Séminaire de Bruno Averbeck

vendredi 20 mai 2022 à 14:30
Publié le 17/05/2022

Bruno Averbeck (Laboratory of Neuropsychology, NIMH, USA)

“Computational   mechanisms   and   neural   systems   underlying reinforcement learning” 

Vendredi 20 mai à 14h30


Meeting ID: 968 6301 4880 – Passcode: 803735

Biological agents adapt behavior using reinforcement learning (RL) to support the survival needs of the individual and the species.  In my talk I will discuss the neural and computational mechanisms that support reinforcement learning in biological agents.  Many theories of RL focus on a simple model.  Anatomically, this model is encompassed by mid-brain dopamine neurons and the striatum.  The striatum is the input nucleus of the basal ganglia.  According to this model mid-brain dopamine neurons code reward prediction errors (RPEs).  RPEs are the difference between the reward that was expected, and the reward that was received, after a choice.  The mid-brain dopamine neurons send a prominent projection to the striatum, and medium spiny neurons in the striatum are thought to integrate the RPEs signaled by the dopamine neurons.  Action values are the integral of RPEs and therefore the striatum is thought to represent the values of actions.  Although these structures are important, and the basic model has substantial predictive validity, our work has shown that a broader set of neural systems are important for RL.  In my talk I will discuss the role of cortical-striatal circuits, including the amygdala and prefrontal cortex, in RL.  Specifically, we will show that the amygdala plays an important role in learning the values of objects in standard bandit paradigms.  I will also show that dorsal-lateral prefrontal cortex carries important signals related to state inference, in the context of a reversal learning experiment in which monkeys learn to rapidly reverse their choice preferences, in a Bayesian manner, when choice-outcome mappings are reversed.  Overall, we believe that a broad-set of cortical-basal ganglia circuits underlie multiple aspects of RL.