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Title: Computational modelling of information processing in deep cerebellar nucleus neurons
Author: Luthman, Johannes
ISNI:       0000 0004 2738 4200
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2012
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The deep cerebellar nuclei (DCN) function as output gates for a large majority of the Purkinje cells of the cerebellar cortex and thereby determine how the cerebellum influences the rest of the brain and body. In my PhD programme I have investigated how the DCN process two kinds of input patterns received from Purkinje cells: irregularity of spike intervals and pauses in Purkinje cell activity resulting from the recognition of patterns received at the synapses with the upstream parallel fibres (PFs). To that objective I have created a network system of biophysically realistic Purkinje cell and DCN neuron models that enables the exploration of a wide range of network structure and cell physiology parameters. With this system I have performed simulations that show how the DCN neuron changes the information modality of its input, consisting of varying regularity in Purkinje cell spike intervals, to varying spike rates in its output to the nervous system outside of the cerebellum. This was confirmed in simulations where I exchanged the artificial Purkinje cell trains for those received from experimental collaborators. In pattern recognition simulations I have found that the morphological arrangement present in the cerebellum, where multiple Purkinje cells connect to each DCN neuron, has the effect of amplifying pattern recognition already performed in the Purkinje cells. Using the metric of signal-to-noise ratio I show that PF patterns previously encountered and stored in PF - Purkinje cell synapses are most clearly distinguished from those novel to the system by a 10-20 ms shortened burst firing of the DCN neuron. This result suggests that the effect on downstream targets of these excitatory projection neurons is a decreased excitation when a stored as opposed to novel pattern is received. My work has contributed to a better understanding of information processing in the cerebellum, with implications for human motor control as well as the increasingly recognised non-motor functions of the cerebellum.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: cerebellum ; deep cerebellar nuclei ; ion channels ; neuronal modelling ; neural information processing ; synaptic plasticity ; pattern recognition