Events Calendar

03 Mar
Bioengineering Graduate Seminar: Dr. Bruno Averbeck
Event Type

Lectures, Symposia, Etc.

Topic

Research

Target Audience

Undergraduate Students, Faculty, Graduate Students, Postdocs

University Unit
Department of Bioengineering
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Bioengineering Graduate Seminar: Dr. Bruno Averbeck

This is a past event.

Computational Mechanisms and Neural Systems Underlying Reinforcement Learning

Bruno Averbeck, PhD
Chief
Section on Learning and Decision Making
National Institute of Mental Health

Abstract:  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 their projections to the striatum. According to this model mid-brain dopamine neurons code reward prediction errors (RPEs), and medium spiny neurons in the striatum 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.

Bio:  Dr. Averbeck attained a B.S. in Electrical Engineering from the University of Minnesota in 1994. After working 3 years in industry, Dr. Averbeck returned to the University of Minnesota and completed a Ph.D. in Neuroscience in 2001, working in the lab of Dr. Apostolos Georgopoulos. His thesis was titled, "Neural Mechanisms of Copying Geometrical Shapes". Following his thesis work, Dr. Averbeck carried out post-doctoral studies at the University of Rochester with Dr. Daeyeol Lee. During this period he studied neural mechanisms underlying sequential learning, coding of vocalizations and population coding. In 2006 Dr. Averbeck moved to University College London as a senior Lecturer, where he began experiments looking at the role of frontal-striatal circuits in learning, combining neurophysiology, brain imaging, and patient studies. In 2009, Dr. Averbeck moved to the NIMH and established the Unit on Learning and Decision Making in the Laboratory of Neuropsychology.

Thursday, March 3 at 4:00 p.m. to 5:00 p.m.

Benedum Hall, Room 157
3700 O'Hara Street, Pittsburgh, PA 15261

Bioengineering Graduate Seminar: Dr. Bruno Averbeck

Computational Mechanisms and Neural Systems Underlying Reinforcement Learning

Bruno Averbeck, PhD
Chief
Section on Learning and Decision Making
National Institute of Mental Health

Abstract:  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 their projections to the striatum. According to this model mid-brain dopamine neurons code reward prediction errors (RPEs), and medium spiny neurons in the striatum 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.

Bio:  Dr. Averbeck attained a B.S. in Electrical Engineering from the University of Minnesota in 1994. After working 3 years in industry, Dr. Averbeck returned to the University of Minnesota and completed a Ph.D. in Neuroscience in 2001, working in the lab of Dr. Apostolos Georgopoulos. His thesis was titled, "Neural Mechanisms of Copying Geometrical Shapes". Following his thesis work, Dr. Averbeck carried out post-doctoral studies at the University of Rochester with Dr. Daeyeol Lee. During this period he studied neural mechanisms underlying sequential learning, coding of vocalizations and population coding. In 2006 Dr. Averbeck moved to University College London as a senior Lecturer, where he began experiments looking at the role of frontal-striatal circuits in learning, combining neurophysiology, brain imaging, and patient studies. In 2009, Dr. Averbeck moved to the NIMH and established the Unit on Learning and Decision Making in the Laboratory of Neuropsychology.

Thursday, March 3 at 4:00 p.m. to 5:00 p.m.

Benedum Hall, Room 157
3700 O'Hara Street, Pittsburgh, PA 15261

Topic

Research

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