Abstract

Abstract: The basal ganglia and dopaminergic systems are well studied for their roles in reinforcement learning and reward-based decision making. Much work focuses on “reward prediction error” (RPE) signals conveyed by dopamine and used for learning. Computational considerations suggest that such signals may be enriched beyond the classical global and scalar RPE computation, to support more structured learning in distinct sub-circuits (“vector RPEs”). Such signals allow an agent to assign credit to the level of action selection most likely responsible for the outcomes, and hence to enhance learning depending on the generative task statistics. Experimental data from mice will be presented, showing spatiotemporal dynamics of dopamine terminal activity and release across the dorsal striatum in the form of traveling waves that support learning about agency.

Biography: Prof Frank received his Ph.D. in Neuroscience & Psychology (joint) at the University of Colorado at Boulder in 2004. After working as a Professor at the University of Arizona, he joined the Brown University’s Cognitive, Linguistic & Psychological Science department in 2011, where he is currently an Edgar L. Marston Professor. His research combines multiple levels of computational modelling and experimental work to understand the neural mechanisms underlying reinforcement learning, decision making and cognitive control. His lab also develops neural circuit and algorithmic models of systems-level interactions between multiple brain areas (primarily prefrontal cortex and basal ganglia and their modulation by dopamine). For detailed biography of Prof Frank, please visit: http://ski.cog.brown.edu/