Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.691321
Title: Using machine learning and computer simulations to analyse neuronal activity in the cerebellar nuclei during absence epilepsy
Author: Alva, Parimala
ISNI:       0000 0004 5917 5999
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2016
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Abstract:
Absence epilepsy is a neurological disorder that commonly occurs in children. Some studies have shown that absence seizures predominantly originate either in the thalamus or the cerebral cortex. Some cerebellar nuclei (CN) neurons project to these brain areas, as explained further in Fig. 2.6 in Chapter 2. Also, some CN neurons have been observed to show modulation during the absence seizures. This indicates that they somehow participate in the seizure and hence are referred to as "participating neurons" in this thesis. In this research, I demonstrate how machine learning techniques and computer simulations can be applied to investigate the properties and the input conditions present in these participating neurons. My investigation found a sub-group of CN neurons, with similar interictal spiking activity, spiking activity between the seizures, that are most likely to participate in seizures. To investigate the input conditions present in the CN neurons that produce this type of interictal activity, I used a morphologically realistic conductance based model of an excitatory CN projection neuron [66] and optimised the input parameters to this model using an Evolutionary Algorithm (EA). The results of the EA revealed that these participating CN neurons receive a synchronous and bursting input from Purkinje cells and bursting input with long intervals(approx. 500ms) from mossy fibre. The same interictal activity can also be produced when the Purkinje cell input to the CN neuron is asynchronous. The excitatory input in this case also had long interburst intervals but there is a decrease in excitatory and inhibitory synaptic weight. Surprisingly, a slight change in these input parameters can change the interictal spiking pattern to an ictal spiking pattern, the spiking pattern observed during absence seizures. I also discovered that it is possible to prevent a participating CN neuron from taking part in the seizures by blocking the Purkinje cell input.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.691321  DOI: Not available
Keywords: machine learning ; computational neuroscience ; absence epilepsy ; computer simulations ; evolutionary algorithms ; cerebellar nuclei
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