FORCE training a Neurobiologically-Motivated Network for Speech Recognition

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dc.contributor.advisorFitz, H.
dc.contributor.advisorQuaresima, A.
dc.contributor.authorCrouzen, Timon
dc.date.issued2022-08-20
dc.description.abstractHow language is processed in the brain is currently not well understood. With the use of neurobiologically-motivated models, brain functions like these can be better studied. Supervised learning methods provide one way to shape the network dynamics of such models. One such method is FORCE training. Based on concepts from reservoir computing, FORCE has been used to reproduce various complex behaviours. In this project, a spiking neural network with realistic neural dynamics is FORCE-trained to classify spike-encoded speech input. It was found that FORCE training does not enhance word recognition in the network. Based on the idea that FORCE training works best when replicating repetitious input, further tests were done with a different input structure, as well as tests with FORCE-training a High Dimensional Temporal Signal. The utility of FORCE training appeared to improve when reproducing a repetitious input, although these results are too limited to be definitive.
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15840
dc.language.isoen
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.thesis.typeBachelor
dc.titleFORCE training a Neurobiologically-Motivated Network for Speech Recognition

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