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Analysis of structured data and big data with application to neuroscience

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dc.contributor.authorSienkiewicz, Ela, author
dc.contributor.authorWang, Haonan, advisor
dc.contributor.authorMeyer, Mary, committee member
dc.contributor.authorBreidt, F. Jay, committee member
dc.contributor.authorHayne, Stephen, committee member
dc.date.accessioned2015-08-28T14:35:02Z
dc.date.available2016-08-14T06:30:24Z
dc.date.issued2015
dc.description.abstractNeuroscience research leads to a remarkable set of statistical challenges, many of them due to the complexity of the brain, its intricate structure and dynamical, non-linear, often non-stationary behavior. The challenge of modeling brain functions is magnified by the quantity and inhomogeneity of data produced by scientific studies. Here we show how to take advantage of advances in distributed and parallel computing to mitigate memory and processor constraints and develop models of neural components and neural dynamics. First we consider the problem of function estimation and selection in time-series functional dynamical models. Our motivating application is on the point-process spiking activities recorded from the brain, which poses major computational challenges for modeling even moderately complex brain functionality. We present a big data approach to the identification of sparse nonlinear dynamical systems using generalized Volterra kernels and their approximation using B-spline basis functions. The performance of the proposed method is demonstrated in experimental studies. We also consider a set of unlabeled tree objects with topological and geometric properties. For each data object, two curve representations are developed to characterize its topological and geometric aspects. We further define the notions of topological and geometric medians as well as quantiles based on both representations. In addition, we take a novel approach to define the Pareto medians and quantiles through a multi-objective optimization problem. In particular, we study two different objective functions which measure the topological variation and geometric variation respectively. Analytical solutions are provided for topological and geometric medians and quantiles, and in general, for Pareto medians and quantiles the genetic algorithm is implemented. The proposed methods are applied to analyze a data set of pyramidal neurons.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifier.urihttp://hdl.handle.net/10217/167082
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectfunction-estimation
dc.subjectMISO
dc.subjectneural spikes
dc.subjectgenetic algorithm
dc.subjectdata object
dc.subjectmulti-objective optimization
dc.titleAnalysis of structured data and big data with application to neuroscience
dc.typeText
dcterms.embargo.expires8/14/2016
dcterms.embargo.terms8/14/2016
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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