education and teaching

Including courses on neuroanatomy, physiology, psychology, medical physics, genetics, psychiatry, neurology, neurosurgery, and radiology

Topics covered: Modeling practice, Model fitting, GLMs, Dimensionality reduction, Bayesian statistics, Linear dynamical systems, Markov process, Decision making, Hidden Markov models, Kalman filter, Optimal control, Markov decision process, Reinforcement learning, Modeling real neuron, Modeling neuronal networks using Wilson-Cowen model, Causality, Deep learning (with Pytorch), Autoencoders

overview of psychiatric disorders, bayesian modeling principles (how to build a model, model fitting, model inversion and model selection), reinforcement learning, models of perception (psychophysics, Bayesian models of perception, predicitve coding), models of action selection (Markov Decision Processes, active inference, drift diffusion models), models of connectivity (dynamic causal modeling and advanced models of connectivity), machine learning, practical applications of computational models to problems from psychiatry + a practical session on using normative modeling on neuroimaging data

molecular mechanisms of synaptic transmision and plasticity, structural and functional organization of the visual system and auditory systems, models of real neurons

Histological and staining methods, Western Immunoblotting, PCR and Electrophysiology