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Rozprawy doktorskie na temat „Computational neuroscience”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Higgins, Irina. "Computational neuroscience of speech recognition." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:daa8d096-6534-4174-b63e-cc4161291c90.

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Physical variability of speech combined with its perceptual constancy make speech recognition a challenging task. The human auditory brain, however, is able to perform speech recognition effortlessly. This thesis aims to understand the precise computational mechanisms that allow the auditory brain to do so. In particular, we look for the minimal subset of sub-cortical auditory brain areas that allow the primary auditory cortex to learn 'good representations' of speech-like auditory objects through spike-timing dependent plasticity (STDP) learning mechanisms as described by Bi & Poo (1998). A '

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Walters, Daniel Matthew. "The computational neuroscience of head direction cells." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:d4afe06a-d44f-4a24-99a3-d0e0a2911459.

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Head direction cells signal the orientation of the head in the horizontal plane. This thesis shows how some of the known head direction cell response properties might develop through learning. The research methodology employed is the computer simulation of neural network models of head direction cells that self-organize through learning. The preferred firing directions of head direction cells will change in response to the manipulation of distal visual cues, but not in response to the manipulation of proximal visual cues. Simulation results are presented of neural network models that learn to

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3

Cronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008.<br>Includes bibliographical references (p. 101-106).<br>Two key fields of computational neuroscience involve, respectively, the analysis of experimental recordings to understand the functional properties of neurons, and modeling how neurons and networks process sensory information in order to represent the environment. In both of these endeavors, it is crucial to understand and quantify uncertainty - when describing how the brain itself draws conclusions about the physical world, and when the e

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4

Stevens, Martin. "Animal camouflage, receiver psychology and the computational neuroscience of avian vision." Thesis, University of Bristol, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432958.

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Tromans, James Matthew. "Computational neuroscience of natural scene processing in the ventral visual pathway." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:b82e1332-df7b-41db-9612-879c7a7dda39.

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Neural responses in the primate ventral visual system become more complex in the later stages of the pathway. For example, not only do neurons in IT cortex respond to complete objects, they also learn to respond invariantly with respect to the viewing angle of an object and also with respect to the location of an object. These types of neural responses have helped guide past research with VisNet, a computational model of the primate ventral visual pathway that self-organises during learning. In particular, previous research has focussed on presenting to the model one object at a time during tr

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6

Vellmer, Sebastian. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21597.

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In dieser Arbeit werden mithilfe der Fokker-Planck-Gleichung die Statistiken, vor allem die Leistungsspektren, von Punktprozessen berechnet, die von mehrdimensionalen Integratorneuronen [Engl. integrate-and-fire (IF) neuron], Netzwerken von IF Neuronen und Entscheidungsfindungsmodellen erzeugt werden. Im Gehirn werden Informationen durch Pulszüge von Aktionspotentialen kodiert. IF Neurone mit radikal vereinfachter Erzeugung von Aktionspotentialen haben sich in Studien die auf Pulszeiten fokussiert sind als Standardmodelle etabliert. Eindimensionale IF Modelle können jedoch beobachtetes Pul

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7

Zhu, Mengchen. "Sparse coding models of neural response in the primary visual cortex." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53868.

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Sparse coding is an influential unsupervised learning approach proposed as a theoretical model of the encoding process in the primary visual cortex (V1). While sparse coding has been successful in explaining classical receptive field properties of simple cells, it was unclear whether it can account for more complex response properties in a variety of cell types. In this dissertation, we demonstrate that sparse coding and its variants are consistent with key aspects of neural response in V1, including many contextual and nonlinear effects, a number of inhibitory interneuron properties, as wel

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8

Woldman, Wessel. "Emergent phenomena from dynamic network models : mathematical analysis of EEG from people with IGE." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/23297.

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In this thesis mathematical techniques and models are applied to electroencephalographic (EEG) recordings to study mechanisms of idiopathic generalised epilepsy (IGE). First, we compare network structures derived from resting-state EEG from people with IGE, their unaffected relatives, and healthy controls. Next, these static networks are combined with a dynamical model describing the ac- tivity of a cortical region as a population of phase-oscillators. We then examine the potential of the differences found in the static networks and the emergent properties of the dynamic network as individual

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9

Nguyen, Harrison Tri Tue. "Computational Neuroscience with Deep Learning for Brain Imaging Analysis and Behaviour Classification." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27313.

