Prof. Rafał Bogacz

Rafał Bogacz abstract
 
🧠 About guest:
 
prof. Rafal Bogacz graduated in computer science at Wroclaw University of Technology in Poland. Then he did a PhD in computational neuroscience at the University of Bristol, and next he worked as a postdoctoral researcher at Princeton University, USA, jointly in the Departments of Applied Mathematics and Psychology. He moved to the University of Oxford in 2013. His research is in the area of computational neuroscience, which seeks to develop mathematical models describing computations in the brain giving raise to our mental abilities. He is particularly interested in modelling the brain networks involved in action selection and decision making, and understanding how brain dynamics change in Parkinson's disease.
During his lecture, "Computational models of reinforcement learning" we'll find out more about classical models describing how the neural circuits in the basal ganglia learn about expected rewards and discuss more recent models of how the basal ganglia also learn about reward uncertainty, and their relationship to experimental data.

📌 Topic: Computational models of reinforcement learning.

👨‍🏫 Abstract:

A great advance in understanding brain networks underlying reinforcement learning has been achieved thanks to joint contributions of experimental and computational neuroscience. The synergy between these fields started with an observation that dopaminergic neurons in the brain encode the same reward prediction error signal that was used in reinforcement learning algorithms from artificial intelligence. Since then, computational models were developed to describe how this signal is generated and how it modulates learning in brain areas innervated by dopaminergic projections. This talk will start with an overview of classical models describing how the neural circuits in the basal ganglia learn about expected rewards. Then it will discuss more recent models of how the basal ganglia also learn about reward uncertainty, and their relationship to experimental data. 

 

 

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