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dc.contributor.supervisor Tatsuno, Masami
dc.contributor.author Balogun, Pauline
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2020-02-20T16:39:03Z
dc.date.available 2020-02-20T16:39:03Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10133/5685
dc.description.abstract This project investigated whether unsupervised machine learning could detect differences in global or local microstates across different rodent brain, in the effects of procedural learning, and clustering validity indices effectiveness. Previously obtained local field potential recordings of M1 and the hippocampus of freely-behaving male rats under naïve and task conditions, including transcranial direct and alternating current stimulation (tDCS; tACS), were analyzed and used to assess several methods. Two local SWS-like REM microstates were detected along with five global microstates. Learning suppressed cortical SWS-like REM microstates, but tDCS negated this effect. Calinski-Harabasz evaluated clusters had the highest sensitivity, specificity and total accuracy. Local and global brain states were effectively detected using PCA and clustering, and measures of phase-amplitude coupling were sensitive to the task conditions. These changes could underlie consolidation windows for procedural learning with potential intervention by tDCS although these results are limited to due the quality of the dataset. en_US
dc.language.iso en_US en_US
dc.publisher Lethbridge, Alta. : Universtiy of Lethbridge, Department of Neuroscience en_US
dc.relation.ispartofseries Thesis (University of Lethbridge. Faculty of Arts and Science) en_US
dc.subject neuroscience en_US
dc.subject k-means en_US
dc.subject machine learning en_US
dc.subject hippocampus en_US
dc.subject memory en_US
dc.subject primary motor cortex en_US
dc.subject clustering en_US
dc.subject cluster validity index en_US
dc.subject PCA en_US
dc.subject behaviour en_US
dc.subject tACS en_US
dc.subject tDCS en_US
dc.subject tPAC en_US
dc.subject Hippocampus (Brain) -- Research en_US
dc.subject Memory -- Research en_US
dc.subject Motor cortex -- Research en_US
dc.subject Sleep -- Research en_US
dc.subject Sleep-wake cycle -- Research en_US
dc.subject Rats as laboratory animals en_US
dc.subject Cluster analysis -- Computer programs en_US
dc.subject Dissertations, Academic en_US
dc.title Detection of sleep and wake substates through unsupervised machine learning in rat hippocampus and primary motor cortex LFP recordings en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Neuroscience en_US
dc.degree.level Masters en_US
dc.proquest.subject 0317 en_US
dc.proquest.subject 0800 en_US
dc.proquest.subject 0463 en_US
dc.proquestyes Yes en_US


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