Teses / dissertações sobre o tema "Computationnal Neuroscience"
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Higgins, Irina. "Computational neuroscience of speech recognition". Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:daa8d096-6534-4174-b63e-cc4161291c90.
Texto completo da fonteWalters, 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.
Texto completo da fonteChateau-Laurent, Hugo. "Modélisation Computationnelle des Interactions Entre Mémoire Épisodique et Contrôle Cognitif". Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0019.
Texto completo da fonteEpisodic memory is often illustrated with the madeleine de Proust excerpt as the ability to re-experience a situation from the past following the perception of a stimulus. This simplistic scenario should not lead into thinking that memory works in isolation from other cognitive functions. On the contrary, memory operations treat highly processed information and are themselves modulated by executive functions in order to inform decision making. This complex interplay can give rise to higher-level functions such as the ability to imagine potential future sequences of events by combining contextually relevant memories. How the brain implements this construction system is still largely a mystery. The objective of this thesis is to employ cognitive computational modeling methods to better understand the interactions between episodic memory, which is supported by the hippocampus, and cognitive control, which mainly involves the prefrontal cortex. It provides elements as to how episodic memory can help an agent to act. It is shown that Neural Episodic Control, a fast and powerful method for reinforcement learning, is in fact mathematically close to the traditional Hopfield Network, a model of associative memory that has greatly influenced the understanding of the hippocampus. Neural Episodic Control indeed fits within the Universal Hopfield Network framework, and it is demonstrated that it can be used to store and recall information, and that other kinds of Hopfield networks can be used for reinforcement learning. The question of how executive functions can control episodic memory operations is also tackled. A hippocampus-inspired network is constructed with as little assumption as possible and modulated with contextual information. The evaluation of performance according to the level at which contextual information is sent provides design principles for controlled episodic memory. Finally, a new biologically inspired model of one-shot sequence learning in the hippocampus is proposed. The model performs very well on multiple datasets while reproducing biological observations. It ascribes a new role to the recurrent collaterals of area CA3 and the asymmetric expansion of place fields, that is to disambiguate overlapping sequences by making retrospective splitter cells emerge. Implications for theories of the hippocampus are discussed and novel experimental predictions are derived
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.
Texto completo da fonteIncludes bibliographical references (p. 101-106).
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 experimenter interprets neuronal data. Bayesian modeling and inference methods provide many advantages for doing so. Three projects are presented that illustrate the advantages of the Bayesian approach. In the first, Markov chain Monte Carlo (MCMC) sampling methods were used to answer a range of scientific questions that arise in the analysis of physiological data from tuning curve experiments; in addition, a software toolbox is described that makes these methods widely accessible. In the second project, the model developed in the first project was extended to describe the detailed dynamics of orientation tuning in neurons in cat primary visual cortex. Using more sophisticated sampling-based inference methods, this model was applied to answer specific scientific questions about the tuning properties of a recorded population. The final project uses a Bayesian model to provide a normative explanation of sensory adaptation phenomena. The model was able to explain a range of detailed physiological adaptation phenomena.
by Beau D. Cronin.
Ph.D.
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.
Texto completo da fonteAllen, 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.
Texto completo da fonteShepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.
Texto completo da fonteCommittee 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.
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.
Texto completo da fonteTromans, 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.
Texto completo da fonteVellmer, 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.
Texto completo da fonteThis thesis is concerned with the calculation of statistics, in particular the power spectra, of point processes generated by stochastic multidimensional integrate-and-fire (IF) neurons, networks of IF neurons and decision-making models from the corresponding Fokker-Planck equations. In the brain, information is encoded by sequences of action potentials. In studies that focus on spike timing, IF neurons that drastically simplify the spike generation have become the standard model. One-dimensional IF neurons do not suffice to accurately model neural dynamics, however, the extension towards multiple dimensions yields realistic behavior at the price of growing complexity. The first part of this work develops a theory of spike-train power spectra for stochastic, multidimensional IF neurons. From the Fokker-Planck equation, a set of partial differential equations is derived that describes the stationary probability density, the firing rate and the spike-train power spectrum. In the second part of this work, a mean-field theory of large and sparsely connected homogeneous networks of spiking neurons is developed that takes into account the self-consistent temporal correlations of spike trains. Neural input is approximated by colored Gaussian noise generated by a multidimensional Ornstein-Uhlenbeck process of which the coefficients are initially unknown but determined by the self-consistency condition and define the solution of the theory. To explore heterogeneous networks, an iterative scheme is extended to determine the distribution of spectra. In the third part, the Fokker-Planck equation is applied to calculate the statistics of sequences of binary decisions from diffusion-decision models (DDM). For the analytically tractable DDM, the statistics are calculated from the corresponding Fokker-Planck equation. To determine the statistics for nonlinear models, the threshold-integration method is generalized.
