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Dickson, Scott M. "Stochastic neural network dynamics : synchronisation and control." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16508.
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Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain's performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson's disease. As a result, it is desirable to research under which conditions synchronisation arises in neural networks and the possibility of controlling its prevalence. Stochastic ensembles of FitzHugh-Nagumo elements are used to model neural networks for numerical simulations and bifurcation analysis. The FitzHugh-Nagumo model is employed because of its realistic representation of the flow of sodium and potassium ions in addition to its advantageous property of allowing phase plane dynamics to be observed. Network characteristics such as connectivity, configuration and size are explored to determine their influences on global synchronisation generation in their respective systems. Oscillations in the mean-field are used to detect the presence of synchronisation over a range of coupling strength values. To ensure simulation efficiency, coupling strengths between neurons that are identical and fixed with time are investigated initially. Such networks where the interaction strengths are fixed are referred to as homogeneously coupled. The capacity of controlling and altering behaviours produced by homogeneously coupled networks is assessed through the application of weak and strong delayed feedback independently with various time delays. To imitate learning, the coupling strengths later deviate from one another and evolve with time in networks that are referred to as heterogeneously coupled. The intensity of coupling strength fluctuations and the rate at which coupling strengths converge to a desired mean value are studied to determine their impact upon synchronisation performance. The stochastic delay differential equations governing the numerically simulated networks are then converted into a finite set of deterministic cumulant equations by virtue of the Gaussian approximation method. Cumulant equations for maximal and sub-maximal connectivity are used to generate two-parameter bifurcation diagrams on the noise intensity and coupling strength plane, which provides qualitative agreement with numerical simulations. Analysis of artificial brain networks, in respect to biological brain networks, are discussed in light of recent research in sleep theory.
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Shin, Jiyun. "Perirhinal feedback input controls neocortical memory formation via layer 1." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22312.
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Das deklarative Gedächtnis beruht auf Wechselwirkungen zwischen dem medialen Temporallappens (MTL) und Neokortex. Aufgrund der verteilten Natur neokortikaler Netzwerke bleiben zelluläre Ziele und Mechanismen der Gedächtnisbildung im Neokortex jedoch schwer fassbar. Im sechsschichtigen Säugetier-Neokortex konvergieren die Top-Down-Inputs auf Schicht 1 (L1). Wir untersuchten, wie Top-Down-Inputs von MTL die neokortikale Aktivität während der Gedächtnisbildung modulieren. Wir haben zunächst ein Kortex- und Hippocampus-abhängiges Lernparadigma angepasst, in dem Tiere gelernt haben, direkte kortikale Mikrostimulation und Belohnung zu assoziieren. Neuronen in den tiefen Schichten des perirhinalen Kortex lieferten monosynaptische Eingaben in L1 des primären somatosensorischen Kortex (S1), wo die Mikrostimulation vorgestellt wurde. Die chemogenetische Unterdrückung der perirhinalen Inputs in L1 von S1 störte die Gedächtnisbildung, hatte jedoch keinen Einfluss auf die Leistung der Tiere nach abgeschlossenem Lernen. Dem Lernen folgte das Auftreten einer klaren Subpopulation von Pyramidenneuronen der Schicht 5 (L5), die durch hochfrequentes Burst-Feuern gekennzeichnet war und durch Blockieren der perirhinalen Inputs zu L1 reduziert werden konnte. Interessanterweise zeigte ein ähnlicher Anteil an apikalen Dendriten von L5-Pyramidenneuronen ebenfalls eine signifikant erhöhte Ca2+-Aktivität während des Gedächtnisabrufs bei Expertentieren. Wichtig ist, dass die Störung der dendritischen Ca2+-Aktivität das Lernen beeinträchtigte, was darauf hindeutet, dass apikale Dendriten von L5-Pyramidenneuronen eine entscheidende Rolle bei der Bildung des neokortikalen Gedächtnisses spielen. Wir schließen daraus, dass MTL-Eingaben das Lernen über einen perirhinalen vermittelten Gating-Prozess in L1 steuern, der sich in einer erhöhten dendritischen Ca2+-Aktivität und einem Burst-Firing in pyramidalen L5-Neuronen manifestiert.Declarative memory relies on interactions between the medial temporal lobe (MTL) and neocortex. However, due the distributed nature of neocortical networks, cellular targets and mechanisms of memory formation in the neocortex remain elusive. In the six-layered mammalian neocortex, top-down inputs converge on its outermost layer, layer 1 (L1). We examined how layer-specific top-down inputs from MTL modulate neocortical activity during memory formation. We first adapted a cortical- and hippocampal-dependent learning paradigm, in which animals learned to associate direct cortical microstimulation and reward, and characterized the learning behavior of rats and mice. We next showed that neurons in the deep layers of the perirhinal cortex not only provide monosynaptic inputs to L1 of the primary somatosensory cortex (S1), where microstimulation was presented, but also actively reflect the behavioral outcome. Chemogenetic suppression of perirhinal inputs to L1 of S1 disrupted early memory formation but did not affect animals’ performance after learning. The learning was followed by an emergence of a distinct subpopulation of layer 5 (L5) pyramidal neurons characterized by high-frequency burst firing, which could be reduced by blocking perirhinal inputs to L1. Interestingly, a similar proportion of apical dendrites (~10%) of L5 pyramidal neurons also displayed significantly enhanced calcium (Ca2+) activity during memory retrieval in expert animals. Importantly, disrupting dendritic Ca2+ activity impaired learning, suggesting that apical dendrites of L5 pyramidal neurons have a critical role in neocortical memory formation. Taken together, these results suggest that MTL inputs control learning via a perirhinal-mediated gating process in L1, manifested by elevated dendritic Ca2+ activity and burst firing in L5 pyramidal neurons. The present study provides insights into cellular mechanisms of learning and memory representations in the neocortex.
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Kromer, Justus Alfred. "Noise in adaptive excitable systems and small neural networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17683.
