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Dissertations / Theses on the topic 'Models of neural elements'

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Published: 30 May 2022

Last updated: 31 July 2025

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1

Andrzej, Tuchołka. "Methodology for assessing the construction of machine elements using neural models and antipatterns : doctoral dissertation." Rozprawa doktorska, [s.n.], 2020. http://dlibra.tu.koszalin.pl/Content/1317.

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2

Miocinovic, Svjetlana. "THEORETICAL AND EXPERIMENTAL PREDICTIONS OF NEURAL ELEMENTS ACTIVATED BY DEEP BRAIN STIMULATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1181758206.

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3

Tomov, Petar Georgiev. "Interplay of dynamics and network topology in systems of excitable elements." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17464.

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Wir untersuchen globale dynamische Phänomene, die sich von dem Zusammenspiel zwischen Netzwerktopologie und Dynamik der einzelnen Elementen ergeben. Im ersten Teil untersuchen wir relativ kleine strukturierte Netzwerke mit überschaubarer Komplexität. Als geeigneter theoretischer Rahmen für erregbare Systeme verwenden wir das Kuramoto und Shinomoto Modell der sinusförmig-gekoppelten "aktiven Rotatoren" und studieren das Kollektivverhalten des Systems in Bezug auf Synchronisation. Wir besprechen die Einschränkungen, die durch die Netzwerktopologie auf dem Fluss im Phasenraum des Systems geste

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4

Wadagbalkar, Pushkar. "Real-time prediction of projectile penetration to laminates by training machine learning models with finite element solver as the trainer." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592169428128864.

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5

Citipitioglu, Ahmet Muhtar. "Development and assessment of response and strength models for bolted steel connections using refined nonlinear 3D finite element analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31691.

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Thesis (Ph.D)--Civil and Environmental Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Haj-Ali, Rami; Committee Co-Chair: Leon, Roberto; Committee Co-Chair: White, Donald; Committee Member: DesRoches, Reginald; Committee Member: Gentry, Russell. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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6

Паржин, Юрій Володимирович. "Моделі і методи побудови архітектури і компонентів детекторних нейроморфних комп'ютерних систем". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/34755.

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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.05 – комп'ютерні системи та компоненти. – Національний технічний університет "Харківський політехнічний інститут", Міністерство освіти і науки України, Харків, 2018. Дисертація присвячена вирішенню проблеми підвищення ефективності побудови та використання нейроморфних комп'ютерних систем (НКС) в результаті розробки моделей побудови їх компонентів та загальної архітектури, а також методів їх навчання на основі формалізованого детекторного принципу. В результаті аналізу і класифікації архітектури та компонент

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7

Паржин, Юрій Володимирович. "Моделі і методи побудови архітектури і компонентів детекторних нейроморфних комп'ютерних систем". Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/34756.

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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.05 – комп'ютерні системи та компоненти. – Національний технічний університет "Харківський політехнічний інститут", Міністерство освіти і науки України, Харків, 2018. Дисертація присвячена вирішенню проблеми підвищення ефективності побудови та використання нейроморфних комп'ютерних систем (НКС) в результаті розробки моделей побудови їх компонентів та загальної архітектури, а також методів їх навчання на основі формалізованого детекторного принципу. В результаті аналізу і класифікації архітектури та компонен

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8

Levin, Robert Ian. "Dynamic Finite Element model updating using neural networks." Thesis, University of Bristol, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264075.

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9

Stevenson, King Douglas Beverley. "Robust hardware elements for weightless artificial neural networks." Thesis, University of Central Lancashire, 2000. http://clok.uclan.ac.uk/1884/.

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This thesis investigates novel robust hardware elements for weightless artificial neural systems with a bias towards high integrity avionics applications. The author initially reviews the building blocks of physiological neural systems and then chronologically describes the development of weightless artificial neural systems. Several new design methodologies for the implementation of robust binary sum-and-threshold neurons are presented. The new techniques do not rely on weighted binary counters or registered arithmetic units for their operation making them less susceptible to transient single

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10

Venkov, Nikola A. "Dynamics of neural field models." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517742.

