A Model of Smooth Pursuit based on Learning of the Target Dynamics Using Only Retinal Signals

Smooth pursuit is an oculomotor behavior to stabilize the image of a moving target on the fovea. Its velocity gain, i.e., the ratio of eye velocity and target velocity, can reach one for targets moving at a speed up to 20 deg/s, and its phase delay can be zero for targets moving periodically at up to 1.0 Hz. Given the signi cant delays in the smooth pursuit feedback loop, some form of predictive control is required to achieve this performance. In this paper, we present a novel and highly simple model of smooth pursuit that can account for both neurophysiological and behavior ndings of this oculomotor behavior. It consists of two cascaded subsystems, capturing the relationship of the higher visual cortices and the cerebellum. One subsystem is a recurrent neural network (RNN) for learning the visual target dynamics, assumed to be implemented in the medial superior temporal (MST) area. The other subsystem forms an inverse model controller for the oculomotor system, most likely realized in the cerebellum. In our model, the target state estimation and the target velocity prediction is realized in the RNN based on learning of the visual target dynamics solely using retinal signals, i.e., without proprioceptive feedback. We demonstrate the performance of our model with simulations and exemplify its robustness in robotic experiments.

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