Available at: https://digitalcommons.calpoly.edu/theses/3193
Date of Award
12-2025
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Franz J. Kurfess
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
The Neuroidal model poses a neurobiologically plausible theory for modeling the brain. This symbolic network has been shown to capture realistic memorization behaviors using the JOIN algorithm. The model has also been recently improved by incorporating Watts-Strogatz small-worlds within its base structure. From the efforts of neuroscience researchers, we have access to the Drosophila melanogaster (D. melanogaster) fruit fly’s connectome, which has been found to also contain small-worlds in this thesis. By synthesizing the Ocellar Ganglion (OCG) region of Drosophila, we compare a digitized version of a real-world brain with an instance of the Neuroidal model. In this thesis, we offer novel results that display the stability of JOIN within OCG, and a striking comparative evaluation between the Neuroidal model and OCG’s capacity for memories. This study further establishes the Neuroidal model as both an efficient and plausible neural network for general cognition.