Dissertations / Theses on the topic 'Computational neuroscience'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Computational neuroscience.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
Higgins, Irina. "Computational neuroscience of speech recognition." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:daa8d096-6534-4174-b63e-cc4161291c90.
Full textWalters, 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.
Full textCronin, Beau D. "Quantifying uncertainty in computational neuroscience with Bayesian statistical inference." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45336.
Full textIncludes 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.
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.
Full textTromans, 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.
Full textVellmer, 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.
Full textThis 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.
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.
Full textWoldman, 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.
Full textNguyen, 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.
Full textLundh, 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.
Full textLee, 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.
Full textMarsh, Steven Joseph Thomas. "Efficient programming models for neurocomputation." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709268.
Full textPhilippides, 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.
Full textO'Leary, Timothy S. "Homeostatic regulation of intrinsic excitability in hippocampal neurons." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3079.
Full textKazer, 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.
Full textVellmer, 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.
Full textWright, 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.
Full textPLEASE 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
Yancey, Madison E. "Computational Simulation and Analysis of Neuroplasticity." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1622582138544632.
Full textLai, 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.
Full textAllen, 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.
Full textZysman, 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.
Full textEllaithy, Amr. "Metabotropic Glutamate Receptor 2 Activation: Computational Predictions and Experimental Validation." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5319.
Full textGing-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.
Full textBattista, 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.
Full textHow 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
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.
Full textPendyam, 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.
Full textHarkin, Emerson. "A Simplified Serotonin Neuron Model." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38533.
Full textChakrabarty, Nilaj. "Computational Study of Axonal Transport Mechanisms of Actin and Neurofilaments." Ohio University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1584441310326918.
Full textMerrison-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.
Full textBenigni, 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.
Full textDe, 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.
Full textMilano, 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.
Full textShepardson, Dylan. "Algorithms for inverting Hodgkin-Huxley type neuron models." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31686.
Full textCommittee 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.
Lieuw, Iris. "Time Frequency Analysis of Neural Oscillations in Multi-Attribute Decision-Making." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/556.
Full textLundqvist, 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.
Full textAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper8: In press.
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.
Full textFöldiak, Peter. "Models of sensory coding." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239097.
Full textBIDDELL, 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.
Full textEndres, Dominik M. "Bayesian and information-theoretic tools for neuroscience." Thesis, St Andrews, 2006. http://hdl.handle.net/10023/162.
Full textMender, 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.
Full textGoings, Sydney Pia. "Neural Synchrony in the Zebra Finch Brain." Scholarship @ Claremont, 2012. https://scholarship.claremont.edu/scripps_theses/41.
Full textNaze, Sebastien. "Multiscale Computational Modeling of Epileptic Seizures : from macro to microscopic dynamics." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4023/document.
Full textThis 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
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.
Full textNeuromodulation 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 portrayed in Chapters 1-4 provide empirical and theoretical evidence for neuromodulatory influence over selected actions through predictions of expected costs and rewards. The simulation experiments described in Chapters 5-6 illustrate how sensory integration influences action execution across different neural architectures in visually and memory guided sensorimotor transformation tasks. The implications of these results and future endeavors are discussed in Chapter 7, along with a proposed computational model of both action selection and sensory integration to investigate the dynamics of decision-making influenced by the integration of multiple sensory inputs in order to execute an action.
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.
Full textBanks, 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.
Full textBerthet, 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.
Full textAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.
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.
Full textDoctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
Strack, Beata. "Multi-column multi-layer computational model of neocortex." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3279.
Full textHam, 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.
Full textAbeysuriya, Romesh Gerald. "Physiologically-based Brain State Modeling." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/14469.
Full text