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dc.contributor.advisorDowning, Keithnb_NO
dc.contributor.authorAndersen, Larsnb_NO
dc.contributor.authorHaus, Tormund Sandvenb_NO
dc.date.accessioned2014-12-19T13:40:18Z
dc.date.available2014-12-19T13:40:18Z
dc.date.created2013-10-12nb_NO
dc.date.issued2013nb_NO
dc.identifier655640nb_NO
dc.identifierntnudaim:9242nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/253403
dc.description.abstractIn recent years artificial neural networks have become increasing popular. New methods and ever increasing computational resources are turning second generation artificial neural networks into powerful tools. Most of the work done with second generation artificial neuron networks do, however, at one point or another involve a phase of supervised learning. Supervised learning methods are inherently limited by the need for labeled training examples. One way of solving this scaling problem is to rely on reinforcement learning, which is a form of unsupervised learning. The more biologically plausible third generation of artificial neural networks have recently been shown capable of tackling the distal reward problem that is at the core of reinforcement learning. Using dopamine modulated spike-timing-dependent plasticity in a spiking neural network, we successfully demonstrate classical conditioning, instrumental conditioning, extinction and second order conditioning in an embodied context.nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.titleDopamine modulated STDP and reinforcement learning in an embodied contextnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber87nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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