NeuroAI: Deep Learning in Brains, Minds, and Machines

Catalog description

This graduate module explores the rapidly converging fields of brain and cognitive science and artificial intelligence. Students will examine how modern deep learning architectures relate to biological neural computation, and how insights from brain science can inform the design of more capable and robust AI systems. Topics include convolutional and recurrent neural networks as models of sensory processing; the transformer architecture and foundation/frontiers models more broadly; their relevance to cognitive science, representation learning, benchmarking AI against human behavior and neural data; and current debates on the limits of AI as a tool for understanding the brain. The course emphasizes critical reading of primary literature on landmark and emerging papers.