Sequence learning in a model of the basal ganglia
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This thesis presents a computational model of the basal ganglia that is able to learn sequences and perform action selection. The basal ganglia is a set of structures in the human brain involved in everything from action selection to reinforcement learning, inspiring research in psychology, neuroscience and computer science. Two temporal difference models of the basal ganglia based on previous work have been reimplemented. Several experiments and analyses help understand and describe the original works. This uncovered flaws and problems that is addressed.