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Title: A comparative analysis of Purkinje cells across species combining modelling, machine learning and information theory
Author: Kidd, Kirsty
ISNI:       0000 0004 7656 2210
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2017
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There have been a number of computational modelling studies that aim to replicate the cerebellar Purkinje cell, though these typically use the morphology of rodent cells. While many species, including rodents, display intricate dendritic branching, it is not a universal feature among Purkinje cells. This study uses morphological reconstructions of 24 Purkinje cells from seven species to explore the changes that occur to the cell through evolution and examine whether this has an effect on the processing capacity of the cell. This is achieved by combining several modes of study in order to gain a comprehensive overview of the variations between the cells in both morphology and behaviour. Passive and active computational models of the cells were created, using the same electrophysiological parameters and ion channels for all models, to characterise the voltage attenuation and electrophysiological behaviour of the cells. These results and several measures of branching and size were then used to look for clusters in the data set using machine learning techniques. They were also used to visualise the differences within each species group. Information theory methods were also employed to compare the estimated information transfer from input to output across each cell. Along with a literature review into what is known about Purkinje cells and the cerebellum across the phylogenetic tree, these results show that while there are some obvious differences in morphology, the variation within species groups in electrophysiological behaviour is often as high as between them. This suggests that morphological changes may occur in order to conserve behaviour in the face of other changes to the cerebellum.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Cerebellum ; Purkinje cell ; Single cell models ; Computational modelling ; Information theory ; Transfer entropy ; Clustering ; Growing neural gas ; Machine learning ; Phylogenetic rank ; Species comparisons ; Neuronal morphology ; Dendrites