@Article{Supelec838,
author = {Julien Vitay and Jérémy Fix and Fred Hamker and Henning Schroll and Frederik Beuth},
title = {{Biological Models of Reinforcement Learning}},
journal = {Künstliche Intelligenz},
year = {2009},
volume = {3-09},
pages = {12--18},
abstract = {This review focuses on biological issues of reinforcement
learning. Since the influential discovery of W. Schultz of an
analogy between the reward prediction error signal of the
temporal difference algorithm and the firing pattern of some
dopaminergic neurons in the midbrain during classical
conditioning, biological models have emerged that use
computational reinforcement learning concepts to explain
adaptative behavior. In particular, the basal ganglia has been
proposed to implement among other things reinforcement learning
for action selection, motor control or working memory. We discuss
to which extent the analogy between the temporal difference
algorithm and the firing of dopamine cells can be considered as
valid. Our review then focuses on the basal ganglia, their
anatomy and key computational properties as demonstrated by three
recent, influential models.}
}