Learning to make predictions in the cerebellum may explain the anticipatory modulation of the vestibulo-ocular reflex (VOR) gain with vergence
Changes in the eye vergence modify the gain of the vestibuloocular reflex (VOR). In a previous dynamical model, this modulation was controlled by the cerebellum using vergence angle information (Coenen & Sejnowski; NIPS 96). However, during a vergence eye movement, the change in the VOR gain anticipates the vergence change (Snyder & King; Vision Res. 323. 92). We demonstrate here how our previous dynamical model and a predictive cerebellar model may be combined to explain these findings. In the predictive model, the cerebellum receives inputs from vergence-disparity cells in the cortex to construct a prediction of vergence angle. By replacing the regular vergence input in our previous dynamical model by the vergence prediction, results similar to the experimental anticipatory gain changes are observed. The inferior olive in the predictive cerebellar model is responsible for computing a prediction error and for selecting the Purkinje cells to be recruited for learning a particular prediction. The learning model is based on the least-mean square (LMS) algorithm to construct predictions from previous context information. This model is a special case of a more general predictive function for the cerebellum that may provide a consistent explanation for the apparently ubiquitous task participation of the cerebellum. We discuss briefly how this more general model may explain some experimental results observed in the cerebellum with positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In conclusion, a predictive cerebellar model receiving vergence+parity cell inputs learns to predict the time course of vergence eye movement and successfully changes ihe VOR gain in anticipation of vergence changes. 3rd Joint Symposium on Neural Computation Proceedings 203 ,