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Neuronen sind erregbare Systeme. Ihre Antwort auf Anregungen oberhalb eines bestimmten Schwellwertes sind Pulse. Häufig wird die Pulserzeugung von verschiedenen Rückkopplungsmechanismen beeinflusst, die auf langsamen Zeitskalen agieren. Das kann zu Phänomenen wie Feuerraten-Adaptation, umgekehrter Feuerraten-Adaptation oder zum Feuern von Pulsen in Salven führen. Weiterhin sind Neuronen verschiedenen Rauschquellen ausgesetzt und wechselwirken mit anderen Neuronen, in neuronalen Netzen. Doch wie beeinflusst das Zusammenspiel von Rückkopplungsmechanismen, Rauschen und der Wechselwirkung mit anderen Neuronen die Pulserzeugung? Diese Arbeit untersucht, wie die Pulserzeugung in rauschgetriebenen erregbaren Systemen von langsamen Rückkopplungsmechanismen und der Wechselwirkung mit anderen erregbaren Systemen beeinflusst wird. Dabei wird die Pulserzeugung in drei Szenarien betrachtet: (i) in einem einzelnen erregbaren System, das um einen langsamen Rückkopplungsmechanismus erweitert wurde, (ii) in gekoppelten erregbaren Systemen und (iii) in stark gekoppelten salvenfeuernden Neuronen. In jedem dieser Szenarien wird die Pulsstatistik mit Hilfe von analytischen Methoden und Computersimulationen untersucht. Das wichtigste Resultat im ersten Szenario ist, dass das Zusammenspiel von einer stark anregenden Rückkopplung und Rauschen zu rauschkontrollierter Bistabilität führt. Das erlaubt es dem System zwischen verschiedenen Modi der Pulserzeugung zu wechseln. In (ii) wird die Pulserzeugung stark von der Wahl der Kopplungsstärken und der Anzahl der Verbindungen beeinflusst. Analytische Näherungen werden abgeleitet, die einen Zusammenhang zwischen der Anzahl der Verbindungen und der Pulsrate, sowie der Pulszugvariabilität herstellen. In (iii) wird festgestellt, dass eine hemmende Rückkopplung zu sehr unregelmäßigem Verhalten der isolierten Neuronen führt, wohingegen eine starke Kopplung mit dem Netzwerk ein regelmäßigeres Feuern von Salven hervorruft.Neurons are excitable systems. Their responses to excitations above a certain threshold are spikes. Usually, spike generation is shaped by several feedback mechanisms that can act on slow time scales. These can lead to phenomena such as spike-frequency adaptation, reverse spike-frequency adaptation, or bursting. In addition to these, neurons are subject to several sources of noise and interact with other neurons, in the connected complexity of a neural network. Yet how does the interplay of feedback mechanisms, noise as well as interaction with other neurons affect spike generation? This thesis examines how spike generation in noise-driven excitable systems is influenced by slow feedback processes and coupling to other excitable systems. To this end, spike generation in three setups is considered: (i) in a single excitable system, which is complemented by a slow feedback mechanism, (ii) in a set of coupled excitable systems, and (iii) in a set of strongly-coupled bursting neurons. In each of these setups, the statistics of spiking is investigated by a combination of analytical methods and computer simulations. The main result of the first setup is that the interplay of strong positive (excitatory) feedback and noise leads to noise-controlled bistability. It enables excitable systems to switch between different modes of spike generation. In (ii), spike generation is strongly affected by the choice of the coupling strengths and the number of connections. Analytical approximations are derived that relate the number of connections to the firing rate and the spike train variability. In (iii), it is found that negative (inhibitory) feedback causes very irregular behavior of the isolated bursters, while strong coupling to the network regularizes the bursting.
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Gill, Jeffrey Paul. "Neural Correlates of Adaptive Responses to Changing Load in Feeding Aplysia." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1579795905638273.
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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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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.
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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network.
Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory feedback and the input bias is what produces the divisive decrease in the slope (known as the gain) of the f-I curve. Our work predicts that an increase in noise is required along with increase in inhibitory feedback to attain a divisive and subtractive shift of the gain as seen in experiments [1].
Our results also show that, although the spatial profile of the feedback does not effect the mean activity of the network, it does influence the overall dynamics of the network. Local feedback generates a network oscillation, which is more robust against disruption by noise or uncorrelated input or network heterogeneity, than that for the global feedback (all-to-all coupling) case. For example uncorrelated input completely disrupts the network oscillation generated by global feedback, but only diminishes the network oscillation due to local feedback. This is characterized by 1st and 2nd order spike train statistics. Further, our theory agrees well with numerical simulations of network dynamics.
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Williams, Ian. "Methods and microelectronics for proprioceptive neural feedback." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24566.
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A neural implant giving an amputee a sense of feeling back in their prosthetic limb could help millions of people live happier, more productive lives. Tactile feedback is commonly targeted, however, it is the lesser known sense of proprioception that is crucial for smooth, coordinated limb control and non-visual limb awareness - both of which are high priorities for amputees. This thesis describes research carried out to progress the development and creation of aproprioceptive neural prosthesis targeted at trans-humeral upper limb amputees. Firstly a review of proprioceptive neural prosthesis design considerations and challenges is presented. The purpose of which is to identify areas requiring further development and to identify a prototype target system that focuses and scopes design effort. Then 3 technical chapters cover research into: (1) Combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns. A neuromusculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. (2) An 8 channel energy-efficient neural stimulator for generating charge-balanced asymmetric pulses. Power consumption is reduced by implementing a fully-integrated DC-DC converter that uses a reconfigurable switched capacitor topology to provide 4 output voltages for Dynamic Voltage Scaling (DVS). A novel charge balancing method is implemented which has a low level of accuracy on a single pulse and a much higher accuracy over a series of pulses. The method used is robust to process and component variation and does not require any initial or ongoing calibration. (3) A non-invasive proprioceptive feedback trial platform (using vibration induced proprioception) for testing modelled neural signals. A low cost vibration device is designed and tested, identifying key issues with this form of non-invasive feedback.
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Fumuro, Tomoyuki. "Bereitschaftspotential augmentation by neuro-feedback training in Parkinson's disease." Kyoto University, 2013. http://hdl.handle.net/2433/174832.
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Habte, Samson. "Snap-drift neural computing for intelligent diagnostic feedback." Thesis, London Metropolitan University, 2017. http://repository.londonmet.ac.uk/1247/.
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Information and communication technologies have been playing a crucial role in improving the efficiency and effectiveness of learning and teaching in higher education. Two decades ago, research studies were focused on how to use artificial intelligence techniques to imitate teachers or tutors in delivering learning sessions. Machine learning techniques have been applied in several research studies to construct a student model in the context of intelligent tutoring systems. However, the usage of intelligent tutoring systems has been very limited in higher education as most educational institutions are in favour of using virtual learning environments (VLEs). VLEs are computer-based systems that support all aspects of teaching and learning from provision of course materials to managing coursework. In this research study, the emphasis is on the assessment aspect of VLEs. A literature review revealed that existing computer-based formative assessments have never utilised unsupervised machine learning to improve their feedback mechanisms. Machine learning techniques have been applied to construct student models, which is represented as categories of knowledge levels such as beginning, intermediate and advanced. The student model does not specify what concepts are understood, the gap of understanding and misconceptions. Previously, a snap-drift modal learning neural network has been applied to improve the feedback mechanisms of computer-based formative assessments. This study investigated the application of snap-drift modal learning neural network for analysing student responses to a set of multiple choice questions to identify student groups. This research study builds on this previous study and its aim is to improve the effectiveness of the application of snap-drift modal learning neural network in modelling student responses to a set of multiple choice questions and to extend its application in modelling student responses gathered from object-oriented programming exercises. A novel method was proposed and evaluated using trials that improves the effectiveness of snap-drift modal learning neural network in identifying useful student group profiles, representing them to facilitate generation of diagnostic feedback and assigning an appropriate diagnostic feedback automatically based on a given student response. Based on the insight gained into the use of this novel method, we extend it to identify useful student group profiles that represent different programming abilities for writing an object-oriented class. The purpose of identifying student group profiles is to facilitate construction of diagnostic feedback that improves the development of basic object-oriented programming abilities. Overall, the main objectives of this research project were addressed successfully. New insights are gained into the application of unsupervised learning in general and snap-drift modal learning in particular. The proposed methods are capable of improving the feedback mechanisms of existing computer-based formative assessment tools. The improved computer-based formative assessments could have a huge impact on students in improving conceptual understanding of topics and development of basic object-oriented programming abilities.
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Andréasson, Per. "Emotional Empathy, Facial Reactions, and Facial Feedback." Doctoral thesis, Uppsala universitet, Institutionen för psykologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-126825.
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The human face has a fascinating capability to express emotions. The facial feedback hypothesis suggests that the human face not only expresses emotions but is also able to send feedback to the brain and modulate the ongoing emotional experience. It has furthermore been suggested that this feedback from the facial muscles could be involved in empathic reactions. This thesis explores the concept of emotional empathy and relates it to two aspects concerning activity in the facial muscles. First, do people high versus low in emotional empathy differ in regard to in what degree they spontaneously mimic emotional facial expressions? Second, is there any difference between people with high as compared to low emotional empathy in respect to how sensitive they are to feedback from their own facial muscles? Regarding the first question, people with high emotional empathy were found to spontaneously mimic pictures of emotional facial expressions while people with low emotional empathy were lacking this mimicking reaction. The answer to the second question is a bit more complicated. People with low emotional empathy were found to rate humorous films as funnier in a manipulated sulky facial expression than in a manipulated happy facial expression, whereas people with high emotional empathy did not react significantly. On the other hand, when the facial manipulations were a smile and a frown, people with low as well as high emotional empathy reacted in line with the facial feedback hypothesis. In conclusion, the experiments in the present thesis indicate that mimicking and feedback from the facial muscles may be involved in emotional contagion and thereby influence emotional empathic reactions. Thus, differences in emotional empathy may in part be accounted for by different degree of mimicking reactions and different emotional effects of feedback from the facial muscles.
