Artificial intelligence is transforming how we study the brain and behavior. Modern AI methods provide both new computational models of neural and cognitive processes and powerful tools for extracting knowledge from complex neural and behavioral data. However, current models and tools remain limited in their ability to capture and uncover many important aspects of biological neural systems, from circuit dynamics to cognitive function.

This three-week course is a hands-on introduction to modern NeuroAI approaches for understanding brain and behavior. You will learn how to:

• model: develop computational models of brain and behavior, from modern AI architectures to biologically constrained neural and circuit models.
• compare: evaluate models against neural and behavioral data, from single neurons to population activity and behavior.
• interpret: use AI to extract insights from high-dimensional neural and behavioral data, from representation learning to parameter inference and model discovery.
• experiment: use models to generate hypotheses, design experiments and interventions, and investigate neural mechanisms in silico.

The three weeks will combine lectures, hands-on tutorials, and student-led research projects mentored by faculty and TAs, alongside advanced lectures by keynote speakers. The course is designed for graduate students and postdocs in neuroscience, cognitive science, machine learning, and related fields who have at least basic programming experience.