  We present a model of the self-calibration of active binocular vision
comprising the simultaneous learning of visual representations, vergence, and
pursuit eye movements. The model follows the principle of Active Efficient
Coding (AEC), a recent extension of the classic Efficient Coding Hypothesis to
active perception. In contrast to previous AEC models, the present model uses
deep autoencoders to learn sensory representations. We also propose a new
formulation of the intrinsic motivation signal that guides the learning of
behavior. We demonstrate the performance of the model in simulations.
