbers called Virtual Measurement Values (VMV). The methodology is tested with start-up data from an experimental nuclear reactor. The results demonstrate the very good capacity of such virtual instruments for failure-tolerance and suggest the possibility of developing alternative algorithms for diagnostics and control.

  5. Reduced Neural Recruitment for Bayesian Adjustment of Inhibitory Control in Methamphetamine Dependence.

    Science.gov (United States)

    Harlé, Katia M; Zhang, Shunan; Ma, Ning; Yu, Angela J; Paulus, Martin P

    2016-09-01

    Delineating the processes that contribute to the progression and maintenance of substance dependence is critical to understanding and preventing addiction. Several previous studies have shown inhibitory control deficits in individuals with stimulant use disorder. We used a Bayesian computational approach to examine potential neural deficiencies in the dynamic predictive processing underlying inhibitory function among recently abstinent methamphetamine-dependent individuals (MDIs), a population at high risk of relapse. Sixty-two MDIs were recruited from a 28-day inpatient treatment program at the San Diego Veterans Affairs Medical Center and compared with 34 healthy control subjects. They completed a stop-signal task during functional magnetic resonance imaging. A Bayesian ideal observer model was used to predict individuals' trial-to-trial probabilistic expectations of inhibitory response, P(stop), to identify group differences specific to Bayesian expectation and prediction error computation. Relative to control subjects, MDIs were more likely to make stop errors on difficult trials and had attenuated slowing following stop errors. MDIs further exhibited reduced sensitivity as measured by the neural tracking of a Bayesian measure of surprise (unsigned prediction error), which was evident across all trials in the left posterior caudate and orbitofrontal cortex (Brodmann area 11), and selectively on stop error trials in the right thalamus and inferior parietal lobule. MDIs are less sensitive to surprising task events, both across trials and upon making commission errors, which may help explain why these individuals may not engage in switching strategy when the environment changes, leading to adverse consequences.

  6. Self-control and Task Timing Shift Self-efficacy and Influence Willingness to Engage in Effortful Tasks.

    Science.gov (United States)

    Ein-Gar, Danit; Steinhart, Yael

    2017-01-01

    Self-efficacy constitutes a key factor that influences people's inclination to engage in effortful tasks. In this study, we focus on an interesting interplay between two prominent factors known to influence engagement in effortful tasks: the timing of the task (i.e., whether the task is scheduled to take place in the near or distant future) and individuals' levels of self-control. Across three studies, we show that these two factors have an interacting effect on self-efficacy. Low self-control (LSC) individuals report higher self-efficacy for distant-future effortful tasks than for near-future tasks, whereas high self-control (HSC) individuals report higher self-efficacy for near-future tasks than for distant future tasks. We further demonstrate how self-efficacy then molds individuals' willingness to engage in those effortful tasks. Given that a particular task may comprise effortful aspects alongside more enjoyable aspects, we show that the effects we observe emerge with regard to a task whose effortful aspects are salient and that the effects are eliminated when the enjoyable aspects of that same task are highlighted.

  7. Self-control and Task Timing Shift Self-efficacy and Influence Willingness to Engage in Effortful Tasks

    Science.gov (United States)

    Ein-Gar, Danit; Steinhart, Yael

    2017-01-01

    Self-efficacy constitutes a key factor that influences people's inclination to engage in effortful tasks. In this study, we focus on an interesting interplay between two prominent factors known to influence engagement in effortful tasks: the timing of the task (i.e., whether the task is scheduled to take place in the near or distant future) and individuals' levels of self-control. Across three studies, we show that these two factors have an interacting effect on self-efficacy. Low self-control (LSC) individuals report higher self-efficacy for distant-future effortful tasks than for near-future tasks, whereas high self-control (HSC) individuals report higher self-efficacy for near-future tasks than for distant future tasks. We further demonstrate how self-efficacy then molds individuals' willingness to engage in those effortful tasks. Given that a particular task may comprise effortful aspects alongside more enjoyable aspects, we show that the effects we observe emerge with regard to a task whose effortful aspects are salient and that the effects are eliminated when the enjoyable aspects of that same task are highlighted. PMID:29075225

  8. Self-control and Task Timing Shift Self-efficacy and Influence Willingness to Engage in Effortful Tasks

    Directory of Open Access Journals (Sweden)

    Danit Ein-Gar

    2017-10-01

    Full Text Available Self-efficacy constitutes a key factor that influences people's inclination to engage in effortful tasks. In this study, we focus on an interesting interplay between two prominent factors known to influence engagement in effortful tasks: the timing of the task (i.e., whether the task is scheduled to take place in the near or distant future and individuals' levels of self-control. Across three studies, we show that these two factors have an interacting effect on self-efficacy. Low self-control (LSC individuals report higher self-efficacy for distant-future effortful tasks than for near-future tasks, whereas high self-control (HSC individuals report higher self-efficacy for near-future tasks than for distant future tasks. We further demonstrate how self-efficacy then molds individuals' willingness to engage in those effortful tasks. Given that a particular task may comprise effortful aspects alongside more enjoyable aspects, we show that the effects we observe emerge with regard to a task whose effortful aspects are salient and that the effects are eliminated when the enjoyable aspects of that same task are highlighted.

  9. 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.

  10. A model for the neural control of pineal periodicity

    Science.gov (United States)

    de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

    2016-12-01

    The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

  11. Practical Application of Neural Networks in State Space Control

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train....... Then the controller is shown to work on a simulation example. We also address the potential problem of too rapidly fluctuating parameters by including regularization in the learning rule. Next we develop a direct adaptive certainty-equivalence controller based on neurofuzzy models. The control loop is proven...

  12. Neural control of muscle relaxation in echinoderms.

    Science.gov (United States)

    Elphick, M R; Melarange, R

    2001-03-01

    Smooth muscle relaxation in vertebrates is regulated by a variety of neuronal signalling molecules, including neuropeptides and nitric oxide (NO). The physiology of muscle relaxation in echinoderms is of particular interest because these animals are evolutionarily more closely related to the vertebrates than to the majority of invertebrate phyla. However, whilst in vertebrates there is a clear structural and functional distinction between visceral smooth muscle and skeletal striated muscle, this does not apply to echinoderms, in which the majority of muscles, whether associated with the body wall skeleton and its appendages or with visceral organs, are made up of non-striated fibres. The mechanisms by which the nervous system controls muscle relaxation in echinoderms were, until recently, unknown. Using the cardiac stomach of the starfish Asterias rubens as a model, it has been established that the NO-cGMP signalling pathway mediates relaxation. NO also causes relaxation of sea urchin tube feet, and NO may therefore function as a 'universal' muscle relaxant in echinoderms. The first neuropeptides to be identified in echinoderms were two related peptides isolated from Asterias rubens known as SALMFamide-1 (S1) and SALMFamide-2 (S2). Both S1 and S2 cause relaxation of the starfish cardiac stomach, but with S2 being approximately ten times more potent than S1. SALMFamide neuropeptides have also been isolated from sea cucumbers, in which they cause relaxation of both gut and body wall muscle. Therefore, like NO, SALMFamides may also function as 'universal' muscle relaxants in echinoderms. The mechanisms by which SALMFamides cause relaxation of echinoderm muscle are not known, but several candidate signal transduction pathways are discussed here. The SALMFamides do not, however, appear to act by promoting release of NO, and muscle relaxation in echinoderms is therefore probably regulated by at least two neuronal signalling systems acting in parallel. Recently, other

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

    Directory of Open Access Journals (Sweden)

    Carlos López-Franco

    2015-01-01

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

  14. Control strategies for underactuated neural ensembles driven by optogenetic stimulation

    Directory of Open Access Journals (Sweden)

    ShiNung eChing

    2013-04-01

    Full Text Available Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

  15. Control strategies for underactuated neural ensembles driven by optogenetic stimulation.

    Science.gov (United States)

    Ching, ShiNung; Ritt, Jason T

    2013-01-01

    Motivated by experiments employing optogenetic stimulation of cortical regions, we consider spike control strategies for ensembles of uncoupled integrate and fire neurons with a common conductance input. We construct strategies for control of spike patterns, that is, multineuron trains of action potentials, up to some maximal spike rate determined by the neural biophysics. We emphasize a constructive role for parameter heterogeneity, and find a simple rule for controllability in pairs of neurons. In particular, we determine parameters for which common drive is not limited to inducing synchronous spiking. For large ensembles, we determine how the number of controllable neurons varies with the number of observed (recorded) neurons, and what collateral spiking occurs in the full ensemble during control of the subensemble. While complete control of spiking in every neuron is not possible with a single input, we find that a degree of subensemble control is made possible by exploiting dynamical heterogeneity. As most available technologies for neural stimulation are underactuated, in the sense that the number of target neurons far exceeds the number of independent channels of stimulation, these results suggest partial control strategies that may be important in the development of sensory neuroprosthetics and other neurocontrol applications.

  16. Action control in task switching: do action effects modulate N - 2 repetition costs in task switching?

    Science.gov (United States)

    Schuch, Stefanie; Sommer, Angelika; Lukas, Sarah

    2017-11-17

    Ideomotor theory posits that actions are controlled by the anticipation of their effects. In line with this theoretical framework, response-contingent action effects have been shown to influence performance in choice-reaction time tasks, both in single-task and task-switching context. Using a task-switching paradigm, the present study investigated whether task-contingent action effects influenced N - 2 repetition costs in task switching. N - 2 repetition costs are thought to be related to task-switch costs, and reflect inhibitory control in task switching. It was expected that task-contingent action effects reduce between-task interference, leading to reduced N - 2 repetition costs. An experimental group (N = 24) performed eight blocks of trials with task-contingent action effects, followed by one block with non-contingent action effects; a control group (N = 24) performed nine blocks of trials with non-contingent action effects. In line with our expectations, a three-way interaction of group, block, and task sequence was obtained, indicating differential data patterns for the two groups: In error rates, the group who had received contingent action effects throughout blocks 1-8 showed larger N - 2 repetition costs in the random block 9 than in block 8, whereas the control group showed a reversed data pattern. The RT data pattern was in the same direction, although no significant three-way interaction was obtained. Taken together, we tentatively conclude that task-contingent action effects reduce task inhibition in task switching, and we outline directions for future research on the role of action effects in multitasking performance.

  17. Application of Discrete Event Control to the Insertion Task of Electric Line Using 6-Link Electro-Hydraulic Manipulators with Dual Arm

    Science.gov (United States)

    Ahn, Kyoungkwan; Yokota, Shinichi

    Uninterrupted power supply has become indispensable during the maintenance task of active electric power lines as a result of today's highly information-oriented society and increasing demand of electric utilities. The maintenance task has the risk of electric shock and the danger of falling from high place. Therefore it is necessary to realize an autonomous robot system using electro-hydraulic manipulator because hydraulic manipulators have the advantage of electric insulation. Meanwhile it is relatively difficult to realize autonomous assembly tasks particularly in the case of manipulating flexible objects such as electric lines. In this report, a discrete event control system is introduced for automatic assembly task of electric lines into sleeves as one of a typical task of active electric power lines. In the implementation of a discrete event control system, LVQNN (learning vector quantization neural network) is applied to the insertion task of electric lines to sleeves. In order to apply these proposed control system to the unknown environment, virtual learning data for LVQNN was generated by fuzzy inference. By the experimental results of two types of electric lines and sleeves, these proposed discrete event control and neural network learning algorithm are confirmed very effective to the insertion tasks of electric lines to sleeves as a typical task of active electric power maintenance tasks.

  18. Neural Network Control for a Batch Distillation Column

    Directory of Open Access Journals (Sweden)

    Duraid Fadhil Ahmed

    2016-07-01

    Full Text Available The  present  work  deals  with  studying  the  dynamic  behavior  of  a  batch  distillation  column  and implemented  two  types  of  control  strategies  for  the  separation  different  types  of  binary  systems.  The model  was  derived  and  then  simulated  using  "MATLAB"  program.  The  experimental  data  of  dynamic behavior  were  to  tune  the  parameters  of  PID  controller  and  developed  the  training  of  neural  networks controller by using supervised  learning algorithms. The simulation results show a qualitatively acceptable behavior.  This  study  shows  also  that  the  response  of  PID  controller  was  oscillatory  behavior  with  high offset value while neural network controller gave less offset value and less  time to reach the steady state. In general, a good improvement is achieved when the  neural network controller  is used compared with PID control.

  19. An artificial neural network controller based on MPSO-BFGS hybrid optimization for spherical flying robot

    Science.gov (United States)

    Liu, Xiaolin; Li, Lanfei; Sun, Hanxu

    2017-12-01

    Spherical flying robot can perform various tasks in the complex and varied environment to reduce labor costs. However, it is difficult to guarantee the stability of the spherical flying robot in the case of strong coupling and time-varying disturbance. In this paper, an artificial neural network controller (ANNC) based on MPSO-BFGS hybrid optimization algorithm is proposed. The MPSO algorithm is used to optimize the initial weights of the controller to avoid the local optimal solution. The BFGS algorithm is introduced to improve the convergence ability of the network. We use Lyapunov method to analyze the stability of ANNC. The controller is simulated under the condition of nonlinear coupling disturbance. The experimental results show that the proposed controller can obtain the expected value in shoter time compared with the other considered methods.

  20. Neural Mobilization: A Systematic Review of Randomized Controlled Trials with an Analysis of Therapeutic Efficacy

    Science.gov (United States)

    Ellis, Richard F.; Hing, Wayne A.

    2008-01-01

    Neural mobilization is a treatment modality used in relation to pathologies of the nervous system. It has been suggested that neural mobilization is an effective treatment modality, although support of this suggestion is primarily anecdotal. The purpose of this paper was to provide a systematic review of the literature pertaining to the therapeutic efficacy of neural mobilization. A search to identify randomized controlled trials investigating neural mobilization was conducted using the key words neural mobilisation/mobilization, nerve mobilisation/mobilization, neural manipulative physical therapy, physical therapy, neural/nerve glide, nerve glide exercises, nerve/neural treatment, nerve/neural stretching, neurodynamics, and nerve/neural physiotherapy. The titles and abstracts of the papers identified were reviewed to select papers specifically detailing neural mobilization as a treatment modality. The PEDro scale, a systematic tool used to critique RCTs and grade methodological quality, was used to assess these trials. Methodological assessment allowed an analysis of research investigating therapeutic efficacy of neural mobilization. Ten randomized clinical trials (discussed in 11 retrieved articles) were identified that discussed the therapeutic effect of neural mobilization. This review highlights the lack in quantity and quality of the available research. Qualitative analysis of these studies revealed that there is only limited evidence to support the use of neural mobilization. Future research needs to re-examine the application of neural mobilization with use of more homogeneous study designs and pathologies; in addition, it should standardize the neural mobilization interventions used in the study. PMID:19119380

  1. Computational models of the neural control of breathing.

    Science.gov (United States)

    Molkov, Yaroslav I; Rubin, Jonathan E; Rybak, Ilya A; Smith, Jeffrey C

    2017-03-01

    The ongoing process of breathing underlies the gas exchange essential for mammalian life. Each respiratory cycle ensues from the activity of rhythmic neural circuits in the brainstem, shaped by various modulatory signals, including mechanoreceptor feedback sensitive to lung inflation and chemoreceptor feedback dependent on gas composition in blood and tissues. This paper reviews a variety of computational models designed to reproduce experimental findings related to the neural control of breathing and generate predictions for future experimental testing. The review starts from the description of the core respiratory network in the brainstem, representing the central pattern generator (CPG) responsible for producing rhythmic respiratory activity, and progresses to encompass additional complexities needed to simulate different metabolic challenges, closed-loop feedback control including the lungs, and interactions between the respiratory and autonomic nervous systems. The integrated models considered in this review share a common framework including a distributed CPG core network responsible for generating the baseline three-phase pattern of rhythmic neural activity underlying normal breathing. WIREs Syst Biol Med 2017, 9:e1371. doi: 10.1002/wsbm.1371 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

  2. Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

    CERN Document Server

    Liu, Jinkun

    2013-01-01

    Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

  3. Self-Control of Task Difficulty during Training Enhances Motor Learning of a Complex Coincidence-Anticipation Task

    Science.gov (United States)

    Andrieux, Mathieu; Danna, Jeremy; Thon, Bernard

    2012-01-01

    The aim of the present work was to analyze the influence of self-controlled task difficulty on motor learning. Participants had to intercept three targets falling at different velocities by displacing a stylus above a digitizer. Task difficulty corresponded to racquet width. Half the participants (self-control condition) could choose the racquet…

  4. Neural control of rhythmic arm cycling after stroke

    Science.gov (United States)

    Loadman, Pamela M.; Hundza, Sandra R.

    2012-01-01

    Disordered reflex activity and alterations in the neural control of walking have been observed after stroke. In addition to impairments in leg movement that affect locomotor ability after stroke, significant impairments are also seen in the arms. Altered neural control in the upper limb can often lead to altered tone and spasticity resulting in impaired coordination and flexion contractures. We sought to address the extent to which the neural control of movement is disordered after stroke by examining the modulation pattern of cutaneous reflexes in arm muscles during arm cycling. Twenty-five stroke participants who were at least 6 mo postinfarction and clinically stable, performed rhythmic arm cycling while cutaneous reflexes were evoked with trains (5 × 1.0-ms pulses at 300 Hz) of constant-current electrical stimulation to the superficial radial (SR) nerve at the wrist. Both the more (MA) and less affected (LA) arms were stimulated in separate trials. Bilateral electromyography (EMG) activity was recorded from muscles acting at the shoulder, elbow, and wrist. Analysis was conducted on averaged reflexes in 12 equidistant phases of the movement cycle. Phase-modulated cutaneous reflexes were present, but altered, in both MA and LA arms after stroke. Notably, the pattern was “blunted” in the MA arm in stroke compared with control participants. Differences between stroke and control were progressively more evident moving from shoulder to wrist. The results suggest that a reduced pattern of cutaneous reflex modulation persists during rhythmic arm movement after stroke. The overall implication of this result is that the putative spinal contributions to rhythmic human arm movement remain accessible after stroke, which has translational implications for rehabilitation. PMID:22572949

  5. Critical thresholds and dynamical network states in a neural architecture for cognitive tasks

    NARCIS (Netherlands)

    de Vries, Pieter

    2017-01-01

    Cognitive tasks are represented in a network, in which: - Nodes correspond to cell-assemblies with a critical threshold, which enables explanation of various psychological phenomena, like priming. - Dynamical network states (excitation loops) can propagate in specific, task-dependent ways ranging

  6. Bilinguals use language-control brain areas more than monolinguals to perform non-linguistic switching tasks.

    Science.gov (United States)

    Rodríguez-Pujadas, Aina; Sanjuán, Ana; Ventura-Campos, Noelia; Román, Patricia; Martin, Clara; Barceló, Francisco; Costa, Albert; Avila, César

    2013-01-01

    We tested the hypothesis that early bilinguals use language-control brain areas more than monolinguals when performing non-linguistic executive control tasks. We do so by exploring the brain activity of early bilinguals and monolinguals in a task-switching paradigm using an embedded critical trial design. Crucially, the task was designed such that the behavioural performance of the two groups was comparable, allowing then to have a safer comparison between the corresponding brain activity in the two groups. Despite the lack of behavioural differences between both groups, early bilinguals used language-control areas--such as left caudate, and left inferior and middle frontal gyri--more than monolinguals, when performing the switching task. Results offer direct support for the notion that, early bilingualism exerts an effect in the neural circuitry responsible for executive control. This effect partially involves the recruitment of brain areas involved in language control when performing domain-general executive control tasks, highlighting the cross-talk between these two domains.

