Parameter Estimation of Neuron Models using Subset Selection and Dynamic Optimization

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dc.contributor.advisorHeinkenschloss, Matthiasen_US
dc.creatorKhaddaj, Anwaren_US
dc.date.accessioned2025-05-29T16:01:38Zen_US
dc.date.available2025-05-29T16:01:38Zen_US
dc.date.created2025-05en_US
dc.date.issued2025-04-23en_US
dc.date.submittedMay 2025en_US
dc.date.updated2025-05-29T16:01:38Zen_US
dc.description.abstractThis thesis presents two frameworks for parameter estimation in neuron models and assesses parameter accuracy by constructing confidence regions of parameter estimates. Parameter estimation helps advance the understanding of how neurons process sensory information. Nonlinear least squares have previously been used to fit biophysical neuron models. Yet, little attention has been devoted to handling rank-deficient problems, and to identifying and characterizing possible degeneracy in model parameters. To identify parameter degeneracy and resolve the rank deficiency, an SVD-based subset selection algorithm is used. Additional biophysical experiments are constructed, which constrain the least identifiable parameters. The approach is applied to the HCN neuron model. Moreover, an all-at-once optimization approach is applied, which includes the neuron model as a constraint and views parameters and model solution as optimization variables. This approach is demonstrated on the Pinsky-Rinzel model. The framework is designed to support the goal of applying them to complex compartmental neuron models.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://hdl.handle.net/1911/118369en_US
dc.language.isoenen_US
dc.subjectparameter estimationen_US
dc.subjectcomputational neuroscienceen_US
dc.subjectnonlinear least squaresen_US
dc.subjectsubset selectionen_US
dc.subjectdynamic optimizationen_US
dc.subjectparameter degeneracyen_US
dc.titleParameter Estimation of Neuron Models using Subset Selection and Dynamic Optimizationen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentComputational and Applied Mathematicsen_US
thesis.degree.disciplineComputational & Applied Math, Computational & Applied Mathen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Artsen_US
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