Paul Masset

Paul Masset is an Assistant Professor in the Department of Psychology at McGill University, focusing on the intersection of neuroscience, artificial intelligence, and experimental psychology. His research investigates how neuronal heterogeneity enables efficient distributed computations in brain circuits, particularly through dopamine-based reinforcement learning models validated via large-scale electrophysiological recordings in rodents. He leads an interdisciplinary team developing biologically plausible neural networks and interpretable deep learning tools for analyzing neural signals.

Key research areas include systems neuroscience, machine learning applications in neuroscience, and computational models of neural circuits. His work bridges theoretical models with experimental validation, emphasizing translational insights between natural and artificial intelligence.

Recent publications highlight advancements in multi-timescale reinforcement learning mechanisms, interpretable neural signal analysis, and natural gradient optimization in spiking networks. His lab's funding来源于 Canadian and Quebec governmental grants, alongside private foundations. Collaborations with institutions like Mila and Harvard extend his computational neuroscience expertise.

Team projects include summer research initiatives supported by NSERC and McGill programs, fostering early-career involvement in neuroscience-AI integration. Lab activities emphasize open science practices and cross-disciplinary collaboration.