Wijs, Chiel (2022) The Influence of Connectivity Sparseness and Alzheimer's Disease on Pattern Separation in a Spiking Neuron Model of the EC-DG-CA3 Hippocampal Circuit. Master's Thesis / Essay, Computational Cognitive Science.
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Abstract
This research used a spiking neuron model of the EC-DG-CA3 hippocampal circuit to determine how sparse connectivity affects pattern separation, the phenomenon through which the hippocampus prevents the overwriting of information during memory formation. Analysis of the model indicated that, within the used modelling framework, sparse connectivity as an isolated feature does not promote pattern separation. Rather, the general decrease in neural activity most heavily influences pattern overlap values. Additionally, the circuit was used to explore how changes that are observed in the DG of a transgenic mouse model of Alzheimer's disease (AD), a type of dementia, can be used to model the deficits in long-term memory characteristic of this neurological disorder. Modelling of an increase of the areas of the EC-DG synaptic junction surfaces, which correlate with synaptic efficacy, proved to be a consistent way to increase pattern overlap. This indicated it as a potential method for inducing AD-related cognitive deficits within the model. As this research focused on how neural and connectivity properties, as well as neurological symptoms of AD, affect a model of the hippocampus without any learning, implementation of a model that allows for increased pattern separation through learning across sparse connections presents itself as an interesting direction for future research into the progression of AD.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Supervisor name: | Borst, J.P. |
| Degree programme: | Computational Cognitive Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 30 Sep 2022 09:43 |
| Last Modified: | 30 Sep 2022 09:43 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/28785 |
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