  7. Statistical process control using optimized neural networks: a case study.

    Science.gov (United States)

    Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

    2014-09-01

    The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

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

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

    Science.gov (United States)

    Lee, Chuen-Chien

    1991-01-01

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

  10. Social categories shape the neural representation of emotion: evidence from a visual face adaptation task

    NARCIS (Netherlands)

    Otten, M.; Banaji, M.R.

    2012-01-01

    A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the

  11. Neural Plasticity and the Issue of Mimicry Tasks in L2 Pronunciation Studies.

    Science.gov (United States)

    Stapp, Yvonne F.

    1999-01-01

    In an investigation of the relationship between mimicry skill and neural plasticity, 28 monolingual Japanese subjects aged 4-17 repeated a list of simple English words containing /r/ and /l/. Analyses were made of individual and age-group scores and of consistency of individuals' pronunciation across word tokens. Results indicated mimicry ability…

  12. Social Categories Shape the Neural Representation of Emotion: Evidence from a Visual Face Adaptation Task.

    Directory of Open Access Journals (Sweden)

    Marte eOtten

    2012-02-01

    Full Text Available A number of recent behavioral studies have shown that emotional expressions are differently perceived depending on the race of a face, and that that perception of race cues is influenced by emotional expressions. However, neural processes related to the perception of invariant cues that indicate the identity of a face (such as race are often described to proceed independently of processes related to the perception of cues that can vary over time (such as emotion. Using a visual face adaptation paradigm, we tested whether these behavioral interactions between emotion and race also reflect interdependent neural representation of emotion and race. We compared visual emotion aftereffects when the adapting face and ambiguous test face differed in race or not. Emotion aftereffects were much smaller in different race trials than same race trials, indicating that the neural representation of a facial expression is significantly different depending on whether the emotional face is black or white. It thus seems that invariable cues such as race interact with variable face cues such as emotion not just at a response level, but also at the level of perception and neural representation.

  13. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions.

    Directory of Open Access Journals (Sweden)

    Zixuan Cang

    2017-07-01

    Full Text Available Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH method. ESPH represents 3D complex geometry by one-dimensional (1D topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN. We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes.weilab.math.msu.edu/TDL/.

  14. TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

    Science.gov (United States)

    2017-01-01

    Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

  15. Control of 12-Cylinder Camless Engine with Neural Networks

    Directory of Open Access Journals (Sweden)

    Ashhab Moh’d Sami

    2017-01-01

    Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

  16. Task, muscle and frequency dependent vestibular control of posture

    Directory of Open Access Journals (Sweden)

    Patrick A Forbes

    2015-01-01

    Full Text Available The vestibular system is crucial for postural control; however there are considerable differences in the task dependence and frequency response of vestibular reflexes in appendicular and axial muscles. For example, vestibular reflexes are only evoked in appendicular muscles when vestibular information is relevant to postural control, while in neck muscles they are maintained regardless of the requirement to maintain head on trunk balance. Recent investigations have also shown that the bandwidth of vestibular input on neck muscles is much broader than appendicular muscles (up to a factor of 3. This result challenges the notion that vestibular reflexes only contribute to postural control across the behavioral and physiological frequency range of the vestibular organ (i.e., 0-20 Hz. In this review, we explore and integrate these task-, muscle- and frequency-related differences in the vestibular system’s contribution to posture, and propose that the human nervous system has adapted vestibular signals to match the mechanical properties of the system that each group of muscles controls.

  17. Task, muscle and frequency dependent vestibular control of posture.

    Science.gov (United States)

    Forbes, Patrick A; Siegmund, Gunter P; Schouten, Alfred C; Blouin, Jean-Sébastien

    2014-01-01

    The vestibular system is crucial for postural control; however there are considerable differences in the task dependence and frequency response of vestibular reflexes in appendicular and axial muscles. For example, vestibular reflexes are only evoked in appendicular muscles when vestibular information is relevant to postural control, while in neck muscles they are maintained regardless of the requirement to maintain head on trunk balance. Recent investigations have also shown that the bandwidth of vestibular input on neck muscles is much broader than appendicular muscles (up to a factor of 3). This result challenges the notion that vestibular reflexes only contribute to postural control across the behavioral and physiological frequency range of the vestibular organ (i.e., 0-20 Hz). In this review, we explore and integrate these task-, muscle- and frequency-related differences in the vestibular system's contribution to posture, and propose that the human nervous system has adapted vestibular signals to match the mechanical properties of the system that each group of muscles controls.

  18. Neural correlates of visuospatial working memory in attention-deficit/hyperactivity disorder and healthy controls.

    Science.gov (United States)

    van Ewijk, Hanneke; Weeda, Wouter D; Heslenfeld, Dirk J; Luman, Marjolein; Hartman, Catharina A; Hoekstra, Pieter J; Faraone, Stephen V; Franke, Barbara; Buitelaar, Jan K; Oosterlaan, Jaap

    2015-08-30

    Impaired visuospatial working memory (VSWM) is suggested to be a core neurocognitive deficit in attention-deficit/hyperactivity disorder (ADHD), yet the underlying neural activation patterns are poorly understood. Furthermore, it is unclear to what extent age and gender effects may play a role in VSWM-related brain abnormalities in ADHD. Functional magnetic resonance imaging (fMRI) data were collected from 109 individuals with ADHD (60% male) and 103 controls (53% male), aged 8-25 years, during a spatial span working memory task. VSWM-related brain activation was found in a widespread network, which was more widespread compared with N-back tasks used in the previous literature. Higher brain activation was associated with higher age and male gender. In comparison with controls, individuals with ADHD showed greater activation in the left inferior frontal gyrus (IFG) and the lateral frontal pole during memory load increase, effects explained by reduced activation on the low memory load in the IFG pars triangularis and increased activation during high load in the IFG pars opercularis. Age and gender effects did not differ between controls and individuals with ADHD. Results indicate that individuals with ADHD have difficulty in efficiently and sufficiently recruiting left inferior frontal brain regions with increasing task difficulty. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  19. Training Working Memory in Childhood Enhances Coupling between Frontoparietal Control Network and Task-Related Regions.

    Science.gov (United States)

    Barnes, Jessica J; Nobre, Anna Christina; Woolrich, Mark W; Baker, Kate; Astle, Duncan E

    2016-08-24

    Working memory is a capacity upon which many everyday tasks depend and which constrains a child's educational progress. We show that a child's working memory can be significantly enhanced by intensive computer-based training, relative to a placebo control intervention, in terms of both standardized assessments of working memory and performance on a working memory task performed in a magnetoencephalography scanner. Neurophysiologically, we identified significantly increased cross-frequency phase amplitude coupling in children who completed training. Following training, the coupling between the upper alpha rhythm (at 16 Hz), recorded in superior frontal and parietal cortex, became significantly coupled with high gamma activity (at ∼90 Hz) in inferior temporal cortex. This altered neural network activity associated with cognitive skill enhancement is consistent with a framework in which slower cortical rhythms enable the dynamic regulation of higher-frequency oscillatory activity related to task-related cognitive processes. Whether we can enhance cognitive abilities through intensive training is one of the most controversial topics of cognitive psychology in recent years. This is particularly controversial in childhood, where aspects of cognition, such as working memory, are closely related to school success and are implicated in numerous developmental disorders. We provide the first neurophysiological account of how working memory training may enhance ability in childhood, using a brain recording technique called magnetoencephalography. We borrowed an analysis approach previously used with intracranial recordings in adults, or more typically in other animal models, called "phase amplitude coupling." Copyright © 2016 Barnes et al.

  20. Qualitative differences between bilingual language control and executive control: evidence from task switching

    Directory of Open Access Journals (Sweden)

    Marco eCalabria

    2012-01-01

    Full Text Available Previous research has shown that highly-proficient bilinguals have comparable switch costs in both directions when they switch between languages (L1 and L2, the so called ‘symmetrical switch cost’ effect. Interestingly, the same symmetry is also present when they switch between L1 and a much weaker L3. These findings suggest that highly proficient bilinguals develop a language control system that seems to be insensitive to language proficiency. In the present study, we explore whether the pattern of symmetrical switch costs in language switching tasks generalizes to a non-linguistic switching task in the same group of highly-proficient bilinguals. The end goal of this is to assess whether bilingual language control (bLC can be considered as subsidiary to domain-general executive control (EC. We tested highly-proficient Catalan-Spanish bilinguals both in a linguistic switching task and in a non-linguistic switching task. In the linguistic task, participants named pictures in L1 and L2 (Experiment 1 or L3 (Experiment 2 depending on a cue presented with the picture (a flag. In the non-linguistic task, the same participants had to switch between two card sorting rule-sets (colour and shape. Overall, participants showed symmetrical switch costs in the linguistic switching task, but not in the non-linguistic switching task. In a further analysis, we observed that in the linguistic switching task the asymmetry of the switch costs changed across blocks, while in the non-linguistic switching task an asymmetrical switch cost was observed throughout the task. The observation of different patterns of switch costs in the linguistic and the non-linguistic switching tasks suggest that the bLC system is not completely subsidiary to the domain-general EC system.

  1. Qualitative Differences between Bilingual Language Control and Executive Control: Evidence from Task-Switching.

    Science.gov (United States)

    Calabria, Marco; Hernández, Mireia; Branzi, Francesca M; Costa, Albert

    2011-01-01

    Previous research has shown that highly proficient bilinguals have comparable switch costs in both directions when they switch between languages (L1 and L2), the so-called "symmetrical switch cost" effect. Interestingly, the same symmetry is also present when they switch between L1 and a much weaker L3. These findings suggest that highly proficient bilinguals develop a language control system that seems to be insensitive to language proficiency. In the present study, we explore whether the pattern of symmetrical switch costs in language switching tasks generalizes to a non-linguistic switching task in the same group of highly proficient bilinguals. The end goal of this is to assess whether bilingual language control (bLC) can be considered as subsidiary to domain-general executive control (EC). We tested highly proficient Catalan-Spanish bilinguals both in a linguistic switching task and in a non-linguistic switching task. In the linguistic task, participants named pictures in L1 and L2 (Experiment 1) or L3 (Experiment 2) depending on a cue presented with the picture (a flag). In the non-linguistic task, the same participants had to switch between two card sorting rule-sets (color and shape). Overall, participants showed symmetrical switch costs in the linguistic switching task, but not in the non-linguistic switching task. In a further analysis, we observed that in the linguistic switching task the asymmetry of the switch costs changed across blocks, while in the non-linguistic switching task an asymmetrical switch cost was observed throughout the task. The observation of different patterns of switch costs in the linguistic and the non-linguistic switching tasks suggest that the bLC system is not completely subsidiary to the domain-general EC system.

  2. Point-and-click cursor control with an intracortical neural interface system by humans with tetraplegia.

    Science.gov (United States)

    Kim, Sung-Phil; Simeral, John D; Hochberg, Leigh R; Donoghue, John P; Friehs, Gerhard M; Black, Michael J

    2011-04-01

    We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (one participant). This study suggests that signals from a small ensemble of motor cortical neurons (∼40) can be used for natural point-and-click 2-D cursor control of a personal computer.

  3. Neural basis of superior performance of action videogame players in an attention-demanding task

    National Research Council Canada - National Science Library

    Mishra, Jyoti; Zinni, Marla; Bavelier, Daphne; Hillyard, Steven A

    2011-01-01

    ...) and from non-videogame players (NVGPs) during an attention-demanding task. Participants were presented with a multi-stimulus display consisting of rapid sequences of alphanumeric stimuli presented at rates...

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

    Science.gov (United States)

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

    2016-12-01

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

  5. The neural architecture of age-related dual-task interferences.

    Science.gov (United States)

    Chmielewski, Witold X; Yildiz, Ali; Beste, Christian

    2014-01-01

    In daily life elderly adults exhibit deficits when dual-tasking is involved. So far these deficits have been verified on a behavioral level in dual-tasking. Yet, the neuronal architecture of these deficits in aging still remains to be explored especially when late-middle aged individuals around 60 years of age are concerned. Neuroimaging studies in young participants concerning dual-tasking were, among others, related to activity in middle frontal (MFG) and superior frontal gyrus (SFG) and the anterior insula (AI). According to the frontal lobe hypothesis of aging, alterations in these frontal regions (i.e., SFG and MFG) might be responsible for cognitive deficits. We measured brain activity using fMRI, while examining age-dependent variations in dual-tasking by utilizing the PRP (psychological refractory period) test. Behavioral data showed an increasing PRP effect in late-middle aged adults. The results suggest the age-related deteriorated performance in dual-tasking, especially in conditions of risen complexity. These effects are related to changes in networks involving the AI, the SFG and the MFG. The results suggest that different cognitive subprocesses are affected that mediate the observed dual-tasking problems in late-middle aged individuals.

  6. Task-dependent neural and behavioral effects of verb argument structure features.

    Science.gov (United States)

    Malyutina, Svetlana; den Ouden, Dirk-Bart

    2017-05-01

    Understanding which verb argument structure (VAS) features (if any) are part of verbs' lexical entries and under which conditions they are accessed provides information on the nature of lexical representations and sentence construction. We investigated neural and behavioral effects of three understudied VAS characteristics (number of subcategorization options, number of thematic options and overall number of valency frames) in lexical decision and sentence well-formedness judgment in healthy adults. VAS effects showed strong dependency on processing conditions. As reflected by behavioral performance and neural recruitment patterns, increased VAS complexity in terms of subcategorization options and thematic options had a detrimental effect on sentence processing, but facilitated lexical access to single words, possibly by providing more lexico-semantic associations and access routes (facilitation through complexity). Effects of the number of valency frames are equivocal. We suggest that VAS effects may be mediated semantically rather than by a dedicated VAS module in verbs' representations. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Short-term Music Training Enhances Complex, Distributed Neural Communication during Music and Linguistic Tasks

    OpenAIRE

    Carpentier, Sarah M.; Moreno, Sylvain; McIntosh, Anthony R.

    2016-01-01

    Musical training is frequently associated with benefits to linguistic abilities, and recent focus has been placed on possible benefits of bilingualism to lifelong executive functions; however, the neural mechanisms for such effects are unclear. The aim of this study was to gain better understanding of the whole-brain functional effects of music and second-language training that could support such previously observed cognitive transfer effects. We conducted a 28-day longitudi...

  8. Rethinking volitional control over task choice in multitask environments: use of a stimulus set selection strategy in voluntary task switching.

    Science.gov (United States)

    Arrington, Catherine M; Weaver, Starla M

    2015-01-01

    Under conditions of volitional control in multitask environments, subjects may engage in a variety of strategies to guide task selection. The current research examines whether subjects may sometimes use a top-down control strategy of selecting a task-irrelevant stimulus dimension, such as location, to guide task selection. We term this approach a stimulus set selection strategy. Using a voluntary task switching procedure, subjects voluntarily switched between categorizing letter and number stimuli that appeared in two, four, or eight possible target locations. Effects of stimulus availability, manipulated by varying the stimulus onset asynchrony between the two target stimuli, and location repetition were analysed to assess the use of a stimulus set selection strategy. Considered across position condition, Experiment 1 showed effects of both stimulus availability and location repetition on task choice suggesting that only in the 2-position condition, where selection based on location always results in a target at the selected location, subjects may have been using a stimulus set selection strategy on some trials. Experiment 2 replicated and extended these findings in a visually more cluttered environment. These results indicate that, contrary to current models of task selection in voluntary task switching, the top-down control of task selection may occur in the absence of the formation of an intention to perform a particular task.

  9. Age-related shift in neural complexity related to task performance and physical activity.

    Science.gov (United States)

    Heisz, Jennifer J; Gould, Michelle; McIntosh, Anthony R

    2015-03-01

    The human brain undergoes marked structural changes with age including cortical thinning and reduced connectivity because of the degradation of myelin. Although these changes can compromise cognitive function, the brain is able to functionally reorganize to compensate for some of this structural loss. However, there are interesting individual differences in outcome: When comparing individuals of similar age, those who engage in regular physical activity are less affected by the typical age-related decline in cognitive function. This study used multiscale entropy to reveal a shift in the way the brain processes information in older adults that is related to physical activity. Specifically, older adults who were more physically active engaged in more local neural information processing. Interestingly, this shift toward local information processing was also associated with improved executive function performance in older adults, suggesting that physical activity may help to improve aspects of cognitive function in older adults by biasing the neural system toward local information processing. In the face of age-related structural decline, the neural plasticity that is enhanced through physical activity may help older adults maintain cognitive health longer into their lifespan.

  10. Human-computer interaction in distributed supervisory control tasks

    Science.gov (United States)

    Mitchell, Christine M.

    1989-01-01

    An overview of activities concerned with the development and applications of the Operator Function Model (OFM) is presented. The OFM is a mathematical tool to represent operator interaction with predominantly automated space ground control systems. The design and assessment of an intelligent operator aid (OFMspert and Ally) is particularly discussed. The application of OFM to represent the task knowledge in the design of intelligent tutoring systems, designated OFMTutor and ITSSO (Intelligent Tutoring System for Satellite Operators), is also described. Viewgraphs from symposia presentations are compiled along with papers addressing the intent inferencing capabilities of OFMspert, the OFMTutor system, and an overview of intelligent tutoring systems and the implications for complex dynamic systems.

  11. Analog neural network control method proposed for use in a backup satellite control mode

    Energy Technology Data Exchange (ETDEWEB)

    Frigo, J.R.; Tilden, M.W.

    1998-03-01

    The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a model of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.

  12. Neural network output feedback control of robot formations.

    Science.gov (United States)

    Dierks, Travis; Jagannathan, Sarangapani

    2010-04-01

    In this paper, a combined kinematic/torque output feedback control law is developed for leader-follower-based formation control using backstepping to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers. A neural network (NN) is introduced to approximate the dynamics of the follower and its leader using online weight tuning. Furthermore, a novel NN observer is designed to estimate the linear and angular velocities of both the follower robot and its leader. It is shown, by using the Lyapunov theory, that the errors for the entire formation are uniformly ultimately bounded while relaxing the separation principle. In addition, the stability of the formation in the presence of obstacles, is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation are prevented. Numerical results are provided to verify the theoretical conjectures.

  13. Immediate Neural Plasticity Involving Reaction Time in a Saccadic Eye Movement Task is Intact in Children With Fetal Alcohol Spectrum Disorder.

