Parameter Estimation in Hybrid Dynamical Systems with Application to Neuronal Models

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dc.contributor.advisorManitius, Andrzej Z.
dc.contributor.authorMitra, Anish
dc.creatorMitra, Anish
dc.date.accessioned2014-09-29T18:06:27Z
dc.date.available2014-09-29T18:06:27Z
dc.date.issued2014-08
dc.description.abstractAnalysis and recreation of brain dynamics has been identified as one of the greatest scientific challenge of this century. Detection of electrical impulses in the brain was the first step towards understanding how it functions. An interconnected network of neurons relay information and communicate with one another through these impulses also referred to as 'spikes'. Knowledge of the spiking behavior and connectivity in different regions of the brain will help in the diagnosis and treatment of neurological disorders such as epilepsy and Parkinsons disease. There are also efforts to develop intelligent algorithms inspired by the functioning of the brain and build efficient processing and computing units.
dc.format.extent112 pages
dc.identifier.urihttps://hdl.handle.net/1920/8990
dc.language.isoen
dc.rightsCopyright 2014 Anish Mitra
dc.subjectElectrical engineering
dc.subjectBiomedical engineering
dc.subjectHybrid Dynamical Systems
dc.subjectNeuron Spiking Models
dc.subjectOptimization
dc.subjectParameter Estimation
dc.subjectSystem Identification
dc.titleParameter Estimation in Hybrid Dynamical Systems with Application to Neuronal Models
dc.typeDissertation
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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