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Recent advances of artificial neural networks and deep learning model have produced significant results in problems related to neuroscience. For example, deep learning models have demonstrated superior performance in non-linear, multivariate pattern classification problems such as Alzheimer’s disease classification, brain lesion segmentation, skull stripping and brain age prediction. Deep learning provides unique advantages for high-dimensional data such as MRI data, since it does not require extensive feature engineering. The thesis investigates three problems related to neuroscience and disc

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10

Lundh, Dan. "A computational neuroscientific model for short-term memory." Thesis, University of Exeter, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324742.

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11

Lee, Ray A. "Analysis of Spreading Depolarization as a Traveling Wave in a Neuron-Astrocyte Network." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503308416771087.

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12

Marsh, Steven Joseph Thomas. "Efficient programming models for neurocomputation." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709268.

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13

Philippides, Andrew Owen. "Modelling diffusion of nitric oxide in brains." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250180.

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14

O'Leary, Timothy S. "Homeostatic regulation of intrinsic excitability in hippocampal neurons." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3079.

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The proper functioning of nervous systems requires electrical activity to be tightly regulated. Perturbations in the intrinsic properties of neurons, and in excitatory input, are imposed throughout nervous system development as cell morphology and network activity evolve. In mature nervous systems these changes continue as a result of synaptic plasticity and external stimuli. It is therefore likely that homeostatic mechanisms exist to regulate membrane conductances that determine the excitability of individual neurons, and several mechanisms have been characterised to date. This thesis charact

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15

Kazer, J. F. "The hippocampus in memory and anxiety : an exploration within computational neuroscience and robotics." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339963.

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16

Vellmer, Sebastian [Verfasser]. "Applications of the Fokker-Planck Equation in Computational and Cognitive Neuroscience / Sebastian Vellmer." Berlin : Humboldt-Universität zu Berlin, 2020. http://d-nb.info/1214240682/34.

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17

Wright, Sean Patrick. "Cognitive neuroscience of episodic memory: behavioral, genetic, electrophysiological, and computational approaches to sequence memory." Thesis, Boston University, 2003. https://hdl.handle.net/2144/27805.

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Boston University. University Professors Program Senior theses.<br>PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. 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>2031-01-02

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18

Yancey, Madison E. "Computational Simulation and Analysis of Neuroplasticity." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1622582138544632.

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19

Lai, Yi Ming. "Stochastic population oscillators in ecology and neuroscience." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:f12697fb-23fa-4817-974e-6e188b9ecb38.

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In this thesis we discuss the synchronization of stochastic population oscillators in ecology and neuroscience. Traditionally, the synchronization of oscillators has been studied in deterministic systems with various modes of synchrony induced by coupling between the oscillators. However, recent developments have shown that an ensemble of uncoupled oscillators can be synchronized by a common noise source alone. By considering the eﬀects of noise-induced synchronization on biological oscillators, we are able to explain various biological phenomena in ecological and neurobiological contexts - mo

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20

Allen, John Michael. "Effects of Abstraction and Assumptions on Modeling Motoneuron Pool Output." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1495538117787703.

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21

Zysman, Daniel. "The role of neuronal feedback in the detection of transient signals: a computational approach." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28832.

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This study investigates the role of neuronal feedback in the detection of small amplitude transient signals. We focus specifically on how the weakly electric fish Apteronotus leptorhynchus capture prey such as Daphnia in a noisy environment, by means of its electric sense. Using the electrosensory network as a template, we build a computational model that allows us to evaluate detection performance in two different scenarios: without neuronal feedback (open-loop) and with neuronal feedback (closed-loop). For each network scenario, spike count distributions across realizations are computed in t

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22

Ellaithy, Amr. "Metabotropic Glutamate Receptor 2 Activation: Computational Predictions and Experimental Validation." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5319.

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G protein-coupled receptors (GPCRs) are the largest family of signaling proteins in animals and represent the largest family of druggable targets in the human genome. Therefore, it is of no surprise that the molecular mechanisms of GPCR activation and signal transduction have attracted close attention for the past few decades. Several stabilizing interactions within the GPCR transmembrane (TM) domain helices regulate receptor activation. An example is a salt bridge between 2 highly conserved amino acids at the bottom of TM3 and TM6 that has been characterized for a large number of GPCRs. Throu

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23

Ging-Jehli, Nadja Rita. "On the implementation of Computational Psychiatry within the framework of Cognitive Psychology and Neuroscience." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555338342285251.

