Probabilistic Modelling of Phase-tuned Disparity Energy Neuron Populations

We present a low dimensional Bayes probabilistic model for the population of binocular disparity energy neurons centered at the same retinal location, but selective to different disparities by phase shifts between the left and right monocular receptive fields. The model accurately predicts response distributions of simulated binocular disparity energy neurons. It provides a probabilistic explanation for the decrease in the reliability of the population responses for large disparities. Applied to vergence control, it generates more reliable responses than using the preferred disparity of the most responsive neuron as the control signal.