BCS.50041/DS.60064 Neuroscience-inspired Artificial Intelligence (2026 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 credit assignment , a core computational principle in the brain and AI. The lectures cover:
Spatial credit assignment (Error backpropagation)
Temporal credit assignment (Bellman equation)
Reinforcement learning algorithms (as a solution to the above)
Neuroscience of reinforcement learning (exploring biological computations and beyond)
The final section explores advanced topics, including striatal model-free and cortical model-based learning, meta-reinforcement learning, entorhinal goal-driven learning, hippocampal successor representation, and dopaminergic distributional value coding. Students are expected to apply machine learning ideas to gain deeper insight into how the brain learns.
TAs: TBA
