Research Studentship in Control and Neuroscience

This project will develop data-driven models for predicting seizure-like events in living neural networks. Such events can be induced in brain slices and share many features with epileptic seizures in the human brain. The underlying challenge is to obtain predictive models of extracellular neural activity that remain interpretable from the mechanistic viewpoint of biological neuromodulation. The student will have the freedom to develop their own modelling approaches, drawing on ideas from control theory, machine learning and computational neuroscience. A promising starting point is provided by Recurrent Mechanistic Models (RMMs), which combine state-space systems and artificial neural networks to capture neural dynamics. Models will be developed and validated using high-density multielectrode recordings from mouse brain slices.