Identifying key activity indicators in rats' neuronal data using lasso regularized logistic regression

Author

Avery Woods, University of MississippiFollow

Date of Award

Spring 5-13-2023

Document Type

Undergraduate Thesis

Department

Computer and Information Science

First Advisor

Yixin Chen

Second Advisor

Alberto Jose Del Arco Gonzalez

Third Advisor

Xin Dang

Relational Format

Dissertation/Thesis

Abstract

This thesis aims to identify timestamps of rats’ neuronal activity that best determine behavior using a machine learning model. Neuronal data is a complex and high-dimensional dataset, and identifying the most informative features is crucial for understanding the underlying neuronal processes. The Lasso regularization technique is employed to select the most relevant features of the data to the model’s prediction. The results of this study provide insights into the key activity indicators that are associated with specific behaviors or cognitive processes in rats, as well as the effect that stress can have on neuronal activity and behavior. Ultimately, it was determined that stress affected the neuronal processes in rats differently, and the ventral tegmental area of the brain was much more influential in the decision-making process than the prefrontal cortex.

Recommended Citation

Woods, Avery, "Identifying key activity indicators in rats' neuronal data using lasso regularized logistic regression" (2023). Honors Theses. 2865.
https://egrove.olemiss.edu/hon_thesis/2865

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This work is licensed under a Creative Commons Attribution 4.0 International License.