Topic: Neural population models

Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. Our hybrid approach fuses data-driven with forward-modeling-based techniques and has been successfully applied to expla

Recent studies have demonstrated that brain areas involved in motor control display common representations of latent trajectories for both stereotypical and generalizable dynamical cortical behavior. However, current research often focuses on analyzing single tasks and individual sessions, missing the opportunity to exploit the structural patterns across datasets. By fitting a unified model to a collection of neural data with shared features, we can increase data usage efficiency and enhance our understanding of common neural computation structures. Despite these insights, the neuroscience fi