First Neurogenesis talk of 2025 highlights learning and spatial coding
Yesterday, HBHL hosted the first Neurogenesis Speaker Series talk of 2025 at The Neuro, featuring Paul MassetÌý²¹²Ô»å Pouya Bashivan.
Paul Masset’s presentation -Ìý"Distributed reinforcement learning in the brain" -Ìýexplored how reinforcement learning principles are implemented in neural circuits, while Pouya Bashivan’s talk -"What does spatial tuning tell us about the neural code in the hippocampus?"Ìý- examined the role of spatial representations in encoding and processing information.
The event allowed attendees to engage with HBHL’s faculty recruits, discuss their research and connect with colleagues from various disciplines during the post-event reception.
Mark your calendars for the next Neurogenesis talk on March 26, 2025 at The Neuro. Speaker details will be announced soon—stay tuned!
Ìý
About the February 2025 Speakers
Paul Masset
is an Assistant Professor in the Department of Psychology at º«¹úÂãÎè 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. Prior to joining º«¹úÂãÎè, 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 (EHESS) and his M.Eng/B.A. in Information and Computer Engineering at the University of Cambridge.
Pouya Bashivan
is an Assistant Professor at the Department of Physiology at º«¹úÂãÎè, an associate member of Mila - Quebec Artificial Intelligence 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.