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11

Oglesby, J. "Neural models for speaker recognition." Thesis, Swansea University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638359.

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In recent years a resurgence of interest in neural modeling has taken place. This thesis examines one such class applied to the task of speaker recognition, with direct comparisons made to a contemporary approach based on vector quantisation (VQ). Speaker recognition systems in general, including feature representations and distance measures, are reviewed. The VQ approach, used for comparisons throughout the experimental work, is described in detail. Currently popular neural architectures are also reviewed and associated gradient-based training procedures examined. The performance of a VQ spea

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12

Whitney, William M. Eng (William F. ). Massachusetts Institute of Technology. "Disentangled representations in neural models." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106449.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 57-62).<br>Representation learning is the foundation for the recent success of neural network models. However, the distributed representations generated by neural networks are far from ideal. Due to their highly entangled nature, they are difficult to reuse and interpret, and they do a poor job of capturing the sparsity which is present in real-world transformations. In this paper, I descr

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13

Taylor, Neill Richard. "Neural models of temporal sequences." Thesis, King's College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300844.

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14

PAVIGLIANITI, ANNUNZIATA. "Neural Models in Biomedical Applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2957752.

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15

Gao, Yun. "Statistical models in neural information processing /." View online version; access limited to Brown University users, 2005. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3174606.

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16

Schmidt, Helmut. "Interface dynamics in neural field models." Thesis, University of Nottingham, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597110.

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Neural fields models have been developed. to emulate large scale brain dynamics. They exhibit similar types of patterns as observed in real cortical tissue, such as travelling waves and persistent localised activity. The study of neural field models is yet a growing field of research, and in this thesis we contribute by developing new approaches to the analysis of pattern formation. A particular focus is on interface methods in one and two spatial dimensions. In the first part of this thesis we Study the influence of inhomogeneities on the velocity of propagating waves. We examine periodically

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17

McCabe, Susan Lynda. "Neural models of subcortical auditory processing." Thesis, University of Plymouth, 1994. http://hdl.handle.net/10026.1/2167.

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An important feature of the auditory system is its ability to distinguish many simultaneous sound sources. The primary goal of this work was to understand how a robust, preattentive analysis of the auditory scene is accomplished by the subcortical auditory system. Reasonably accurate modelling of the morphology and organisation of the relevant auditory nuclei, was seen as being of great importance. The formulation of plausible models and their subsequent simulation was found to be invaluable in elucidating biological processes and in highlighting areas of uncertainty. In the thesis, a review o

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18

Murphy, Eric James. "Cell culture models for neural trauma /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487672245901403.

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19

Williams, Bryn V. "Evolutionary neural networks : models and applications." Thesis, Aston University, 1995. http://publications.aston.ac.uk/10635/.

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The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA

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20

Hely, Timothy Alasdair. "Computational models of developing neural systems." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/22303.

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The work of this thesis has focused on crating computational models of developing neurons. Three different but related areas of research have been studied - how cells make connections, what influences the shape of these connections and how neuronal network behaviour can be influenced by local interactions. In order to understand how cells make connections I simulated the dynamics of the neuronal growth cone - a structure which guides the developing axon to its target cells. Results from the first models showed that small interaction effects between structural proteins in the axon called microt

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21

Meng, Liang. "Statistical inferences of biophysical neural models." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12819.

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Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>A fundamental issue in neuroscience is to understand the dynamic properties of, and biological mechanisms underlying, neural spiking activity. Two types of approaches have been developed: statistical and biophy

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22

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:<br/>Traces, Neural Inspired Models, Controls<br/>Autor: <br/>Armand-Mihai Ionescu<br/>Directores: <br/>Dr. Victor Mitrana <br/>(URV)<br/>Dr. Takashi Yokomori <br/>(Universidad Waseda, Japón)<br/>Resumen Castellano:<br/>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.<br/>La disertación contribuye al área de cálculo

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23

Xu, Shuxiang, University of Western Sydney, and of Informatics Science and Technology Faculty. "Neuron-adaptive neural network models and applications." THESIS_FIST_XXX_Xu_S.xml, 1999. http://handle.uws.edu.au:8081/1959.7/275.