    Science.gov (United States)

    Paolozza, Angelina; Munoz, Douglas P; Brien, Donald; Reynolds, James N

    2016-11-01

    Saccades are rapid eye movements that bring an image of interest onto the retina. Previous research has found that in healthy individuals performing eye movement tasks, the location of a previous visual target can influence performance of the saccade on the next trial. This rapid behavioral adaptation represents a form of immediate neural plasticity within the saccadic circuitry. Our studies have shown that children with fetal alcohol spectrum disorder (FASD) are impaired on multiple saccade measures. We therefore investigated these previous trial effects in typically developing children and children with FASD to measure sensory neural plasticity and how these effects vary with age and pathology. Both typically developing control children (n = 102; mean age = 10.54 ± 3.25; 48 males) and children with FASD (n = 66; mean age = 11.85 ± 3.42; 35 males) were recruited from 5 sites across Canada. Each child performed a visually guided saccade task. Reaction time and saccade amplitude were analyzed and then assessed based on the previous trial. There was a robust previous trial effect for both reaction time and amplitude, with both the control and FASD groups displaying faster reaction times and smaller saccades during alternation trials (visual target presented on the opposite side to the previous trial). Children with FASD exhibited smaller overall mean amplitude and smaller amplitude selectively on alternation trials compared with controls. The effect of the previous trial on reaction time and amplitude did not differ across childhood and adolescent development. Children with FASD did not display any significant reaction time differences, despite exhibiting numerous deficits in motor and higher level cognitive control over saccades in other studies. These results suggest that this form of immediate neural plasticity in response to sensory information before saccade initiation remains intact in children with FASD. In contrast, the previous trial effect on

  14. Strongly pressed by the task of population control.

    Science.gov (United States)

    Wei, W

    1987-08-01

    Today, the world population grows at an annual rate of over 80 million. The activities of "the Day of the 5 Billion" sponsored by the United Nations sounds alarm to the world: It is an emerging task to strictly control human reproduction. China, being a developing country, knows only too well the difficulties that over-rapid population growth brings upon economic and social development. Population control is a pressing task. China would like to make the nation prosperous by quadrupling the gross national product (GNP) to US$800 or US$1000 per capita, thus raising the people's living standard to the level of being well-off at the end of the century. The GNP would be quadrupled again to US$4000 per capita with the standard of living raised by the mid-21st century. In order to realize strategic goals, China must strive to control the total population of China at about 1.2 billion at the turn of the century, leaving a better population structure for the next century. At present, China has a population of 1.057 billion and is faced with a new baby boom. It is hoped that under the leadership of the Party's Central Committee and the State Council, governments at all levels, and the people of all nationalities will do a better job in population control by fulfilling the population plan for this year so as to lay down a good foundation for enforcing the plan during the 7th 5-year plan. Meanwhile, China will continue to make new contributions to the stabilization of the world's population together with the UNFPA and other international bodies and friendly countries who support China's population control policy.

  15. A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control.

    Science.gov (United States)

    Donnarumma, Francesco; Prevete, Roberto; Chersi, Fabian; Pezzulo, Giovanni

    2015-09-01

    There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies.

  16. Mental fatigue and task control : Planning and preparation

    NARCIS (Netherlands)

    Lorist, MM; Klein, Martin; Nieuwenhuis, S; De Jong, R; Mulder, G; Meijman, TF

    The effects of mental fatigue on planning and preparation for future actions were examined, using a task switching paradigm. Fatigue was induced by "time on task," with subjects performing a switch task continuously for 2 hr. Subjects had to alternate between tasks on every second trial, so that a

  17. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    Science.gov (United States)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  18. Control processes in voluntary and explicitly cued task switching.

    Science.gov (United States)

    Masson, Michael E J; Carruthers, Sarah

    2014-10-01

    Explicitly cued task switching slows performance relative to performing the same task on consecutive trials. This effect appears to be due partly to more efficient encoding of the task cue when the same cue is used on consecutive trials and partly to an additional task-switching process. These components were examined by comparing explicitly cued and voluntary task switching groups, with external cues presented to both groups. Cue-switch effects varied in predictable ways to dissociate explicitly cued and voluntary task switching, whereas task-switch effects had similar characteristics for both instructional groups. The data were well fitted by a mathematical model of task switching that included a cue-encoding mechanism (whereby cue repetition improves performance) and an additional process that was invoked on task-switch trials. Analyses of response-time distributions suggest that this additional process involves task-set reconfiguration that may or may not be engaged before the target stimulus is presented.

  19. Control of overtime: a department manager's never-ending task.

    Science.gov (United States)

    McConnell, C R

    2001-03-01

    Overtime is an organizational necessity that is misused or abused as often as it is applied appropriately. As an element of labor cost it is sometimes seen as an extra expense to be avoided by all means, yet there are times when overtime is the best response to a particular need. The determination and payment of overtime are dictated by provisions of the Fair Labor Standards Act (FLSA), the federal wage and hour legislation that governs a number of aspects of employment. Overtime is often taken for granted, and it can readily go out of control. It is the task of the department manager to maintain control of overtime, adhering to a budget and taking active steps to ensure that only essential overtime is approved and worked.

  20. Geometrical approach to neural net control of movements and posture

    Science.gov (United States)

    Pellionisz, A. J.; Ramos, C. F.

    1993-01-01

    In one approach to modeling brain function, sensorimotor integration is described as geometrical mapping among coordinates of non-orthogonal frames that are intrinsic to the system; in such a case sensors represent (covariant) afferents and motor effectors represent (contravariant) motor efferents. The neuronal networks that perform such a function are viewed as general tensor transformations among different expressions and metric tensors determining the geometry of neural functional spaces. Although the non-orthogonality of a coordinate system does not impose a specific geometry on the space, this "Tensor Network Theory of brain function" allows for the possibility that the geometry is non-Euclidean. It is suggested that investigation of the non-Euclidean nature of the geometry is the key to understanding brain function and to interpreting neuronal network function. This paper outlines three contemporary applications of such a theoretical modeling approach. The first is the analysis and interpretation of multi-electrode recordings. The internal geometries of neural networks controlling external behavior of the skeletomuscle system is experimentally determinable using such multi-unit recordings. The second application of this geometrical approach to brain theory is modeling the control of posture and movement. A preliminary simulation study has been conducted with the aim of understanding the control of balance in a standing human. The model appears to unify postural control strategies that have previously been considered to be independent of each other. Third, this paper emphasizes the importance of the geometrical approach for the design and fabrication of neurocomputers that could be used in functional neuromuscular stimulation (FNS) for replacing lost motor control.

  1. Web based educational tool for neural network robot control

    Directory of Open Access Journals (Sweden)

    Jure Čas

    2007-05-01

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

  2. Neural adaptations associated with interlimb transfer in a ballistic wrist flexion task

    NARCIS (Netherlands)

    Ruddy, Kathy L.; Rudolf, Anne K.; Kalkman, Barbara; King, Maedbh; Daffertshofer, Andreas; Carroll, Timothy J.; Carson, Richard G.

    2016-01-01

    Cross education is the process whereby training of one limb gives rise to increases in the subsequent performance of its opposite counterpart. The execution of many unilateral tasks is associated with increased excitability of corticospinal projections from primary motor cortex (M1) to the opposite

  3. Isolating the Neural Mechanisms of Interference During Continuous Multisensory Dual-task Performance

    Science.gov (United States)

    2014-01-01

    waveforms. Psychophysiology, 39, 236–245. Strayer, D. L., Drews, F. A., & Crouch, D. J. (2006). A comparison of the cell phone driver and the drunk driver...related potentials and secondary task performance during simulated driving. Accident Analysis & Prevention, 40, 1–7. Woldorff, M. G., Gallen, C. C

  4. When the brain simulates stopping: Neural activity recorded during real and imagined stop-signal tasks.

    Science.gov (United States)

    González-Villar, Alberto J; Bonilla, F Mauricio; Carrillo-de-la-Peña, María T

    2016-10-01

    It has been suggested that mental rehearsal activates brain areas similar to those activated by real performance. Although inhibition is a key function of human behavior, there are no previous reports of brain activity during imagined response cancellation. We analyzed event-related potentials (ERPs) and time-frequency data associated with motor execution and inhibition during real and imagined performance of a stop-signal task. The ERPs characteristic of stop trials-that is, the stop-N2 and stop-P3-were also observed during covert performance of the task. Imagined stop (IS) trials yielded smaller stop-N2 amplitudes than did successful stop (SS) and unsuccessful stop (US) trials, but midfrontal theta power similar to that in SS trials. The stop-P3 amplitude for IS was intermediate between those observed for SS and US. The results may be explained by the absence of error-processing and correction processes during imagined performance. For go trials, real execution was associated with higher mu and beta desynchronization over motor areas, which confirms previous reports of lower motor activation during imagined execution and also with larger P3b amplitudes, probably indicating increased top-down attention to the real task. The similar patterns of activity observed for imagined and real performance suggest that imagination tasks may be useful for training inhibitory processes. Nevertheless, brain activation was generally weaker during mental rehearsal, probably as a result of the reduced engagement of top-down mechanisms and limited error processing.

  5. Adaptive Neural Control for a Class of Outputs Time-Delay Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Ruliang Wang

    2012-01-01

    Full Text Available This paper considers an adaptive neural control for a class of outputs time-delay nonlinear systems with perturbed or no. Based on RBF neural networks, the radius basis function (RBF neural networks is employed to estimate the unknown continuous functions. The proposed control guarantees that all closed-loop signals remain bounded. The simulation results demonstrate the effectiveness of the proposed control scheme.

  6. Individual variation in the neural processes of motor decisions in the stop signal task: the influence of novelty seeking and harm avoidance personality traits.

    Science.gov (United States)

    Hu, Jianping; Lee, Dianne; Hu, Sien; Zhang, Sheng; Chao, Herta; Li, Chiang-Shan R

    2016-06-01

    Personality traits contribute to variation in human behavior, including the propensity to take risk. Extant work targeted risk-taking processes with an explicit manipulation of reward, but it remains unclear whether personality traits influence simple decisions such as speeded versus delayed responses during cognitive control. We explored this issue in an fMRI study of the stop signal task, in which participants varied in response time trial by trial, speeding up and risking a stop error or slowing down to avoid errors. Regional brain activations to speeded versus delayed motor responses (risk-taking) were correlated to novelty seeking (NS), harm avoidance (HA) and reward dependence (RD), with age and gender as covariates, in a whole brain regression. At a corrected threshold, the results showed a positive correlation between NS and risk-taking responses in the dorsomedial prefrontal, bilateral orbitofrontal, and frontopolar cortex, and between HA and risk-taking responses in the parahippocampal gyrus and putamen. No regional activations varied with RD. These findings demonstrate that personality traits influence the neural processes of executive control beyond behavioral tasks that involve explicit monetary reward. The results also speak broadly to the importance of characterizing inter-subject variation in studies of cognition and brain functions.

  7. Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

    Science.gov (United States)

    Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

    2014-03-15

    The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. Output feedback control of a quadrotor UAV using neural networks.

    Science.gov (United States)

    Dierks, Travis; Jagannathan, Sarangapani

    2010-01-01

    In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

  9. Central neural control of thermoregulation and brown adipose tissue.

    Science.gov (United States)

    Morrison, Shaun F

    2016-04-01

    Central neural circuits orchestrate the homeostatic repertoire that maintains body temperature during environmental temperature challenges and alters body temperature during the inflammatory response. This review summarizes the experimental underpinnings of our current model of the CNS pathways controlling the principal thermoeffectors for body temperature regulation: cutaneous vasoconstriction controlling heat loss, and shivering and brown adipose tissue for thermogenesis. The activation of these effectors is regulated by parallel but distinct, effector-specific, core efferent pathways within the CNS that share a common peripheral thermal sensory input. Via the lateral parabrachial nucleus, skin thermal afferent input reaches the hypothalamic preoptic area to inhibit warm-sensitive, inhibitory output neurons which control heat production by inhibiting thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to thermogenesis-controlling premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation of spinal circuits necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus sympathetic premotor neurons controlling cutaneous vasoconstriction. The model proposed for central thermoregulatory control provides a useful platform for further understanding of the functional organization of central thermoregulation and elucidating the hypothalamic circuitry and neurotransmitters involved in body temperature regulation. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

    Science.gov (United States)

    Torghabeh, A. A.; Tousi, A. M.

    2007-08-01

    This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

  11. Neural network based adaptive control for nonlinear dynamic regimes

    Science.gov (United States)

    Shin, Yoonghyun

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

  12. Parametric motion control of robotic arms: A biologically based approach using neural networks

    Science.gov (United States)

    Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

    1993-01-01

    A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

  13. Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control.

    Science.gov (United States)

    Zhu, Jiaming; Cao, Zhiqiang; Zhang, Tianping; Yang, Yuequan; Yi, Yang

    2017-06-20

    In this brief, sufficient conditions are proposed for the existence of the compact sets in the neural network controls. First, we point out that the existence of the compact set in a classical neural network control scheme is unsolved and its result is incomplete. Next, as a simple case, we derive the sufficient condition of the existence of the compact set for the neural network control of first-order systems. Finally, we propose the sufficient condition of the existence of the compact set for the neural-network-based backstepping control of high-order nonlinear systems. The theoretic result is illustrated through a simulation example.

  14. Differences in neural activation between preterm and full term born adolescents on a sentence comprehension task: implications for educational accommodations.

    Science.gov (United States)

    Barde, Laura H F; Yeatman, Jason D; Lee, Eliana S; Glover, Gary; Feldman, Heidi M

    2012-02-15

    Adolescent survivors of preterm birth experience persistent functional problems that negatively impact academic outcomes, even when standardized measures of cognition and language suggest normal ability. In this fMRI study, we compared the neural activation supporting auditory sentence comprehension in two groups of adolescents (ages 9-16 years); sentences varied in length and syntactic difficulty. Preterms (n=18, mean gestational age 28.8 weeks) and full terms (n=14) had scores on verbal IQ, receptive vocabulary, and receptive language tests that were within or above normal limits and similar between groups. In early and late phases of the trial, we found interactions by group and length; in the late phase, we also found a group by syntactic difficulty interaction. Post hoc tests revealed that preterms demonstrated significant activation in the left and right middle frontal gyri as syntactic difficulty increased. ANCOVA showed that the interactions could not be attributed to differences in age, receptive language skill, or reaction time. Results are consistent with the hypothesis that preterm birth modulates brain-behavior relations in sentence comprehension as task demands increase. We suggest preterms' differences in neural processing may indicate a need for educational accommodations, even when formal test scores indicate normal academic achievement. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Effects of aripiprazole and haloperidol on neural activation during a simple motor task in healthy individuals: A functional MRI study.

    Science.gov (United States)

    Goozee, Rhianna; O'Daly, Owen; Handley, Rowena; Reis Marques, Tiago; Taylor, Heather; McQueen, Grant; Hubbard, Kathryn; Pariante, Carmine; Mondelli, Valeria; Reinders, Antje A T S; Dazzan, Paola

    2017-04-01

    The dopaminergic system plays a key role in motor function and motor abnormalities have been shown to be a specific feature of psychosis. Due to their dopaminergic action, antipsychotic drugs may be expected to modulate motor function, but the precise effects of these drugs on motor function remain unclear. We carried out a within-subject, double-blind, randomized study of the effects of aripiprazole, haloperidol and placebo on motor function in 20 healthy men. For each condition, motor performance on an auditory-paced task was investigated. We entered maps of neural activation into a random effects general linear regression model to investigate motor function main effects. Whole-brain imaging revealed a significant treatment effect in a distributed network encompassing posterior orbitofrontal/anterior insula cortices, and the inferior temporal and postcentral gyri. Post-hoc comparison of treatments showed neural activation after aripiprazole did not differ significantly from placebo in either voxel-wise or region of interest analyses, with the results above driven primarily by haloperidol. We also observed a simple main effect of haloperidol compared with placebo, with increased task-related recruitment of posterior cingulate and precentral gyri. Furthermore, region of interest analyses revealed greater activation following haloperidol compared with placebo in the precentral and post-central gyri, and the putamen. These diverse modifications in cortical motor activation may relate to the different pharmacological profiles of haloperidol and aripiprazole, although the specific mechanisms underlying these differences remain unclear. Evaluating healthy individuals can allow investigation of the effects of different antipsychotics on cortical activation, independently of either disease-related pathology or previous treatment. Hum Brain Mapp 38:1833-1845, 2017. © 2017 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  16. SECONDARY VOLTAGE CONTROL BASED ON ADAPTIVE NEURAL PI CONTROLLERS

    Directory of Open Access Journals (Sweden)

    RUBEN TAPIA

    2010-01-01

    Full Text Available Este trabajo tiene como objetivo presentar el desempeño de un controlador basado en redes neuronales Bspline que regula el aporte de potencia reactiva de las máquinas síncronas. Debido a que los sistemas de potencia operan con parámetros no estacionarios y configuración cambiante, es preferible utilizar esquemas de control adaptativos. La tecnología de control debe asegurar su desempeño en términos de condiciones operativas prácticas de los sistemas de potencia, que considere la diversidad de cargas conectadas a la red y maximice la disponibilidad de recursos. La red neuronal Bspline es una herramienta conveniente para implementar el control adaptativo de voltaje, con la posibilidad de llevar a cabo ésta tarea enlínea considerando las no linealidades del sistema. El despacho de potencia reactiva se basa en la premisa de que cada máquina debe aportar en proporción a su capacidad nominal de operación. La aplicabilidad de la propuesta se demuestra mediante simulación en un sistema de potencia multimáquinas.

  17. Control of neural stem cell survival by electroactive polymer substrates.

    Directory of Open Access Journals (Sweden)

    Vanessa Lundin

    Full Text Available Stem cell function is regulated by intrinsic as well as microenvironmental factors, including chemical and mechanical signals. Conducting polymer-based cell culture substrates provide a powerful tool to control both chemical and physical stimuli sensed by stem cells. Here we show that polypyrrole (PPy, a commonly used conducting polymer, can be tailored to modulate survival and maintenance of rat fetal neural stem cells (NSCs. NSCs cultured on PPy substrates containing different counter ions, dodecylbenzenesulfonate (DBS, tosylate (TsO, perchlorate (ClO(4 and chloride (Cl, showed a distinct correlation between PPy counter ion and cell viability. Specifically, NSC viability was high on PPy(DBS but low on PPy containing TsO, ClO(4 and Cl. On PPy(DBS, NSC proliferation and differentiation was comparable to standard NSC culture on tissue culture polystyrene. Electrical reduction of PPy(DBS created a switch for neural stem cell viability, with widespread cell death upon polymer reduction. Coating the PPy(DBS films with a gel layer composed of a basement membrane matrix efficiently prevented loss of cell viability upon polymer reduction. Here we have defined conditions for the biocompatibility of PPy substrates with NSC culture, critical for the development of devices based on conducting polymers interfacing with NSCs.

  18. Neural control of daily and seasonal timing of songbird migration.

    Science.gov (United States)

    Stevenson, Tyler J; Kumar, Vinod

    2017-07-01

    Bird migration is one of most salient annual events in nature. It involves predictable seasonal movements between breeding and non-breeding habitats. Both circadian and circannual clocks are entrained by photoperiodic cues and time daily and seasonal changes in migratory physiology and behavior. This mini-review provides an update on daily and seasonal rhythms of migratory behavior, and examines the neuroendocrine and molecular pathways involved in the timing of migration in songbirds. Recent findings have identified key neural substrates, and suggest the involvement of multiple neuroendocrine regulatory systems in controlling seasonal states in migrants. We propose that four distinct neural substrates are involved in the timing of migration and include (1) pineal gland and suprachiasmatic nucleus (mSCN); (2) a cluster of hypothalamic nuclei, the mediobasal hypothalamus (MBH); (3) dorsomedial hypothalamic nucleus (DMH); and (4) tanycytes along ependymal layer of the 3rd ventricle (3V). Cluster N, a nucleus in the telencephalon involved in the integration of geomagnetic cues, likely maintains functional connectivity with brain regions involved in timing songbird migration. These nuclei form an interconnected network that coordinates daily timing (pineal gland/mSCN), annual photoperiodic response (MBH, 3V), energetic state (MBH, DMH, 3V), and magnetic compass information (i.e., cluster N) for migration in songbirds.

