Skip to main content
Duke University Libraries
DukeSpace Scholarship by Duke Authors
  • Login
  • Ask
  • Menu
  • Login
  • Ask a Librarian
  • Search & Find
  • Using the Library
  • Research Support
  • Course Support
  • Libraries
  • About
View Item 
  •   DukeSpace
  • Theses and Dissertations
  • Duke Dissertations
  • View Item
  •   DukeSpace
  • Theses and Dissertations
  • Duke Dissertations
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series

Thumbnail
View / Download
6.8 Mb
Date
2015
Author
Carlson, David
Advisor
Carin, Lawrence
Repository Usage Stats
284
views
472
downloads
Abstract

This thesis presents novel methods for processing electrophysiological time-series from simultaneously recorded electrodes in a brain, as well as providing new inference techniques that are more generally applicable. On spike sorting, I introduce Bayesian nonparametric methods to process multiple electrodes simultaneously, which improves performance when the electrode spacing is less than 100 microns. Furthermore, by treating the spike sorting problem as a single deconvolutional model instead of the conventional 2-step procedure with detection and clustering steps, the over- lapping spike problem is ameliorated. I then show that these detected neurons and their spike trains have dynamic relationships with local field potentials in distinct brain regions, and that the number of distinct relationships appears to cluster.

While these models approach an important scientific problem, it is necessary to have efficient inference in computationally-intensive models. To this end, I intro- duce novel methods for Variational Bayesian inference, as well as introducing a new stochastic inference algorithm called "Stochastic Spectral Descent," which mimics Stochastic Gradient Descent but operates in the Shatten-infinity norm. I show that several common machine learning problems naturally operate in the Shatten-infinity norm, and that this descent method mimics the natural geometry and greatly improves learning efficiency.

Type
Dissertation
Department
Electrical and Computer Engineering
Subject
Electrical engineering
Permalink
https://hdl.handle.net/10161/9895
Citation
Carlson, David (2015). Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/9895.
Collections
  • Duke Dissertations
More Info
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

Rights for Collection: Duke Dissertations


Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info

Make Your Work Available Here

How to Deposit

Browse

All of DukeSpaceCommunities & CollectionsAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit DateThis CollectionAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit Date

My Account

LoginRegister

Statistics

View Usage Statistics
Duke University Libraries

Contact Us

411 Chapel Drive
Durham, NC 27708
(919) 660-5870
Perkins Library Service Desk

Digital Repositories at Duke

  • Report a problem with the repositories
  • About digital repositories at Duke
  • Accessibility Policy
  • Deaccession and DMCA Takedown Policy

TwitterFacebookYouTubeFlickrInstagramBlogs

Sign Up for Our Newsletter
  • Re-use & Attribution / Privacy
  • Harmful Language Statement
  • Support the Libraries
Duke University