Relevant bibliographies by topics / Neural controls

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural controls'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Neural controls"

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Palmieri, Arianna, Federica Meconi, Antonino Vallesi, Mariagrazia Capizzi, Emanuele Pick, Sonia Marcato, Johann R. Kleinbub, Gianni Sorarù, and Paola Sessa. "Enhanced Neural Empathic Responses in Patients with Spino-Bulbar Muscular Atrophy: An Electrophysiological Study." Brain Sciences 11, no. 1 (December 24, 2020): 16. http://dx.doi.org/10.3390/brainsci11010016.

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Background: Spino-bulbar muscular atrophy is a rare genetic X-linked disease caused by testosterone insensitivity. An inverse correlation has been described between testosterone levels and empathic responses. The present study explored the profile of neural empathic responding in spino-bulbar muscular atrophy patients. Methods: Eighteen patients with spino-bulbar muscular atrophy and eighteen healthy male controls were enrolled in the study. Their event-related potentials were recorded during an “Empathy Task” designed to distinguish neural responses linked with experience-sharing (early response) and mentalizing (late response) components of empathy. The task involved the presentation of contextual information (painful vs. neutral sentences) and facial expressions (painful vs. neutral). An explicit dispositional empathy-related questionnaire was also administered to all participants, who were screened via neuropsychological battery tests that did not reveal potential cognitive deficits. Due to electrophysiological artefacts, data from 12 patients and 17 controls were finally included in the analyses. Results: Although patients and controls did not differ in terms of dispositional, explicit empathic self-ratings, notably conservative event-related potentials analyses (i.e., spatio-temporal permutation cluster analyses) showed a significantly greater experience-sharing neural response in patients compared to healthy controls in the Empathy-task when both contextual information and facial expressions were painful. Conclusion: The present study contributes to the characterization of the psychological profile of patients with spino-bulbar muscular atrophy, highlighting the peculiarities in enhanced neural responses underlying empathic reactions.
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PHILLIPS, M. L., I. M. MARKS, C. SENIOR, D. LYTHGOE, A. M. O'DWYER, O. MEEHAN, S. C. R. WILLIAMS, M. J. BRAMMER, E. T. BULLMORE, and P. K. McGUIRE. "A differential neural response in obsessive–compulsive disorder patients with washing compared with checking symptoms to disgust." Psychological Medicine 30, no. 5 (September 2000): 1037–50. http://dx.doi.org/10.1017/s0033291799002652.

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Background. Patients with obsessive–compulsive disorder (OCD) have symptoms that pre-dominantly concern washing (washers) or checking (checkers), or both. Functional neuroimaging has been used to identify the neural correlates of the urge to ritualize but has not distinguished between washing and checking symptoms in OCD. We used functional magnetic resonance imaging to compare the neural response to emotive pictures in washers and checkers.Methods. In one of two 5-minute experiments, washers (N = 7), checkers (N = 7) and age-matched normal controls (N = 14) were scanned while viewing alternating blocks of normally disgusting (rated as disgusting by all subjects) and neutral pictures. In the other experiment, all patients and a normal subgroup (N = 8) viewed alternating blocks of washer-relevant (rated as more disgusting by washers than normal controls or checkers) and neutral pictures.Results. In all subjects, normally disgusting pictures activated visual regions implicated in perception of aversive stimuli and the insula, important in disgust perception. Only in washers were similar regions activated by washer-relevant pictures. In checkers, these pictures activated fronto-striatal regions associated with the urge to ritualize in OCD. Normal controls were more similar in neural response to checkers than washers to these pictures. Both normal controls and checkers had frontal regions activated significantly more by washer-relevant than normally disgusting pictures, and had these regions activated significantly more than washers by washer-relevant pictures.Conclusions. We demonstrate a differential neural response to washer-relevant disgust in washers and checkers: only washers demonstrate a neural response to washer-relevant disgust associated with emotion perception rather than attention to non-emotive visual detail.
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Moran, Timothy H., and Ellen E. Ladenheim. "Physiologic and Neural Controls of Eating." Gastroenterology Clinics of North America 45, no. 4 (December 2016): 581–99. http://dx.doi.org/10.1016/j.gtc.2016.07.009.

