Explore the neural mechanisms of reinforcement learning in this 39-minute seminar by Dr. Poornima Kumar from McLean Hospital/Harvard Medical School. Delve into the functional and structural connectome underlying reinforcement learning processes. Examine computational models and their applications in understanding neural substrates of prediction error. Investigate the anatomy and circuitry of reinforcement learning, with a focus on its role in depression. Learn about reinforcement learning-based decision-making and its neural correlates. Discover methods for identifying structural and functional connectomes associated with reinforcement learning. Analyze the potential of reinforcement learning in parsing heterogeneity in major depressive disorder. Gain insights into multimodal imaging data fusion techniques and their application in identifying brain patterns related to reinforcement learning.
Overview
Syllabus
Intro
Neural Substrate of Prediction Error
What are Computational Models?
Can Computational Models help?
Anatomy of Reinforcement Learning
Reinforcement Learning Circuitry
Reinforcement Learning in Depression
RL based Decision Making
RL heterogeneity and Neural Correlates
Identifying Structural Connectome of RL
Identifying Functional Connectome of RL
DSM-V Major Depressive Disorder
Can we use Reinforcement Learning to parse heterogeneity?
Parsing heterogeneity and Identifying biotypes
Multimodal Imaging Data fusion
Multimodal Data fusion Steps
Delay-Discount Factor
DLPFC Multimodal Brain Pattern (n=1103)
Taught by
MGH Martinos Center
