Inferring dynamic brain networks with Transformer VAEs on MEG data (joint project with the Oxford Centre for Functional MRI of the Brain) "
This is a joint project between the Oxford Applied and Theoretical Machine Learning Group at CS (https://oatml.cs.ox.ac.uk/) and the Oxford Centre for Functional MRI of the Brain at the department of Clinical Neurosciences (https://www.ndcn.ox.ac.uk/divisions/fmrib/about-fmrib). Scientific objective: Infer dynamic brain networks (ie. which regions of the brains are jointly activated over time) from MEG data of individuals, at rest or while performing specific tasks. The goal is ultimately to improve our understanding of how the brain functions -- how, when, and where information is computed and represented in the brain depending on specific tasks. This may also inform us how to apply brain stimulations as part of a closed loop system, and help track the progression of neurological disorders (e.g., seizures in epilepsy) or the effectiveness of drugs / treatments. Project objectives: The FMRIB team at Oxford already developed several models to tackle this problem (e.g., based on HMM or LSTM). The main challenges at this point are a limited ability to learn long term dependencies in the MEG data given the current model architecture, as well as the necessity to make several simplifying assumptions to make computations tractacle. The project will consist in addressing these issues by leveraging recent progress in Transformer-based architectures, which have demonstrated an increased ability to learn longer-range dependencies in several Natural Language Processing applications over previous methods. Prerequisites * strong python experience * experience with deep learning, sequence models.
|* strong python experience|
|* experience with deep learning, sequence models|