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Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy

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24

Tang, Chuan Zhang. "Artificial neural network models for digital implementation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq30298.pdf.

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25

Braga, AntoÌ‚nio de PaÌdua. "Design models for recursive binary neural networks." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336442.

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26

Ripley, Ruth Mary. "Neural network models for breast cancer prognosis." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244721.

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27

Forrest, B. M. "Memory and optimisation in neural network models." Thesis, University of Edinburgh, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384164.

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A numerical study of two classes of neural network models is presented. The performance of Ising spin neural networks as content-addressable memories for the storage of bit patterns is analysed. By studying systems of increasing sizes, behaviour consistent with fintite-size scaling, characteristic of a first-order phase transition, is shown to be exhibited by the basins of attraction of the stored patterns in the Hopfield model. A local iterative learning algorithm is then developed for these models which is shown to achieve perfect storage of nominated patterns with near-optimal content-addre

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28

Veltz, Romain, and Romain Veltz. "Nonlinear analysis methods in neural field models." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00686695.

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This thesis deals with mesoscopic models of cortex called neural fields. The neural field equations describe the activity of neuronal populations, with common anatomical / functional properties. They were introduced in the 1950s and are called the equations of Wilson and Cowan. Mathematically, they consist of integro-differential equations with delays, the delays modeling the signal propagation and the passage of signals across synapses and the dendritic tree. In the first part, we recall the biology necessary to understand this thesis and derive the main equations. Then, we study these equati

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29

MELLEM, MARCELO TOURASSE NASSIM. "AUTOREGRESSIVE-NEURAL HYBRID MODELS FOR TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1997. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14541@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho apresenta um modelo linear por partes chamado de modelo ARN. Trata-se de uma estrutura híbrida que envolve modelos autoregressivos e redes neurais. Este modelo é comparado com o modelo AR de coeficientes fixos e com a rede neural estática aplicada à previsão. Os resultados mostram que o ARN consegue identificar a estrutura não-linear dos dados simulados e que na maioria dos casos ele possui melhor habilidade preditiva do que os modelos supracitados.<br>In this thesis we develop a piece-wise linear model named ARN mod

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30

Beck, Amanda M. "State space models for isolating neural oscillations." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120408.

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Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 55-56).<br>Information communication in the brain depends on the spiking patterns of neurons. The interaction of these cells at the population level can be observed as oscillations of varying frequency and power, in local field potential recordings as well as non-invasive scalp electroencephalograms (EEG). These oscillations are thought to be responsible fo

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31

Lei, Tao Ph D. Massachusetts Institute of Technology. "Interpretable neural models for natural language processing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108990.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 109-119).<br>The success of neural network models often comes at a cost of interpretability. This thesis addresses the problem by providing justifications behind the model's structure and predictions. In the first part of this thesis, we present a class of sequence operations for text processing. The proposed component generalizes from convolution operations and gated aggregations. As justi

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32

West, Ansgar Heinrich Ludolf. "Role of biases in neural network models." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/11546.

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The capacity problem for multi-layer networks has proven especially elusive. Our calculation of the capacity of multi-layer networks built by constructive algorithms relies heavily on the existence of biases in the basic building block, the binary perceptron. It is the first time where the capacity is explicitly evaluated for large networks and finite stability. One finds that the constructive algorithms studied, a tiling-like algorithm and variants of the upstart algorithm, do not saturate the known Mitchison-Durbin bound. In supervised learning, a student network is presented with training e

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33

Mo, Mimi Shin Ning. "Neural vulnerability in models of Parkinson's disease." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:ac82e1c1-5d9f-473f-97ac-fcb70b2587ca.

