Neural Mechanism for Coding Depth from Motion Parallax in Area MT: Gain Modulation or Tuning Shifts?

There are two distinct sources of retinal image motion: objects moving in the world and observer movement. When the eyes move to track a target of interest, the retinal velocity of some object in the scene will depend on both eye velocity and that object's motion in the world. Thus, to compute the object's velocity relative to the head, a coordinate transformation must be performed by vectorially adding eye velocity and retinal velocity. In contrast, a very different interaction between retinal and eye velocity signals has been proposed to underlie estimation of depth from motion parallax, which involves computing the ratio of retinal and eye velocities. We examined how neurons in the middle temporal (MT) area of male macaques combine eye velocity and retinal velocity, to test whether this interaction is more consistent with a partial coordinate transformation (for computing head-centered object motion) or a multiplicative gain interaction (for computing depth from motion parallax). We find that some MT neurons show clear signatures of a partial coordinate transformation for computing head-centered velocity. Even a small shift toward head-centered velocity tuning can account for the observed depth-sign selectivity of MT neurons, including a strong dependence on speed preference that was previously unexplained. A formal model comparison reveals that the data from many MT neurons are equally well explained by a multiplicative gain interaction or a partial transformation toward head-centered tuning, although some responses can only be well fit by the coordinate transform model. Our findings shed new light on the neural computations performed in area MT, and raise the possibility that depth-sign selectivity emerges from a partial coordinate transformation toward representing head-centered velocity.