  19. Neural contributions to the motivational control of appetite in humans.

    Science.gov (United States)

    Hinton, Elanor C; Parkinson, John A; Holland, Anthony J; Arana, F Sergio; Roberts, Angela C; Owen, Adrian M

    2004-09-01

    The motivation to eat in humans is a complex process influenced by intrinsic mechanisms relating to the hunger and satiety cascade, and extrinsic mechanisms based on the appetitive incentive value of individual foods, which can themselves induce desire. This study was designed to investigate the neural basis of these two factors contributing to the control of motivation to eat within the same experimental design using positron emission tomography. Using a novel counterbalanced approach, participants were scanned in two separate sessions, once after fasting and once after food intake, in which they imagined themselves in a restaurant and considered a number of items on a menu, and were asked to choose their most preferred. All items were tailored to each individual and varied in their incentive value. No actual foods were presented. In response to a hungry state, increased activation was shown in the hypothalamus, amygdala and insula cortex as predicted, as well as the medulla, striatum and anterior cingulate cortex. Satiety, in contrast, was associated with increased activation in the lateral orbitofrontal and temporal cortex. Only activity in the vicinity of the amygdala and orbitofrontal cortex was observed in response to the processing of extrinsic appetitive incentive information. These results suggest that the contributions of intrinsic homeostatic influences, and extrinsic incentive factors to the motivation to eat, are somewhat dissociable neurally, with areas of convergence in the amygdala and orbitofrontal cortex. The findings of this study have implications for research into the underlying mechanisms of eating disorders.

  20. Learning sequential control in a Neural Blackboard Architecture for in situ concept reasoning

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; Besold, Tarek R.; Lamb, Luis; Serafini, Luciano; Tabor, Whitney

    2016-01-01

    Simulations are presented and discussed of learning sequential control in a Neural Blackboard Architecture (NBA) for in situ concept-based reasoning. Sequential control is learned in a reservoir network, consisting of columns with neural circuits. This allows the reservoir to control the dynamics of

  1. Neural Control of Rising and Falling Tones in Mandarin Speakers Who Stutter

    Science.gov (United States)

    Howell, Peter; Jiang, Jing; Peng, Danling; Lu, Chunming

    2012-01-01

    Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. "Brain and Language, 121"(1), 35-46]. Nine PWS and nine controls were scanned. Functional…

  2. Control System Design for Cylindrical Tank Process Using Neural Model Predictive Control Technique

    Directory of Open Access Journals (Sweden)

    M. Sridevi

    2010-10-01

    Full Text Available Chemical manufacturing and process industry requires innovative technologies for process identification. This paper deals with model identification and control of cylindrical process. Model identification of the process was done using ARMAX technique. A neural model predictive controller was designed for the identified model. The performance of the controllers was evaluated using MATLAB software. The performance of NMPC controller was compared with Smith Predictor controller and IMC controller based on rise time, settling time, overshoot and ISE and it was found that the NMPC controller is better suited for this process.

  3. Neurophysiological capacity in a working memory task differentiates dependent from nondependent heavy drinkers and controls.

    Science.gov (United States)

    Wesley, Michael J; Lile, Joshua A; Fillmore, Mark T; Porrino, Linda J

    2017-06-01

    Determining the neurobehavioral profiles that differentiate heavy drinkers who are and are not alcohol dependent will inform treatment efforts. Working memory is linked to substance use disorders and can serve as a representation of the demand placed on the neurophysiology associated with cognitive control. Behavior and brain activity (via fMRI) were recorded during an N-Back working memory task in controls (CTRL), nondependent heavy drinkers (A-ND) and dependent heavy drinkers (A-D). Typical and novel step-wise analyses examined profiles of working memory load and increasing task demand, respectively. Performance was significantly decreased in A-D during high working memory load (2-Back), compared to CTRL and A-ND. Analysis of brain activity during high load (0-Back vs. 2- Back) showed greater responses in the dorsal lateral and medial prefrontal cortices of A-D than CTRL, suggesting increased but failed compensation. The step-wise analysis revealed that the transition to Low Demand (0-Back to 1-Back) was associated with robust increases and decreases in cognitive control and default-mode brain regions, respectively, in A-D and A-ND but not CTRL. The transition to High Demand (1-Back to 2-Back) resulted in additional engagement of these networks in A-ND and CTRL, but not A-D. Heavy drinkers engaged working memory neural networks at lower demand than controls. As demand increased, nondependent heavy drinkers maintained control performance but relied on additional neurophysiological resources, and dependent heavy drinkers did not display further resource engagement and had poorer performance. These results support targeting these brain areas for treatment interventions. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. The neural substrates of musical memory revealed by fMRI and two semantic tasks.

    Science.gov (United States)

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

    2010-12-01

    Recognizing a musical excerpt without necessarily retrieving its title typically reflects the existence of a memory system dedicated to the retrieval of musical knowledge. The functional distinction between musical and verbal semantic memory has seldom been investigated. In this fMRI study, we directly compared the musical and verbal memory of 20 nonmusicians, using a congruence task involving automatic semantic retrieval and a familiarity task requiring more thorough semantic retrieval. In the former, participants had to access their semantic store to retrieve musical or verbal representations of melodies or expressions they heard, in order to decide whether these were then given the right ending or not. In the latter, they had to judge the level of familiarity of musical excerpts and expressions. Both tasks revealed activation of the left inferior frontal and posterior middle temporal cortices, suggesting that executive and selection processes are common to both verbal and musical retrievals. Distinct patterns of activation were observed within the left temporal cortex, with musical material mainly activating the superior temporal gyrus and verbal material the middle and inferior gyri. This cortical organization of musical and verbal semantic representations could explain clinical dissociations featuring selective disturbances for musical or verbal material. Copyright © 2010 Elsevier Inc. All rights reserved.

  5. Functional connectivity among spike trains in neural assemblies during rat working memory task.

    Science.gov (United States)

    Xie, Jiacun; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2014-11-01

    Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Differences in task demands influence the hemispheric lateralization and neural correlates of metaphor.

    Science.gov (United States)

    Yang, Fanpei Gloria; Edens, Jennifer; Simpson, Claire; Krawczyk, Daniel C

    2009-11-01

    This study investigated metaphor comprehension in the broader context of task-difference effects and manipulation of processing difficulty. We predicted that right hemisphere recruitment would show greater specificity to processing difficulty rather than metaphor comprehension. Previous metaphor processing studies have established that the left inferior frontal gyrus strongly correlates with metaphor comprehension but there has been controversy about whether right hemisphere (RH) involvement is specific for metaphor comprehension. Functional MRI data were recorded from healthy subjects who read novel metaphors, conventional metaphors, definition-like sentences, or literal sentences. We investigated metaphor processing in contexts where semantic judgment or imagery modulates linguistic judgment. Our findings support the position that the type of task rather than figurative language processing per se modulates the left inferior gyrus (LIFG). RH involvement was more influenced by processing difficulty and less by the novelty or figurativity of linguistic expressions. Our results suggest that figurative language processing depends upon the effects of task-type and processing difficulty on imaging results.

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

    Science.gov (United States)

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

    2017-09-04

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

  8. Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available This paper focuses on neural learning from adaptive neural control (ANC for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE condition for neural networks (NNs. Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

  9. Folic Acid Supplementation for the Prevention of Neural Tube Defects: An Updated Evidence Report and Systematic Review for the US Preventive Services Task Force.

    Science.gov (United States)

    Viswanathan, Meera; Treiman, Katherine A; Kish-Doto, Julia; Middleton, Jennifer C; Coker-Schwimmer, Emmanuel J L; Nicholson, Wanda K

    2017-01-10

    Neural tube defects are among the most common congenital anomalies in the United States. Periconceptional folic acid supplementation is a primary care-relevant preventive intervention. To review the evidence on folic acid supplementation for preventing neural tube defects to inform the US Preventive Services Task Force for an updated Recommendation Statement. MEDLINE, Cochrane Library, EMBASE, and trial registries through January 28, 2016, with ongoing surveillance through November 11, 2016; references; experts. English-language studies of folic acid supplementation in women. Excluded were poor-quality studies; studies of prepubertal girls, men, women without the potential for childbearing, and neural tube defect recurrence; and studies conducted in developing countries. Two investigators independently reviewed abstracts, full-text articles, and risk of bias of included studies. One investigator extracted data and a second checked accuracy. Because of heterogeneity, data were not pooled. Neural tube defects, harms of treatment (twinning, respiratory outcomes). A total of 24 studies (N > 58 860) were included. In 1 randomized clinical trial from Hungary initiated in 1984, incidence of neural tube defects for folic acid supplementation compared with trace element supplementation was 0% vs 0.25% (Peto odds ratio [OR], 0.13 [95% CI, 0.03-0.65]; n = 4862). Odds ratios from cohort studies recruiting participants between 1984 and 1996 demonstrated beneficial associations and ranged from 0.11 to 0.27 (n = 19 982). Three of 4 case-control studies with data from 1976 through 1998 reported ORs ranging from 0.6 to 0.7 (n > 7121). Evidence of benefit led to food fortification in the United States beginning in 1998, after which no new prospective studies have been conducted. More recent case-control studies drawing from data collected after 1998 have not demonstrated a protective association consistently with folic acid supplementation, with ORs ranging from

  10. Role Identification of Yaw and Sway Motion in Helicopter Yaw Control Tasks

    NARCIS (Netherlands)

    Ellerbroek, J.; Stroosma, O.; Van Paassen, M.M.; Mulder, M.

    2008-01-01

    A set of experiments has been conducted to investigate the relative effect of translational and rotational motion cues on pilot performance. Two helicopter yaw control tasks were performed on the SIMONA: a yaw capture task and a target-tracking task with simulated turbulence. The yaw capture task

  11. Context-Sensitive Adjustment of Cognitive Control in Dual-Task Performance

    Science.gov (United States)

    Fischer, Rico; Gottschalk, Caroline; Dreisbach, Gesine

    2014-01-01

    Performing 2 highly similar tasks at the same time requires an adaptive regulation of cognitive control to shield prioritized primary task processing from between-task (cross-talk) interference caused by secondary task processing. In the present study, the authors investigated how implicitly and explicitly delivered information promotes the…

  12. Neural control and precision of flight muscle activation in Drosophila.

    Science.gov (United States)

    Lehmann, Fritz-Olaf; Bartussek, Jan

    2017-01-01

    Precision of motor commands is highly relevant in a large context of various locomotor behaviors, including stabilization of body posture, heading control and directed escape responses. While posture stability and heading control in walking and swimming animals benefit from high friction via ground reaction forces and elevated viscosity of water, respectively, flying animals have to cope with comparatively little aerodynamic friction on body and wings. Although low frictional damping in flight is the key to the extraordinary aerial performance and agility of flying birds, bats and insects, it challenges these animals with extraordinary demands on sensory integration and motor precision. Our review focuses on the dynamic precision with which Drosophila activates its flight muscular system during maneuvering flight, considering relevant studies on neural and muscular mechanisms of thoracic propulsion. In particular, we tackle the precision with which flies adjust power output of asynchronous power muscles and synchronous flight control muscles by monitoring muscle calcium and spike timing within the stroke cycle. A substantial proportion of the review is engaged in the significance of visual and proprioceptive feedback loops for wing motion control including sensory integration at the cellular level. We highlight that sensory feedback is the basis for precise heading control and body stability in flies.

  13. ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing

    Science.gov (United States)

    Rusakov, Dmitri A.; Savtchenko, Leonid P.

    2017-01-01

    Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT). PMID:28362877

  14. Neural Mechanism of Cognitive Control Impairment in Patients with Hepatic Cirrhosis: A Functional Magnetic Resonance Imaging Study

    Energy Technology Data Exchange (ETDEWEB)

    Long Jiang Zhang; Guifen Yang; Jianzhong Yin; Yawu Liu; Ji Qi [Dept. of Radiology, Tianjin First Central Hospital of Tianjin Medical Univ, Tianjin (China)

    2007-07-15

    Background: Many studies have claimed the existence of attention alterations in cirrhotic patients without overt hepatic encephalopathy (HE). No functional magnetic resonance imaging (fMRI) study in this respect has been published. Purpose: To investigate the neural basis of cognitive control deficiency in cirrhotic patients using fMRI. Material and Methods: 14 patients with hepatic cirrhosis and 14 healthy volunteers were included in the study. A modified Stroop task with Chinese characters was used as the target stimulus, and block-design fMRI was used to acquire resource data, including four stimulus blocks and five control blocks, each presented alternatively. Image analysis was performed using statistical parametric mapping 99. After fMRI examinations were complete, behavior tests of Stroop interference were performed for all subjects. Overall reaction time and error numbers were recorded. Results: Both healthy volunteers and patients with hepatic cirrhosis had Stroop interference effects. Patients with hepatic cirrhosis had more errors and longer reaction time in performing an incongruous color-naming task than healthy volunteers (P<0.001); there was no significant difference in performing an incongruous word-reading task (P 0.066). Compared with controls, patients with hepatic cirrhosis had greater activation of the bilateral prefrontal cortex and parietal cortex when performing the incongruous word-reading task. With increased conflict, activation of the anterior cingulate cortex (ACC), bilateral prefrontal cortex (PFC), parietal lobe, and temporal fusiform gyrus (TFG) was decreased when patients with hepatic cirrhosis performed the incongruous color-naming task. Conclusion: This study demonstrates that patients with hepatic cirrhostic have cognitive control deficiency. The abnormal brain network of the ACC-PFC-parietal lobe-TFG is the neural basis of cognitive control impairment in cirrhotic patients.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-10-01

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

  16. Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

    Science.gov (United States)

    Vazquez, Luis A; Jurado, Francisco; Castaneda, Carlos E; Santibanez, Victor

    2018-02-01

    This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

  17. Neural and Hormonal Control of Postecdysial Behaviors in Insects

    Science.gov (United States)

    White, Benjamin H.; Ewer, John

    2016-01-01

    The shedding of the old exoskeleton that occurs in insects at the end of a molt (a process called ecdysis) is typically followed by the expansion and tanning of a new one. At the adult molt, these postecdysial processes include expanding and hardening the wings. Here we describe recent advances in understanding the neural and hormonal control of wing expansion and hardening, focusing on work done in Drosophila where genetic manipulations have permitted a detailed investigation of postecdysial processes and their modulation by sensory input. To place this work in context, we briefly review recent progress in understanding the neuroendocrine regulation of ecdysis, which appears to be largely conserved across insect species. Investigations into the neuroendocrine networks that regulate ecdysial and postecdysial behaviors, will provide insights into how stereotyped, yet environmentally-responsive, sequences are generated, as well as into how they develop and evolve. PMID:24160420

  18. What are task-sets: a single, integrated representation or a collection of multiple control representations?

    Directory of Open Access Journals (Sweden)

    Dragan eRangelov

    2013-09-01

    Full Text Available Performing two randomly alternating tasks typically results in higher reaction times (RTs following a task switch, relative to a task repetition. These task switch costs (TSC reflect processes of switching between control settings for different tasks. The present study investigated whether task sets operate as a single, integrated representation or as an agglomeration of relatively independent components. In a cued task switch paradigm, target detection (present/absent and discrimination (blue/green/right-/left-tilted tasks alternated randomly across trials. The target was either a color or an orientation singleton among homogeneous distractors. Across two trials, the task and target-defining dimension repeated or changed randomly. For task switch trials, agglomerated task sets predict a difference between dimension changes and repetitions: joint task and dimension switches require full task set reconfiguration, while dimension repetitions permit re-using some control settings from the previous trial. By contrast, integrated task sets always require full switches, predicting dimension repetition effects (DREs to be absent across task switches. RT analyses showed significant DREs across task switches as well as repetitions supporting the notion of agglomerated task sets. Additionally, two event-related potentials (ERP were analyzed: the Posterior-Contralateral-Negativity (PCN indexing spatial selection dynamics, and the Sustained-Posterior-Contralateral-Negativity (SPCN indexing post-selective perceptual/semantic analysis. Significant DREs across task switches were observed for both the PCN and SPCN components. Together, DREs across task switches for RTs and two functionally distinct ERP components suggest that re-using control settings across different tasks is possible. The results thus support the ‘agglomerated-task-set’ hypothesis, and are inconsistent with ‘integrated task sets’.

  19. Abnormal Neural Responses to Emotional Stimuli but Not Go/NoGo and Stroop Tasks in Adults with a History of Childhood Nocturnal Enuresis.

    Directory of Open Access Journals (Sweden)

    Mengxing Wang

    Full Text Available Nocturnal enuresis (NE is a common disorder in school-aged children. Previous studies have reported that children with NE exhibit structural, functional and neurochemical abnormalities in the brain, suggesting that children with NE may have cognitive problems. Additionally, children with NE have been shown to process emotions differently from control children. In fact, most cases of NE resolve with age. However, adults who had experienced NE during childhood may still have potential cognitive or emotion problems, and this possibility has not been thoroughly investigated.In this work, we used functional magnetic resonance imaging (fMRI to evaluate brain functional changes in adults with a history of NE. Two groups, consisting of 21 adults with NE and 21 healthy controls, were scanned using fMRI. We did not observe a significant abnormality in activation during the Go/NoGo and Stroop tasks in adults with a history of NE compared with the control group. However, compared to healthy subjects, young adults with a history of NE mainly showed increased activation in the bilateral temporoparietal junctions, bilateral dorsolateral prefrontal cortex, and bilateral anterior cingulate cortex while looking at negative vs. neutral pictures.Our results demonstrate that adults with a history of childhood NE have no obvious deficit in response inhibition or cognitive control but showed abnormal neural responses to emotional stimuli.

  20. Multilayer neural-net robot controller with guaranteed tracking performance.

    Science.gov (United States)

    Lewis, F L; Yegildirek, A; Liu, K

    1996-01-01

    A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed. The structure of the NN controller is derived using a filtered error/passivity approach. No off-line learning phase is needed for the proposed NN controller and the weights are easily initialized. The nonlinear nature of the NN, plus NN functional reconstruction inaccuracies and robot disturbances, mean that the standard delta rule using backpropagation tuning does not suffice for closed-loop dynamic control. Novel online weight tuning algorithms, including correction terms to the delta rule plus an added robust signal, guarantee bounded tracking errors as well as bounded NN weights. Specific bounds are determined, and the tracking error bound can be made arbitrarily small by increasing a certain feedback gain. The correction terms involve a second-order forward-propagated wave in the backpropagation network. New NN properties including the notions of a passive NN, a dissipative NN, and a robust NN are introduced.

