EMERGENCE OF POSITION-INDEPENDENT DETECTORS OF SENSE OF ROTATION AND DILATION WITH HEBBIAN LEARNING - AN ANALYSIS

We previously demonstrated that it is possible to learn position-indep endent responses to rotation and dilation by filtering rotations and d ilations with different centers through an input layer with MT-like sp eed and direction tuning curves and connecting them to an MST-like lay er with simple Hebbian synapses (Sereno and Sereno 1991). By analyzing an idealized version of the network with broader, sinusoidal directio n-tuning and linear speed-tuning, we show analytically that a Hebb rul e trained with arbitrary rotation, dilation/contraction, and translati on velocity fields yields units with weight fields that are a rotation plus a dilation or contraction field, and whose responses to a rotati ng or dilating/contracting disk are exactly position independent. Diff erences between the performance of this idealized model and our origin al model (and real MST neurons) are discussed.