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Thomas, E. J., R. Elliott, S. McKie, D. Arnone, D. Downey, G. Juhasz, J. F. W. Deakin, and I. M. Anderson. "Interaction between a history of depression and rumination on neural response to emotional faces." Psychological Medicine 41, no. 9 (February 9, 2011): 1845–55. http://dx.doi.org/10.1017/s0033291711000043.

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BackgroundBoth past depressive episodes and the personality trait of depressive rumination are strong risk factors for future depression. Depression is associated with abnormal emotional processing, which may be a neurobiological marker for vulnerability to depression. A consistent picture has yet to emerge as to how a history of depression and the tendency to ruminate influence emotional processing. The aim of this study was to investigate the relationship between rumination, past depression and neural responses when processing face emotions.MethodThe Ruminative Responses Scale (RRS) was completed by 30 remitted depressives and 37 controls who underwent functional magnetic resonance imaging (fMRI) scanning while viewing happy, sad, fearful and neutral faces.ResultsThe remitted depressives showed overall reductions in neural responses to negative emotions relative to the controls. However, in the remitted depressives, but not the controls, RRS scores were correlated with increased neural responses to negative emotions and decreased responses to happiness in limbic regions.ConclusionsAutomatic emotion processing biases and rumination seem to be correlated to aspects of vulnerability to depression. However, remission from depression may be maintained by a general suppression of limbic responsiveness to negative emotion.
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Schiefer, C., F. X. Rubenzucker, H. P. Jorgl, and H. R. Aberl. "A neural network controls the galvannealing process." IEEE Transactions on Industry Applications 35, no. 1 (1999): 114–18. http://dx.doi.org/10.1109/28.740854.

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Horan, W. P., G. Hajcak, J. K. Wynn, and M. F. Green. "Impaired emotion regulation in schizophrenia: evidence from event-related potentials." Psychological Medicine 43, no. 11 (January 28, 2013): 2377–91. http://dx.doi.org/10.1017/s0033291713000019.

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BackgroundAlthough several aspects of emotion seem to be intact in schizophrenia, there is emerging evidence that patients show an impaired ability to adaptively regulate their emotions. This event-related potential (ERP) study examined whether schizophrenia is associated with impaired neural responses to appraisal frames, that is when negative stimuli are presented in a less negative context.MethodThirty-one schizophrenia out-patients and 27 healthy controls completed a validated picture-viewing task with three conditions: (1) neutral pictures preceded by neutral descriptions (‘Neutral’), (2) unpleasant pictures preceded by negative descriptions (‘Preappraised negative’), and (3) unpleasant pictures preceded by more neutral descriptions (‘Preappraised neutral’). Analyses focused on the late positive potential (LPP), an index of facilitated attention to emotional stimuli that is reduced following cognitive emotion regulation strategies, during four time windows from 300 to 2000 ms post-picture onset.ResultsReplicating prior studies, controls showed smaller LPP in Preappraised neutral and Neutral versus Preappraised negative conditions throughout the 300–2000-ms time period. By contrast, patients showed (a) larger LPP in Preappraised neutral and Preappraised negative versus Neutral conditions in the initial period (300–600 ms) and (b) an atypical pattern of larger LPP to Preappraised neutral versus Preappraised negative and Neutral conditions in the 600–1500-ms epochs.ConclusionsModulation of neural responses by a cognitive emotion regulation strategy seems to be impaired in schizophrenia during the first 2 s after exposure to unpleasant stimuli.
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Prajapati, Ravindra S., Mark Hintze, and Andrea Streit. "PRDM1 controls the sequential activation of neural, neural crest and sensory progenitor determinants." Development 146, no. 24 (December 5, 2019): dev181107. http://dx.doi.org/10.1242/dev.181107.

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Tada, S., T. Yasui, T. Okuno, Y. Nakatsuji, H. Mochizuki, S. Sakoda, and H. Kikutani. "BAFF controls neural cell survival through BAFF receptor." Journal of the Neurological Sciences 333 (October 2013): e689. http://dx.doi.org/10.1016/j.jns.2013.07.2380.

