Sequence learning in a model of the basal ganglia

dc.contributor.advisor	Downing, Keith
dc.contributor.author	Søiland, Stian
dc.date.accessioned	2018-11-05T15:00:30Z
dc.date.available	2018-11-05T15:00:30Z
dc.date.created	2006-06-08
dc.date.issued	2006
dc.identifier	ntnudaim:1551
dc.identifier.uri	http://hdl.handle.net/11250/2571042
dc.description.abstract	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.
dc.language	eng
dc.publisher	NTNU
dc.subject	Informatikk, Kunstig intelligens og læring
dc.title	Sequence learning in a model of the basal ganglia
dc.type	Master thesis