A large part of computational brain science is concerned with generating models of the brain that can help us understand its function. These models range in complexity and biological accuracy, with system-level models often being the least biologically detailed ones. Increasing computational power has opened the possibility to turn models of the brain, or of its components, into simulations with an unprecedented level of detail. However, there is still a gap between biologically inspired models, used to address molecular and cellular questions, and models based on dynamical systems which can account for complex neurological (e.g. EEG, fMRI) and behavioral observations. For example, traditionally, the epileptic brain has been interpreted using dynamical system models (e.g. neural mass) with loose biological bases, while the molecular mechanisms causing epileptic phenotypes were investigated experimentally and using biophysical simulations. Recent academic work has shown that models of cellular features can be matched to standard clinical measures such as EEG showing seizure-like behavior. Here, we propose to expand the molecules-to-organism simulation approach by generating a framework that allows the implementation of different biological mechanisms to test whether their therapeutic modulation would be efficacious in reducing the likelihood of seizures.
For brain diseases, such as epilepsy, extracting high-level dynamic features from biophysically accurate neuronal and network simulations can provide predictive information on seizures and on whether these can be mitigated by a given treatment. To test this hypothesis, we will use sophisticated computational methods, including machine learning techniques, to build a simulation framework spanning across different levels, from molecules to patients. The framework will be informed by experimental and clinical data and it will lay its foundation in well tested open-source simulation environments such as NEURON., NEST and The Virtual Brain, part of the EBRAIN infrastructure funded through the Human Brain Project.
The supervision team has expertise in machine learning, computational neuroscience, animal and human neuroscience, brain disease, clinical neuroscience, with regularly published research papers at the very top venues in related fields.
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