Ellaithy, Amr. "Metabotropic Glutamate Receptor 2 Activation: Computational Predictions and Experimental Validation". VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5319.
Texto completo da fonteNguyen, 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.
Texto completo da fonteYancey, Madison E. "Computational Simulation and Analysis of Neuroplasticity". Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1622582138544632.
Texto completo da fonteChakrabarty, Nilaj. "Computational Study of Axonal Transport Mechanisms of Actin and Neurofilaments". Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1584441310326918.
Texto completo da fonteLai, 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.
Texto completo da fonteNaze, Sebastien. "Multiscale Computational Modeling of Epileptic Seizures : from macro to microscopic dynamics". Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4023/document.
Texto completo da fonteThis thesis consists in the development of a network model of spiking neurons and the systematic investigation of conditions under which the network displays the emergent dynamic behaviors known from the Epileptor, a well-investigated abstract model of epileptic neural activity. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings between neurons play an essential role in seizure genesis. We demonstrate that spike-waves discharges, including interictal spikes, can be generated primarily by inhibitory neurons only, whereas excitatory neurons are responsible for the fast discharges during the wave part. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes locally, and excitatory synaptic and extracellular couplings participate in seizure spread globally across brain regions
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.
Texto completo da fonteVellmer, 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.
Texto completo da fonteZhu, Mengchen. "Sparse coding models of neural response in the primary visual cortex". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53868.
Texto completo da fonteHudson, 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.
Texto completo da fonteBanks, 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.
Texto completo da fonteGing-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.
Texto completo da fonteBattista, 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.
Texto completo da fonteHow sensory information is encoded and processed by neuronal circuits is a central question in computational neuroscience. In many brain areas, the activity of neurons is found to depend strongly on some continuous sensory correlate; examples include simple cells in the V1 area of the visual cortex coding for the orientation of a bar presented to the retina, and head direction cells in the subiculum or place cells in the hippocampus, whose activities depend, respectively, on the orientation of the head and the position of an animal in the physical space. Over the past decades, continuous attractor neural networks were introduced as an abstract model for the representation of a few continuous variables in a large population of noisy neurons. Through an appropriate set of pairwise interactions between the neurons, the dynamics of the neural network is constrained to span a low-dimensional manifold in the high-dimensional space of activity configurations, and thus codes for a few continuous coordinates on the manifold, corresponding to spatial or sensory information. While the original model was based on how to build a single continuous manifold in an high-dimensional space, it was soon realized that the same neural network should code for many distinct attractors, {em i.e.}, corresponding to different spatial environments or contextual situations. An approximate solution to this harder problem was proposed twenty years ago, and relied on an ad hoc prescription for the pairwise interactions between neurons, summing up the different contributions corresponding to each single attractor taken independently of the others. This solution, however, suffers from two major issues: the interference between maps strongly limit the storage capacity, and the spatial resolution within a map is not controlled. In the present manuscript, we address these two issues. We show how to achieve optimal storage of continuous attractors and study the optimal trade-off between capacity and spatial resolution, that is, how the requirement of higher spatial resolution affects the maximal number of attractors that can be stored, proving that recurrent neural networks are very efficient memory devices capable of storing many continuous attractors at high resolution. In order to tackle these problems we used a combination of techniques from statistical physics of disordered systems and random matrix theory. On the one hand we extended Gardner's theory of learning to the case of patterns with strong spatial correlations. On the other hand we introduced and studied the spectral properties of a new ensemble of random matrices, {em i.e.}, the additive superimposition of an extensive number of independent Euclidean random matrices in the high-density regime. In addition, this approach defines a concrete framework to address many questions, in close connection with ongoing experiments, related in particular to the discussion of the random remapping hypothesis and to the coding of spatial information and the development of brain circuits in young animals. Finally, we discuss a possible mechanism for the learning of continuous attractors from real images
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.
