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 qualitatively and quantitatively with a large variety of horizontal disparity vergence data. The model consists of seven functional stages, namely: (1) computation of instantaneous disparity; (2) generation of a disparity map; (3) conversion of the disparity into a velocity signal; (4) push–pull integration of velocity to generate a position signal; (5) conversion of the position signal to motoneuron/plant activity for each eye; (6) gating of velocity overdrive signal to motoneuron/plant system; and finally (7) discharge path for position cells. Closed-loop (normal binocular viewing) symmetric step and staircase disparity vergence data were collected from three subjects and model parameters were determined to quantitatively match each subject’s data. The simulated closed-loop as well as open-loop (disparity clamped viewing) symmetric step, sinusoidal, pulse, staircase, square and ramp wave responses closely resemble experimental results either recorded in our laboratory or reported in the literature. Where possible, the firing pattern of the neurons in the model have been compared to actual cellular recordings reported in the literature. The model provides insights into neural correlates underlying the dynamics of vergence eye movements. It also makes novel predictions about the huma