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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorPoppe, R.W.
dc.contributor.authorKoninx, M.
dc.date.accessioned2020-04-28T18:00:10Z
dc.date.available2020-04-28T18:00:10Z
dc.date.issued2020
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/35716
dc.description.abstractOur behavior is driven by a small subset of all information available to us. As our processing resources are limited, we select information to attend to in order to learn from our environment. Visual attention has been studied for many decades. Still, current attentional models do not explain how attentional modulations affect trial-and-error learning in the visual cortex. This study is the first to define synaptic plasticity as a function of attentional modulations observed prior to receiving rewards. The attention-modulated Hebbian plasticity rule is used to simulate attention-guided learning for a series of classification tasks. Despite exclusively receiving reward feedback for the predicted label, our attention-guided reinforcement learning framework is able to perform comparably to supervised error-backpropagation. This holds for datasets with up to 100 class labels. Our results are obtained by redefining learning to reflect biological mechanisms which ultimately govern behavior.
dc.description.sponsorshipUtrecht University
dc.format.extent3786091
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleAttention-guided Visual Learning, A Computational Model
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsreinforcement learning; attention; neural network, visual attention; computational model; attention-guided learning
dc.subject.courseuuComputing Science


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