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Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.
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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.
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Longtin, André. "Nonlinear oscillations, noise and chaos in neural delayed feedback." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74311.
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Bifurcations and complex oscillations in the human pupil light reflex (PLR) are studied. Autonomous pupil area oscillations are produced by substituting electronically controllable nonlinear feedback for the normal negative feedback of this reflex. A physiologically sound theoretical framework in which to study pupillary oscillations is developed. The model, framed as a delay-differential equation (DDE), agrees quantitatively with the simpler periodic behaviors and qualitatively with the complex behaviors. Much of the aperiodicity in the data can be ascribed to noise and transients rather than to chaos. The critical behavior of the PLR at oscillation onset is different with piecewise constant rather than smooth negative feedback. In the former, relative fluctuations in period are larger than those in amplitude, and vice versa in the latter. Properties of the time solutions and densities of a stochastic DDE are used to explain this experimental result. The Hopf bifurcation in this system is postponed by both additive and multiplicative colored noise. Theoretical insight into the behavior of stationary densities of DDE's and the origin of the postponement is given, and implications for analyzing bifurcations in neural delayed feedback systems are discussed.
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Yang, Bong-Jun. "Adaptive Output Feedback Control of Flexible Systems." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5248.
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Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process.
Within this context, adaptive compensation for external disturbances is addressed.
In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The
effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction.
While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be
applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator.
The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an
adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in both the observer and the
controller. Simulations to illustrate these approaches include an inverted pendulum with its cart serially attached to two carts
(one unmodelled), three spring-coupled inverted pendulums, and an inverted pendulum with its initial condition in a range in which a linear controller is destabilizing.
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Kutay, Ali Turker. "Neural Network Based Adaptive Output Feedback Control: Applications and Improvements." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-11282005-122234/.
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Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2006.J. Eric Corban, Committee Member ; Panagiotis Tsiotras, Committee Member ; Eric N. Johnson, Committee Member ; Nader Sadegh, Committee Member ; Anthony J. Calise, Committee Chair.
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Fyfe, Colin. "Negative feedback as an organising principle for artificial neural networks." Thesis, University of Strathclyde, 1995. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21390.
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We investigate the properties of an unsupervised neural network which uses simple Hebbian learning and negative feedback of activation in order to self-organise. The negative feedback circumvents the well-known difficulty of positive feedback in Hebbian learning systems which causes the networks' weights to increase without bound. We show, both analytically and experimentally, that not only do the weights of networks with this architecture converge, they do so to values which give the networks important information processing properties: linear versions of the model are shown to perform a Principal Component Analysis of the input data while a non-linear version is shown to be capable of Exploratory Projection Pursuit. While there is no claim that the networks described herein represent the complexity found in biological networks, we believe that the networks investigated are not incompatible with known neurobiology. However, the main thrust of the thesis is a mathematical analysis of the emergent properties of the network; such analysis is backed by empirical evidence at all times.
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Franklin, D. R. "Neural networks for visual feedback control of an industrial robot." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599180.
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The majority of industrial robots in use today are configured by on-line programming at the start of each production run. The workpieces are located using precision indexing. The robots have little or no sensory input, other than joint position feedback, and are unable to operate in changing or loosely constrained environments. To overcome these constraints and to increase the range of practical applications, robots need to be able to apply adaptive intelligence to manufacturing operations. This calls for enhanced sensory capabilities. Vision systems have been introduced successfully into many production processes to perform component identification, inspection and location. When introduced into the robot workspace as part of a dynamic visual feedback control scheme they have the potential to reduce the costs associated with precise component fixturing, to compensate for calibration errors, to extend the working life of the robot, to align a robot program developed off-line with the part it is operating on, and to compensate for variations in components. The research presented here used a world-based stereo vision system to control an industrial robot in 3-dimensional space. A visual tracking algorithm was developed to follow the robot end-effector. Iterative and dynamic visual feedback control strategies were investigated. To achieve this it was necessary to translate between the visually observed position of the robot end-effector and its position in the workspace. The bulk of the experimental work was devoted to techniques for achieving this. Methods based on an affine stereo algorithm, a geometric perspective stereo algorithm, and a neural gas network were investigated. The neural gas network is an artificial neural network algorithm that uses a rapid interpolative training scheme. The network was used to implement either an image to robot joint space mapping or an image to Cartesian space mapping. The neural network algorithm had no prior knowledge of the positions of the cameras or the kinematics of the robot, but instead learned the mapping by making a series of trial movements and by updating the network weights based on the results. A number of different training scheme variations were investigated and optimised. The most accurate mapping algorithms were used to implement a dynamic dual loop visual control system. The resulting system was capable of driving the end-effector along a visually defined path. The system was able to tolerate a degree of robot miscalibration as well as serious image to robot miscalibration.
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Rollins, Elizabeth S. M. Massachusetts Institute of Technology. "Optimization of neural network feedback control systems using automatic differentiation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/59691.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 95-97).
Optimal control problems can be challenging to solve, whether using analytic or numerical methods. This thesis examines the application of an adjoint method for optimal feedback control, which combines various algorithmic techniques into an original numerical method. In the method investigated here, a neural network defines the control input in both trajectory and feedback control optimization problems. The weights of the neural network that minimize a cost function are determined by an unconstrained optimization routine. By using automatic differentiation on the code that evaluates the cost function, the gradient of the cost with respect to the weights is obtained for the gradient search phase of the optimization process. Automatic differentiation is more efficient than hand-differentiating code for the user and provides exact gradients, allowing the optimization of the neural network weights to proceed more rapidly. Another benefit of this method comes from its use of neural networks, which are able to represent complex feedback control policies, because they are general nonlinear function approximators. Neural networks also have the potential to be generalizable, meaning that a control policy found using a sufficiently rich training set will often work well for other initial conditions outside of the training set. Finally, the software implementation is modular, which means that the user only needs to adjust a few codes in order to set up the method for a specific problem. The application of the adjoint method to three control problems with known solutions demonstrates the ability of the method to determine neural networks that produce near-optimal trajectories and control policies.
by Elizabeth Rollins.
S.M.
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Weaver, Adam L. "The functional roles of the Lateral Pyloric and Ventricular Dilator neurons in the pyloric network of the lobster, Panulirus interruptus." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1010521587.
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Wilson, Luke. "The influence of feedback connections on the dynamics of populations of spiking neurons /." Leeds : University of Leeds, School of Computer Studies, 2008. http://www.comp.leeds.ac.uk/fyproj/reports/0708/Wilson.pdf.
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Jiang, Yiwu. "Neural network based feedback linearization control of an unmanned aerial vehicle." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/26937.
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In this thesis, a flight control design of an unmanned aerial vehicle (UAV) using neural network based feedback linearization and the output redefinition technique is presented. The UAV model we chose in this research is a nonlinear nonminimum phase system. The output redefinition technique is used in a way such that the resulting system is minimum phase and can be inverted. The nonlinear autoregressive moving average (NARMA-L2) neural network is trained off-line to identify the forward dynamics of the UAV model with the redefined output, and then inverted to force the real output to approximately track the desired trajectory. The results shows that a good tracking performance can be achieved using this control scheme.
