AC2: Modelling in Neuroscience and Psychiatry

Description

We will start by getting an overview of modelling approaches in neuroscience and psychiatry, followed by a brief peek at some interesting results of these methods. Then we will delve deeper into the general challenges of developing, fitting, checking, and comparing models, with an emphasis on Bayesian approaches. Finally, we will take a detailed look at three modelling approaches: dynamic causal modelling (DCM), the hierarchical Gaussian filter (HGF), and active inference.

Objectives

A general familiarity with the state of the art of modelling in neuroscience and psychiatry

Familiarity with the approaches of dynamic causal modelling, hierarchical Gaussian filtering, and active inference

Literature

Stephan, K.E. & Mathys, C. (2014). Computational approaches to psychiatry. Current Opinion in Neurobiology, 25, 85-92.

Mathys, C., (2016). How could we get nosology from computation? in: Computational Psychiatry: What Can Theoretical Neuroscience and Psychiatry Teach Each Other? ed. A. D. Redish and J. A. Gordon. Strüngmann Forum Reports, 20, J. Lupp, series editor. Cambridge, MA: MIT Press, ISBN 978-0-262-03542-2.

Mathys, C., Lomakina, E.I., Daunizeau, J., Iglesias, S., Brodersen, K.H., Friston, K.J., & Stephan, K.E. (2014). Uncertainty in perception and the Hierarchical Gaussian Filter. Frontiers in Human Neuroscience, 8:825.

Friston, K.J., Harrison, L., Penny, W., 2003. Dynamic causal modelling. NeuroImage 19, 1273–1302.

Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., Dolan, R.J., 2013. The anatomy of choice: active inference and agency. Front. Hum. Neurosci 7:598.

Course location

Günne

Course requirements

None

Instructor information.

Instructor
Christoph Mathys

Vita


Christoph Mathys is Assistant Professor of Neuroscience at Scuola Internazionale Superiore di Studi Avanzati (SISSA) in Trieste, Italy. Originally a theoretical physicist, he worked in the IT industry for several years before doing a PhD in information technology at ETH Zurich and a master's degree in psychology and psychopathology at the University of Zurich. During his graduate studies, he developed the hierarchical Gaussian filter (HGF), a generic hierarchical Bayesian model of inference in volatile environments. Based on this, he develops and maintain the HGF Toolbox, a Matlab-based free software package for the analysis of behavioural and neuroimaging experiments. His research focus is on the hierarchical message passing that supports inference in the brain, and on failures in this kind of predictive coding in relation to psychopathology.

Website

https://chrismathys.com