Hamiltonian Monte Carlo

…and non-conjugate models, Gibbs and Hamiltonian Monte Carlo, convergence checks, hierarchical regression, regularisation and variational inference. Later sessions compare data-fusion strategies and Bayesian factor models while quantifying uncertainty in integrated biomedical datasets. Sessions: 30 November–4 December, 14:00–18:00 daily, Europe/Berlin (CET, UTC+01:00), with Slack discussion between meetings. Liv

Aims: Social cognition hinges on the fundamental capability to perceive the presence of others. Research indicates that the mere presence of conspecifics can enhance task performance, particularly in simpler tasks, and is closely associated with the activity of context-dependent neural subpopulations. While attentional modulation (attentional-conflict theory) has long been posited as the primary mechanism driving social facilitation, the literature on the neurobiological underpinnings of this modulation remains scant at best. Understanding these mechanisms could yield critical insights into so

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

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