FIGURE 9.

Invariant object-based global motion in the dorsal visual system. This shows two wheels at different locations in the visual field rotating in the same direction. One rotating wheel is presented at a time, and a representation is needed in the case illustrated that the rotating flow field produced by the wheel in either location is always clockwise. The local flow field in V1 and V2 is ambiguous about the direction of rotation of the two wheels, because of the small receptive field size. Rotation that is clockwise or counterclockwise can only be identified by a global flow computation, with larger receptive fields. The diagram shows how a network with stages like those found in the brain can solve the problem to produce position invariant global motion-sensitive neurons by Layer 3. The computation involved is convergence from stage to stage as illustrated, combined with a short-term memory trace synaptic learning rule to help the network learn that it is the same wheel rotating in the same direction as it moves across the visual field during training (during development). This is the computational architecture of VisNet. It was demonstrated that VisNet can learn translation invariant representations of these types of object-based motion, by substituting the normal Gabor filters as the input neurons in the input Layer corresponding to V1 with local optic flow motion neurons also present in V1. (After Rolls and Stringer, 2006b).