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Krauss, Patrick, Marc Schuster, Verena Dietrich, Achim Schilling, Holger Schulze, and Claus Metzner. "Weight statistics controls dynamics in recurrent neural networks." PLOS ONE 14, no. 4 (April 9, 2019): e0214541. http://dx.doi.org/10.1371/journal.pone.0214541.

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Fortin, D. L. "Neural Activity Controls the Synaptic Accumulation of -Synuclein." Journal of Neuroscience 25, no. 47 (November 23, 2005): 10913–21. http://dx.doi.org/10.1523/jneurosci.2922-05.2005.

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Dissertations / Theses on the topic "Neural controls"

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Ng, Justin. "Artificial Neural Network-Based Robotic Control." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1846.

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Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.
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Ionescu, Armand-Mihai. "Membrane computing: traces, neural inspired models, controls." Doctoral thesis, Universitat Rovira i Virgili, 2008. http://hdl.handle.net/10803/8790.

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Membrane Computing:
Traces, Neural Inspired Models, Controls
Autor:
Armand-Mihai Ionescu
Directores:
Dr. Victor Mitrana
(URV)
Dr. Takashi Yokomori
(Universidad Waseda, Japón)
Resumen Castellano:
El presente trabajo está dedicado a una área muy activa del cálculo natural (que intenta descubrir la odalidad en la cual la naturaleza calcula, especialmente al nivel biológico), es decir el cálculo con membranas, y más preciso, a los modelos de membranas inspirados de la funcionalidad biológica de la neurona.
La disertación contribuye al área de cálculo con membranas en tres direcciones principales. Primero, introducimos una nueva manera de definir el resultado de una computación siguiendo los rastros de un objeto especificado dentro de una estructura celular o de una estructura neuronal. A continuación, nos acercamos al ámbito de la biología del cerebro, con el objetivo de obtener varias maneras de controlar la computación por medio de procesos que inhiben/de-inhiben. Tercero, introducimos e investigamos en detallo - aunque en una fase preliminar porque muchos aspectos tienen que ser clarificados - una clase de sistemas inspirados de la manera en la cual las neuronas cooperan por medio de spikes, pulsos eléctricos de formas idénticas.
English summary:
The present work is dedicated to a very active branch of natural computing (which tries to discover the way nature computes, especially at a biological level), namely membrane computing, more precisely, to those models of membrane systems mainly inspired from the functioning of the neural cell.
The present dissertation contributes to membrane computing in three main directions. First, we introduce a new way of defining the result of a computation by means of following the traces of a specified object within a cell structure or a neural structure. Then, we get closer to the biology of the brain, considering various ways to control the computation by means of inhibiting/de-inhibiting processes. Third, we introduce and investigate in a great - though preliminary, as many issues remain to be clarified - detail a class of P systems inspired from the way neurons cooperate by means of spikes, electrical pulses of identical shapes.
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Pegoraro, Caterina. "Finding novel Neural Crest regulators : Pfkfb4, a key glycolysis partner, controls Neural Crest early patterning in Xenopus laevis." Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112374.

