The development of models for neuronal systems have matured in recent years and they exhibit increasing complexity thanks to computer resources for simulation. In parallel, the increasing availability of data poses the challenge to quantitatively related those models to data, going beyond reproducing qualitative activity patterns and behavior. Model inference is thus becoming an indispensable tool for unraveling the mechanisms underlying brain dynamics, behavior, and (dys)function. A critical aspect of this endeavor is the ability to infer changes across multiple scales, from neurotransmitters and synaptic interactions to neural circuits and whole-brain networks. Recent approaches that have been adopted by the neuroscience community include methods for directed effective connectivity (e.g. dynamical causal modeling), simulation-based inference on whole-brain models, and active inference for understanding perception, action and behavior. They have significantly enhanced our ability to interpret data by modeling underlying mechanisms and neuronal processes. This workshop will bring together experts from diverse fields to explore the state-of-the-art methodologies, taking specific applications as examples to compare them and highlight remaining challenges.
