NEURAL-NETWORK MODEL OF SHORT-TERM HORIZONTAL DISPARITY VERGENCE DYNAMICS

We present a neural network model of short-term dynamics of the human horizontal vergence system (HVS) and compare its predictions qualitati vely and quantitatively with a large variety of horizontal disparity v ergence data. The model consists of seven functional stages, namely: ( 1) computation of instantaneous disparity; (2) generation of a dispari ty map; (3) conversion of the disparity into a velocity signal; (4) pu sh-pull integration of velocity to generate a position signal; (5) con version of the position signal to motoneuron/plant activity for each e ye; (6) gating of velocity overdrive signal to motoneuron/plant system ; and finally (7) discharge path for position cells. Closed-loop (norm al binocular viewing) symmetric step and staircase disparity vergence data were collected from three subjects and model parameters were dete rmined to quantitatively match each subject's data, The simulated clos ed-loop as well as open-loop (disparity clamped viewing) symmetric ste p, sinusoidal, pulse, staircase, square and ramp wave responses closel y resemble experimental results either recorded in our laboratory or r eported in the literature. Where possible, the firing pattern of the n eurons in the model have been compared to actual cellular recordings r eported in the literature. The model provides insights into neural cor relates underlying the dynamics of vergence eye movements. It also mak es novel predictions about the human vergence system. (C) 1997 Elsevie r Science Ltd.