Abstract
Computational neuroscience combines theory and experiment to shed light on the principles and mechanisms of neural computation. This approach has been highly fruitful in the ongoing effort to understand velocity computation by the primate visual system. This Review describes the success of spatiotemporal-energy models in representing local-velocity detection. It shows why local-velocity measurements tend to differ from the velocity of the object as a whole. Certain cells in the middle temporal area are thought to solve this problem by combining local-velocity estimates to compute the overall pattern velocity. The Review discusses different models for how this might occur and experiments that test these models. Although no model is yet firmly established, evidence suggests that computing pattern velocity from local-velocity estimates involves simple operations in the spatiotemporal frequency domain.