A Recurrent Neural Network Based Model of Predictive Smooth Pursuit Eye Movement in Primates

A predictive mechanism in the brain enables primates to visually track a target with almost zero lag smooth pursuit eye movements, overcoming the delays in processing retinal inputs. Interestingly, it also allows pursuit of occluded targets with nonlinear motion patterns. We propose a recurrent neural network (RNN) model that rapidly learns the target velocity sequence and generates eye velocity signals to eliminate the initial lag between target and eye velocities, and to track occluded targets with nonlinear velocity. Moreover, the model is able to adapt to unpredictable perturbation and phase shift of target velocity and qualitatively reproduce the initial pursuit acceleration in experimentally observed timescales. We propose that the frontal eye field (FEF) region of the primate brain is homologous to the proposed RNN based on its persistent predictive activities during pursuit and location on the pursuit pathway.