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La crête neurale (CN) est une population transitoire de cellules multipotentes qui émerge à la frontière entre l’ectoderme neural et non-neural, dans une région appelée la bordure neurale (BN). Lorsque la BN se soulève pour former le tube neural, les cellules de la CN subissent une transition épithélium-mésenchyme (TEM), et migrent de façon intensive dans l’ensemble de l’embryon pour atteindre leur destination finale et se différencier. Elles sont à l’origine de nombreux types de dérivés : neurones, cellules gliales, cartilage de la tête, os et tissus connectifs, cellules pigmentaires, cellules sympatho-adrenales. Tous ces processus sont régulés par l’action coordonnée de nombreux gènes qui forment un réseau de régulations génétiques complexe, au sein duquel de nombreuses interactions ont été décrites, même si de nombreuses relations restent à élucider à ce jour. Une mauvaise régulation de gènes normalement impliqués dans la formation de la CN provoque des malformations congénitales appelées neurocristopathies. Par ailleurs, la TEM subie par les cellules de CN avant leur migration est également observée dans les cellules cancéreuses acquérant des propriétés métastatiques. Les événements moléculaires et de nombreux gènes impliqués dans la TEM sont communs au développement de la CN et au cancer.Les liens existant entre le développement de la CN et les neurocristopathies, ainsi que les métastases, soulignent l’importance de l’étude du réseau de régulations génétiques permettant la formation de la CN et l’EMT.Au laboratoire, nous nous intéressons aux événements précoces d’induction et de spécification de la CN. Dans le but d’identifier les gènes préférentiellement impliqués dans le développement précoce de la CN et non dans la formation de l’ectoderme neural et non-neural, un crible a été effectué sur le transcriptome de différents tissus embryonnaires micro-disséqués. La validation des résultats de ce crible a permis d’identifier plusieurs gènes intéressants possédant une fonction potentielle dans la formation de la CN. Nous nous sommes particulièrement intéressés à deux d’entre eux, en raison de leur fonction originale comparée à la majorité des gènes impliqués dans le développement de la CN : serca1 et pfkfb4, un régulateur de l’homéostasie calcique et un régulateur de la glycolyse respectivement.Nous avons analysé les patrons d’expression des gènes des familles serca et pfkfb au cours du développement de Xenopus laevis. En raison de son expression spécifique dans la CN, nous avons étudié plus en détails le rôle de pfkfb4 dans la formation de la CN. Cette analyse a montré que pfkfb4 est nécessaire pour la spécification neurale et de la crête neurale.Toutefois, malgré son rôle documenté dans la glycolyse, le phénotype des morphants pfkfb4 dans l’embryon de Xenopus laevis n’est pas dû à une altération de la glycolyse.En conclusion, nos résultats démontrent l’existence d’un nouveau rôle non glycolytique pour Pfkfb4 au cours du développement embryonnaire de Xenopus Laevis
Neural Crest (NC) is a transient population of multipotent cells that arises at the border between neural and non-neural ectoderm, in a region named the neural border (NB). As the neural border elevates to form the neural tube, NC cells undergo an Epithelial-To-Mesenchymal Transition (EMT), migrate extensively into the whole body to reach their final destinations and differentiate. They give rise to multiple derivatives: neurons and glia, head cartilage, bones and connective tissue, pigment cells, sympatho-adrenal cells. All these processes are regulated by the concerted actions of several genes that form a complex Gene Regulatory Network (GRN), in which many interactions have been elucidated, but even more relationships still need to be understood. Misregulation of genes normally involved in NC formation causes birth defects called neurocristopathies. Moreover, the EMT that NC cells undergo before migration also takes place when cancer cells become metastatic: the molecular events and many of the genes involved in EMT and migration are shared between NC development and cancer. The links with metastasis, neurocristopathies and the fact that still little is known about the earliest steps of NC formation, highlight the importance and the interest in understanding the Gene Regulatory Network (GRN) leading to NC formation and EMT.In the laboratory, we are interested in the early steps of NC induction and specification. In order to identify genes preferentially involved in early NC development compared to genes involved in neural and non-neural ectoderm formation, a transcriptome screen on different microdissected embryonic tissues has been performed. The validation of the results of the screen revealed several interesting genes with a potential function in NC formation. We focused particularly on two of them, due to their original function compared to the majority of the genes involved in NC development: serca1 and pfkfb4, a calcium homeostasis regulator and a glycolysis regulator respectively. We analysed the expression patterns of serca and pfkfb family genes during Xenopus laevis development. Then, due to its specific expression in NC, we studied more in details the role of pfkfb4 in NC formation. This analysis revealed that pfkfb4 is necessary for neural and neural crest specification. However, despite its known role in glycolysis, pfkfb4 morphant phenotype in Xenopus laevis embryos is not due to an alteration of the glycolytic pathway.In conclusion, our results reveal a novel extra-glycolytic role for Pfkfb4 during Xenopus laevis embryonic development
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Layshot, Nicholas Joseph. "MODELING OF A GYRO-STABILIZED HELICOPTER CAMERA SYSTEM USING NEURAL NETWORKS." DigitalCommons@CalPoly, 2010. https://digitalcommons.calpoly.edu/theses/421.