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Parkinson's disease (PD) is a neurodegenerative disorder with no known cure. This thesis explores the degenerative process in two neurotoxin-based models, the 6-hydroxydopamine and the chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)/probenecid mouse models, to yield important information about the pathogenesis of PD. Neuronal survival patterns in Parkinsonian patients and animals are heterogeneous. More dopaminergic neurons are lost from the ventral tier of the substantia nigra (SN) than from the dorsal tier or the adjacent ventral tegmental area, possibly due to differential expres

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34

Wedgwood, Kyle C. A., Kevin K. Lin, Ruediger Thul, and Stephen Coombes. "Phase-Amplitude Descriptions of Neural Oscillator Models." BioMed Central, 2013. http://hdl.handle.net/10150/610255.

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Phase oscillators are a common starting point for the reduced description of many single neuron models that exhibit a strongly attracting limit cycle. The framework for analysing such models in response to weak perturbations is now particularly well advanced, and has allowed for the development of a theory of weakly connected neural networks. However, the strong-attraction assumption may well not be the natural one for many neural oscillator models. For example, the popular conductance based Morris-Lecar model is known to respond to periodic pulsatile stimulation in a chaotic fashion that cann

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35

Yau, Hon Wah. "Phase space techniques in neural network models." Thesis, University of Edinburgh, 1992. http://hdl.handle.net/1842/14713.

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We present here two calculations based on the <i>phase-space of interactions</i> treatment of neural network models. As a way of introduction we begin by discussing the type of neural network models we wish to study, and the analytical techniques available to us from the branch of disordered systems in statistical mechanics. We then detail a neural network which models a <i>content addressable memory</i>, and sketch the mathematical methods we shall use. The model is a mathematical realisation of a neural network with its synaptic efficacies optimised in its phase space of interactions through

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36

Tomes, Hayley Sarah. "Investigating neural responses in models of neurocysticercosis." Doctoral thesis, Faculty of Health Sciences, 2021. http://hdl.handle.net/11427/33046.

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Epilepsy is more frequent in sub-Saharan Africa than the rest of the world due to high levels of brain infections by larvae of the pig cestode Taenia solium, a condition termed neurocysticercosis. Despite the large nature of the problem, little is known about how neurocysticercosis modulates neuronal responses to result in the development of seizures. In this thesis I have used the cestode Taenia crassiceps to develop multiple in vitro and in vivo models of neurocysticercosis in rodents. Utilising patch-clamp electrophysiology in organotypic hippocampal brain slices and chronic, wireless elect

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37

Fan, Xuetong. "Laminar Flow Control Models with Neural Networks /." The Ohio State University, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487929745334864.

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38

Veltz, Romain. "Nonlinear analysis methods in neural field models." Thesis, Paris Est, 2011. http://www.theses.fr/2011PEST1056/document.

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Cette thèse traite de modèles mésoscopiques de cortex appelés champs neuronaux. Les équations des champs neuronaux décrivent l'activité corticale de populations de neurones, ayant des propriétés anatomiques/fonctionnelles communes. Elles ont été introduites dans les années 1950 et portent le nom d'équations de Wilson et Cowan. Mathématiquement, elles consistent en des équations intégro-différentielles avec retards, les retards modélisant les délais de propagation des signaux ainsi que le passage des signaux à travers les synapses et l'arbre dendritique. Dans la première partie, nous rappelons

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39

Westermann, Gert. "Constructivist neural network models of cognitive development." Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/22733.

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In this thesis I investigate the modelling of cognitive development with Constructivist neural networks. I argue that the constructivist nature of development, that is, the building of a cognitive system through active interactions with its environment, is an essential property of human development and should be considered in models of cognitive development. I evaluate this claim on the basis of evidence from cortical development, cognitive development, and learning theory. In an empirical evaluation of this claim, I then present a constructivist neural network model of the acquisition of the

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40

Xu, Shuxiang. "Neuron-adaptive neural network models and applications." Thesis, [Campbelltown, N.S.W. : The Author], 1999. http://handle.uws.edu.au:8081/1959.7/275.

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Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy

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41

Xu, Shuxiang. "Neuron-adaptive neural network models and applications /." [Campbelltown, N.S.W. : The Author], 1999. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030702.085320/index.html.

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42

Kryściński, Wojciech. "Training Neural Models for Abstractive Text Summarization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-236973.