Texto completo da fontePLEASE 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.
2031-01-02
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.
Texto completo da fonteEndres, Dominik M. "Bayesian and information-theoretic tools for neuroscience". Thesis, St Andrews, 2006. http://hdl.handle.net/10023/162.
Texto completo da fontevan, de Ven Gido. "Reactivation and reinstatement of hippocampal assemblies". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:edd60944-381e-4c7d-8029-4d7abb811fc9.
Texto completo da fonteDe, 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.
Texto completo da fonteO'Leary, Timothy S. "Homeostatic regulation of intrinsic excitability in hippocampal neurons". Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3079.
Texto completo da fonteMarsh, Steven Joseph Thomas. "Efficient programming models for neurocomputation". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709268.
Texto completo da fontePhilippides, 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.
Texto completo da fonteLundh, 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.
Texto completo da fonteHendrickson, Eric B. "Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33905.
Texto completo da fonteGuclu, Burak Bolanowski Stanley J. "Computational studies on rapidly-adapting mechanoreceptive fibers". Related Electronic Resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2003. http://wwwlib.umi.com/cr/syr/main.
Texto completo da fonteZysman, 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.
Texto completo da fonteLaw, 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.
Texto completo da fontePendyam, 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.
Texto completo da fonteHarkin, Emerson. "A Simplified Serotonin Neuron Model". Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38533.
Texto completo da fonteCogliati, 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.
Texto completo da fonteDoctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
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.
Texto completo da fonteHjorth, Johannes. "Information processing in the Striatum : a computational study". Licentiate thesis, Stockholm, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3999.
Texto completo da fonteNguyen, Tung Le. "Computational Modeling of Slow Neurofilament Transport along Axons". Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547036394834075.
Texto completo da fonteBenichoux, Victor. "Timing cues for azimuthal sound source localization". Phd thesis, Université René Descartes - Paris V, 2013. http://tel.archives-ouvertes.fr/tel-00931645.
Texto completo da fonteMilano, 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.
Texto completo da fonteMerrison-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.
Texto completo da fonteHunt, Alexander Jacob. "Neurologically Based Control for Quadruped Walking". Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1445947104.
Texto completo da fonteLieuw, Iris. "Time Frequency Analysis of Neural Oscillations in Multi-Attribute Decision-Making". Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/556.
Texto completo da fonteTopalidou, Meropi. "Neuroscience of decision making : from goal-directed actions to habits". Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0174/document.
Texto completo da fonteAction-outcome and stimulus-response processes are two important components of behavior. The former evaluates the benefit of an action in order to choose the best action among those available (action selection) while the latter is responsible for automatic behavior, eliciting a response as soon as a known stimulus is present. Such habits are generally associated (and mostly opposed) to goal-directed actions that require a deliberative process to evaluate the best option to take in order to reach a given goal. Using a computational model, we investigated the classic hypothesis of habits formation and expression in the basal ganglia and proposed a new hypothesis concerning the respective role for both the basal ganglia and the cortex. Inspired by previous theoretical and experimental works (Leblois et al., 2006; Guthrie et al., 2013), we designed a computational model of the basal ganglia-thalamus-cortex that uses segregated loops (motor, cognitive and associative) and makes the hypothesis that basal ganglia are only necessary for the acquisition of habits while the expression of such habits can be mediated through the cortex. Furthermore, this model predicts the existence of covert learning within the basal ganglia ganglia when their output is inhibited. Using a two-armed bandit task, this hypothesis has been experimentally tested and confirmed in monkey. Finally, this works suggest to revise the classical idea that automatism is a subcortical feature
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.
Texto completo da fonteGoings, Sydney Pia. "Neural Synchrony in the Zebra Finch Brain". Scholarship @ Claremont, 2012. https://scholarship.claremont.edu/scripps_theses/41.
Texto completo da fonte