Characterising multiscale brain-wide neural dynamics

State-of-the-art experimental techniques can record neural dynamics of human brain with fine
details. Recordings can be done across different brain areas and different scales from single cells,
local filed potentials (LFPs) to intracranial electroencephalograms (iEEG). Particularly, these data
can be collected in humans, typically human patients. For instance, abnormal brain activity in
certain areas of the brain causes seizures and in some cases loss of awareness and characterised
behaviour in epileptic patients. Computational modelling has allowed us to be able to characterise
these interactions to be able to find the influence of brain regions on the activity of our brain. This
project is dedicated to establishing a connection between iEEG data recorded and LFP signals,
which will be used in a machine learning model to be able to stablish characterizing features for a
computational model of the connection. This model will be a generalised brain dynamics model,
which could be manipulated into a functional epilepsy seizure characterising model.