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Hassibi, Khosrow M. "A study of the application of neural networks to feedback linearization." Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055527640.
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Tukhlina, Natalia. "Feedback control of complex oscillatory systems." Phd thesis, Universität Potsdam, 2008. http://opus.kobv.de/ubp/volltexte/2008/1854/.
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In the present dissertation paper an approach which ensures an efficient control of such diverse systems as noisy or chaotic oscillators and neural ensembles is developed. This approach is implemented by a simple linear feedback loop. The dissertation paper consists of two main parts.
One part of the work is dedicated to the application of the suggested technique to a population of neurons with a goal to suppress their synchronous collective dynamics.
The other part is aimed at investigating linear feedback control of coherence of a noisy or chaotic self-sustained oscillator.
First we start with a problem of suppressing synchronization in a large population of interacting neurons.
The importance of this task is based on the hypothesis that emergence of pathological brain activity in the case of Parkinson's disease and other neurological disorders is caused by synchrony of many thousands of neurons.
The established therapy for the patients with such disorders is a permanent high-frequency electrical stimulation via the depth microelectrodes, called Deep Brain Stimulation (DBS). In spite of efficiency of such stimulation, it has several side effects and mechanisms underlying DBS remain unclear.
In the present work an efficient and simple control technique is suggested. It is designed to ensure suppression of synchrony in a neural ensemble by a minimized stimulation that vanishes as soon as the tremor is suppressed. This vanishing-stimulation technique would be a useful tool of experimental neuroscience; on the other hand, control of collective dynamics in a large population of units represents an interesting physical problem. The main idea of suggested approach is related to the classical problem of oscillation theory, namely the interaction between a self-sustained (active) oscillator and a passive load (resonator). It is known that under certain conditions the passive oscillator can suppress the oscillations of an active one. In this thesis a much more complicated case of active medium, which itself consists of thousands of oscillators is considered. Coupling this medium to a specially designed passive oscillator, one can control the collective motion of the ensemble, specifically can enhance or suppress it. Having in mind a possible application in neuroscience, the problem of suppression is concentrated upon.
Second, the efficiency of suggested suppression scheme is illustrated by considering more complex case, i.e. when the population of neurons generating the undesired rhythm consists of two non-overlapping subpopulations: the first one is affected by the stimulation,
while the collective activity is registered from the second one. Generally speaking, the second population can be by itself both active and passive; both cases are considered here. The possible applications of suggested technique are discussed.
Third, the influence of the external linear feedback on coherence of a noisy or chaotic self-sustained oscillator is considered. Coherence is one of the main properties of self-oscillating systems and plays a key role in the construction of clocks, electronic generators, lasers, etc. The coherence of a noisy limit cycle oscillator in the context of phase dynamics is evaluated by the phase diffusion constant, which is in its turn proportional to the width of the spectral peak of oscillations. Many chaotic oscillators can be described within the framework of phase dynamics, and, therefore, their coherence can be also quantified by the way of the phase diffusion constant. The analytical theory for a general linear feedback, considering noisy systems in the linear and Gaussian approximation is developed and validated by numerical results.In der vorliegenden Dissertation wird eine Näherung entwickelt, die eine effiziente Kontrolle verschiedener Systeme wie verrauschten oder chaotischen Oszillatoren und Neuronenensembles ermöglicht. Diese Näherung wird durch eine einfache lineare Rückkopplungsschleife implementiert. Die Dissertation besteht aus zwei Teilen. Ein Teil der Arbeit ist der Anwendung der vorgeschlagenen Technik auf eine Population von Neuronen gewidmet, mit dem Ziel ihre synchrone Dynamik zu unterdrücken. Der zweite Teil ist auf die Untersuchung der linearen Feedback-Kontrolle der Kohärenz eines verrauschten oder chaotischen, selbst erregenden Oszillators gerichtet. Zunächst widmen wir uns dem Problem, die Synchronisation in einer großen Population von aufeinander wirkenden Neuronen zu unterdrücken. Da angenommen wird, dass das Auftreten pathologischer Gehirntätigkeit, wie im Falle der Parkinsonschen Krankheit oder bei Epilepsie, auf die Synchronisation großer Neuronenpopulation zurück zu führen ist, ist das Verständnis dieser Prozesse von tragender Bedeutung. Die Standardtherapie bei derartigen Erkrankungen besteht in einer dauerhaften, hochfrequenten, intrakraniellen Hirnstimulation mittels implantierter Elektroden (Deep Brain Stimulation, DBS). Trotz der Wirksamkeit solcher Stimulationen können verschiedene Nebenwirkungen auftreten, und die Mechanismen, die der DBS zu Grunde liegen sind nicht klar. In meiner Arbeit schlage ich eine effiziente und einfache Kontrolltechnik vor, die die Synchronisation in einem Neuronenensemble durch eine minimierte Anregung unterdrückt und minimalinvasiv ist, da die Anregung stoppt, sobald der Tremor erfolgreich unterdrückt wurde. Diese Technik der "schwindenden Anregung" wäre ein nützliches Werkzeug der experimentellen Neurowissenschaft. Desweiteren stellt die Kontrolle der kollektiven Dynamik in einer großen Population von Einheiten ein interessantes physikalisches Problem dar. Der Grundansatz der Näherung ist eng mit dem klassischen Problem der Schwingungstheorie verwandt - der Interaktion eines selbst erregenden (aktiven) Oszillators und einer passiven Last, dem Resonator. Ich betrachte den deutlich komplexeren Fall eines aktiven Mediums, welches aus vielen tausenden Oszillatoren besteht. Durch Kopplung dieses Mediums an einen speziell hierür konzipierten, passiven Oszillator kann man die kollektive Bewegung des Ensembles kontrollieren, um diese zu erhöhen oder zu unterdrücken. Mit Hinblick auf eine möglichen Anwendung im Bereich der Neurowissenschaften, konzentriere ich mich hierbei auf das Problem der Unterdrückung. Im zweiten Teil wird die Wirksamkeit dieses Unterdrückungsschemas im Rahmen eines komplexeren Falles, bei dem die Population von Neuronen, die einen unerwünschten Rhythmus erzeugen, aus zwei nicht überlappenden Subpopulationen besteht, dargestellt. Zunächst wird eine der beiden Subpopulationen durch Stimulation beeinflusst und die kollektive Aktivität an der zweiten Subpopulation gemessen. Im Allgemeinen kann sich die zweite Subpopulation sowohl aktiv als auch passiv verhalten. Beide Fälle werden eingehend betrachtet. Anschließend werden die möglichen Anwendungen der vorgeschlagenen Technik besprochen. Danach werden verschiedene Betrachtungen über den Einfluss des externen linearen Feedbacks auf die Kohärenz eines verrauschten oder chaotischen selbst erregenden Oszillators angestellt. Kohärenz ist eine Grundeigenschaft schwingender Systeme und spielt ein tragende Rolle bei der Konstruktion von Uhren, Generatoren oder Lasern. Die Kohärenz eines verrauschten Grenzzyklus Oszillators im Sinne der Phasendynamik wird durch die Phasendiffusionskonstante bewertet, die ihrerseits zur Breite der spektralen Spitze von Schwingungen proportional ist. Viele chaotische Oszillatoren können im Rahmen der Phasendynamik beschrieben werden, weshalb ihre Kohärenz auch über die Phasendiffusionskonstante gemessen werden kann. Die analytische Theorie eines allgemeinen linearen Feedbacks in der Gaußschen, als auch in der linearen, Näherung wird entwickelt und durch numerische Ergebnisse gestützt.
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Dall´Agnol, Marcelo. "Aplicação de controladores feedback em sistema experimental de refrigeração e desenvolvimento de modelo preditivo baseado em redes neurais." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266964.