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On-board gimbal systems for camera stabilization in helicopters are typically based on linear models. Such models, however, are inaccurate due to system nonlinearities and complexities. As an alternative approach, artificial neural networks can provide a more accurate model of the gimbal system based on their non-linear mapping and generalization capabilities. This thesis investigates the applications of artificial neural networks to model the inertial characteristics (on the azimuth axis) of the inner gimbal in a gyro-stabilized multi-gimbal system. The neural network is trained with time-domain data obtained from gyro rate sensors of an actual camera system. The network performance is evaluated and compared with measured data and a traditional linear model. Computer simulation results show the neural network model fits well with the measured data and significantly outperforms a traditional model.
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Newman, Jonathan P. "Optogenetic feedback control of neural activity." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52973.

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Optogenetics is a set of technologies that enable optically triggered gain or loss of function in genetically specified populations of cells. Optogenetic methods have revolutionized experimental neuroscience by allowing precise excitation or inhibition of firing in specified neuronal populations embedded within complex, heterogeneous tissue. Although optogenetic tools have greatly improved our ability manipulate neural activity, they do not offer control of neural firing in the face of ongoing changes in network activity, plasticity, or sensory input. In this thesis, I develop a feedback control technology that automatically adjusts optical stimulation in real-time to precisely control network activity levels. I describe hardware and software tools, modes of optogenetic stimulation, and control algorithms required to achieve robust neural control over timescales ranging from seconds to days. I then demonstrate the scientific utility of these technologies in several experimental contexts. First, I investigate the role of connectivity in shaping the network encoding process using continuously-varying optical stimulation. I show that synaptic connectivity linearizes the neuronal response, verifying previous theoretical predictions. Next, I use long-term optogenetic feedback control to show that reductions in excitatory neurotransmission directly trigger homeostatic increases in synaptic strength. This result opposes a large body of literature on the subject and has significant implications for memory formation and maintenance. The technology presented in this thesis greatly enhances the precision with which optical stimulation can control neural activity, and allows causally related variables within neural circuits to be studied independently.
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Kelly, Bronwen Noreen. "The Coordination of Breathing and Swallowing Across the Human Lifespan: Implications for Neural Control." Thesis, University of Canterbury. Communication Disorders, 2006. http://hdl.handle.net/10092/1295.

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Our understanding of the neural control of breathing-swallowing coordination (BSC) is largely unclear. Although brainstem control is undoubtedly predominant, this research investigated the hypothesis that the cortex becomes increasingly influential in BSC between birth and adulthood. The main paradigm used to test this primary hypothesis was a comparison of BSC in conditions along a continuum of volitional through non-volitional swallowing on the basis of a decreasing level of cortical activation along this continuum. Voluntarily-initiated swallows during wakefulness were at one end of the continuum and reflexively-initiated swallows during sleep were at the other extreme. Non-volitional wakeful swallows were considered between these two conditions. The BSC of ten infants between birth and 1 year of age and twenty adults between the ages of 20 and 75 years was recorded using non-invasive time-locked recording methods. In order to apply the 'continuum-of-volition' paradigm to swallowing conditions in infants, BSC was monitored during nutritive (breast- or bottle-feeding), non-nutritive wake, and sleep swallows. Infants were monitored longitudinally to determine whether maturation of the cortex and corticobulbar tracts during the first year of life influenced the patterns of BSC. In adults, BSC was monitored during three non-nutritive conditions: volitional, spontaneous wake, and sleep conditions. Post-swallow expiration was found to be predominant in all conditions for all participants at all ages. In addition, the infant results revealed that nutritive BSC matured during the first year of life and differed to non-nutritive wakeful BSC, particularly in the first 2 months of life. Non-nutritive wakeful and sleep BSC did not differ from one another. In summary, the infant results support increasing cortical input into volitional nutritive BSC, an early impact of feeding on BSC, and no difference between BSC when asleep and non-volitional non-nutritive swallows when awake. The results obtained from adults revealed that irrespective of the level of arousal, volitional BSC is different to non-volitional BSC. These results imply that cortical influence on BSC is limited to conditions in which swallowing is voluntarily initiated. The combined interpretation of infant and adult results suggest that cortical influence over BSC, although increasing with maturation, is limited to the volitional swallowing conditions of feeding in infants and during non-nutritive but volitional swallows in adults. From this, it can be deduced that the most likely cortical sites involved in BSC are those involved in the voluntary initiation or planning of swallowing. Infant and adult swallowing apnoea duration (SAD) was also compared across all of the above conditions. SAD was influenced by feeding throughout the first year of life but was not influenced by level of arousal at any stage in the first year or in adulthood. Also, SAD did not change with age in any swallowing condition during infancy. However, comparison of non-nutritive wake SAD across the lifespan revealed that SAD of newborns and young adults is shorter than that of elderly adults, with no difference between consecutive age-groups: newborns, one-year-olds, and young adults. These results suggest SAD is largely mature at birth and impervious to descending suprabulbar influence. Finally, the effects of volitional swallowing and level of arousal on peak submental surface electromyography (SEMG) was investigated in adults. Like BSC, submental muscle activity was influenced only by volitional swallowing, being longer for volitional than non-volitional swallows without being influenced by level of arousal. Since peak submental SEMG activity represents a measure of relative hyolaryngeal excursion, these results suggest that the cortex has some degree of influence over this particular feature of pharyngeal-stage swallowing.
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Kimball, Nicholas. "Utilizing Trajectory Optimization In The Training Of Neural Network Controllers." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2071.

