Using Machine Learning and Computer Simulations to Analyse Neuronal Activity in the Cerebellar Nuclei During Absence Epilepsy

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

A potential role for the cerebellar nuclei in absence seizures

© 2013 Alva et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Poster presented ar CNS 2013Non peer reviewe

The Mining and Analysis of Data with Mixed Attribute Types

Ed Wakelam, Neil Davey, Yi Sun, Amanda Jefferies, Parimala Alva, and Alex Hocking, ‘The Mining and Analysis of Data with Mixed Attribute Types’, paper presented at the IMMM 2016: Sixth International Conference on Advances in Information Mining and Management, 22 May 2016 – 26 May 2016, Valencia, Spain. Published by IARIA XPS Press, Archived in the free access ThinkMind™ Digital Library. Available online at http://www.thinkmind.org/index.php?view=article&articleid=immm_2016_3_20_50067 © IARIA, 2016Mining and analysis of large data sets has become a major contributor to the exploitation of Artificial Intelligence in a wide range of real life challenges, including education, business intelligence and research. In the field of education, the mining, extraction and exploitation of useful information and patterns from student data provides lecturers, trainers and organisations with the potential to tailor learning paths and materials to maximize teaching efficiency and to predict and influence student success rates. Progress in this important area of student data analytics can provide useful techniques for exploitation in the development of adaptive learning systems. Student data often includes a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. In this paper, we summarise our progress in applying a combination of what we believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs, followed by numeric data analysis, providing the opportunity to focus on promising correlations for deeper analysis.Final Accepted Versio

Optimization of input parameters to a CN neuron model to simulate its activity during and between epileptic absence seizures

Peer reviewe

Combining machine learning and simulations of a morphologically realistic model to study modulation of neuronal activity in cerebellar nuclei

Abstract from 23rd Annual Computational Neuroscience Meeting: CNS 2014 © 2014 Alva et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Epileptic absence seizures are characterized by synchronized oscillatory activity in the cerebral cortex that can be recorded as so-called spike-and-wave discharges (SWDs) by electroencephalogram. Although the cerebral cortex and the directly connected thalamus are paramount to this particular form of epilepsy, several other parts of the mammalian brain are likely to influence this oscillatory activity. We have recently shown that some of the cerebellar nuclei (CN) neurons, which form the main output of the cerebellum, show synchronized oscillatory activity during episodes of cortical SWDs in two independent mouse models of absence epilepsy [1]. The CN neurons that show this significant correlation with the SWDs are deemed to “participate” in the seizure activity and are therefore used in our current study designed to unravel the potential causes of such oscillatory firing patternsPeer reviewe

Cerebellar output controls generalized spike-and-wave discharge occurence

© 2015 The Authors Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (CC BY-NC-ND 4.0) which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike-and-wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizuresPeer reviewedFinal Published versio

Evolution of Dendritic Morphologies Using Deterministic and Nondeterministic Genotype to Phenotype Mapping

In this study, two morphological representations in the genotype, a deterministic and a nondeterministic representation, are compared when evolving a neuronal morphology for a pattern recognition task. The deterministic approach represents the dendritic morphology explicitly as a set of partitions in the genotype which can give rise to a single phenotype. The nondeterministic method used in this study encodes only the branching probability in the genotype which can produce multiple phenotypes. The main result is that the nondeterministic method instigates the selection of more symmetric dendritic morphologies which was not observed in the deterministic metho