Advisor(s)
Ryoichi Kawai
Committee Member(s)
Gopikrishna Deshpande
Harrison Walker
Julie Quinet
Kristina Visscher
Matthew Nelson
Paul Gamlin
School
College of Arts and Sciences
Document Type
Dissertation
Department (new version)
Physics
Date of Award
9-11-2025
Abstract
Saccadic eye movements provide a well-defined behavioral readout for investigating how the basal ganglia (BG) contribute to action selection. While saccades are controlled by multiple brain regions, including direct excitatory inputs from various cortices, the BG exerts a key inhibitory influence. Decades of experimental work using saccadic behavioral paradigms in non-human primates have yielded important insights into the activity of BG populations. These include the caudate nucleus, subthalamic nucleus, globus pallidus externus, and substantia nigra pars reticulata, as well as dopaminergic afferents from the substantia nigra pars compacta and efferent projections to the superior colliculus, which controls eye movements. Despite this extensive body of research, the data remain fragmented, with most studies focusing on individual nuclei or specific conditions. To integrate these observations into a coherent framework, we developed a physiologically grounded computational model of the BG and simulated two widely studied tasks: visually-guided saccades (VGS) and memory-guided saccades (MGS). The model architecture reflects the experimentally supported concept of BG local \emph{channels}- parallel, functionally segregated loops that process specific task-related information through the BG. In our current computational model, each channel consists of 64 leaky integrate-and-fire neurons per nucleus, with connectivity drawn probabilistically on each run to reflect structural variability and support statistical robustness. Building on this framework, we use the model network to demonstrate that experimental data, semi-quantitatively reproduced in the firing patterns of the modeled BG nuclei, give rise to BG output dynamics consistent with the differing saccade metrics observed during VGS and MGS tasks. These metrics include peak saccade velocity (PSV) and saccadic reaction time (SRT). Through simulation, we reproduced BG outputs that may account for the higher PSV in VGS compared to MGS, while also explaining variations in SRT (shorter, comparable or longer) in VGS vs. MGS, that may similarly arise from underlying BG dynamics. To enable this analysis, cortical and dopaminergic inputs are synthetically generated to match experimental constraints, and the network is reconstructed anew in each of 100 independent trials. Probabilistic connectivity between neurons introduces variability across runs, allowing for population-level averaging across stochastic realizations.
ProQuest ID
Recommended Citation
Chitale, Saarth Anjali, "Modeling The Basal Ganglia Network Using Leaky Integrate-And-Fire Neurons To Explore Its Role In Facilitating Purposive Saccades" (2025). All ETDs from UAB. 7394.
https://digitalcommons.library.uab.edu/etd-collection/7394