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24

Battista, Aldo. "Low-dimensional continuous attractors in recurrent neural networks : from statistical physics to computational neuroscience." Thesis, Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLE012.

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La manière dont l'information sensorielle est codée et traitée par les circuits neuronaux est une question centrale en neurosciences computationnelles. Dans de nombreuses régions du cerveau, on constate que l'activité des neurones dépend fortement de certains corrélats sensoriels continus ; on peut citer comme exemples les cellules simples de la zone V1 du cortex visuel codant pour l'orientation d'une barre présentée à la rétine, et les cellules de direction de la tête dans le subiculum ou les cellules de lieu dans l'hippocampe, dont les activités dépendent, respectivement, de l'orientation de

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25

Law, Judith S. "Modeling the development of organization for orientation preference in primary visual cortex." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3935.

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The cerebral cortex of mammals comprises a series of topographic maps, forming sensory and motor areas such as those in the visual, auditory, and somatosensory systems. Understanding the rules that govern the development of these maps and how this topographic organization relates to information processing is critical for the understanding of cortical processing and whole brain function. Previous computational models have shown that topographic maps can develop through a process of self-organization, if spatially localized patches of cortical neurons are activated by particular stimuli. This th

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26

Pendyam, Sandeep Nair Satish S. "Computational neural modeling at the cellular and network levels two case studies /." Diss., Columbia, Mo. : University of Missouri--Columbia, 2007. http://hdl.handle.net/10355/4899.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on September 15, 2009). Thesis advisor: Satish S. Nair. Includes bibliographical references.

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27

Harkin, Emerson. "A Simplified Serotonin Neuron Model." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38533.

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28

Chakrabarty, Nilaj. "Computational Study of Axonal Transport Mechanisms of Actin and Neurofilaments." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1584441310326918.

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29

Merrison-Hort, Robert. "Computational study of the mechanisms underlying oscillation in neuronal locomotor circuits." Thesis, University of Plymouth, 2014. http://hdl.handle.net/10026.1/3107.

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In this thesis we model two very different movement-related neuronal circuits, both of which produce oscillatory patterns of activity. In one case we study oscillatory activity in the basal ganglia under both normal and Parkinsonian conditions. First, we used a detailed Hodgkin-Huxley type spiking model to investigate the activity patterns that arise when oscillatory cortical input is transmitted to the globus pallidus via the subthalamic nucleus. Our model reproduced a result from rodent studies which shows that two anti-phase oscillatory groups of pallidal neurons appear under Parkinsonian c

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30

Benigni, Barbara. "Exploring the interplay between the human brain and the mind: a complex systems approach." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/346541.

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The understanding of human brain mechanisms has captured the imagination of scientists for ages. From the quantitative perspective, there is evidence that damages to brain structure affect brain function and, as a consequence, cognitive aspects. As there is evidence that brain structure might be affected by altered cognition. However, the complex interplay between the human brain and the mind remains still poorly understood. This fact has important clinical consequences, limiting applications devoted to the prevention and treatment of brain diseases. In the present thesis, we aim to enhance ou

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31

De, Pisapia Nicola. "A framework for implicit planning : towards a cognitive/computational neuroscience theory of prefrontal cortex function." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/24519.

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In this thesis we review available experimental findings on rodents, monkeys and humans, and we suggest a unifying interpretation of the role of the Prefrontal Cortex (PFC) in behaviour. We implement computational models to test this interpretation, and propose novel experiments. Our suggestion is, as also other researchers have proposed, that the PFC is involved in Planning, i.e. in evaluating which course of actions to execute in order to reach a goal. Unlike previous researchers, we emphasize and limit ourselves to unconscious aspects of Planning, and describe a view of this process that is

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32

Milano, Isabel. "The Characterization of Alzheimer’s Disease and the Development of Early Detection Paradigms: Insights from Nosology, Biomarkers and Machine Learning." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2192.

