Date of Award
Computer and Information Science
Alberto Jose Del Arco Gonzalez
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.
Woods, Avery, "Identifying key activity indicators in rats' neuronal data using lasso regularized logistic regression" (2023). Honors Theses. 2865.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.