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Orientadores: Ana Maria Frattini Fileti, Flávio Vasconcelos da SilvaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
Made available in DSpace on 2018-08-16T02:02:41Z (GMT). No. of bitstreams: 1 Dall´Agnol_Marcelo_M.pdf: 3167173 bytes, checksum: fc8b21f6fe3204996f4d61c2febac662 (MD5) Previous issue date: 2010
Resumo: O presente trabalho teve por objetivo propor uma sistemática para identificação de um sistema de refrigeração dotado de compressor e bomba de fluido secundário de rotação variável, para a aplicação futura de alternativas de controle multivariáveis com foco na redução do consumo de energia e otimização do coeficiente de performance (COP) de tais sistemas. Uma primeira etapa de ensaios experimentais foi realizada para gerar um conhecimento prévio do sistema, caracterizando-o como não-linear. Foram utilizadas diversas arquiteturas de redes neurais artificiais (RNA) para gerar modelos não-lineares MIMO (Multiple Input-Multiple Output), através do software MATLAB® para fazer a predição das temperaturas de evaporação e do fluido secundário. Para treinamento da rede neural foram utilizados dados de ensaios em malha aberta e malha fechada com um controlador PID desenvolvido especialmente para este fim. Neste sistema de controle PID foram utilizadas técnicas como anti-windup e derivative-kick na tentativa de melhorar a atuação de tal controlador. O PID, apesar de conseguir controlar o sistema em torno de 2°C, mostrou-se pouco eficiente quando eram aplicados degraus no set-point e na carga elétrica. A modelagem não-linear resultou em uma rede neural com arquitetura 11x2x8x2 que foi capaz de prever as duas temperaturas com precisão de mais ou menos 0,5°C, mesmo quando o sistema de refrigeração se manteve instável.
Abstract: The main aim of the present work was to propose a systematic identification procedure for an experimental refrigeration system operating under variable rotation speed compressor and pump of secondary fluid. This work will find future applications in alternative multivariable control development with focus on reducing energy consumption and on the optimization of the coefficient of performance (COP) of such systems. The nonlinear feature of the system was proved by means of imposed speed disturbances process reaction curves. Using the software MATLAB? Various artificial neural networks (ANN) architectures were used to generate non-linear models with Multiple-Input-Multiple-Output (MIMO) variables, in order to predict the evaporation and the secondary fluid temperatures. Open-loop and also closed loop assays were carried out and the experimental data used in the neural network training procedure. A specially designed PID, using anti-windup and derivative-kick techniques, was employed in the closed-loop runs. This conventional temperature controller showed an acceptable off-set of 2°C, however some instability occurred when set point and thermal load changed. The best non-linear modeling resulted in a neural network with architecture 11x2x8x2. This neural model was able to predict the two temperatures with precision of about 0.5°C, even when the refrigeration system remained unstable.
Mestrado
Sistemas de Processos Quimicos e Informatica
Mestre em Engenharia Química
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許建平 and Kin-ping Hui. "Computer texture boundary detection based on texton model and neural positive feedback." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B42574298.
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Hui, Kin-ping. "Computer texture boundary detection based on texton model and neural positive feedback." [Hong Kong] : The University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record/B42574298.
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Ingram, Stephen D. "Visual Feedback Stabilisation of a Cart Inverted Pendulum A." Thesis, University of Bradford, 2016. http://hdl.handle.net/10454/17375.
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Vision-based object stabilisation is an exciting and challenging area of research, and is one that promises great technical advancements in the field of computer vision. As humans, we are capable of a tremendous array of skilful interactions, particularly when balancing unstable objects that have complex, non-linear dynamics. These complex dynamics impose a difficult control problem, since the object must be stabilised through collaboration between applied forces and vision-based feedback. To coordinate our actions and facilitate delivery of precise amounts of muscle torque, we primarily use our eyes to provide feedback in a closed-loop control scheme. This ability to control an inherently unstable object by vision-only feedback demonstrates an exceptionally high degree of voluntary motor skill. Despite the pervasiveness of vision-based stabilisation in humans and animals, relatively little is known about the neural strategies used to achieve this task.
In the last few decades, with advancements in technology, we have tried to impart the skill of vision-based object stabilisation to machines, with varying degrees of success. Within the context of this research, we continue this pursuit by employing the classic Cart Inverted Pendulum; an inherently unstable, non-linear system to investigate dynamic object balancing by vision-only feedback. The Inverted Pendulum is considered to be one of the most fundamental benchmark systems in control theory; as a platform, it provides us with a strong, well established test bed for this research.
We seek to discover what strategies are used to stabilise the Cart Inverted Pendulum, and to determine if these strategies can be deployed in Real-Time, using cost-effective solutions. The thesis confronts, and overcomes the problems imposed by low-bandwidth USB cameras; such as poor colour-balance, image noise and low frame rates etc., to successfully achieve vision-based stabilisation.
The thesis presents a comprehensive vision-based control system that is capable of balancing an inverted pendulum with a resting oscillation of approximately ±1º. We employ a novel, segment-based location and tracking algorithm, which was found to have excellent noise immunity and enhanced robustness. We successfully demonstrate the resilience of the tracking and pose estimation algorithm against visual disturbances in Real-Time, and with minimal recovery delay. The algorithm was evaluated against peer reviewed research; in terms of processing time, amplitude of oscillation, measurement accuracy and resting oscillation. For each key performance indicator, our system was found to be superior in many cases to that found in the literature.
The thesis also delivers a complete test software environment, where vision-based algorithms can be evaluated. This environment includes a flexible tracking model generator to allow customisation of visual markers used by the system. We conclude by successfully performing off-line optimization of our method by means of Artificial Neural Networks, to achieve a significant improvement in angle measurement accuracy.Goodrich Engine Control Systems and Balfour Beatty Rail Technologies
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Böhm, Urs Lucas. "Physiological inputs to cerebrospinal fluid-contacting neurons." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066196/document.
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Les neurones au contact du liquide céphalorachidien (CSF-cNs) sont des cellules ciliées présentes tout autour du canal central de la moelle épinière. Ces cellules sont GABAergiques, déploient une brosse de microvillosités à l'intérieur de la lumière du canal et sont caractérisées par une expression du canal ionique Pkd2l1. Ceci les désigne comme de potentielles cellules sensorielles. Il a été montré que les CSF-cNs peuvent moduler la locomotion et qu'elles réagissent aux variations de pH in vitro. Cependant les modalités sensorielles transmises par ces cellules et leur implication dans la fonction locomotrice nous échappent encore. Dans ma thèse, j'étudie la fonction sensorielle des CSF-cNs dans la moelle épinière de la larve de poisson zèbre. En combinant le relargage de proton et l'imagerie pH avec l'imagerie calcique, nous avons pu montrer que les CSF-cNs répondent à des pics d'acidification in vivo et que cette réponse persiste dans des mutants pkd2l1. Nous démontrons également que les CSF-cNs ne sont pas activés de façon coordonnée lors de la locomotion fictive. Les mouvements actifs ou passifs de la queue conduisent toutefois à l'activation spécifique des CSF-cNs ipsilatérales de la contraction musculaire. Ces observations suggèrent que les CSF-cNs sont recrutées par une stimulation mécanique. Les mutants pkd2l1 ont montré une diminution de la réponse à la flexion active et passive de la queue et une diminution de la fréquence de battement de la queue. Dans son ensemble, le travail présenté ici met donc en évidence que les CSF-cNs répondent aux variations de pH in vivo et révèle leur rôle d'organe mécanosensoriel permettant la modulation du réseau locomoteur spinalCerebrospinal fluid-contacting neurons (CSF-cNs) are ciliated cells surrounding the central canal. These cells are GABAergic, extend a brush of microvilli into the lumen and are specified by the expression of the transient receptor potential ion channel Pkd2l1. The atypical morphology of CSF-cNs and their location make them candidates for sensory cells. It has been shown that CSF-cNs modulate locomotion by projecting onto the locomotor central pattern generators (CPGs) and that CSF-cNs can react to changes of pH in vitro, but the sensory modality these cells convey to spinal circuits and their relevance to locomotion remain elusive. In my thesis I investigate the sensory function of CSF-cNs in the zebrafish larva spinal cord. By combining proton uncaging together with pH imaging and calcium imaging, we could show that CSF-cNs respond to pulses of acidification in vivo and that this response persists in pkd2l1 mutants. Using genetically encoded calcium sensors we showed that CSF-cNs are not coordinately activated during fictive locomotion. Active or passive tail movement, however, led to CSF-cN activation restrained to cells ipsilateral to muscle contraction. These observations suggest that CSF-cNs are recruited by ipsilateral muscle contraction and/or tail torsion. Pkd2l1 mutants showed a decreased response to active and passive bending of the tail and a subtle but consistent decrease of tail-beat frequency was observed in the startle response. Altogether, the presented work shows evidence that CSF-cNs respond to changes in CSF pH and reveals that CSF-cNs constitute a mechanosensory organ which operates during locomotion to modulate spinal CPGs
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Deng, Jiamei. "Predictive control of nonlinear systems using feedback linearisation based on dynamic neural networks." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433463.