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Alzheimer’s Disease (AD) is the only condition in the top ten leading causes of death for which we do not have an effective treatment that prevents, slows, or stops its progression. Our ability to design useful interventions relies on (a) increasing our understanding of the pathological process of AD and (b) improving our ability for its early detection. These goals are impeded by our current reliance on the clinical symptoms of AD for its diagnosis. This characterizations of AD often falsely assumes a unified, underlying AD-specific pathology for similar presentations of dementia that leads t

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33

Shepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.

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Thesis (Ph.D)--Algorithms, Combinatorics, and Optimization, Georgia Institute of Technology, 2010.<br>Committee Chair: Tovey, Craig; Committee Member: Butera, Rob; Committee Member: Nemirovski, Arkadi; Committee Member: Prinz, Astrid; Committee Member: Sokol, Joel. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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34

Lieuw, Iris. "Time Frequency Analysis of Neural Oscillations in Multi-Attribute Decision-Making." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/556.

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In our daily lives, we often make decisions that require the use of self-control, weighing trade-offs between various attributes: for example, selecting a food based on its health rather than its taste. Previous research suggests that re-weighting attributes may rely on selective attention, associated with decreased neural oscillations over posterior brain regions in the alpha (8-12 Hz) frequency range. Here, we utilized the high temporal resolution and whole-brain coverage of electroencephalography (EEG) to test this hypothesis in data collected from hungry human subjects exercising dietary s

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35

Lundqvist, Mikael. "Oscillations and spike statistics in biophysical attractor networks." Doctoral thesis, Stockholms universitet, Numerisk analys och datalogi (NADA), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-93316.

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The work of this thesis concerns how cortical memories are stored and retrieved. In particular, large-scale simulations are used to investigate the extent to which associative attractor theory is compliant with known physiology and in vivo dynamics. The first question we ask is whether dynamical attractors can be stored in a network with realistic connectivity and activity levels. Using estimates of biological connectivity we demonstrated that attractor memories can be stored and retrieved in biologically realistic networks, operating on psychophysical timescales and displaying firing rate pat

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36

Hilgetag, Claus-Christian. "Mathematical approaches to the analysis of neural connectivity in the mammalian brain." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310171.

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FoÌˆldiak, Peter. "Models of sensory coding." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239097.

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38

BIDDELL, KEVIN MICHAEL. "CREATION OF A BIOPHYSICAL MODEL OF A STRIATAL DORSAL LATERAL MEDIUM SPINY NEURON INCORPORATING DENDRITIC EXCITATION BY NMDA AND AMPA RECEPTOR MODELS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196211076.

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39

Endres, Dominik M. "Bayesian and information-theoretic tools for neuroscience." Thesis, St Andrews, 2006. http://hdl.handle.net/10023/162.

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40

Mender, Bedeho M. W. "Models of primate supraretinal visual representations." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ce1fff8e-db5c-46e4-b5aa-7439465c2a77.

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This thesis investigates a set of non-classical visual receptive field properties observed in the primate brain. Two main phenomena were explored. The first phenomenon was neurons with head-centered visual receptive fields, in which a neuron responds maximally to a visual stimulus in the same head-centered location across all eye positions. The second phenomenon was perisaccadic receptive field dynamics, which involves a range of experimentally observed response behaviours of an eye-centered neuron associated with the advent of a saccade that relocates the neuron's receptive field. For each of

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41

Goings, Sydney Pia. "Neural Synchrony in the Zebra Finch Brain." Scholarship @ Claremont, 2012. https://scholarship.claremont.edu/scripps_theses/41.

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I am interested in discovering the role of field potential oscillations in producing synchrony within the song system of the male zebra finch brain. An important function attributed to neural synchrony is sensorimotor integration. In the production of birdsong, sensorimotor integration is crucial, as auditory feedback is necessary for the maintenance of the song. A cortical-thalamic-cortical feedback loop is thought to play a role in the integration of auditory and motor information for the purpose of producing song. Synchronous activity has been observed between at least two nuclei in this fe

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42

Naze, Sebastien. "Multiscale Computational Modeling of Epileptic Seizures : from macro to microscopic dynamics." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4023/document.

