Comparative Topological Analysis of Neuronal Arbors via Sequence Representation and Alignment

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dc.contributor.advisorAscoli, Giorgio A.
dc.contributor.authorGillette, Todd Aaron
dc.creatorGillette, Todd Aaron
dc.date.accessioned2015-07-29T18:35:17Z
dc.date.available2015-07-29T18:35:17Z
dc.date.issued2015
dc.description.abstractNeuronal morphology is a key mediator of neuronal function, defining the profile of connectivity and shaping signal integration and propagation. Reconstructing neurite processes is technically challenging and thus data has historically been relatively sparse. Data collection and curation along with more efficient and reliable data production methods provide opportunities for the application of informatics to find new relationships and more effectively explore the field. This dissertation presents a method for aiding the development of data production as well as a novel representation and set of analyses for extracting morphological patterns.
dc.format.extent274 pages
dc.identifier.urihttps://hdl.handle.net/1920/9628
dc.language.isoen
dc.rightsCopyright 2015 Todd Aaron Gillette
dc.subjectNanoscience
dc.subjectBioinformatics
dc.subjectMotif analysis
dc.subjectNeuroinformatics
dc.subjectNeuronal morphology
dc.subjectNeuronal reconstruction
dc.subjectSequence alignment
dc.titleComparative Topological Analysis of Neuronal Arbors via Sequence Representation and Alignment
dc.typeDissertation
thesis.degree.disciplineNeuroscience
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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