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Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Policy Search algorithm is able to learn a desired control policy much faster than the other algorithms. In the inverted pendulum system, it learns an effective policy up to three times faster than the other algorithms. In the cartpole system, it learns an effective policy up to nearly fifteen times faster than the other algorithms.
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Smith, Bradley R. "Neural Network Enhancement of Closed-Loop Controllers for Ill-Modeled Systems with Unknown Nonlinearities." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29607.

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The nonlinearities of a nonlinear system can degrade the performance of a closed-loop system. In order to improve the performance of the closed-loop system, an adaptive technique, using a neural network, was developed. A neural network is placed in series between the output of the fixed-gain controller and the input into the plant. The weights are initialized to values that result in a unity gain across the neural network, which is referred to as a "feed-through neural network." The initial unity gain causes the output of the neural network to be equal to the input of neural network at the beginning of the convergence process. The result is that the closed-loop system's performance with the neural network is, initially, equal to the closed-loop system's performance without the neural network. As the weights of the neural network converge, the performance of the system improves. However, the back propagation algorithm was developed to update the weights of the feed-forward neural network in the open loop. Although the back propagation algorithm converged the weights in the closed loop, it worked very slowly. Two new update algorithms were developed for converging the weights of the neural network inside the closed-loop. The first algorithm was developed to make the convergence process independent of the plants dynamics and to correct for the effects of the closed loop. The second algorithm does not eliminate the effects of the plant's dynamics, but still does correct for the effects of the closed loop. Both algorithms are effective in converging the weights much faster than the back propagation algorithm. All of the update algorithms have been shown to work effectively on stable and unstable nonlinear plants.
Ph. D.
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Barrett, Andrea Lynn. "A FGF-Hh feedback loop controls stem cell proliferation in the developing larval brain of drosophila melanogaster." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2017.

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Hudson, Christopher Allen. "Single-Phase, Single-Switch, Sensorless Switched Reluctance Motor Drive Utilizing a Minimal Artificial Neural Net." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34735.

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Artificial Neural Networks (ANNs) have proved to be useful in approximating non- linear systems in many applications including motion control. ANNs advocated in switched reluctance motor (SRM) control typically have a large number of neurons and several layers which impedes their real time implementation in embedded sys- tems. Real time estimation at high speeds using these ANNs is diffcult due to the high number of operations required to process the ANN controller. An insuffcient availability of time between two sampling intervals limits the available computation time for both processing the neural net and the other functions required for the motor drive. One ideal application of ANNs in SRM control is rotor position estimation. Due to reliability issues, elimination of the rotor position sensors is absolutely required for high volume, high speed and low cost applications of SRM's. ANNs provide a means by which drive designers can implement position sensorless drive technology that is both robust and easily implemented. It is demonstrated that a new and novel ANN configuration can be implemented for accurate rotor position estimation in a sensorless SRM drive. Consisting of just 4 neurons, the neural estimator is the smallest of its kind for SRM rotor position estimation. The breakthrough that provided the reduction was the addition of a non- linear input. Typical input spaces for SRM position neural estimators consist of both current,and fux-linkage. The neural network was trained on-line using these inputs and a third, non-linear input provided by a preprocessed product of the two typical inputs.
Master of Science
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Books on the topic "Neural controls"

