Sequence learning in a model of the basal ganglia

dc.contributor.advisor	Downing, Keith	nb_NO
dc.contributor.author	Søiland, Stian	nb_NO
dc.date.accessioned	2014-12-19T13:33:22Z
dc.date.available	2014-12-19T13:33:22Z
dc.date.created	2010-09-03	nb_NO
dc.date.issued	2006	nb_NO
dc.identifier	348179	nb_NO
dc.identifier	ntnudaim:1551	nb_NO
dc.identifier.uri	http://hdl.handle.net/11250/251073
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.	nb_NO
dc.language	eng	nb_NO
dc.publisher	Institutt for datateknikk og informasjonsvitenskap	nb_NO
dc.subject	ntnudaim	no_NO
dc.subject	MIT informatikk	no_NO
dc.subject	Kunstig intelligens og læring	no_NO
dc.title	Sequence learning in a model of the basal ganglia	nb_NO
dc.type	Master thesis	nb_NO
dc.source.pagenumber	118	nb_NO
dc.contributor.department	Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskap	nb_NO