Bayesian processing of vestibular information

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

2. 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

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

4. Tilt Translation 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)

5. 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

6. 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

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

8. 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