Primary supervisor
Levin KuhlmannResearch area
Machine Learning and Deep LearningThis project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis methods. In this work we study the effects putative NMDA antagonists xenon, a potent anaesthetic, and nitrous oxide, a weak anaesthetic, on anesthetic-induced changes in brain mechanisms and networks. The goal is to find common brain mechanisms and networks that are effected by different kinds anaesthetics to see if this points to a 'backbone' for the generation of consciousness.
Required knowledge
Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a willingness to learn about the other areas involved.