¿´Æ¬ÊÓƵ

Neurogenesis Speaker Series

Wednesday, February 5, 2025
4:30–5:30 p.m. (with post-event reception)

TheÌýNeurogenesisÌýSpeaker Series will give you the opportunity to get to know HBHL’s new recruits firsthand, learn about their research, ask questions and network with your peers during the post-event reception.

Each event in this series will feature two HBHL faculty recruits whose research areas provide an interesting contrast or intersection for discussion.

December's Speakers:Ìý

• Paul Masset, PhDÌý- "Distributed reinforcement learning in the brain"
• Pouya Bashivan, PhDÌý- "What does spatial tuning tell us about the neural code in the hippocampus?"

Speakers

Paul Masset

Paul Masset is an Assistant Professor in the Department of Psychology at ¿´Æ¬ÊÓƵ University and an Affiliate member at Mila - Quebec Artificial Intelligence Institute, working at the intersection of neuroscience, AI and cognitive science. The focus of his research group is to understand how the structure of neural circuits endows the brain with efficient distributed computations underlying cognition and how we can leverage these principles to design more efficient learning algorithms.ÌýPrior to joining ¿´Æ¬ÊÓƵ, he was a Postdoctoral Fellow at Harvard University. He obtained his PhD at Cold Spring Harbor Laboratory, his Masters in Cognitive Science at the École des hautes études en sciences sociales Ìý(EHESS) and his M.Eng/B.A. in Information and Computer Engineering at the University of Cambridge.

Pouya Bashivan

Pouya Bashivan is an Assistant Professor at the Department of Physiology at ¿´Æ¬ÊÓƵ University, an associate member of Mila, the Quebec AI Institute, and a William Dawson Scholar. Bashivan’s past research has spanned the fields of control theory, machine learning, and neuroscience. The research in his group is at the intersection of artificial neural networks and neuroscience and is focused on developing computational models of visual processing in the primate brain with a focus on visual memory. Specifically, he uses artificial neural network models trained to perform ecologically-relevant tasks to model the cortical responses in primate’s brain. His ultimate research goal is to leverage the predictive power in such models of brain activity to modulate the brain’s function in disease.