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Bereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.
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Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing to new users. We build our proposed recommender system for recommending the articles of Peltarion Knowledge Center. By incorporating two data sources, implicit user feedback based on pageview data as well as the content of articles, we propose a hybrid recommender solution. Throughout our experiments, we compare our proposed solution with a matrix factorization approach as well as a popularity- based and a random baseline, analyse the hyperparameters of our model, and examine the capability of our solution to give recommendations to new users who were not part of the training data set. Our model results in slightly lower, but similar Mean Average Precision and Mean Reciprocal Rank scores to the matrix factorization approach, and outperforms the popularity- based and random baselines. The main advantages of our model are computational efficiency and its ability to give relevant recommendations to new users without the need for retraining the model, which are key features for real- world use cases.Rekommendationssystem används ofta på webbplatser och applikationer för att hjälpa användare att hitta relevant innehåll baserad på deras intressen. Med utvecklingen av grafneurala nätverk nådde toppmoderna resultat inom rekommendationssystem och representerade data i form av en graf. De flesta grafbaserade lösningar har dock svårt med beräkningskomplexitet eller att generalisera till nya användare. Därför föreslår vi ett nytt grafbaserat rekommendatorsystem genom att modifiera Simple Graph Convolution. De här tillvägagångssätt är en effektiv grafnodsklassificering och lägga till möjligheten att generalisera till nya användare. Vi bygger vårt föreslagna rekommendatorsystem för att rekommendera artiklarna från Peltarion Knowledge Center. Genom att integrera två datakällor, implicit användaråterkoppling baserad på sidvisningsdata samt innehållet i artiklar, föreslår vi en hybridrekommendatörslösning. Under våra experiment jämför vi vår föreslagna lösning med en matrisfaktoriseringsmetod samt en popularitetsbaserad och en slumpmässig baslinje, analyserar hyperparametrarna i vår modell och undersöker förmågan hos vår lösning att ge rekommendationer till nya användare som inte deltog av träningsdatamängden. Vår modell resulterar i något mindre men liknande Mean Average Precision och Mean Reciprocal Rank poäng till matrisfaktoriseringsmetoden och överträffar de popularitetsbaserade och slumpmässiga baslinjerna. De viktigaste fördelarna med vår modell är beräkningseffektivitet och dess förmåga att ge relevanta rekommendationer till nya användare utan behov av omskolning av modellen, vilket är nyckelfunktioner för verkliga användningsfall.
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Nardi, Flavio. "Neural network based adaptive alogrithms for nonlinear control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/12012.
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Boitard, Constance. "Identification des réseaux neurobiologiques gouvernant les apprentissages ambigus chez l'abeille Apis mellifera." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30125/document.
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L'apprentissage associatif recouvre des niveaux variables de complexité, des tâches cognitives simples jusqu'à des tâches complexes qui nécessitent la résolution de discriminations ambigües. Cette thèse traite de deux protocoles présentant des ambigüités chez l'abeille, au cours desquels le blocage de la signalisation GABAergique des neurones récurrents sur les corps pédonculés, structures cérébrales majeures de l'apprentissage, est à l'origine de la perte de capacité de résolution ambigüe. Ces neurones, non requis pour les apprentissages simples, semblent donc indispensables à la résolution des ambigüités propres aux discriminations cognitives complexes et élaborées chez l'abeilleAssociative learning spans different levels of complexity, from simple tasks involving simple causal relationships between events, to ambiguous tasks, in which animals have to solve complex discriminations based on non-linear associative links. We focused on two protocols presenting a temporal or configural ambiguity at the level of stimulus contingencies in honey bees (\textit{Apis mellifera}). We performed selective blockades of GABAergic signalisation from recurrent feedback neurons in the mushroom bodies (MBs), higher-order insect brain structures associated with memory storage and retrieval, and found that this blockade within the MB calyces impaired both ambiguous learning tasks, although if did not affect simple conditioning counterparts. We suggest that the A3v cluster of the GABA feedback neurons innervating the MBs calyces are thus dispensable for simple learning, but are required for counteracting stimulus ambiguity in complex discriminations in honey bees
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Zhong, Junpei [Verfasser], and Stefan [Akademischer Betreuer] Wermter. "Artificial Neural Models for Feedback Pathways for Sensorimotor Integration / Junpei Zhong. Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2015. http://d-nb.info/1073248208/34.
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Pérez, Dattari Rodrigo Javier. "Interactive learning with corrective feedback for continuous-action policies based on deep neural networks." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/170535.
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Tesis para optar al grado de Magíster en Ciencias de la Ingeniería, Mención EléctricaMemoria para optar al título de Ingeniero Civil Eléctrico
El Aprendizaje Reforzado Profundo (DRL) se ha transformado en una metodología poderosa para resolver problemas complejos de toma de decisión secuencial. Sin embargo, el DRL tiene varias limitaciones cuando es usado en problemas del mundo real (p.ej. aplicaciones de robótica). Por ejemplo, largos tiempos de entrenamiento (que no se pueden acelerar) son requeridos, en contraste con ambientes simulados, y las funciones de recompensa pueden ser difíciles de especificar/modelar y/o computar. Más aún, el traspaso de políticas aprendidas en simulaciones al mundo real no es directo (\emph{reality gap}). Por otro lado, métodos de aprendizaje de máquinas basados en la transferencia de conocimiento humano a un agente han mostrado ser capaces de obtener políticas con buenos desempeños sin necesariamente requerir el uso de una función de recompensa, siendo eficientes en lo que respecta al tiempo. En este contexto, en esta tesis se introduce una estrategia de Aprendizaje Interactivo de Máquinas (IML) para entrenar políticas modeladas como Redes Neuronales Profundas (DNNs), basada en retroalimentación correctiva humana con un método llamado D-COACH. Se combina Aprendizaje Profundo (DL) con el método Asesoramiento Correctivo Comunicado por Humanos (COACH), en donde humanos no expertos pueden entrenar políticas corrigiendo las acciones que va tomando el agente en ejecución. El método D-COACH tiene el potencial de resolver problemas complejos sin la necesidad de utilizar muchos datos o tiempo. Resultados experimentales validan la eficiencia del método propuesto en plataformas simuladas y del mundo real, en espacios de estados de baja y alta dimensionalidad, mostrando la capacidad de aprender políticas en espacios de acción continuos de manera efectiva. El método propuesto mostró resultados particularmente interesantes cuando políticas parametrizadas con Redes Neuronales Convolucionales (CNNs) fueron usadas para resolver problemas con espacios de estado de alta dimensionalidad, como pixeles desde una imagen. Al usar CNNs, los agentes tienen la capacidad de construir valiosas representaciones del estado del ambiente sin la necesidad de hacer ingeniería de características por el lado del diseñador (lo que era siempre necesario en el Aprendizaje Reforzado (RL) clásico). Estas propiedades pueden ser muy útiles en robótica, ya que es común encontrar aplicaciones en donde la información adquirida por los sensores del sistema es de alta dimensionalidad, como imágenes RGB. Darles la habilidad a los robots de aprender desde datos del alta dimensionalidad va a permitir aumentar la complejidad de los problemas que estos pueden resolver. A lo largo de esta tesis se proponen y validan tres variaciones de D-COACH. La primera introduce una estructura general para resolver problemas de estado de baja y alta dimensionalidad. La segunda propone una variación del primer método propuesto para problemas de estado de alta dimensionalidad, reduciendo el tiempo y esfuerzo de un humano al entrenar una política. Y por último, la tercera introduce el uso de Redes Neuronales Recurrentes para añadirle memoria a los agentes en problemas con observabilidad parcial.