  1. Composite learning from adaptive backstepping neural network control.

    Science.gov (United States)

    Pan, Yongping; Sun, Tairen; Liu, Yiqi; Yu, Haoyong

    2017-11-01

    In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Facilitation of Memory Encoding in Primate Hippocampus by a Neuroprosthesis that Promotes Task Specific Neural Firing

    Science.gov (United States)

    Hampson, Robert E.; Song, Dong; Opris, Ioan; Santos, Lucas M.; Shin, Dae C.; Gerhardt, Greg A.; Marmarelis, Vasilis Z.; Berger, Theodore W.; Deadwyler, Sam A.

    2014-01-01

    Objective Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s’, aging and dementia resulting from impaired hippocampal function in medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. Approach NHPs trained to perform a short-term delayed match to sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. Main Results The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for successful encoding of Sample phase information on more difficult DMS trials. This was validated by delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the Sample phase which facilitated task performance in the subsequent delayed Match phase on difficult trials that required more precise encoding of Sample information. Significance These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain. PMID:24216292

  3. Neural correlates of emotional intelligence in a visual emotional oddball task: an ERP study.

    Science.gov (United States)

    Raz, Sivan; Dan, Orrie; Zysberg, Leehu

    2014-11-01

    The present study was aimed at identifying potential behavioral and neural correlates of Emotional Intelligence (EI) by using scalp-recorded Event-Related Potentials (ERPs). EI levels were defined according to both self-report questionnaire and a performance-based ability test. We identified ERP correlates of emotional processing by using a visual-emotional oddball paradigm, in which subjects were confronted with one frequent standard stimulus (a neutral face) and two deviant stimuli (a happy and an angry face). The effects of these faces were then compared across groups with low and high EI levels. The ERP results indicate that participants with high EI exhibited significantly greater mean amplitudes of the P1, P2, N2, and P3 ERP components in response to emotional and neutral faces, at frontal, posterior-parietal and occipital scalp locations. P1, P2 and N2 are considered indexes of attention-related processes and have been associated with early attention to emotional stimuli. The later P3 component has been thought to reflect more elaborative, top-down, emotional information processing including emotional evaluation and memory encoding and formation. These results may suggest greater recruitment of resources to process all emotional and non-emotional faces at early and late processing stages among individuals with higher EI. The present study underscores the usefulness of ERP methodology as a sensitive measure for the study of emotional stimuli processing in the research field of EI. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Implications of movement-related cortical potential for understanding neural adaptations in muscle strength tasks

    Science.gov (United States)

    2014-01-01

    This systematic review aims to provide information about the implications of the movement-related cortical potential (MRCP) in acute and chronic responses to the counter resistance training. The structuring of the methods of this study followed the proposals of the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). It was performed an electronically search in Pubmed/Medline and ISI Web of Knowledge data bases, from 1987 to 2013, besides the manual search in the selected references. The following terms were used: Bereitschaftspotential, MRCP, strength and force. The logical operator “AND” was used to combine descriptors and terms used to search publications. At the end, 11 studies attended all the eligibility criteria and the results demonstrated that the behavior of MRCP is altered because of different factors such as: force level, rate of force development, fatigue induced by exercise, and the specific phase of muscular action, leading to an increase in the amplitude in eccentric actions compared to concentric actions, in acute effects. The long-term adaptations demonstrated that the counter resistance training provokes an attenuation in the amplitude in areas related to the movement, which may be caused by neural adaptation occurred in the motor cortex. PMID:24602228

  5. Neurale Netværk anvendt indenfor Proceskontrol. Neural Network for Process Control

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    Dette projekt omhandler anvendelsen af neurale netværksmodeller til proceskontrol. Neurale netværksmodeller er simple modeller af de processer, der forløber i det biologiske neurale netværk. Det biologiske neurale netværk er det netværk af nerveceller, der tilsammen danner centralnervesystemet hos...... beskrivelige inputsignaler. Det biologiske neurale netværk dvs. hjernen er således gennem indlæring i stand til at læse, hvorledes der skal stryes og reguleres på baggrund af disse inputsignaler, så det ønskede resultat opnås. Det er derfor nærliggende at undersøge, hvorvidt neurale netværk er anvendelige...... indenfor proceskontrol i almindelighed. Med anvendelser til proceskontrol menes der her anvendeler til prediction, simulering og regulering af dynamiske systemer. For at teste, hvorvidt neurale netværk er anvendelig til prediction og simulering, er der anvendt en tre-trinsoverheder simulator til...

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

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

    DEFF Research Database (Denmark)

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

    2001-01-01

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

  8. Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task.

    Science.gov (United States)

    Lo, Chung-Chuan; Wang, Xiao-Jing

    2016-08-01

    Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a "Stop" process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.

  9. Hexavalent chromium exposure and control in welding tasks.

    Science.gov (United States)

    Meeker, John D; Susi, Pam; Flynn, Michael R

    2010-11-01

    Studies of exposure to the lung carcinogen hexavalent chromium (CrVI) from welding tasks are limited, especially within the construction industry where overexposure may be common. In addition, despite the OSHA requirement that the use of engineering controls such as local exhaust ventilation (LEV) first be considered before relying on other strategies to reduce worker exposure to CrVI, data on the effectiveness of LEV to reduce CrVI exposures from welding are lacking. The goal of the present study was to characterize breathing zone air concentrations of CrVI during welding tasks and primary contributing factors in four datasets: (1) OSHA compliance data; (2) a publicly available database from The Welding Institute (TWI); (3) field survey data of construction welders collected by the Center for Construction Research and Training (CPWR); and (4) controlled welding trials conducted by CPWR to assess the effectiveness of a portable LEV unit to reduce CrVI exposure. In the OSHA (n = 181) and TWI (n = 124) datasets, which included very few samples from the construction industry, the OSHA permissible exposure level (PEL) for CrVI (5 μg/m(3)) was exceeded in 9% and 13% of samples, respectively. CrVI concentrations measured in the CPWR field surveys (n = 43) were considerably higher, and 25% of samples exceeded the PEL. In the TWI and CPWR datasets, base metal, welding process, and LEV use were important predictors of CrVI concentrations. Only weak-to-moderate correlations were found between total particulate matter and CrVI, suggesting that total particulate matter concentrations are not a good surrogate for CrVI exposure in retrospective studies. Finally, in the controlled welding trials, LEV reduced median CrVI concentrations by 68% (p = 0.02). In conclusion, overexposure to CrVI in stainless steel welding is likely widespread, especially in certain operations such as shielded metal arc welding, which is commonly used in construction. However, exposure could be

  10. Neural network based adaptive output feedback control: Applications and improvements

    Science.gov (United States)

    Kutay, Ali Turker

    Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in

  11. Effects of working memory training on neural correlates of Go/Nogo response control in adults with ADHD: A randomized controlled trial.

    Science.gov (United States)

    Liu, Zhong-Xu; Lishak, Victoria; Tannock, Rosemary; Woltering, Steven

    2017-01-27

    Working memory and response control are conceptualized as functions that are part of a closely connected and integrated executive function system mediated by the prefrontal cortex and other related brain structures. In the present paper, we asked whether effects of intensive and adaptive computerized working memory training (CWMT) would generalize to enhancements in response control at behavioral and neural levels. A total of 135 postsecondary students with Attention-Deficit/Hyperactivity Disorder (ADHD), a condition associated with executive function impairments, were randomized into a Standard-length CWMT (45-min /session, 25 sessions), Shortened-length CWMT (15min/session, 25 sessions), and a waitlist group. Both training groups received CWMT for 5 days a week for 5 weeks long. All participants completed a Go-Nogo task while neural activity was measured using Electroencephalography (EEG), before and after CWMT. Behavioral results showed trend level evidence (p=0.061) for benefits of CWMT on response control (i.e., improved accuracy of Go responses). Among several neural measures results showed statistically significant changes after CWMT only for the Go trial ERP N2 and P3 in frontal electrodes (p=0.039 and 0.001, respectively). However, given the lack of relationship between behavioral and neural changes and especially the clear lack of predicted does effects (i.e., standard length > short length > control), we conclude that there is no convincing evidence that the working memory training per se changes neural activation patterns in untrained executive functions. The positive finding of general training related changes in this study should have no clinical implications, but may contribute to the literature in better understanding the relationship between neural plasticity and transfer. Published by Elsevier Ltd.

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

    Science.gov (United States)

    Bao, Gang; Zeng, Zhigang; Shen, Yanjun

    2018-02-01

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

  13. Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller.

    Science.gov (United States)

    Lopez-Franco, Carlos; Gomez-Avila, Javier; Alanis, Alma Y; Arana-Daniel, Nancy; Villaseñor, Carlos

    2017-08-12

    In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results.

  14. Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

    Science.gov (United States)

    Lopez-Franco, Carlos; Alanis, Alma Y.; Arana-Daniel, Nancy; Villaseñor, Carlos

    2017-01-01

    In recent years, unmanned aerial vehicles (UAVs) have gained significant attention. However, we face two major drawbacks when working with UAVs: high nonlinearities and unknown position in 3D space since it is not provided with on-board sensors that can measure its position with respect to a global coordinate system. In this paper, we present a real-time implementation of a servo control, integrating vision sensors, with a neural proportional integral derivative (PID), in order to develop an hexarotor image based visual servo control (IBVS) that knows the position of the robot by using a velocity vector as a reference to control the hexarotor position. This integration requires a tight coordination between control algorithms, models of the system to be controlled, sensors, hardware and software platforms and well-defined interfaces, to allow the real-time implementation, as well as the design of different processing stages with their respective communication architecture. All of these issues and others provoke the idea that real-time implementations can be considered as a difficult task. For the purpose of showing the effectiveness of the sensor integration and control algorithm to address these issues on a high nonlinear system with noisy sensors as cameras, experiments were performed on the Asctec Firefly on-board computer, including both simulation and experimenta results. PMID:28805689

  15. Classification of autistic individuals and controls using cross-task characterization of fMRI activity

    Directory of Open Access Journals (Sweden)

    Guillaume Chanel

    2016-01-01

    Full Text Available Multivariate pattern analysis (MVPA has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI, a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based approach that we apply to two different fMRI experiments with social stimuli (faces and bodies. The method, based on Support Vector Machines (SVMs and Recursive Feature Elimination (RFE, is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%. Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations.

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

    Directory of Open Access Journals (Sweden)

    Minoru Sasaki

    2012-01-01

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

  17. Neural Control System in Obstacle Avoidance in Mobile Robots Using Ultrasonic Sensors

    Directory of Open Access Journals (Sweden)

    A. Medina-Santiago

    2014-02-01

    Full Text Available This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing. An Arduino embedded platform is used to implement the neural control for field results.

  18. Neural network based adaptive control of nonlinear plants using random search optimization algorithms

    Science.gov (United States)

    Boussalis, Dhemetrios; Wang, Shyh J.

    1992-01-01

    This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.

  19. Exploring the Neural Representation of Novel Words Learned through Enactment in a Word Recognition Task.

    Science.gov (United States)

    Macedonia, Manuela; Mueller, Karsten

    2016-01-01

    Vocabulary learning in a second language is enhanced if learners enrich the learning experience with self-performed iconic gestures. This learning strategy is called enactment. Here we explore how enacted words are functionally represented in the brain and which brain regions contribute to enhance retention. After an enactment training lasting 4 days, participants performed a word recognition task in the functional Magnetic Resonance Imaging (fMRI) scanner. Data analysis suggests the participation of different and partially intertwined networks that are engaged in higher cognitive processes, i.e., enhanced attention and word recognition. Also, an experience-related network seems to map word representation. Besides core language regions, this latter network includes sensory and motor cortices, the basal ganglia, and the cerebellum. On the basis of its complexity and the involvement of the motor system, this sensorimotor network might explain superior retention for enactment.

  20. Spacecraft Neural Network Control System Design using FPGA

    OpenAIRE

    Hanaa T. El-Madany; Faten H. Fahmy; Ninet M. A. El-Rahman; Hassen T. Dorrah

    2011-01-01

    Designing and implementing intelligent systems has become a crucial factor for the innovation and development of better products of space technologies. A neural network is a parallel system, capable of resolving paradigms that linear computing cannot. Field programmable gate array (FPGA) is a digital device that owns reprogrammable properties and robust flexibility. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffer...

  1. Modeling of the height control system using artificial neural networks

    Directory of Open Access Journals (Sweden)

    A. R Tahavvor

    2016-09-01

    Full Text Available Introduction Automation of agricultural and machinery construction has generally been enhanced by intelligent control systems due to utility and efficiency rising, ease of use, profitability and upgrading according to market demand. A broad variety of industrial merchandise are now supplied with computerized control systems of earth moving processes to be performed by construction and agriculture field vehicle such as grader, backhoe, tractor and scraper machines. A height control machine which is used in measuring base thickness is consisted of two mechanical and electronic parts. The mechanical part is consisted of conveyor belt, main body, electrical engine and invertors while the electronic part is consisted of ultrasonic, wave transmitter and receiver sensor, electronic board, control set, and microcontroller. The main job of these controlling devices consists of the topographic surveying, cutting and filling of elevated and spotted low area, and these actions fundamentally dependent onthe machine's ability in elevation and thickness measurement and control. In this study, machine was first tested and then some experiments were conducted for data collection. Study of system modeling in artificial neural networks (ANN was done for measuring, controlling the height for bases by input variable input vectors such as sampling time, probe speed, conveyer speed, sound wave speed and speed sensor are finally the maximum and minimum probe output vector on various conditions. The result reveals the capability of this procedure for experimental recognition of sensors' behavior and improvement of field machine control systems. Inspection, calibration and response, diagnosis of the elevation control system in combination with machine function can also be evaluated by some extra development of this system. Materials and Methods Designing and manufacture of the planned apparatus classified in three dissimilar, mechanical and electronic module, courses of

  2. Neural network control of mobile robot formations using RISE feedback.

    Science.gov (United States)

    Dierks, Travis; Jagannathan, S

    2009-04-01

    In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader-follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are AS and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with most NN controllers. Additionally, the stability of the formation in the presence of obstacles is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation do not occur. The asymptotic stability of the follower robots as well as the entire formation during an obstacle avoidance maneuver is demonstrated using Lyapunov methods, and numerical results are provided to verify the theoretical conjectures.

  3. Neural correlates of rumination in adolescents with remitted major depressive disorder and healthy controls.

    Science.gov (United States)

    Burkhouse, Katie L; Jacobs, Rachel H; Peters, Amy T; Ajilore, Olu; Watkins, Edward R; Langenecker, Scott A

    2017-04-01

    The aim of the present study was to use fMRI to examine the neural correlates of engaging in rumination among a sample of remitted depressed adolescents, a population at high risk for future depressive relapse. A rumination induction task was used to assess differences in the patterns of neural activation during rumination versus a distraction condition among 26 adolescents in remission from major depressive disorder (rMDD) and in 15 healthy control adolescents. Self-report depression and rumination, as well as clinician-rated depression, were also assessed among all participants. All of the participants recruited regions in the default mode network (DMN), including the posterior cingulate cortex, medial prefrontal cortex, inferior parietal lobe, and medial temporal gyrus, during rumination. Increased activation in these regions during rumination was correlated with increased self-report rumination and symptoms of depression across all participants. Adolescents with rMDD also exhibited greater activation in regions involved in visual, somatosensory, and emotion processing than did healthy peers. The present findings suggest that during ruminative thought, adolescents with rMDD are characterized by increased recruitment of regions within the DMN and in areas involved in visual, somatosensory, and emotion processing.

  4. CHRNA5 variants moderate the effect of nicotine deprivation on a neural index of cognitive control.

    Science.gov (United States)

    Evans, D E; MacQueen, D A; Jentink, K G; Park, J Y; Lin, H-Y; Drobes, D J

    2014-09-01

    Individuals with reduced attention and memory cognitive control-related processes may be motivated to smoke as a result of the cognitive enhancing effects of nicotine. Further, nicotine deprivation-induced reductions in cognitive control may negatively reinforce smoking. Minor allele carriers at rs16969968 in the nicotinic acetylcholine receptor α5 subunit gene (CHRNA5) have been shown to exhibit both reduced cognitive control and greater nicotine dependence. It is therefore of interest to see if variants in this gene moderate the influence of nicotine deprivation on cognitive control. P3b and P3a components of the event-related brain potential waveform evoked by a three-stimulus visual oddball task are widely viewed as positive indices of cognitive control-related processes. We tested the hypothesis that individuals possessing at least one minor allele at rs16969968 in CHRNA5 would show greater nicotine deprivation-induced reductions in P3b and P3a amplitude. The sample included 72 non-Hispanic, Caucasian heavy smokers (54 men and 18 women) with a mean age of 36.11 years (SD = 11.57). Participants completed the visual oddball task during counterbalanced nicotine and placebo smoking sessions. Findings indicated that rs16969968 status did not moderate nicotine effects on P3b or P3a, whereas variation in other CHRNA5 polymorphisms, which are not as well characterized and are not in linkage disequilibrium with rs16969968, predicted nicotine deprivation-induced reduction of P3a amplitude: rs588765 (F1,68 = 7.74, P = 0.007) and rs17408276 (F1,67 = 7.34, P = 0.009). Findings are interpreted in the context of vulnerability alleles that may predict nicotine effects on cognitive control. © 2014 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  5. Dissociation of neural substrates of response inhibition to negative information between implicit and explicit facial Go/Nogo tasks: evidence from an electrophysiological study.

    Science.gov (United States)

    Yu, Fengqiong; Ye, Rong; Sun, Shiyue; Carretié, Luis; Zhang, Lei; Dong, Yi; Zhu, Chunyan; Luo, Yuejia; Wang, Kai

    2014-01-01

    Although ample evidence suggests that emotion and response inhibition are interrelated at the behavioral and neural levels, neural substrates of response inhibition to negative facial information remain unclear. Thus we used event-related potential (ERP) methods to explore the effects of explicit and implicit facial expression processing in response inhibition. We used implicit (gender categorization) and explicit emotional Go/Nogo tasks (emotion categorization) in which neutral and sad faces were presented. Electrophysiological markers at the scalp and the voxel level were analyzed during the two tasks. We detected a task, emotion and trial type interaction effect in the Nogo-P3 stage. Larger Nogo-P3 amplitudes during sad conditions versus neutral conditions were detected with explicit tasks. However, the amplitude differences between the two conditions were not significant for implicit tasks. Source analyses on P3 component revealed that right inferior frontal junction (rIFJ) was involved during this stage. The current source density (CSD) of rIFJ was higher with sad conditions compared to neutral conditions for explicit tasks, rather than for implicit tasks. The findings indicated that response inhibition was modulated by sad facial information at the action inhibition stage when facial expressions were processed explicitly rather than implicitly. The rIFJ may be a key brain region in emotion regulation.

  6. Dissociation of neural substrates of response inhibition to negative information between implicit and explicit facial Go/Nogo tasks: evidence from an electrophysiological study.

