SC6 Temporal Sensory Integration in the Brain

Description

Integration of information from multiple sources and sensors can be described mathematically in terms of Bayesian inference, and there is evidence that the brain integrates information approximately optimally from an information theoretic perspective. This course will start by looking at the mathematical description of sensory integration in terms of Bayesian inference. Specifically, we will see how sensory integration across modalities as well as time can be reduced to belief updates by precision-weighted prediction errors. We will then familiarize ourselves with theories applying this framework to the brain and sample empirical results on how the brain implements sensory integration, with an emphasis on the temporal domain.

Objectives

- To understand the application of Bayesian inference to sensory integration
- To understand the reduction of Bayesian inference to precision-weighting of prediction errors
- Familiarity with recent empirical advances in the study of temporal sensory integration

Literature

Ernst, M.O., Banks, M.S., 2002. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433.

Powers, A.R., Mathys, C., Corlett, P.R., 2017. Pavlovian conditioning–induced hallucinations result from overweighting of perceptual priors. Science 357, 596–600.

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.

Mathys, C., Daunizeau, J., Friston, K.J., Stephan, K.E., 2011. A Bayesian foundation for individual learning under uncertainty. Front. Hum. Neurosci. 5, 39.

Course location

Guenne

Course requirements

None

Instructor information.

Instructor
Christoph Mathys

Affiliation

Scuola Internazionale Superiore di Studi Avanzati

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 of inference that lead to psychopathology.

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