Date of Submission
Campus Only Senior Thesis
Bachelor of Arts
In this thesis, I explore the use of artificial neural networks as a mechanism for decoding organismal location from LFP (local field potential) data in the entorhinal cortex. Success has already been found in doing this for individual neurons, using traditional practices of measuring spike data at the cell-level. Additionally, position information has already been decoded from LFP data in the hippocampus. As of now, there is only other known paper that explores the decoding of this information from the entorhinal cortex.
Conceptually, data from the hippocampus and entorhinal cortex differs greatly but shares a focus on spatial navigation. The hippocampus encodes information about position through place cells that activate within absolute fields of locations. The entorhinal cortex encodes information about relative location using grid cells.
I have used Agarwal (2014) as a starting point for this technical challenge. By making adjustments to the preprocessing techniques and reusing the artificial neural network structure that Dr. Agarwal used, promising results have been obtained. It is clear from the results that modeling LFP from the entorhinal cortex does better than chance at predicting the position of an organism in space.
marin, bryan, "Communication through Rhythms in the Hippocampus and Entorhinal Cortex" (2022). CMC Senior Theses. 2967.
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.