    Directory of Open Access Journals (Sweden)

    Fengqiong Yu

    Full Text Available BACKGROUND: Although ample evidence suggests that emotion and response inhibition are interrelated at the behavioral and neural levels, neural substrates of response inhibition to negative facial information remain unclear. Thus we used event-related potential (ERP methods to explore the effects of explicit and implicit facial expression processing in response inhibition. METHODS: We used implicit (gender categorization and explicit emotional Go/Nogo tasks (emotion categorization in which neutral and sad faces were presented. Electrophysiological markers at the scalp and the voxel level were analyzed during the two tasks. RESULTS: We detected a task, emotion and trial type interaction effect in the Nogo-P3 stage. Larger Nogo-P3 amplitudes during sad conditions versus neutral conditions were detected with explicit tasks. However, the amplitude differences between the two conditions were not significant for implicit tasks. Source analyses on P3 component revealed that right inferior frontal junction (rIFJ was involved during this stage. The current source density (CSD of rIFJ was higher with sad conditions compared to neutral conditions for explicit tasks, rather than for implicit tasks. CONCLUSIONS: The findings indicated that response inhibition was modulated by sad facial information at the action inhibition stage when facial expressions were processed explicitly rather than implicitly. The rIFJ may be a key brain region in emotion regulation.

  7. Neural signatures of language co-activation and control in bilingual spoken word comprehension.

    Science.gov (United States)

    Chen, Peiyao; Bobb, Susan C; Hoshino, Noriko; Marian, Viorica

    2017-06-15

    To examine the neural signatures of language co-activation and control during bilingual spoken word comprehension, Korean-English bilinguals and English monolinguals were asked to make overt or covert semantic relatedness judgments on auditorily-presented English word pairs. In two critical conditions, participants heard word pairs consisting of an English-Korean interlingual homophone (e.g., the sound /mu:n/ means "moon" in English and "door" in Korean) as the prime and an English word as the target. In the homophone-related condition, the target (e.g., "lock") was related to the homophone's Korean meaning, but not related to the homophone's English meaning. In the homophone-unrelated condition, the target was unrelated to either the homophone's Korean meaning or the homophone's English meaning. In overtly responded situations, ERP results revealed that the reduced N400 effect in bilinguals for homophone-related word pairs correlated positively with the amount of their daily exposure to Korean. In covertly responded situations, ERP results showed a reduced late positive component for homophone-related word pairs in the right hemisphere, and this late positive effect was related to the neural efficiency of suppressing interference in a non-linguistic task. Together, these findings suggest 1) that the degree of language co-activation in bilingual spoken word comprehension is modulated by the amount of daily exposure to the non-target language; and 2) that bilinguals who are less influenced by cross-language activation may also have greater efficiency in suppressing interference in a non-linguistic task. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

    Directory of Open Access Journals (Sweden)

    Heryanto M Ary

    2015-01-01

    Full Text Available UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.

  9. Obstacle avoidance and smooth trajectory control: Neural areas highlighted during improved locomotor performance.

    Directory of Open Access Journals (Sweden)

    Jaclyn eBillington

    2013-02-01

    Full Text Available Visual control of locomotion typically involves both detection of current egomotion as well as anticipation of impending changes in trajectory. To determine if there are distinct neural systems involved in these aspects of steering control we used a slalom paradigm, which required participants to steer around objects in a computer simulated environment using a joystick. In some trials the whole slalom layout was visible (steering ‘preview’ trials so planning of the trajectory around future waypoints was possible, whereas in other trials the slalom course was only revealed one object at a time (steering ‘near’ trials so that future planning was restricted. In order to control for any differences in the motor requirements and visual properties between ‘preview’ and ‘near’ trials, we also interleaved control trials which replayed a participants’ previous steering trials, with the task being to mimic the observed steering. Behavioral and fMRI results confirmed previous findings of superior parietal lobe (SPL recruitment during steering trials, with a more extensive parietal and sensorimotor network during steering ‘preview’ compared to steer ‘near’ trials. Correlational analysis of fMRI data with respect to individual behavioral performance revealed that there was increased activation in the SPL in participants who exhibited smoother steering performance. These findings indicate that there is a role for the SPL in spatial encoding and updating of future targets or obstacles during forward locomotion, which also provides a potential neural underpinning to explain improved steering performance on an individual basis.

  10. Fixation to features and neural processing of facial expressions in a gender discrimination task.

    Science.gov (United States)

    Neath, Karly N; Itier, Roxane J

    2015-10-01

    Early face encoding, as reflected by the N170 ERP component, is sensitive to fixation to the eyes. Whether this sensitivity varies with facial expressions of emotion and can also be seen on other ERP components such as P1 and EPN, was investigated. Using eye-tracking to manipulate fixation on facial features, we found the N170 to be the only eye-sensitive component and this was true for fearful, happy and neutral faces. A different effect of fixation to features was seen for the earlier P1 that likely reflected general sensitivity to face position. An early effect of emotion (∼120 ms) for happy faces was seen at occipital sites and was sustained until ∼350 ms post-stimulus. For fearful faces, an early effect was seen around 80 ms followed by a later effect appearing at ∼150 ms until ∼300 ms at lateral posterior sites. Results suggests that in this emotion-irrelevant gender discrimination task, processing of fearful and happy expressions occurred early and largely independently of the eye-sensitivity indexed by the N170. Processing of the two emotions involved different underlying brain networks active at different times. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. A task-based quality control metric for digital mammography

    Science.gov (United States)

    Maki Bloomquist, A. K.; Mainprize, J. G.; Mawdsley, G. E.; Yaffe, M. J.

    2014-11-01

    A reader study was conducted to tune the parameters of an observer model used to predict the detectability index (dʹ ) of test objects as a task-based quality control (QC) metric for digital mammography. A simple test phantom was imaged to measure the model parameters, namely, noise power spectrum, modulation transfer function and test-object contrast. These are then used in a non-prewhitening observer model, incorporating an eye-filter and internal noise, to predict dʹ. The model was tuned by measuring dʹ of discs in a four-alternative forced choice reader study. For each disc diameter, dʹ was used to estimate the threshold thicknesses for detectability. Data were obtained for six types of digital mammography systems using varying detector technologies and x-ray spectra. A strong correlation was found between measured and modeled values of dʹ, with Pearson correlation coefficient of 0.96. Repeated measurements from separate images of the test phantom show an average coefficient of variation in dʹ for different systems between 0.07 and 0.10. Standard deviations in the threshold thickness ranged between 0.001 and 0.017 mm. The model is robust and the results are relatively system independent, suggesting that observer model dʹ shows promise as a cross platform QC metric for digital mammography.

  12. Neural network model to control an experimental chaotic pendulum

    NARCIS (Netherlands)

    Bakker, R; Schouten, JC; Takens, F; vandenBleek, CM

    1996-01-01

    A feedforward neural network was trained to predict the motion of an experimental, driven, and damped pendulum operating in a chaotic regime. The network learned the behavior of the pendulum from a time series of the pendulum's angle, the single measured variable. The validity of the neural

  13. Central chemoreceptors and neural mechanisms of cardiorespiratory control

    Directory of Open Access Journals (Sweden)

    T.S. Moreira

    2011-09-01

    Full Text Available The arterial partial pressure (P CO2 of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

  14. Neural mechanisms of attentional control in mindfulness meditation

    Directory of Open Access Journals (Sweden)

    Peter eMalinowski

    2013-02-01

    Full Text Available The scientific interest in meditation and mindfulness practice has recently seen an unprecedented surge. After an initial phase of presenting beneficial effects of mindfulness practice in various domains, research is now seeking to unravel the underlying psychological and neurophysiological mechanisms. Advances in understanding these processes are required for improving and fine-tuning mindfulness-based interventions that target specific conditions such as eating disorders or attention deficit hyperactivity disorders. This review presents a theoretical framework that emphasizes the central role of attentional control mechanisms in the development of mindfulness skills. It discusses the phenomenological level of experience during meditation, the different attentional functions that are involved, and relates these to the brain networks that subserve these functions. On the basis of currently available empirical evidence specific processes as to how attention exerts its positive influence are considered and it is concluded that meditation practice appears to positively impact attentional functions by improving resource allocation processes. As a result, attentional resources are allocated more fully during early processing phases which subsequently enhance further processing. Neural changes resulting from a pure form of mindfulness practice that is central to most mindfulness programs are considered from the perspective that they constitute a useful reference point for future research. Furthermore, possible interrelations between the improvement of attentional control and emotion regulation skills are discussed.

  15. Reinforced ART (ReART) for Online Neural Control

    Science.gov (United States)

    Ediriweera, Damjee D.; Marshall, Ian W.

    Fuzzy ART has been proposed for learning stable recognition categories for an arbitrary sequence of analogue input patterns. It uses a match based learning mechanism to categorise inputs based on similarities in their features. However, this approach does not work well for neural control, where inputs have to be categorised based on the classes which they represent, rather than by the features of the input. To address this we propose and investigate ReART, a novel extension to Fuzzy ART. ReART uses a feedback based categorisation mechanism supporting class based input categorisation, online learning, and immunity from the plasticity stability dilemma. ReART is used for online control by integrating it with a separate external function which maps each ReART category to a desired output action. We test the proposal in the context of a simulated wireless data reader intended to be carried by an autonomous mobile vehicle, and show that ReART training time and accuracy are significantly better than both Fuzzy ART and Back Propagation. ReART is also compared to a Naïve Bayesian Classifier. Naïve Bayesian Classification achieves faster learning, but is less accurate in testing compared to both ReART, and Bach Propagation.

  16. Neural network based optimal control of HVAC&R systems

    Science.gov (United States)

    Ning, Min

    Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the

  17. Visual Attention Allocation Between Robotic Arm and Environmental Process Control: Validating the STOM Task Switching Model

    Science.gov (United States)

    Wickens, Christopher; Vieanne, Alex; Clegg, Benjamin; Sebok, Angelia; Janes, Jessica

    2015-01-01

    Fifty six participants time shared a spacecraft environmental control system task with a realistic space robotic arm control task in either a manual or highly automated version. The former could suffer minor failures, whose diagnosis and repair were supported by a decision aid. At the end of the experiment this decision aid unexpectedly failed. We measured visual attention allocation and switching between the two tasks, in each of the eight conditions formed by manual-automated arm X expected-unexpected failure X monitoring- failure management. We also used our multi-attribute task switching model, based on task attributes of priority interest, difficulty and salience that were self-rated by participants, to predict allocation. An un-weighted model based on attributes of difficulty, interest and salience accounted for 96 percent of the task allocation variance across the 8 different conditions. Task difficulty served as an attractor, with more difficult tasks increasing the tendency to stay on task.

  18. Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Ruijun Liu

    2014-01-01

    Full Text Available Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

  19. Part 2: Prediktion, Simulering og Regulering med Neurale Netværk. Prediction, Simulation and Control using Neural Network

    DEFF Research Database (Denmark)

    Schiøler, Henrik

    til Del 1, idet de to rapporter kan opfattes som en enhed. Herefter introduceres de grundlæggende begreber inden for prediktion, samt for mål og integralteorien. Det beskrives, hvorledes neurale net kan fungere som ulinære prediktionsmodeller og den nødvendige teori for Multi Lags Perceptronen (MLP......) samt alternative strukturer baseret på Parzen Window estimationsmetoden, præsenteres med detaljerne af analysen henlagt til appendices. Herefter demonstreres ved en simpel test, hvorledes de forskellige nettyper fungerer i prediktionsanvendelser. Herefter er neurale net anvendt til simulering behandlet...... på tilsvarende måde, dog i en lidt forkortet udgave. Til sidst behandles, hvorledes de behandlede nettyper anvendes i en regulatorstruktur baseret på såkaldte Sliding mode control. Teorien for de neurale net er her den samme som for simulering. Det konkluderes at de alternative strukturer, baseret på...

  20. Hybrid task priority-based motion control of a redundant free-floating space robot

    Directory of Open Access Journals (Sweden)

    Cheng ZHOU

    2017-12-01

    Full Text Available This paper presents a novel hybrid task priority-based motion planning algorithm of a space robot. The satellite attitude control task is defined as the primary task, while the least-squares-based non-strict task priority solution of the end-effector plus the multi-constraint task is viewed as the secondary task. Furthermore, a null-space task compensation strategy in the joint space is proposed to derive the combination of non-strict and strict task-priority motion planning, and this novel combination is termed hybrid task priority control. Thus, the secondary task is implemented in the primary task’s null-space. Besides, the transition of the state of multiple constraints between activeness and inactiveness will only influence the end-effector task without any effect on the primary task. A set of numerical experiments made in a real-time simulation system under Linux/RTAI shows the validity and feasibility of the proposed methodology. Keywords: Base attitude control, Hybrid task-priority, Motion planning, Multiple constraints, Redundant space robot

  1. Neural Predictors of Decisions to Cognitively Control Emotion.

    Science.gov (United States)

    Doré, Bruce Pierre; Weber, Jochen; Ochsner, Kevin Nicholas

    2017-03-08

    Deciding to control emotional responses is a fundamental means of responding to environmental challenges, but little is known about the neural mechanisms that predict the outcome of such decisions. We used fMRI to test whether human brain responses during initial viewing of negative images could be used to predict decisions to regulate affective responses to those images. Our results revealed the following: (1) decisions to regulate were more frequent in individuals exhibiting higher average levels of activity within the amygdala and regions of PFC known a priori to be involved in the cognitive control of emotion and (2) within-person expression of a distributed brain pattern associated with regulating emotion predicted choosing to regulate responses to particular stimuli beyond the predictive value of stimulus intensity or self-reports of emotion. These results demonstrate the behavioral relevance of variability in brain responses to aversive stimuli and provide a model that leverages this variability to predict behavior. SIGNIFICANCE STATEMENT Everyone experiences stressors, but how we respond to them can range from protracted disability to resilience and growth. One key process underlying this variability is the agentic decision to exert control over emotional responses. We present an fMRI-based model predicting decisions to control emotion, finding that activity in brain regions associated with the generation and regulation of emotion was predictive of which people choose to regulate frequently and a distributed brain pattern associated with regulating emotion was predictive of which stimuli regulation was chosen. These brain variables predicted future decisions to regulate emotion beyond what could be predicted from stimulus and self-report variables. Copyright © 2017 the authors 0270-6474/17/372580-09$15.00/0.

  2. A neural tracking and motor control approach to improve rehabilitation of upper limb movements

    Directory of Open Access Journals (Sweden)

    Schmid Maurizio

    2008-02-01

    have been calculated in order to determine the accuracy of the system. Results The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements. Conclusion A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES. The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.

  3. A neural tracking and motor control approach to improve rehabilitation of upper limb movements.

    Science.gov (United States)

    Goffredo, Michela; Bernabucci, Ivan; Schmid, Maurizio; Conforto, Silvia

    2008-02-05

    accuracy of the system. The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of +/- 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements. A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.

  4. Longitudinal associations among family environment, neural cognitive control, and social competence among adolescents

    Directory of Open Access Journals (Sweden)

    Jungmeen Kim-Spoon

    2017-08-01

    Full Text Available During adolescence, prefrontal cortex regions, important in cognitive control, undergo maturation to adapt to changing environmental demands. Ways through which social-ecological factors contribute to adolescent neural cognitive control have not been thoroughly examined. We hypothesize that household chaos is a context that may modulate the associations among parental control, adolescent neural cognitive control, and developmental changes in social competence. The sample involved 167 adolescents (ages 13–14 at Time 1, 53% male. Parental control and household chaos were measured using adolescents’ questionnaire data, and cognitive control was assessed via behavioral performance and brain imaging at Time 1. Adolescent social competence was reported by adolescents at Time 1 and at Time 2 (one year later. Structural equation modeling analyses indicated that higher parental control predicted better neural cognitive control only among adolescents living in low-chaos households. The association between poor neural cognitive control at Time 1 and social competence at Time 2 (after controlling for social competence at Time 1 was significant only among adolescents living in high-chaos households. Household chaos may undermine the positive association of parental control with adolescent neural cognitive control and exacerbate the detrimental association of poor neural cognitive control with disrupted social competence development.

  5. Developing a 3-choice serial reaction time task for examining neural and cognitive function in an equine model.

    Science.gov (United States)

    Roberts, Kirsty; Hemmings, Andrew J; McBride, Sebastian D; Parker, Matthew O

    2017-12-01

    Large animal models of human neurological disorders are advantageous compared to rodent models due to their neuroanatomical complexity, longevity and their ability to be maintained in naturalised environments. Some large animal models spontaneously develop behaviours that closely resemble the symptoms of neural and psychiatric disorders. The horse is an example of this; the domestic form of this species consistently develops spontaneous stereotypic behaviours akin to the compulsive and impulsive behaviours observed in human neurological disorders such as Tourette's syndrome. The ability to non-invasively probe normal and abnormal equine brain function through cognitive testing may provide an extremely useful methodological tool to assess brain changes associated with certain human neurological and psychiatric conditions. An automated operant system with the ability to present visual and auditory stimuli as well as dispense salient food reward was developed. To validate the system, ten horses were trained and tested using a standard cognitive task (three choice serial reaction time task (3-CSRTT)). All animals achieved total learning criterion and performed six probe sessions. Learning criterion was met within 16.30±0.79 sessions over a three day period. During six probe sessions, level of performance was maintained at 80.67±0.57% (mean±SEM) accuracy. This is the first mobile fully automated system developed to examine cognitive function in the horse. A fully-automated operant system for mobile cognitive function of a large animal model has been designed and validated. Horses pose an interesting complementary model to rodents for the examination of human neurological dysfunction. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. Which neural mechanisms mediate the effects of a parenting intervention program on parenting behavior: design of a randomized controlled trial.

    Science.gov (United States)

    Kolijn, Laura; Euser, Saskia; van den Bulk, Bianca G; Huffmeijer, Renske; van IJzendoorn, Marinus H; Bakermans-Kranenburg, Marian J

    2017-03-21

    The Video-feedback Intervention to promote Positive Parenting and Sensitive Discipline (VIPP-SD) has proven effective in increasing parental sensitivity. However, the mechanisms involved are largely unknown. In a randomized controlled trial we examine parental neurocognitive factors that may mediate the intervention effects on parenting behavior. Our aims are to (1) examine whether the intervention influences parents' neural processing of children's emotional expressions and the neural precursors of response inhibition and to (2) test whether neural changes mediate intervention effects on parenting behavior. We will test 100 mothers of 4-6 year old same-sex twins. A random half of the mothers will receive the VIPP-SD Twins (i.e. VIPP-SD adapted for twin families), consisting of 5 home visits in a 3-months period; the other half will receive a dummy intervention. Neurocognitive measures are acquired approximately 2 weeks before and 2 weeks after the intervention. Mothers' electroencephalographic (EEG) activity is measured while performing a stop signal task and in response to children's facial expressions. To obtain a complementary behavioral measure, mothers also perform an emotion recognition task. Parenting behavior will be assessed during parent-child interactions at pre and post intervention lab visits. Our results will shed light on the neurocognitive factors underlying changes in parenting behavior after a parenting support program, which may benefit the development of such programs. Dutch Trial Register: NTR5312 ; Date registered: January 3, 2017.

  7. Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System

    OpenAIRE

    Liu, Da; Li, Muguo

    2015-01-01

    This paper presents a wavelet neural network backstepping sliding mode controller (WNNBSSM) for permanent-magnet synchronous motor (PMSM) position servo control system. Backstepping sliding mode (BSSM) is utilized to guarantee favorable tracking performance and stability of the whole system, meanwhile, wavelet neural network (WNN) is used for approximating nonlinear uncertainties. The designed controller combined the merits of the backstepping sliding mode control with robust characteristics ...

