Computational modelling of dopaminergic networks and exploring the effects of protein aggregates on the function of these neuronal circuits

This project will bridge the fields of experimental neuroscience and theoretical computational mathematical modelling. Patch Clamping will be used to explore the way in which dopamine neurons are connected and how they respond to protein aggregation. The project will also expand on previous neuronal microcircuit network work, both in the hippocampus and also of dopaminergic neurons in the SNc. Acquisition of such network information will be greatly assisted by the production of simplified computational models, which allow the production of large networks without requiring large amount of computational power.
In order to translate this initial electrophysiology data into a network model, matlab will be used to generate dynamic IV curves. These can then be compared across different cell types and conditions. We propose to modify the rEIF model, which is already published for pyramidal neurons in the hippocampus, but does not accurately describe the electrophysiological properties of dopaminergic neurons. It will need to be altered using the electrophysiology data to give accurate spike prediction and hence a reliable model.
Our initial experimental plan is to inject a-syn oligomers (Kaufman et al) into dopaminergic neurons and measure early changes in electrophysiology of the neurons over time. We also plan to inject tau oligomers into pyramidal cells to see how the aggregation affects the neuronal responses. As protein aggregation is a problem that develops primarily with increasing age, different ages of mice will be used to compare the response to the addition of oligomers.