Computational analysis of animal model memory systems
Abstract/Contents
- Abstract
- The field of neuroscience uses a variety of technologies and animal models to ask fundamental questions about the functionality of the brain. Advancements in biology and recording technologies work together to provide new neurological insight. However, as the recorded information becomes more complex, new analytical methods are needed to extract meaning from the data. This dissertation will specifically look at computational techniques applied to various neural imaging methodologies to advance our understanding of animal model memory systems. In the following chapters, I will describe analysis pipelines developed to study various aspects of both flies and mice. First, I will discuss a method for computing the cross-sectional head and body areas of Drosophila using a dexterous high-throughput robot. This project provides one of the first applications for automatically assessing fly attributes without the need for anesthesia methods that can potentially disrupt the neurological function of the fly. Next, I will present an algorithm to extract and clean GCaMP signals from the mushroom bodies of freely moving flies. This technology will enable the recording of neural function in Drosophila during behaviors that cannot be studied while the fly is head fixed. Lastly, I will present the results of an experiment studying context modulated associations in the mouse hippocampus. Using a miniature microscope, we record ensembles of neurons in mice as they perform a novel task that requires the use of both associative and procedural memory. The analysis presented will show how both memory types are represented in the hippocampus and how procedural learning can transition to accommodate higher order associations.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Maxey, Jessica Rosa |
|---|---|
| Degree supervisor | Schnitzer, Mark Jacob, 1970- |
| Thesis advisor | Schnitzer, Mark Jacob, 1970- |
| Thesis advisor | El Gamal, Abbas A |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Degree committee member | El Gamal, Abbas A |
| Degree committee member | Shenoy, Krishna V. (Krishna Vaughn) |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jessica Rosa Maxey. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/jn205vd7739 |
Access conditions
- Copyright
- © 2021 by Jessica Rosa Maxey
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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