Characterising multiscale brain-wide neural dynamics

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State-of-the-art experimental techniques can record neural dynamics of human brain with finedetails. Recordings can be done across different brain areas and different scales from single cells,local filed potentials (LFPs) to intracranial electroencephalograms (iEEG). Particularly, these datacan be collected in humans, typically human patients. For instance, abnormal brain activity incertain areas of the brain causes seizures and in some cases loss of awareness and characterisedbehaviour in epileptic patients. Computational modelling has allowed us to be able to characterisethese interactions to be able to find the influence of brain regions on the activity of our brain. Thisproject 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 acomputational model of the connection. This model will be a generalised brain dynamics model,which could be manipulated into a functional epilepsy seizure characterising model.