FONDECYT 1161500
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Sdrulla, Dan Alexandru. "Adenosine-dependent short- and long-term changes in hippocampal synaptic plasticity /." Connect to full text via ProQuest. IP filtered, 2005.
Find full textAbstract:
Thesis (Ph.D. in Neuroscience) -- University of Colorado, 2005.Typescript. Includes bibliographical references (leaves 96-111). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;
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Tan, Daniel. "Restoring Sensation in Human Upper Extremity Amputees using Chronic Peripheral Nerve Interfaces." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405070015.
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Guglielmi, Luca [Verfasser], and Matthias [Akademischer Betreuer] Carl. "A Wif1 mediated feedback loop suppresses premature Wnt signaling in nascent habenular neurons / Luca Guglielmi ; Betreuer: Matthias Carl." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177385058/34.
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Du, Hongliu. "Control of systems with uncertainties /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841139.
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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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Kim, Nakwan. "Improved Methods in Neural Network-Based Adaptive Output Feedback Control, with Applications to Flight Control." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5282.
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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.
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Gibb, Leif. "Inhibition, recurrent excitation, and neural feedback in computational models of sparse bursting and birdsong sequencing." Diss., [La Jolla, Calif.] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3344677.
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Thesis (Ph. D.)--University of California, San Diego, 2009.Title from first page of PDF file (viewed March 19, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Trujillo, Logan Thomas. "Electrophysiological Correlates of the Influences of Past Experience on Conscious and Unconscious Figure-Ground Perception." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/194981.
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Figure-ground perception can be modeled as a competitive process with mutual inhibition between shape properties on opposite sides of an edge. This dissertation reports brain-based evidence that such competitive inhibition can be induced by access to preexisting object memory representations during figure assignment. Silhouette stimuli were used in which the balance of properties along an edge biased the inner, bounded, region to be seen as a novel figure. Experimental silhouettes (EXP) suggested familiar objects on their outside edges, which nonetheless appeared as shapeless grounds. Control silhouettes (CON) suggested novel shapes on the outside.In an initial task, human observers categorized masked EXP and CON silhouettes (175 ms exposure) as "novel" versus a third group of silhouettes depicting "familiar" objects on the inside. Signal detection measures verified that observers were unconscious of the familiar shapes within the EXP stimuli. Across three experiments, novel categorizations were highly accurate with shorter RTs for EXP than CON. Event-related potential (ERP) indices of observers' brain activity (Experiments 2 and 3) revealed a Late Potential (~300 ms) to be less positive for EXP than CON, a reduction in neural activity consistent with the presence of greater competitive inhibition for EXP stimuli. After controlling for stimulus confounds (Experiment 3), the P1 ERP (~100 ms) was larger for EXP than CON conditions, perhaps reflecting unconscious access to object memories.In a second task, observers were informed about familiar shapes suggested on the outsides of the EXP silhouettes before viewing masked (Experiments 1 and 2) or unmasked (Experiment 3) EXP and CON silhouettes to report whether they saw familiar shapes on the outside. Experiment 3 observers were more accurate to categorize CON vs. EXP stimuli as novel vs. familiar, with shorter RTs for EXP than CON. Task 2 N170 ERPs (~170 ms) were larger for EXP than CON in Experiments 2 and 3, reflecting the conscious perception of familiar shape in the outsides of EXP silhouettes. LP magnitudes were greater for CON than EXP, although ERP polarity was dependent on the presence/absence of a mask. Task 2 LPs may reflect competitive inhibition or longer processing times for CON stimuli.
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Eyng, Eduardo. "Controle feedforward-feedback aplicado as colunas de absorção do processo de produção de etanol por fermentação." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267254.
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Orientador: Ana Maria Frattini FiletiTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica
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Resumo: O etanol perdido por evaporacao durante o processo de producao por fermentação pode ser recuperado por uma coluna de absorcao, a qual requer um sistema de controle robusto de acordo com sua complexidade. Este equipamento tambem e utilizado no tratamento do CO2, um co-produto deste processo. Neste sentido, no presente trabalho foi proposto e testado, o emprego de controladores feedforward-feedback, baseados em modelo inverso de redes neurais, os quais manipulam as vazoes de solvente que sao alimentados as colunas, para controlar a concentracao de etanol na corrente gasosa a saida da primeira coluna, e a concentracao de agua residual no efluente gasoso da segunda. Simulacoes foram feitas, sendo abordado tanto o problema regulatorio quanto o problema servo para as duas colunas de absorcao do processo estudado. O desempenho do controlador neural foi superior ao apresentado por um controlador convencional PID, para ambas as colunas de absorcao estudadas, pois o tempo de resposta, assim como o overshoot foi menor. A superioridade do controlador neural foi comprovada pelos valores obtidos para os parametros ITAE (integral do erro absoluto ponderada pelo tempo), IAE (integral do erro absoluto) e ISE (integral do quadrado do erro). Outro objetivo deste trabalho foi avaliar a influencia das incertezas nos sensores sobre o desempenho do sistema de controle. Foram testados tres niveis: 5, 10 e 15%, sendo as incertezas inseridas nas variaveis de entrada do tipo concentracao de etanol/agua residual na corrente gasosa. Para a coluna de recuperacao de etanol, tanto para o problema regulatorio quanto servo, nenhum controlador conseguiu estabilizar a variavel controlada no set point, no entanto, quando empregado o controlador neural a amplitude da faixa de oscilacao da variavel controlada foi menor para todos os niveis de incerteza testados. Já para a coluna de tratamento de CO2, os controladores encontraram dificuldades em manter a estabilidade do sistema. Neste sentido, o controlador neural apresentou um desempenho satisfatorio para incertezas de 5 e 10%, enquanto que o PID nao conseguiu manter o sistema estavel para incertezas superiores a 5%. Com base nos testes realizados foi constatado que o controlador neural proposto constitui uma opcao atrativa para o controle das colunas de absorcao do processo deproducao de etanol por fermentacao, principalmente quando os niveis de incerteza forem de ate 10%.