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Maeland, Jahn Andreas. Model-reference neural control. Salford: University of Salford, 1994.

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Ferrell, William R., and Uwe Proske. Neural control of movement. New York: Springer Science+Business Media, LLC, 1995.

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Moshou, Dimitrios. Neural control of robot. Manchester: UMIST, 1993.

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Ferrell, William R., and Uwe Proske, eds. Neural Control of Movement. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1985-0.

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P, Banks Stephen. Optimal control by neural networks. Sheffield: Universityof Sheffield, Dept. of Control Engineering, 1990.

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Ge, S. S. Stable Adaptive Neural Network Control. Boston, MA: Springer US, 2002.

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Ge, Shuzhi S., Chang C. Hang, Tong H. Lee, and Tao Zhang. Stable Adaptive Neural Network Control. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-6577-9.

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Willems, Timotheus Martinus. Neural networks in control?: Proefschrift. Eindhoven: Technische Universiteit Eindhoven, 1993.

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The neural basis of motor control. New York: Oxford University Press, 1986.

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Nie, Junhong. Fuzzy-neural control: Principles, algorithms andapplications. New York: Prentice-Hall, 1995.

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Book chapters on the topic "Neural controls"

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Li, Na, Bin Hu, Jing Chen, Hong Peng, Qinglin Zhao, and Mingqi Zhao. "Investigation of Chronic Stress Differences between Groups Exposed to Three Stressors and Normal Controls by Analyzing EEG Recordings." In Neural Information Processing, 512–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_64.

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Borgens, Richard B. "Physiological Gradients of Voltage as Controls of Neural Morphogenesis." In Electricity and Magnetism in Biology and Medicine, 541–43. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4867-6_128.

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Eccles, John C. "New Light on the Mind-Brain Problem: How Mental Events Could Influence Neural Events." In How the SELF Controls Its BRAIN, 55–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-49224-2_4.

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Eccles, John C. "Do Mental Events Cause Neural Events Analogously to the Probability Fields of Quantum Mechanics?" In How the SELF Controls Its BRAIN, 71–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-49224-2_5.

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Cutsuridis, Vassilis. "Modeling Cognitive Processing of Healthy Controls and Obsessive-Compulsive Disorder Subjects in the Antisaccade Task." In Springer Series in Cognitive and Neural Systems, 91–103. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18830-6_9.

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Qiu, Yue, Qiu-Hua Lin, Li-Dan Kuang, Wen-Da Zhao, Xiao-Feng Gong, Fengyu Cong, and Vince D. Calhoun. "Classification of Schizophrenia Patients and Healthy Controls Using ICA of Complex-Valued fMRI Data and Convolutional Neural Networks." In Advances in Neural Networks – ISNN 2019, 540–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22808-8_53.

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Hatori, Yasuhiro, Tatsuroh Mashita, and Ko Sakai. "Sparseness Controls the Receptive Field Characteristics of V4 Neurons: Generation of Curvature Selectivity in V4." In Artificial Neural Networks and Machine Learning – ICANN 2013, 327–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40728-4_41.

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Li, Wei, Nicholas S. Szczecinski, Alexander J. Hunt, and Roger D. Quinn. "A Neural Network with Central Pattern Generators Entrained by Sensory Feedback Controls Walking of a Bipedal Model." In Biomimetic and Biohybrid Systems, 144–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42417-0_14.

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Zhou, Wuneng, Jun Yang, Liuwei Zhou, and Dongbing Tong. "Stability and Synchronization of Neutral-Type Neural Networks." In Studies in Systems, Decision and Control, 153–267. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47833-2_5.

