A neural net model of the adaptation of binocular vertical eye alignment

Abstract

Binocular eye alignment is continuously recalibrated and readjusted to maintain a single view of the world. Once this process is complete, visual feedback is no longer required to maintain alignment. Rather, alignment is maintained through non-visual or extra-retinal information. The calibration process can be demonstrated by producing a cross-coupling or association between vertical vergence and another type of eye movement. This paper presents a neural net model of a plausible biological mechanism that could be involved with maintaining alignment in the context of vertical vergence. The model couples conjugate eye-position-sensitive neurons with a vertical vergence response. Weight training of the input neurons is accomplished with a modified Hebbian rule that minimizes the vertical eye alignment error during adaptation to vertical disparities. The experimental results are simulated with a class of input neurons that has randomly distributed sensitivities and thresholds similar to those found in premotor sites in the brainstem. For simultaneous adaptation to three vertical disparities, the weighted inputs of the input class are reshaped such that the inputs qualitatively obtain a sensitivity - threshold relationship similar to that of motoneurons in the brainstem.