  8. Tai Chi and meditation-plus-exercise benefit neural substrates of executive function: a cross-sectional, controlled study.

    Science.gov (United States)

    Hawkes, Teresa D; Manselle, Wayne; Woollacott, Marjorie H

    2014-12-01

    We report the first controlled study of Tai Chi effects on the P300 event-related potential, a neuroelectric index of human executive function. Tai Chi is a form of exercise and moving meditation. Exercise and meditation have been associated with enhanced executive function. This cross-sectional, controlled study utilized the P300 event-related potential (ERP) to compare executive network neural function between self-selected long-term Tai Chi, meditation, aerobic fitness, and sedentary groups. We hypothesized that because Tai Chi requires moderate aerobic and mental exertion, this group would show similar or better executive neural function compared to meditation and aerobic exercise groups. We predicted all health training groups would outperform sedentary controls. Fifty-four volunteers (Tai Chi, n=10; meditation, n=16; aerobic exercise, n=16; sedentary, n=12) were tested with the Rockport 1-mile walk (estimated VO2 Max), a well-validated measure of aerobic capacity, and an ecologically valid visuo-spatial, randomized, alternating runs Task Switch test during dense-array electroencephalographic (EEG) recording. Only Tai Chi and meditation plus exercise groups demonstrated larger P3b ERP switch trial amplitudes compared to sedentary controls. Our results suggest long-term Tai Chi practice, and meditation plus exercise may benefit the neural substrates of executive function.

  9. Luminance controlled pupil size affects Landolt C task performance

    Energy Technology Data Exchange (ETDEWEB)

    Berman, S.M. (Lawrence Berkeley Lab., CA (United States)); Fein, G. (Neurobehavioral Lab. Software, San Rafael, CA (United States)); Jewett, D.L.; Ashford, F. (ABRATech Corp., Mill Valley, CA (United States))

    1993-02-01

    Subjects judged the orientation of a 2 min. gap Landolt C located at a distance of 2.4 m. The stimuli were presented in central vision on a CRT, at low to medium contrast. The effects of varying the spectrum and luminance of surround lighting were assessed on both pupil size (measured using infrared pupillometry during task performance) and task accuracy. The task display was protected from the surround lighting, so that its luminance and contrast could be varied independently of the changes in the surround lighting. Indirect surround illumination was provided by either two illuminants of very different scotopic spectral content but with the same photopic luminance (Experiments 1 and 3), or by using the same illuminant at two different luminance levels (Experiment 2). In Experiment 3, the effect of changing surround spectrum was compared to the effect of varying task background luminance between 12 cd/m[sup 2] and 73 cd/m[sup 2]. In all experiments, scotopically enhanced surround lighting produced pupil areas which were reduced by almost 50% in comparison with surround lighting with relatively less scotopic luminance. Concomitantly there was improvement in Landolt C task performance with the scotopically enhanced surround lighting at all contrast and luminance levels. In these experiments, smaller pupil sizes were associated with significantly better visual-task performance in spite of lower task retinal illuminance when compared to the condition with larger pupils. These results suggest that changes in surround spectrum can compensate for the effect on task performance of a reduction in task luminance and supports the hypothesis that lighting energy savings could accrue in the workplace by shifting lamp spectra to obtain greater scotopic efficacy.

  10. Luminance controlled pupil size affects Landolt C task performance. Revision

    Energy Technology Data Exchange (ETDEWEB)

    Berman, S.M. [Lawrence Berkeley Lab., CA (United States); Fein, G. [Neurobehavioral Lab. Software, San Rafael, CA (United States); Jewett, D.L.; Ashford, F. [ABRATech Corp., Mill Valley, CA (United States)

    1993-02-01

    Subjects judged the orientation of a 2 min. gap Landolt C located at a distance of 2.4 m. The stimuli were presented in central vision on a CRT, at low to medium contrast. The effects of varying the spectrum and luminance of surround lighting were assessed on both pupil size (measured using infrared pupillometry during task performance) and task accuracy. The task display was protected from the surround lighting, so that its luminance and contrast could be varied independently of the changes in the surround lighting. Indirect surround illumination was provided by either two illuminants of very different scotopic spectral content but with the same photopic luminance (Experiments 1 and 3), or by using the same illuminant at two different luminance levels (Experiment 2). In Experiment 3, the effect of changing surround spectrum was compared to the effect of varying task background luminance between 12 cd/m{sup 2} and 73 cd/m{sup 2}. In all experiments, scotopically enhanced surround lighting produced pupil areas which were reduced by almost 50% in comparison with surround lighting with relatively less scotopic luminance. Concomitantly there was improvement in Landolt C task performance with the scotopically enhanced surround lighting at all contrast and luminance levels. In these experiments, smaller pupil sizes were associated with significantly better visual-task performance in spite of lower task retinal illuminance when compared to the condition with larger pupils. These results suggest that changes in surround spectrum can compensate for the effect on task performance of a reduction in task luminance and supports the hypothesis that lighting energy savings could accrue in the workplace by shifting lamp spectra to obtain greater scotopic efficacy.

  11. Prefrontal control during a semantic decision task that involves idiom comprehension: a transcranial direct current stimulation study.

    Science.gov (United States)

    Sela, Tal; Ivry, Richard B; Lavidor, Michal

    2012-07-01

    Language processing and comprehension can be understood in terms of both linguistic and non-linguistic processes. To make a decision regarding the meaning of complex linguistic inputs such as idiomatic expressions, one has to perform multiple complex cognitive operations such as prediction, selection and inhibition. In the current study, we used transcranial direct current stimulation (tDCS) to test the hypotheses that (I) a prefrontal cognitive control network is involved in directing decisions required for the comprehension of idioms, and (II) that this prefrontal control may be biased by motivational mechanisms. Participants were randomly allocated to one of two stimulation groups (LH anodal/RH cathodal or RH anodal/LH Cathodal). Over a one-week interval, participants were tested twice, completing a semantic decision task after either receiving active or sham stimulation. The semantic decision task required participants to judge the relatedness of an idiom and a target word, with the idiom being predictable or not. The target word was either figuratively related, literally related, or unrelated to the idiom. Each participant also completed a trait motivation questionnaire and a control task. After DC stimulation, a general deceleration in reaction times to targets was found. In addition, the results indicate that the neural enhancement of a left lateralized prefrontal network improved performance when participants had to make decisions to figurative targets of highly predictable idioms, whereas the neural enhancement of the opposite network improved participants' performance to literal targets of unpredictable idioms. These effects were more pronounced in individuals rated as being most sensitive to reward likelihood. The results are discussed in terms of cognitive control over semantic processing. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task.

    Science.gov (United States)

    Goto, Nobuhiko; Mushtaq, Faisal; Shee, Dexter; Lim, Xue Li; Mortazavi, Matin; Watabe, Motoki; Schaefer, Alexandre

    2017-09-01

    We investigated whether well-known neural markers of selective attention to motivationally-relevant stimuli were modulated by variations in subjective preference towards consumer goods in a virtual shopping task. Specifically, participants viewed and rated pictures of various goods on the extent to which they wanted each item, which they could potentially purchase afterwards. Using the event-related potentials (ERP) method, we found that variations in subjective preferences for consumer goods strongly modulated positive slow waves (PSW) from 800 to 3000 milliseconds after stimulus onset. We also found that subjective preferences modulated the N200 and the late positive potential (LPP). In addition, we found that both PSW and LPP were modulated by subsequent buying decisions. Overall, these findings show that well-known brain event-related potentials reflecting selective attention processes can reliably index preferences to consumer goods in a shopping environment. Based on a large body of previous research, we suggest that early ERPs (e.g. the N200) to consumer goods could be indicative of preferences driven by unconditional and automatic processes, whereas later ERPs such as the LPP and the PSW could reflect preferences built upon more elaborative and conscious cognitive processes. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Why are friends special? Implementing a social interaction simulation task to probe the neural correlates of friendship.

    Science.gov (United States)

    Güroğlu, Berna; Haselager, Gerbert J T; van Lieshout, Cornelis F M; Takashima, Atsuko; Rijpkema, Mark; Fernández, Guillén

    2008-01-15

    Friendships form one of the most proximal contexts with a critical role in mental health and social and psychological development. Yet, the neurobiological basis of this crucial developmental factor is largely uninvestigated. In this study, we tested the hypothesis that the interaction with friends is associated with specific activity increases in brain areas known to be involved in interpersonal phenomena, such as empathy, and in reward expectancy. Using functional magnetic resonance imaging (fMRI), we assessed neural activity in a social interaction simulation task implementing the factors 'type of relationship' (peers vs. familiar celebrities) and 'emotional valence' (positive (liked), negative (disliked), and neutral (neither liked nor disliked)). In this design, all stimuli were selected individually for each of the 28 participants and positive peers constituted the friends. Participants were asked to approach a stimulus, to avoid it, or remain neutral. Behavioral results confirmed the expectations in the sense that the participants approached positive stimuli more often than they approached neutral, which were also more often approached than negative stimuli. Moreover, peers were more often approached than celebrities were. Imaging results revealed, among others, three regions of particular interest as selectively more strongly activated when subjects interacted with their friends than with other peers and celebrities: the amygdala and hippocampus, the nucleus accumbens, and the ventro-medial prefrontal cortex. These results might highlight the role of empathy and reward-related processes in friendship. Thus, we may have identified a potential mechanism by which friendships exert such a critical role in development and mental health.

  14. Identification of the neural correlates of cyclothymic temperament using an esthetic judgment for paintings task in fMRI.

    Science.gov (United States)

    Mizokami, Yoshinori; Terao, Takeshi; Hatano, Koji; Kodama, Kensuke; Kohno, Kentaro; Makino, Mayu; Hoaki, Nobuhiko; Araki, Yasuo; Izumi, Toshihiko; Shimomura, Tsuyoshi; Fujiki, Minoru; Kochiyama, Takanori

    2014-12-01

    There is a well-known association between artistic creativity and cyclothymic temperament but the neural correlates of cyclothymic temperament have not yet been fully identified. Recently, we showed that the left lingual gyrus and bilateral cuneus may be associated with esthetic judgment of representational paintings, we therefore sought to investigate brain activity during esthetic judgment of paintings in relation to measures of cyclothymic temperament. Regions of interest (ROI) were set at the left lingual gyrus and bilateral cuneus using automated anatomical labeling, and percent signal changes of the ROIs were measured by marsbar toolbox. The associations between percent signal changes of the ROIs during esthetic judgments of paintings and cyclothymic temperament scores were investigated by Pearson׳s coefficient. Moreover, the associations were further analyzed using multiple regression analysis whereby cyclothymic temperament scores were a dependent factor and percent signal changes of the 3 ROIs and the other 4 temperament scores were independent factors. There was a significantly negative association of cyclothymic temperament scores with the percent signal changes of the left lingual gyrus during esthetic judgments of paintings, but not with those of bilateral cuneus. Even after adjustment using multiple regression analysis, this finding remained unchanged. The number of subjects was relatively small and the task was limited to appreciation of paintings. The present findings suggest that cyclothymic temperament may be associated with the left lingual gyrus. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Epinephrine biosynthesis: hormonal and neural control during stress.

    Science.gov (United States)

    Wong, Dona Lee

    2006-01-01

    1. Stress contributes to the pathophysiology of many diseases, including psychiatric disorders, immune dysfunction, nicotine addiction and cardiovascular illness. Epinephrine and the glucocorticoids, cortisol and corticosterone, are major stress hormones. 2. Release of epinephrine from the adrenal medulla and glucocorticoids from the adrenal cortex initiate the biological responses permitting the organism to cope with adverse psychological, physiological and environmental stressors. Following its massive release during stress, epinephrine must be restored to replenish cellular pools and sustain release to maintain the heightened awareness and sequelae of responses to re-establish homeostasis and ensure survival. 3. Epinephrine is regulated in part through its biosynthesis catalyzed by the final enzyme in the catecholamine pathway, phenylethanolamine N-methyltransferase (E.C. 2.1.1.28, PNMT). PNMT expression, in turn, is controlled through hormonal and neural stimuli, which exert their effects on gene transcription through protein stability. 4. The pioneering work of Julius Axelrod forged the path to our present understanding of how the stress hormone and neurotransmitter epinephrine, is regulated, in particular via its biosynthesis by PNMT.

  16. Neural control of tuneable skin iridescence in squid.

    Science.gov (United States)

    Wardill, T J; Gonzalez-Bellido, P T; Crook, R J; Hanlon, R T

    2012-10-22

    Fast dynamic control of skin coloration is rare in the animal kingdom, whether it be pigmentary or structural. Iridescent structural coloration results when nanoscale structures disrupt incident light and selectively reflect specific colours. Unlike animals with fixed iridescent coloration (e.g. butterflies), squid iridophores (i.e. aggregations of iridescent cells in the skin) produce dynamically tuneable structural coloration, as exogenous application of acetylcholine (ACh) changes the colour and brightness output. Previous efforts to stimulate iridophores neurally or to identify the source of endogenous ACh were unsuccessful, leaving researchers to question the activation mechanism. We developed a novel neurophysiological preparation in the squid Doryteuthis pealeii and demonstrated that electrical stimulation of neurons in the skin shifts the spectral peak of the reflected light to shorter wavelengths (greater than 145 nm) and increases the peak reflectance (greater than 245%) of innervated iridophores. We show ACh is released within the iridophore layer and that extensive nerve branching is seen within the iridophore. The dynamic colour shift is significantly faster (17 s) than the peak reflectance increase (32 s), revealing two distinct mechanisms. Responses from a structurally altered preparation indicate that the reflectin protein condensation mechanism explains peak reflectance change, while an undiscovered mechanism causes the fast colour shift.

  17. Gastrointestinal parasites and the neural control of gut functions

    Directory of Open Access Journals (Sweden)

    Marie Christiane Halliez

    2015-11-01

    Full Text Available Gastrointestinal motility and transport of water and electrolytes play key roles in the pathophysiology of diarrhea upon exposure to enteric parasites. These processes are actively modulated by the enteric nervous system (ENS, which includes efferent, and afferent neurons, as well as interneurons. ENS integrity is essential to the maintenance of homeostatic gut responses. A number of gastrointestinal parasites are known to cause disease by altering the enteric nervous system. The mechanisms remain incompletely understood. Cryptosporidium parvum, Giardia duodenalis (syn. G. intestinalis, G. lamblia, Trypanosoma cruzi, Schistosoma sp and others alter gastrointestinal motility, absorption, or secretion at least in part via effects on the ENS. Recent findings also implicate enteric parasites such as Cryptosporidium parvum and Giardia duodenalis in the development of post-infectious complications such as irritable bowel syndrome, which further underscores their effects on the gut-brain axis. This article critically reviews recent advances and the current state of knowledge on the impact of enteric parasitism on the neural control of gut functions, and provides insights into mechanisms underlying these abnormalities.

  18. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    Directory of Open Access Journals (Sweden)

    Poramate eManoonpong

    2013-02-01

    Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  19. Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines.

    Science.gov (United States)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

  20. Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

    Science.gov (United States)

    Wan, Ying; Cao, Jinde; Wen, Guanghui

    In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

  1. Physical and neural entrainment to rhythm: human sensorimotor coordination across tasks and effector systems

    Directory of Open Access Journals (Sweden)

    Jessica Marie Ross

    2014-08-01

    Full Text Available The human sensorimotor system can be readily entrained to environmental rhythms, through multiple sensory modalities. In this review, we provide an overview of theories of timekeeping that make this neuroentrainment possible. First, we present recent evidence that contests the assumptions made in classic timekeeper models. The role of state estimation, sensory feedback and movement parameters on the organization of sensorimotor timing are discussed in the context of recent experiments that examined simultaneous timing and force control. This discussion is extended to the study of coordinated multi-effector movements and how they may be entrained.

  2. Translating Principles of Neural Plasticity into Research on Speech Motor Control Recovery and Rehabilitation

    Science.gov (United States)

    Ludlow, Christy L.; Hoit, Jeannette; Kent, Raymond; Ramig, Lorraine O.; Shrivastav, Rahul; Strand, Edythe; Yorkston, Kathryn; Sapienza, Christine M.

    2008-01-01

    Purpose: To review the principles of neural plasticity and make recommendations for research on the neural bases for rehabilitation of neurogenic speech disorders. Method: A working group in speech motor control and disorders developed this report, which examines the potential relevance of basic research on the brain mechanisms involved in neural…

  3. Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2017-07-01

    In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

  4. Internalizing versus Externalizing Control: Different Ways to Perform a Time-Based Prospective Memory Task

    Science.gov (United States)

    Huang, Tracy; Loft, Shayne; Humphreys, Michael S.

    2014-01-01

    "Time-based prospective memory" (PM) refers to performing intended actions at a future time. Participants with time-based PM tasks can be slower to perform ongoing tasks (costs) than participants without PM tasks because internal control is required to maintain the PM intention or to make prospective-timing estimates. However, external…

  5. Neural Control and Adaptive Neural Forward Models for Insect-like, Energy-Efficient, and Adaptable Locomotion of Walking Machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

    2013-01-01

    Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate......, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...

  6. Distributed neural representation of saliency controlled value and category during anticipation of rewards and punishments.

    Science.gov (United States)

    Zhang, Zhihao; Fanning, Jennifer; Ehrlich, Daniel B; Chen, Wenting; Lee, Daeyeol; Levy, Ifat

    2017-12-04

    An extensive literature from cognitive neuroscience examines the neural representation of value, but interpretations of these existing results are often complicated by the potential confound of saliency. At the same time, recent attempts to dissociate neural signals of value and saliency have not addressed their relationship with category information. Using a multi-category valuation task that incorporates rewards and punishments of different nature, we identify distributed neural representation of value, saliency, and category during outcome anticipation. Moreover, we reveal category encoding in multi-voxel blood-oxygen-level-dependent activity patterns of the vmPFC and the striatum that coexist with value signals. These results help clarify ambiguities regarding value and saliency encoding in the human brain and their category independence, lending strong support to the neural "common currency" hypothesis. Our results also point to potential novel mechanisms of integrating multiple aspects of decision-related information.

  7. Glucocorticoid control of gene transcription in neural tissue

    NARCIS (Netherlands)

    Morsink, Maarten Christian

    2007-01-01

    Glucocorticoid hormones exert modulatory effects on neural function in a delayed genomic fashion. The two receptor types that can bind glucocorticoids, the mineralocorticoid receptor (MR) and the glucocorticoid receptor (GR), are ligand-inducible transcription factors. Therefore, changes in gene

  8. Distributed task-specific processing of somatosensory feedback for voluntary motor control.

    Science.gov (United States)

    Omrani, Mohsen; Murnaghan, Chantelle D; Pruszynski, J Andrew; Scott, Stephen H

    2016-04-14

    Corrective responses to limb disturbances are surprisingly complex, but the neural basis of these goal-directed responses is poorly understood. Here we show that somatosensory feedback is transmitted to many sensory and motor cortical regions within 25 ms of a mechanical disturbance applied to the monkey's arm. When limb feedback was salient to an ongoing motor action (task engagement), neurons in parietal area 5 immediately (~25 ms) increased their response to limb disturbances, whereas neurons in other regions did not alter their response until 15 to 40 ms later. In contrast, initiation of a motor action elicited by a limb disturbance (target selection) altered neural responses in primary motor cortex ~65 ms after the limb disturbance, and then in dorsal premotor cortex, with no effect in parietal regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal circuits that provide the neural substrate for goal-directed corrections, an essential aspect of highly skilled motor behaviors.

