Prace Call: 17th
ID: 2018184373, Leader: Egidio D'Angelo
Affiliation: University of Pavia, IT
Research Field: BiochemistryBioinformatics and Life sciences
Collaborators: Stefano Masoli University of Pavia IT , Martina Francesca Rizza University of Pavia IT , Claudia Casellato University of Pavia IT , Stefano Casali University of Pavia IT , Elisa Marenzi University of Pavia IT , Chaitanya Medini University of Pavia IT
Resource Awarded: 30 Mil. core hours on Joliot Curie - KNL
The cerebellum is part of the central nervous system (CNS) and performs its physiological function as a massive parallel processor, composed by many parallel modules. It receives a massive quantity of information from the motor and somatosensory brain cortex and physical feedback, from the peripheral nervous system (PNS). Its purpose is to elaborate, as fast as possible, motor and postural information to keep muscle tone, balance and other motor related behaviours, which, if it is disrupted, causes motor dysfunction and ataxias. In the last two decades, its involvement in the higher cognitive functions was proved even more fundamental. This is due to the discovery of specific pathways connecting the cerebellum to cognition related part of the brain, like the amygdala, involved in the fair and emotional responses or the basal ganglia, part of a system dedicated to procedural learning, cognition and emotions. These reciprocal connections uncovered the involvement of the cerebellum in known neurodegenerative diseases, such as frontotemporal dementia, psychosis, Alzheimer and Parkinson diseases. Until the last decade, the only way to investigate the cerebellum was trough the animal model and non-invasive techniques in humans. Since supercomputers have progressed a lot in their computational power, the reconstructed and simulated, of the cerebellum network, through realistic network models formed by hundred thousand neurons. The first step is the reconstruction of the biophysical properties of each neuron to a high degree of detail (realistic modelling), based on available experimental data. This approach allows to understand the fundamental physiological properties of the neurons and also to propagate these properties throughout neuronal networks. The realistic modeling approach requires more data for construction and validation as well as more computational power, but it eventually delivers high quality predictions about the biological functions of the network, that can thereafter be tested experimentally. The second step is the assembly of these neurons into local microcircuits and multiscale networks able to reproduce, with a high degree of detail, the synaptic connectivity. The final step is the simulation of the network using various representative tasks in order to determine its behaviors. The ability to simulate multi-scale networks, composed of hundred thousand neuron models, can help to shed light on the overall network activity, intrinsic and extrinsic signals coming from other parts of the brain, to better diagnose the supra mentioned diseases.