LCN - Laboratory of Computational Neuroscience

Conditioning and Reinforcement learning

Conditioning experiments have a long tradition in biology and psychology. The basic pardigm can be formulated as reward-based `reinforcement' learning. From a mathematical point of view, reinforcement learning (Sutton and Barto) is a class of machine learning algorithms that can be understood as iterative solutions of the Bellman equation (dynamic programing). A basic element in the learning rule is a reinforcement signal that is positive only if the actual reward is larger than the expected reward. The group of Wolfgang Schultz (previously in Fribourg, now in Cambridge) has found exactly this type of activity in dopaminergic neurons in the basal ganglia. The connection between basal ganglia signals on the one side and conditioning and reinforcement learning on the other side has been recognized for a long time. In this project, we adress, among others, the following questions:

Collaborators: Julien Mayor, PhD student at the LCN. This project (financed by the Swiss National Science Foundation) is performed in collaboration with the group of W. Schultz in Cambridge/England.


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LCN - Laboratory of Computational Neuroscience