Bayesian processing of vestibular information Maarten van der Heijden Supervisors: Rens Vingerhoets, Jan van Gisbergen, Pieter Medendorp 6 Nov 2006

Introduction Vestibular system provides information on spatial orientation The system consists of three canals and two otoliths –Canals sense rotational acceleration –Otoliths sense linear acceleration

Canals Canals measure rotation High-pass filter characteristics Results in decay of signal during prolonged motion

Otoliths Activated by tilt and rotation Signal is ambiguous –Normally resolved by visual information –Can cause illusory motion percepts –E.g. somatogravic effect (perceive tilt instead of acceleration) TiltTranslation

Model Brain has to construct percept out of noisy and ambiguous sensor signals Laurens & Droulez propose a Bayesian approach to model this Probability based reasoning (Bayes’ rule) Bayesian inference is convenient to use with noisy systems Requires prior information –Laurens & Droulez argue the brain also uses these priors

Model (con’d) Model assumptions: –Brain uses an internal model of vestibular dynamics –Brain is aware of laws of physics (gravity in particular) –Prior information on the likelihood of motion percepts A priori situations with low acceleration and rotation are the most likely (intuitively correct in everyday life) The model calculates a probability distribution of head motion given the vestibular signals

Example Replicating results from Laurens & Droulez E.g.: forward acceleration

Further work Test whether the model can explain OVAR data from experiments done here Evaluate model parameters –Width of priors –Noise on otoliths –Prior on tilt Test model performance with new experiments