This course provides an overview of current Bayesian methods in translational neuromodeling and computational psychiatry. The course focuses on (i) dynamic causal models (DCM) for inferring neuronal system mechanisms from fMRI and EEG data, and (ii) hierarchical Bayesian and Bayesian reinforcement learning models for inference on computational mechanisms from behavioural data.
Learning objective
To obtain a comprehensive overview of state-of-the-art Bayesian methods for translational neuromodeling and computational psychiatry, with applications to neuroimaging (fMRI, EEG) and behavioural data.
Content
This course provides an overview of current Bayesian methods in translational neuromodeling and computational psychiatry. The course focuses on (i) dynamic causal models (DCM) for inferring neuronal system mechanisms from fMRI and EEG data, and (ii) hierarchical Bayesian and Bayesian reinforcement learning models for inference on computational mechanisms from behavioural data. A particular emphasis is on how these models can be applied for characterizing neuronal and computational mechanisms of aberrant learning and pathological belief formation in psychiatric diseases.