  9. The Minimal Control Principle Predicts Strategy Shifts in the Abstract Decision Making Task

    Science.gov (United States)

    Taatgen, Niels A.

    2011-01-01

    The minimal control principle (Taatgen, 2007) predicts that people strive for problem-solving strategies that require as few internal control states as possible. In an experiment with the Abstract Decision Making task (ADM task; Joslyn & Hunt, 1998) the reward structure was manipulated to make either a low-control strategy or a high-strategy…

  10. Neuromodulation of the neural circuits controlling the lower urinary tract.

    Science.gov (United States)

    Gad, Parag N; Roy, Roland R; Zhong, Hui; Gerasimenko, Yury P; Taccola, Giuliano; Edgerton, V Reggie

    2016-11-01

    The inability to control timely bladder emptying is one of the most serious challenges among the many functional deficits that occur after a spinal cord injury. We previously demonstrated that electrodes placed epidurally on the dorsum of the spinal cord can be used in animals and humans to recover postural and locomotor function after complete paralysis and can be used to enable voiding in spinal rats. In the present study, we examined the neuromodulation of lower urinary tract function associated with acute epidural spinal cord stimulation, locomotion, and peripheral nerve stimulation in adult rats. Herein we demonstrate that electrically evoked potentials in the hindlimb muscles and external urethral sphincter are modulated uniquely when the rat is stepping bipedally and not voiding, immediately pre-voiding, or when voiding. We also show that spinal cord stimulation can effectively neuromodulate the lower urinary tract via frequency-dependent stimulation patterns and that neural peripheral nerve stimulation can activate the external urethral sphincter both directly and via relays in the spinal cord. The data demonstrate that the sensorimotor networks controlling bladder and locomotion are highly integrated neurophysiologically and behaviorally and demonstrate how these two functions are modulated by sensory input from the tibial and pudental nerves. A more detailed understanding of the high level of interaction between these networks could lead to the integration of multiple neurophysiological strategies to improve bladder function. These data suggest that the development of strategies to improve bladder function should simultaneously engage these highly integrated networks in an activity-dependent manner. Copyright © 2016. Published by Elsevier Inc.

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

    Science.gov (United States)

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

    2018-02-01

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

  12. TEST CONTROL OF CALCULATED TASKS EXECUTION AT THE ALGORITHMIC LEVEL OF EDUCATIONAL ACTIVITY

    Directory of Open Access Journals (Sweden)

    Oleksandr O. Petkov

    2014-10-01

    Full Text Available The article deals with the question of the open calculated tasks test control. These materials are focused on basic algorithmic level of educational activity. A method for estimating the control results based on the implementation of the minimum number of open computational tasks is proposed. The technique allows the possibility of refutation mark, exposed by a computer program. It also describes, the characteristics of the controlling program, realizing the method. There are presented the results of calculated tasks execution using the developed program. Information of the article can be used for control tests developing at any algorithmic level of educational activities and for researching of complexity of open calculated tasks.

  13. Online adaptive neural control of a robotic lower limb prosthesis

    Science.gov (United States)

    Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

    2018-02-01

    Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

  14. Position Control of a Pneumatic Muscle Actuator Using RBF Neural Network Tuned PID Controller

    Directory of Open Access Journals (Sweden)

    Jie Zhao

    2015-01-01

    Full Text Available Pneumatic Muscle Actuator (PMA has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

  15. Experiments in Neural-Network Control of a Free-Flying Space Robot

    Science.gov (United States)

    Wilson, Edward

    1995-01-01

    Four important generic issues are identified and addressed in some depth in this thesis as part of the development of an adaptive neural network based control system for an experimental free flying space robot prototype. The first issue concerns the importance of true system level design of the control system. A new hybrid strategy is developed here, in depth, for the beneficial integration of neural networks into the total control system. A second important issue in neural network control concerns incorporating a priori knowledge into the neural network. In many applications, it is possible to get a reasonably accurate controller using conventional means. If this prior information is used purposefully to provide a starting point for the optimizing capabilities of the neural network, it can provide much faster initial learning. In a step towards addressing this issue, a new generic Fully Connected Architecture (FCA) is developed for use with backpropagation. A third issue is that neural networks are commonly trained using a gradient based optimization method such as backpropagation; but many real world systems have Discrete Valued Functions (DVFs) that do not permit gradient based optimization. One example is the on-off thrusters that are common on spacecraft. A new technique is developed here that now extends backpropagation learning for use with DVFs. The fourth issue is that the speed of adaptation is often a limiting factor in the implementation of a neural network control system. This issue has been strongly resolved in the research by drawing on the above new contributions.

  16. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

    Science.gov (United States)

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.

  17. Neural substrates of the interaction of emotional stimulus processing and motor inhibitory control: an emotional linguistic go/no-go fMRI study.

    Science.gov (United States)

    Goldstein, Martin; Brendel, Gary; Tuescher, Oliver; Pan, Hong; Epstein, Jane; Beutel, Manfred; Yang, Yihong; Thomas, Katherine; Levy, Kenneth; Silverman, Michael; Clarkin, Jonathon; Posner, Michael; Kernberg, Otto; Stern, Emily; Silbersweig, David

    2007-07-01

    Neural substrates of behavioral inhibitory control have been probed in a variety of animal model, physiologic, behavioral, and imaging studies, many emphasizing the role of prefrontal circuits. Likewise, the neurocircuitry of emotion has been investigated from a variety of perspectives. Recently, neural mechanisms mediating the interaction of emotion and behavioral regulation have become the focus of intense study. To further define neurocircuitry specifically underlying the interaction between emotional processing and response inhibition, we developed an emotional linguistic go/no-go fMRI paradigm with a factorial block design which joins explicit inhibitory task demand (i.e., go or no-go) with task-unrelated incidental emotional stimulus valence manipulation, to probe the modulation of the former by the latter. In this study of normal subjects focusing on negative emotional processing, we hypothesized activity changes in specific frontal neocortical and limbic regions reflecting modulation of response inhibition by negative stimulus processing. We observed common fronto-limbic activations (including orbitofrontal cortical and amygdalar components) associated with the interaction of emotional stimulus processing and response suppression. Further, we found a distributed cortico-limbic network to be a candidate neural substrate for the interaction of negative valence-specific processing and inhibitory task demand. These findings have implications for elucidating neural mechanisms of emotional modulation of behavioral control, with relevance to a variety of neuropsychiatric disease states marked by behavioral dysregulation within the context of negative emotional processing.

  18. Synergistic control of forearm based on accelerometer data and artificial neural networks

    Directory of Open Access Journals (Sweden)

    B. Mijovic

    2008-05-01

    Full Text Available In the present study, we modeled a reaching task as a two-link mechanism. The upper arm and forearm motion trajectories during vertical arm movements were estimated from the measured angular accelerations with dual-axis accelerometers. A data set of reaching synergies from able-bodied individuals was used to train a radial basis function artificial neural network with upper arm/forearm tangential angular accelerations. The trained radial basis function artificial neural network for the specific movements predicted forearm motion from new upper arm trajectories with high correlation (mean, 0.9149-0.941. For all other movements, prediction was low (range, 0.0316-0.8302. Results suggest that the proposed algorithm is successful in generalization over similar motions and subjects. Such networks may be used as a high-level controller that could predict forearm kinematics from voluntary movements of the upper arm. This methodology is suitable for restoring the upper limb functions of individuals with motor disabilities of the forearm, but not of the upper arm. The developed control paradigm is applicable to upper-limb orthotic systems employing functional electrical stimulation. The proposed approach is of great significance particularly for humans with spinal cord injuries in a free-living environment. The implication of a measurement system with dual-axis accelerometers, developed for this study, is further seen in the evaluation of movement during the course of rehabilitation. For this purpose, training-related changes in synergies apparent from movement kinematics during rehabilitation would characterize the extent and the course of recovery. As such, a simple system using this methodology is of particular importance for stroke patients. The results underlie the important issue of upper-limb coordination.

  19. Control system of hexacopter using color histogram footprint and convolutional neural network

    Science.gov (United States)

    Ruliputra, R. N.; Darma, S.

    2017-07-01

    The development of unmanned aerial vehicles (UAV) has been growing rapidly in recent years. The use of logic thinking which is implemented into the program algorithms is needed to make a smart system. By using visual input from a camera, UAV is able to fly autonomously by detecting a target. However, some weaknesses arose as usage in the outdoor environment might change the target's color intensity. Color histogram footprint overcomes the problem because it divides color intensity into separate bins that make the detection tolerant to the slight change of color intensity. Template matching compare its detection result with a template of the reference image to determine the target position and use it to position the vehicle in the middle of the target with visual feedback control based on Proportional-Integral-Derivative (PID) controller. Color histogram footprint method localizes the target by calculating the back projection of its histogram. It has an average success rate of 77 % from a distance of 1 meter. It can position itself in the middle of the target by using visual feedback control with an average positioning time of 73 seconds. After the hexacopter is in the middle of the target, Convolutional Neural Networks (CNN) classifies a number contained in the target image to determine a task depending on the classified number, either landing, yawing, or return to launch. The recognition result shows an optimum success rate of 99.2 %.

  20. Identification of Time-Varying Pilot Control Behavior in Multi-Axis Control Tasks

    Science.gov (United States)

    Zaal, Peter M. T.; Sweet, Barbara T.

    2012-01-01

    Recent developments in fly-by-wire control architectures for rotorcraft have introduced new interest in the identification of time-varying pilot control behavior in multi-axis control tasks. In this paper a maximum likelihood estimation method is used to estimate the parameters of a pilot model with time-dependent sigmoid functions to characterize time-varying human control behavior. An experiment was performed by 9 general aviation pilots who had to perform a simultaneous roll and pitch control task with time-varying aircraft dynamics. In 8 different conditions, the axis containing the time-varying dynamics and the growth factor of the dynamics were varied, allowing for an analysis of the performance of the estimation method when estimating time-dependent parameter functions. In addition, a detailed analysis of pilots adaptation to the time-varying aircraft dynamics in both the roll and pitch axes could be performed. Pilot control behavior in both axes was significantly affected by the time-varying aircraft dynamics in roll and pitch, and by the growth factor. The main effect was found in the axis that contained the time-varying dynamics. However, pilot control behavior also changed over time in the axis not containing the time-varying aircraft dynamics. This indicates that some cross coupling exists in the perception and control processes between the roll and pitch axes.

  1. Anxiety and cognitive efficiency: differential modulation of transient and sustained neural activity during a working memory task.

    Science.gov (United States)

    Fales, C L; Barch, D M; Burgess, G C; Schaefer, A; Mennin, D S; Gray, J R; Braver, T S

    2008-09-01

    According to the processing-efficiency hypothesis (Eysenck, Derakshan, Santos, & Calvo, 2007), anxious individuals are thought to require greater activation of brain systems supporting cognitive control (e.g.,dorsolateral prefrontal cortex; DLPFC) in order to maintain equivalent performance to nonanxious subjects. A recent theory of cognitive control (Braver, Gray, & Burgess, 2007) has proposed that reduced cognitive efficiency might occur as a result of changes in the temporal dynamics of DLPFC recruitment. In this study, we used a mixed blocked/ event-related fMRI design to track transient and sustained activity in DLPFC while high- and low-anxious participants performed a working memory task. The task was performed after the participants viewed videos designed to induce neutral or anxiety-related moods. After the neutral video, the high-anxious participants had reduced sustained but increased transient activation in working memory areas, in comparison with low-anxious participants. The high-anxious group also showed extensive reductions in sustained activation of "default-network" areas (possible deactivation). After the negative video,the low-anxiety group shifted their activation dynamics in cognitive control regions to resemble those of the high-anxious group. These results suggest that reduced cognitive control in anxiety might be due to a transient, rather than sustained, pattern of working memory recruitment. Supplementary information for this study may be found at www.psychonomic.org/archive.

  2. Effects of aversive odour presentation on inhibitory control in the Stroop colour-word interference task

    OpenAIRE

    Nießen Thomas; Bude Daniela; Kellermann Thilo; Finkelmeyer Andreas; Schwenzer Michael; Mathiak Klaus; Reske Martina

    2010-01-01

    Abstract Background Due to the unique neural projections of the olfactory system, odours have the ability to directly influence affective processes. Furthermore, it has been shown that emotional states can influence various non-emotional cognitive tasks, such as memory and planning. However, the link between emotional and cognitive processes is still not fully understood. The present study used the olfactory pathway to induce a negative emotional state in humans to investigate its effect on i...

  3. Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

    Science.gov (United States)

    Long, Lijun; Zhao, Jun

    2015-07-01

    This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

  4. Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

    Science.gov (United States)

    Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

    2016-12-01

    This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Active vision task and postural control in healthy, young adults: Synergy and probably not duality.

    Science.gov (United States)

    Bonnet, Cédrick T; Baudry, Stéphane

    2016-07-01

    In upright stance, individuals sway continuously and the sway pattern in dual tasks (e.g., a cognitive task performed in upright stance) differs significantly from that observed during the control quiet stance task. The cognitive approach has generated models (limited attentional resources, U-shaped nonlinear interaction) to explain such patterns based on competitive sharing of attentional resources. The objective of the current manuscript was to review these cognitive models in the specific context of visual tasks involving gaze shifts toward precise targets (here called active vision tasks). The selection excluded the effects of early and late stages of life or disease, external perturbations, active vision tasks requiring head and body motions and the combination of two tasks performed together (e.g., a visual task in addition to a computation in one's head). The selection included studies performed by healthy, young adults with control and active - difficult - vision tasks. Over 174 studies found in Pubmed and Mendeley databases, nine were selected. In these studies, young adults exhibited significantly lower amplitude of body displacement (center of pressure and/or body marker) under active vision tasks than under the control task. Furthermore, the more difficult the active vision tasks were, the better the postural control was. This underscores that postural control during active vision tasks may rely on synergistic relations between the postural and visual systems rather than on competitive or dual relations. In contrast, in the control task, there would not be any synergistic or competitive relations. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Age-related changes in posture control are differentially affected by postural and cognitive task complexity.

    Science.gov (United States)

    Bernard-Demanze, L; Dumitrescu, M; Jimeno, P; Borel, L; Lacour, M

    2009-07-01

    The simple postural task of quiet standing, which requires minimal attentional resources, is generally paired with cognitive activity. Competition for attentional resources is a consequence of simultaneously performing balance tasks and cognitive tasks, and impairment of attentional resource allocation with aging leads to increased risks of fall. We investigated age-related changes in posture control during dual task performance, using a paradigm that crossed a static (quiet standing) and a dynamic (keeping balance on a translational force plate) postural task and cognitive tasks of low demand (mental arithmetic) and high demand (spatial memory). Postural performance was analyzed through center-of-pressure displacements using both statistical (body sway area/sway velocity) and nonlinear (wavelet transform) methods in three age groups (younger, middle-aged, and older healthy participants). Results showed that 1) the nonlinear analysis method was more sensitive than the traditional approach in distinguishing performance between age groups, a result that explains discrepancies in the dual-task literature; 2) dual-tasking costs were dependent on both postural task difficulty and cognitive task complexity, corroborating previous investigations; 3) younger adults improved their postural performance during dual-tasking, but older adults lowered their performance; 4) balance recovery strategies in the dynamic postural task appeared to differ in younger versus older adults. Together, our findings on dual-tasking can be interpreted within the conceptual frame of task prioritization: shifting attention away from postural task automates posture control in the younger adults, whereas prioritization of postural task and selection of compensatory strategy are the main characteristics of posture control in the older population.

  7. Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

    Directory of Open Access Journals (Sweden)

    Tat-Bao-Thien Nguyen

    2014-01-01

    Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

  8. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

    Science.gov (United States)

    Li, Lin; Park, Il Memming; Brockmeier, Austin; Chen, Badong; Seth, Sohan; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2013-07-01

    The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate

  9. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  10. Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network

    Directory of Open Access Journals (Sweden)

    Yundi Chu

    2015-01-01

    Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.

  11. Smart earphone: Controlling tasks by earphone in smart phone by ...

    African Journals Online (AJOL)

    ... earphone in the ears, on plugging out the speaker of the earphone in the ears. Using this technique Authors have implemented that a smart earphone system can be used to make the earphone from task oriented to multitasking by different gestures of the user. Keywords: Android app, ATMEGA 8, Capacitive touch Sensor, ...

  12. Adaptive Control of Response Preparedness in Task Switching

    Science.gov (United States)

    Steinhauser, Marco; Hubner, Ronald; Druey, Michel

    2009-01-01

    When rapidly switching between two tasks, bivalent stimuli can accidentally trigger the previously executed and therefore still activated response. Recently, it has been suggested that behavioral response-repetition effects reflect response inhibition that reduces the risk of such erroneous response repetitions. The present study investigated…

  13. Sequential Analysis of Postural Control Resource Allocation During a Dual Task Test

    Science.gov (United States)

    Hwang, Ji Hye; Chang, Hyun Jung; Park, Dae-Sung

    2013-01-01

    Objective To investigate the postural control factors influencing the automatic (reflex-controlled) and attentional (high cortical) factors on dual task. Methods We used a dual task model to examine the attentional factors affecting the control of posture, subjecting test subjects to vibration stimulation, one-leg standing and verbal or nonverbal task trials. Twenty-three young, healthy participants were asked to stand on force plates and their centers of pressure were measured during dual task trials. We acquired 15 seconds of data for each volunteer during six dual task trials involving varying task combinations. Results We observed significantly different sway patterns between the early and late phases of dual task trials, which probably reflect the attentional demands. Vibration stimulation perturbed sway more during the early than the late phases; with or without vibration stimulation, the addition of secondary tasks decreased sway in all phases, and greater decreases in sway were observed in the late phases, when subjects were assigned nonverbal tasks. Less sway was observed during the nonverbal task in a sequential study. Conclusion The attentional and automatic factors were analyzed during a sequential study. By controlling the postural control factors, optimal parameters and training methods might be used in clinical applications. PMID:23869332

  14. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

    Science.gov (United States)

    Simeral, J. D.; Kim, S.-P.; Black, M. J.; Donoghue, J. P.; Hochberg, L. R.

    2011-04-01

    The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point

  15. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...... a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present...

  16. Multifamily Quality Control Inspector Job/Task Analysis and Report: September 2013

    Energy Technology Data Exchange (ETDEWEB)

    Owens, C. M.

    2013-09-01

    The development of job/task analyses (JTAs) is one of three components of the Guidelines for Home Energy Professionals project and will allow industry to develop training resources, quality assurance protocols, accredited training programs, and professional certifications. The Multifamily Quality Control Inspector JTA identifies and catalogs all of the tasks performed by multifamily quality control inspectors, as well as the knowledge, skills, and abilities (KSAs) needed to perform the identified tasks.

  17. Adaptive control using a hybrid-neural model: application to a polymerisation reactor

    Directory of Open Access Journals (Sweden)

    Cubillos F.

    2001-01-01

    Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.