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Parameter Estimation in Hybrid Dynamical Systems with Application to Neuronal Models

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dc.contributor.advisor Manitius, Andrzej Z.
dc.contributor.author Mitra, Anish
dc.creator Mitra, Anish en_US
dc.date.accessioned 2014-09-29T18:06:27Z
dc.date.available 2014-09-29T18:06:27Z
dc.date.issued 2014-08 en_US
dc.identifier.uri https://hdl.handle.net/1920/8990
dc.description.abstract Analysis 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.extent 112 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2014 Anish Mitra en_US
dc.subject Electrical engineering en_US
dc.subject Biomedical engineering en_US
dc.subject Hybrid Dynamical Systems en_US
dc.subject Neuron Spiking Models en_US
dc.subject Optimization en_US
dc.subject Parameter Estimation en_US
dc.subject System Identification en_US
dc.title Parameter Estimation in Hybrid Dynamical Systems with Application to Neuronal Models en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Electrical and Computer Engineering en
thesis.degree.grantor George Mason University en


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