A neural model of binocular saccade planning and vergence control

Abstract

The human visual system uses saccadic and vergence eye movements to foveate visual targets. To mimic this aspect of the biological visual system the PC/BC-DIM neural network is used as an omni-directional basis function network for learning and performing sensory-sensory and sensory-motor transformations without using any hard-coded geometric information. A hierarchical PC/BC-DIM network is used to learn a head-centred representation of visual targets by dividing the whole problem into independent subtasks. The learned head-centred representation is then used to generate saccade and vergence motor commands. The performance of the proposed system is tested using the iCub humanoid robot simulator.