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Abstractive text summarization aims to condense long textual documents into a short, human-readable form while preserving the most important information from the source document. A common approach to training summarization models is by using maximum likelihood estimation with the teacher forcing strategy. Despite its popularity, this method has been shown to yield models with suboptimal performance at inference time. This work examines how using alternative, task-specific training signals affects the performance of summarization models. Two novel training signals are proposed and evaluated as

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Kunz, Jenny. "Neural Language Models with Explicit Coreference Decision." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371827.

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Coreference is an important and frequent concept in any form of discourse, and Coreference Resolution (CR) a widely used task in Natural Language Understanding (NLU). In this thesis, we implement and explore two recent models that include the concept of coreference in Recurrent Neural Network (RNN)-based Language Models (LM). Entity and reference decisions are modeled explicitly in these models using attention mechanisms. Both models learn to save the previously observed entities in a set and to decide if the next token created by the LM is a mention of one of the entities in the set, an entit

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Labeau, Matthieu. "Neural language models : Dealing with large vocabularies." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS313/document.

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Le travail présenté dans cette thèse explore les méthodes pratiques utilisées pour faciliter l'entraînement et améliorer les performances des modèles de langues munis de très grands vocabulaires. La principale limite à l'utilisation des modèles de langue neuronaux est leur coût computationnel: il dépend de la taille du vocabulaire avec laquelle il grandit linéairement. La façon la plus aisée de réduire le temps de calcul de ces modèles reste de limiter la taille du vocabulaire, ce qui est loin d'être satisfaisant pour de nombreuses tâches. La plupart des méthodes existantes pour l'entraînement

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Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/367784.

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Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language understanding (SLU) which understands what the user means. We focus on the language model (LM) component of SLS. LMs constrain the search space that is used in the search for the best hypothesis. Therefore, they play a crucial role in the performance of SLS. It has long been discussed that an improvement in the

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Bayer, Ali Orkan. "Semantic Language models with deep neural Networks." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1578/1/bayer_thesis.pdf.

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Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language understanding (SLU) which understands what the user means. We focus on the language model (LM) component of SLS. LMs constrain the search space that is used in the search for the best hypothesis. Therefore, they play a crucial role in the performance of SLS. It has long been discussed that an improvement in the

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Lineaweaver, Sean Kenneth Ridgway. "Dynamic spiral lumped element model of electrical field distribution and neural excitation in the implanted cochlea /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/6092.

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Herrington, William Frederick Jr. "Micro-optic elements for a compact opto-electronic integrated neural coprocessor." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97800.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 165-167).<br>The research done for this thesis was aimed at developing the optical elements needed for the Compact Opto-electronic Integrated Neural coprocessor (COIN coprocessor) project. The COIN coprocessor is an implementation of a feed forward neural network using free-space optical interconnects to communicate between neurons. Prior work on this project had assumed these interconnects

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Ayed, Ibrahim. "Neural Models for Learning Real World Dynamics and the Neural Dynamics of Learning." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS434.

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Cette thèse se situe à l'intersection de deux domaines : d'une part celui des systèmes dynamiques, et notamment ceux qui peuvent être représentés par des équations différentielles d'évolution, et, d'autre part, celui des l'apprentissage profond. Son objectif est alors double : Il s'agit d'abord de chercher à modéliser, grâce aux techniques modernes de l'apprentissage profond, des phénomènes physiques complexes, dans divers cadres d'intérêt pour les praticiens. Ensuite, nous avons également tenté d'employer des outils issus des théories mathématiques permettant l'étude des équations différentie

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Fischer, Shain Ann. "A Three-Dimensional Anatomically Accurate Finite Element Model for Nerve Fiber Activation Simulation Coupling." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1365.

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Improved knowledge of human nerve function and recruitment would enable innovation in the Biomedical Engineering field. Better understanding holds the potential for greater integration between devices and the nervous system as well as the ability to develop therapeutic devices to treat conditions affecting the nervous system. This work presents a three-dimensional volume conductor model of the human arm for coupling with code describing nerve membrane characteristics. The model utilizes an inhomogeneous medium composed of bone, muscle, skin, nerve, artery, and vein. Dielectric properties of ea

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