BCS.50041/DS.60064 Neuroscience-inspired Artificial Intelligence (2025 Fall)
[Important notice] This course is the second part of the Brain x AI lecture series:
How AI and the brain work (undergraduate course; previously BiS 429):
focuses on inverse problems. It covers linear methods, deep learning, and neuroscience of deep learning.Neuroscience-inspired artificial intelligence (graduate course; previously BCS541):
focuses on control problems. It covers biological error backpropagation, temporal credit assignment problems, reinforcement learning theory/algorithms, neuroscience of reinforcement learning, and brain-inspired reinforcement learning.I will no longer teach BiS 429 and BCE 772.
Summary: This course explores the theory, algorithms, and neuroscience of "temporal credit assignment," a fundamental computation in the brain and AI. Lectures cover the following topics:
Spatial credit assignment (Biological error backpropagation)
Temporal credit assignment (Bellman equation)
Reinforcement learning algorithms (as a solution to the above)
Neuroscience of reinforcement learning (Biological evidence and beyond)
The final section delves into striatal model-free and cortical model-based learning, prefrontal meta-reinforcement learning, entorhinal goal-driven learning, hippocampal successor representation, and dopaminergic distributional value coding. Students are expected to master necessary skills to make the best use of machine learning theory to understand how and why the brain learns.
