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We present a model of the self-calibration of active binocular visioncomprising the simultaneous learning of visual representations, vergence, andpursuit eye movements. The model follows the principle of Active EfficientCoding (AEC), a recent extension of the classic Efficient Coding Hypothesis toactive perception. In contrast to previous AEC models, the present model usesdeep autoencoders to learn sensory representations. We also propose a newformulation of the intrinsic motivation signal that guides the learning ofbehavior. We demonstrate the performance of the model in simulations.

Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders