Machine learning meets mammalian learning : statistical tools for large-scale calcium imaging and the study of changing neural codes
Abstract/Contents
- Abstract
- In this thesis, I describe my work creating computational and statistical tools for neuroscience, and the application of these tools to the study of how neural coding changes with learning. In particular, I apply these tools to study several facets of the dynamics of place cells in hippocampal area CA1. First, I describe a Bayesian decoding method for predicting an animal's position in a room solely from his brain imaging data. I use this method to demonstrate retention of spatial information despite changes in place coding population. Further, I demonstrate that place coding ensemble turnover resembles a random process that nevertheless contains significant information about the time of the experience. Secondly, I introduce and validate a method for extracting neural images and fluorescence traces from large-scale calcium imaging movies. I use this method to study the relationship between the place-coding properties of nearby cells in area CA1. Lastly, I apply these decoding and cell extraction methods to the study of how place cells change during learning. I find that there is significant refinement in the place code, and that the neural spatial information is correlated with the animal's ability to navigate. This final demonstration is consistent with the hypothesis that improved fidelity of the internal place-coding map underlies the animal's navigational learning. Taken together, the tools in this thesis are advancements that contribute to the ability of neuroscientists to process and glean meaning from large datasets, a challenge that is presently at the forefront of the field.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kitch, Lacey Jane |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Schnitzer, Mark Jacob, 1970- |
| Thesis advisor | Schnitzer, Mark Jacob, 1970- |
| Thesis advisor | El Gamal, Abbas A |
| Thesis advisor | Goldsmith, Andrea, 1964- |
| Advisor | El Gamal, Abbas A |
| Advisor | Goldsmith, Andrea, 1964- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Lacey Jane Kitch. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Lacey Kitch
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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