Delay Learning in a Biologically Plausible Reservoir Computing Model

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dc.contributor.advisorSprinkhuizen-Kuyper, I.G.
dc.contributor.advisorGerven, M.A.J. van
dc.contributor.authorDatadien, A.H.R.
dc.date.issued2013-08-22
dc.description.abstractIn this thesis, a reservoir model created by Paugam-Moisy et al. (2008) was replicated, and subsequently modified to be more biologically plausible. The use of a di erent neuron model and STDP function had no negative impact on performance. For the delay learning mechanism, replacing delay adaptation with delay selection proved viable, but did result in somewhat lower performance. It was discovered here that the reservoir in this model served no purpose. Therefore, the use of a small-world Watts-Strogatz reservoir instead of a randomly connected reservoir, could not affect performance.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/204
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleDelay Learning in a Biologically Plausible Reservoir Computing Modelen_US
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