BraiNets is a research team in computational neuroscience working at the interface between system/cognitive neuroscience and artificial intelligence. Our vision is that computational neuroscience will lead to ground-breaking applications by formalising how brain functions emerge from the interaction between neural populations and brain networks. The BraiNets team exploits most advanced theoretical approaches and computational tools from artificial intelligence and statistics to study how brain interactions support neural computations underlying cognitive functions.
Computational and theoretical approaches
The main theoretical approaches and methodology characterizing BraiNets are:
- Functional Connectivity analysis of brain networks (MEG, intracranial EEG, LFP)
- Effective Connectivity modelling of brain interactions
- Bayesian probabilistic modelling and reinforcement learning
- Neural networks models and neurocomputational modeling
- Machine learning