Abstract: Some of ethanol lost by evaporation during its fermentation production process may be recovered using an absorption column, which requires a robust control system. This equipment also is used on carbonic gas treatment, a by-product of this process. In the present work, the development of nonlinear feedforward-feedback controllers, based on a neural network inverse model, was proposed and tested to manipulate the absorbent flow rates in order to control the residual ethanol concentration in the effluent gas phase at the first absorption column, and the residual water at the second one. Simulation studies were carried out for the regulator and servo problem, for both absorption columns studied. The neural controller proposed outperformed a conventional PID, because the response time, and also the overshoot were smaller when the neural controller was applied. The results were confirmed by the ITAE (integral of time multiplied by the absolute error), IAE (integral of absolute error) and ISE (integral of square error) parameters. The measurement uncertainties influence on control system performance was tested for three levels: 5, 10 and 15%. The uncertainties were introduced on ethanol/residual water concentration on gas phase. For the ethanol recovery column, neither PID nor the neural controller drove the controlled variable exactly to the set point, however, the neural controller provided a smaller oscillation for all uncertainty levels tested, for regulator and servo problem. The neural controller also outperformed PID in CO2 treatment column. For the regulator and servo problems the neural controller successfully proceeded when the uncertainty level was 5% or 10%, while the PID did not deal adequately with uncertainties above 5%. Therefore, the proposed neural controller proved be an attractive control solution for the absorption columns of ethanol production process by fermentation, especially when the input variables carry small uncertainties ( less than 10%) from the sensors.
Doutorado
Sistemas de Processos Quimicos e Informatica
Doutor em Engenharia Química
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Williams, Carrie. "Influence of Sensory Feedback on Rhythmic Movement: A Computational Study of Resonance Tuning in Biological Systems." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11172006-180642/.
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Thesis (Ph. D.)--Biomedical Engineering, Georgia Institute of Technology, 2007.DeWeerth, Stephen, Committee Chair ; Lee, Robert, Committee Member ; Ting, Lena, Committee Member ; Katz, Paul, Committee Member ; Butera, Robert, Committee Member.
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Vance, Jonathan Blake. "Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Vance_09007dcc8043fb11.pdf.
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Thesis (Ph. D.)--University of Missouri--Rolla, 2007.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed March 26, 2008) Includes bibliographical references.
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Thurston, Peter William. "Sequentiality in artificial neural networks : an account of three models exploring the benefit of time delayed feedback." Thesis, University of Kent, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257204.
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Quérée, Philip. "Feedback control in the central 5-HT system : evidence for a role of 5-HT₂c receptors." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670061.
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Gong, Jianwei. "NON-SILICON MICROFABRICATED NANOSTRUCTURED CHEMICAL SENSORS FOR ELECTRIC NOSE APPLICATION." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4082.
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A systematic investigation has been performed for "Electric Nose", a system that can identify gas samples and detect their concentrations by combining sensor array and data processing technologies. Non-silicon based microfabricatition has been developed for micro-electro-mechanical-system (MEMS) based gas sensors. Novel sensors have been designed, fabricated and tested. Nanocrystalline semiconductor metal oxide (SMO) materials include SnO2, WO3 and In2O3 have been studied for gas sensing applications. Different doping material such as copper, silver, platinum and indium are studied in order to achieve better selectivity for different targeting toxic gases including hydrogen, carbon monoxide, hydrogen sulfide etc. Fundamental issues like sensitivity, selectivity, stability, temperature influence, humidity influence, thermal characterization, drifting problem etc. of SMO gas sensors have been intensively investigated. A novel approach to improve temperature stability of SMO (including tin oxide) gas sensors by applying a temperature feedback control circuit has been developed. The feedback temperature controller that is compatible with MEMS sensor fabrication has been invented and applied to gas sensor array system. Significant improvement of stability has been achieved compared to SMO gas sensors without temperature compensation under the same ambient conditions. Single walled carbon nanotube (SWNT) has been studied to improve SnO2 gas sensing property in terms of sensitivity, response time and recovery time. Three times of better sensitivity has been achieved experimentally. The feasibility of using TSK Fuzzy neural network algorithm for Electric Nose has been exploited during the research. A training process of using TSK Fuzzy neural network with input/output pairs from individual gas sensor cell has been developed. This will make electric nose smart enough to measure gas concentrations in a gas mixture. The model has been proven valid by gas experimental results conducted.Ph.D.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering
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Klinke, Ina [Verfasser]. "Associative plasticity and context modulation in GABAergic feedback neurons of the mushroom body output in the honeybee (Apis mellifera) / Ina Klinke." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1026992354/34.
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Shih, Peter. "Reinforcement-learning based output-feedback controller for nonlinear discrete-time system with application to spark ignition engines operating lean and EGR." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Shih_thesis_2007_030207_09007dcc80336990.pdf.
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Thesis (M.S.)--University of Missouri--Rolla, 2007.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 16, 2007) Includes bibliographical references.
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Fidelin, Kevin. "Modulation of premotor circuits controlling locomotor activity by spinal GABAergic sensory neurons in zebrafish : connectivity mapping of an intraspinal sensory feedback circuit." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066200/document.
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Comprendre les mécanismes mis en place au sein du système nerveux pour générer des répertoires locomoteurs complexes reste l'un des grands défis des neurosciences systémiques. Le travail présenté dans ce manuscrit vise à comprendre comment les neurones de la moelle épinière contribuent à la production et à la modulation de l'activité locomotrice. Pour répondre à ce problème, nous utilisons le poisson-zèbre comme organisme modèle et avons développé de nouvelles approches génétiques et optiques afin de disséquer l'architecture du circuit formé par une classe de neurones sensoriels de la moelle et qui est conservée chez tous les vertébrés. Ces neurones sont appelés les neurones au contact du liquide céphalo-rachidien (Nc-LCR) et nous proposons de sonder leur(s) fonction(s) in vivo. Ces neurones sensoriels forment une interface unique entre le liquide céphalo-rachidien et le réseau de neurones impliqué dans le contrôle du mouvement dans la moelle épinière. Cependant, leur diagramme de connectivité demeure complètement inconnu. Afin de comprendre comment ces " Nc-LCR ou CSF-cNs " modulent la locomotion chez les vertébrés, nous avons développé un projet combinant des approches génétiques, électrophysiologiques, d'imagerie, et d'analyse du comportement, afin de cartographier le circuit qu'elles forment avec les neurones de la moelle épinière. Nos résultats montrent que les CSF-cNs projettent sur de nombreux éléments du centre générateur de rythme de la moelle. Notre approche révèle également la capacité des CSF-cNs à moduler la locomotion selon l'état dans lequel se trouve l'animal, une propriété caractéristique des circuits proprioceptifs dans la moelle épinièreUnderstanding how the central nervous system generates motor sequences, coordinates limbs and body orientation in an ever-changing environment, while adapting to sensory cues remains a central question in the field of systems neuroscience. The work presented here aims to understand how local sensory neurons in the spinal cord contribute to the production and/ or the modulation of locomotor activity. We focused our work on a conserved class of spinal sensory neurons termed cerebrospinal fluid contacting neurons (CSF-cNs). These neurons lie at the interface between the CSF and spinal interneurons controlling motor output and represent an interesting yet poorly understood sensorimotor loop in the vertebrate spinal cord. However, the connectivity of CSF-cNs remains completely uncharacterized. To understand how CSF-cNs modulate locomotion in vertebrates, we combined genetics, imaging, optogenetics, electrophysiology, and behavior analysis to map the functional connectivity of these sensory neurons and test their function in the zebrafish larva. Our results demonstrate that CSF-cNs target several elements thought to be part of the locomotor central pattern generator in zebrafish, including glutamatergic spinal neurons involved in slow and fast swimming. We show that CSF-cNs can modulate the duration and occurrence of spontaneous locomotor events in a state dependent manner and tune the frequency of evoked fast escape responses. Altogether our work dissecting sensorimotor integration in the spinal cord bridged single cell function in vivo to behavior in zebrafish and should contribute to a better understanding of the role of sensory feedback during locomotion in vertebrates