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Reconstructing Neuronal Network Dynamics Using Data Assimilation

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dc.contributor.advisor Peixoto, Nathalia
dc.contributor.advisor Sauer, Timothy
dc.contributor.author Hamilton, Franz William
dc.creator Hamilton, Franz William
dc.date.accessioned 2015-07-29T18:42:49Z
dc.date.available 2015-07-29T18:42:49Z
dc.date.issued 2015
dc.identifier.uri https://hdl.handle.net/1920/9699
dc.description.abstract Understanding the dynamics of the in vivo brain under normal and diseased states is one of the great challenges of modern scientific study. The overall complexity and dimension of the brain though can make this problem intractable. In an effort to study these dynamics in a more controlled, manageable setting, in vitro experimental and computational models have developed. Additionally, the advancement of mathematical analysis and techniques has led to a prominent role for data-assisted modeling whereby experimental data is fused with computational models allowing for data-driven predictions.
dc.format.extent 115 pages
dc.language.iso en
dc.rights Copyright 2015 Franz William Hamilton
dc.subject Mathematics en_US
dc.subject Neurosciences en_US
dc.subject Electrical engineering en_US
dc.subject data assimilation en_US
dc.subject ensemble Kalman filter en_US
dc.subject microelectrode array en_US
dc.subject neuronal networks en_US
dc.title Reconstructing Neuronal Network Dynamics Using Data Assimilation
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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