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L’évaluation expérimentale des mécanismes de l’initiation, de la propagation, et de la fin des crises d’épilepsie est un problème complexe. Cette thèse consiste en le développement d’un modèle de réseau de neurones aux caractéristiques biologiques pertinentes à la compréhension des mécanismes de genèse et de propagation de crises d’épilepsie. Nous démontrons que les décharges de type pointes ondes peuvent être générées par les neurones inhibiteurs seuls, tandis que les décharges rapides sont dues en grande partie aux neurones excitateurs. Nous concluons que les variations lentes d’excitabilité

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43

Asher, Derrik E. "Action Selection and Execution with Computational Neural Networks of Neuromodulation and Sensory Integration." Thesis, University of California, Irvine, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3626926.

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<p> Neuromodulation is a neurophysiological process by which a single neuron can regulate the neural activity of a diverse population of neurons. Sensory integration is a neurobiological process by which the brain combines multiple sensory modality inputs (i.e., vision, proprioception, audition, tactile, olfactory, vestibular, interoception, and taste) into usable functional outputs. In biological systems, neuromodulation and sensory integration have been shown to have a strong influence over action selection (decision-making) and action execution (motor output) respectively. The experiments p

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44

Hudson, Amber Elise. "Neuronal mechanisms for the maintenance of consistent behavior in the stomatogastric ganglion of Cancer borealis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47654.

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Each neuron needs to maintain a careful balance between the changes implicit in experience, and the demands of stability required by its function. This balance tips depending on the neuronal system, but in any role, disease or neural disorders can develop when the regulatory mechanisms involved in neuronal stability fail. The objective of this thesis was to characterize mechanisms underlying neuronal stability and activity maintenance, in the hopes that further understanding of these processes might someday lead to novel interventions for neurological disorders. The pyloric circuit of decapo

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45

Banks, Jess M. "Chaos and Learning in Discrete-Time Neural Networks." Oberlin College Honors Theses / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1445945609.

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46

Berthet, Pierre. "Computational Modeling of the Basal Ganglia : Functional Pathways and Reinforcement Learning." Doctoral thesis, Stockholms universitet, Numerisk analys och datalogi (NADA), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-123747.

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We perceive the environment via sensor arrays and interact with it through motor outputs. The work of this thesis concerns how the brain selects actions given the information about the perceived state of the world and how it learns and adapts these selections to changes in this environment. Reinforcement learning theories suggest that an action will be more or less likely to be selected if the outcome has been better or worse than expected. A group of subcortical structures, the basal ganglia (BG), is critically involved in both the selection and the reward prediction. We developed and investi

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47

Cogliati, Dezza Irene. "“Vanilla, Vanilla .but what about Pistachio?” A Computational Cognitive Clinical Neuroscience Approach to the Exploration-Exploitation Dilemma." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/278730/3/Document1.pdf.

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On the 24th November of 1859, Charles Darwin published the first edition of The Origin of Species. One hundred fifty-nine years later, our understanding of human and animal adaptation to the surrounding environment remains a major scientific challenge. How do humans and animals generate apt decision strategies in order to achieve this adaptation? How does their brain efficiently carry out complex computations in order to produce such adaptive behaviors? Although an exhaustive answer to these questions continues to feel out of reach, the investigation of adaptive processing results relevant in

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Strack, Beata. "Multi-column multi-layer computational model of neocortex." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3279.

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We present a multi-layer multi-column computational model of neocortex that is built based on the activity and connections of known neuronal cell types and includes activity-dependent short term plasticity. This model, a network of spiking neurons, is validated by showing that it exhibits activity close to biology in terms of several characteristics: (1) proper laminar flow of activity; (2) columnar organization with focality of inputs; (3) low-threshold-spiking (LTS) and fast-spiking (FS) neurons function as observed in normal cortical circuits; and (4) different stages of epileptiform activ

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Ham, Michael I. Gross Guenter W. "Exploration of hierarchical leadership and connectivity in neural networks in vitro." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9775.

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Abeysuriya, Romesh Gerald. "Physiologically-based Brain State Modeling." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/14469.

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The brain is the most complex organ in the body, and exhibits rich multiscale dynamics. Changes in arousal state (such as falling asleep) are accompanied by large scale changes in whole-brain dynamics that are readily observable by a range of methods including electroencephalography (EEG). However, most analysis of arousal state focuses either on coarse quantitative measures like the relative amount of power in certain frequency bands, or by assigning the state into one of several classes based largely on qualitative observations. Both of these approaches are ultimately phenomenological, and p

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