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Omatu, Sigeru, Marzuki Khalid, and Rubiyah Yusof. "Neural Networks." In Advances in Industrial Control, 7–27. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3058-1_2.

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Conference papers on the topic "Neural controls"

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Kinser, Jason M. "Simplified pulse-coupled neural network." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers and Dennis W. Ruck. SPIE, 1996. http://dx.doi.org/10.1117/12.235951.

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Popovic, Dejan B., Mirjana B. Popovic, and Thomas Sinkjaer. "Life-like Control for Neural Prostheses: "Proximal Controls Distal"." In 2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616283.

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Ali, Kamal, and Dia L. Ali. "Neural network approach to digital control." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers, David B. Fogel, James C. Bezdek, and Bruno Bosacchi. SPIE, 1998. http://dx.doi.org/10.1117/12.304807.

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Sugiyama, Shigeki. "Self-evolutional neural network knowledge base." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers, David B. Fogel, James C. Bezdek, and Bruno Bosacchi. SPIE, 1998. http://dx.doi.org/10.1117/12.304819.

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Sarkar, Dilip, and Harald J. Schmidl. "Classification of chords by neural networks." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers, David B. Fogel, James C. Bezdek, and Bruno Bosacchi. SPIE, 1998. http://dx.doi.org/10.1117/12.304836.

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Wolfe, William J., and Frank A. Duca. "Summary of the neural centroid TSP." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers, David B. Fogel, James C. Bezdek, and Bruno Bosacchi. SPIE, 1998. http://dx.doi.org/10.1117/12.304856.

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Johnson, John L., Marius P. Schamschula, Ramarao Inguva, and H. John Caulfield. "Pulse-coupled neural network sensor fusion." In Aerospace/Defense Sensing and Controls, edited by Belur V. Dasarathy. SPIE, 1998. http://dx.doi.org/10.1117/12.303682.

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Odorico, Roberto. "Neural implementations for on-line triggers." In Aerospace/Defense Sensing and Controls, edited by Steven K. Rogers and Dennis W. Ruck. SPIE, 1996. http://dx.doi.org/10.1117/12.235974.

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Lee, Chulhee, Bongjun Lee, and Kwanghoon Sohn. "Directional interpolation using neural networks." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Stephen K. Park, Zia-ur Rahman, and Robert A. Schowengerdt. SPIE, 2001. http://dx.doi.org/10.1117/12.438261.

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Guo, Yang, Xianghong Yin, and Weibo Gong. "ART2 neural network clustering for hierarchical simulation." In Aerospace/Defense Sensing and Controls, edited by Alex F. Sisti. SPIE, 1998. http://dx.doi.org/10.1117/12.319351.

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Reports on the topic "Neural controls"

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Herbert, Thorwald, and Joseph Haritonidis. Flow Control Using Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, August 1996. http://dx.doi.org/10.21236/ada353984.

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Nasr, Chaiban. Neural Networks For Robot Control. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada387882.

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Hintz, Kenneth J., Z. Zhang, and D. Duane. Evolving Neural Networks for Nonlinear Control. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada317072.

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Shenoy, Krishna. Toward Neural Control of Prosthetic Devices. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada468691.

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Mumme, D. C., and D. R. Chick. Design of a neural-network control system. Office of Scientific and Technical Information (OSTI), September 1988. http://dx.doi.org/10.2172/6410833.

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Mears, Mark J., and Marios M. Polycarpou. Stable Neural Control of Uncertain Multivariable Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada411951.

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Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones, and K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10178912.

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Gotten, Jr, and William M. Robotic Control Using Muscular and Neural Electrical Signals. Fort Belvoir, VA: Defense Technical Information Center, May 1994. http://dx.doi.org/10.21236/ada284908.

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Niederer, J. Particle Beam Control Design Notes for Neural Models. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/1151384.

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Nasr, Chaiban. Neural Networks Control of a Magnetic Levitation System. Fort Belvoir, VA: Defense Technical Information Center, April 2001. http://dx.doi.org/10.21236/ada388065.

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