We previously demonstrated that it is possible to learn position-independent responses to rotation and dilation by filtering rotations and dilations with different centers through an input layer with MT-like speed and direction tuning curves and connecting them to an MST-like layer with simple Hebbian synapses (Sereno and Sereno 1991). By analyzing an idealized version of the network with broader, sinusoidal direction-tuning and linear speed-tuning, Kechen Zhang showed analytically that a Hebb rule trained with arbitrary rotation, dilation/contraction, and translation velocity fields yields units with weight fields that are a rotation plus a dilation or contraction field, and whose responses to a rotating or dilating/contracting disk are exactly position-independent. Differences between the performance of this idealized model and our original model (and real MST neurons) are discussed.

Proof of position-invariant response to flow fields

Response elicited by a rotating (a) or dilating (b) ring in a receptive field with circular or radial distribution of direction selectivity is independent of the position of the ring (see Zhang, Sereno, and Sereno, 1993).