Active efficient coding explains the development of binocular vision and its failure in amblyopia

The development of vision during the first months of life is an active process that comprises the learning of appropriate neural representations and the learning of accurate eye movements. While it has long been suspected that the two learning processes are coupled, there is still no widely accepted theoretical framework describing this joint development. Here we propose a computational model of the development of active binocular vision to fill this gap. The model is based on a new formulation of the Active Efficient Coding theory, which proposes that eye movements, as well as stimulus encoding, are jointly adapted to maximize the overall coding efficiency of the system. Under healthy conditions, the model self-calibrates to perform accurate vergence and accommodation eye movements. It exploits disparity cues to deduce the direction of defocus, which leads to coordinated vergence and accommodation responses. In a simulated anisometropic case, where the refraction power of the two eyes differs, an amblyopia-like state develops, in which the foveal region of one eye is suppressed due to inputs from the other eye. After correcting for refraction errors, the model can only reach healthy performance levels if receptive fields are still plastic, in line with findings on a critical period for binocular vision development. Overall, our model offers a unifying conceptual framework for understanding the development of binocular vision under healthy conditions and in amblyopia. Significance Statement Brains must operate in an energy efficient manner. The efficient coding hypothesis states that sensory systems achieve this by adapting neural representations to the statistics of sensory input signals. Importantly, however, these statistics are influenced by the organism’s behavior and how it samples information from the environment. Therefore, optimal performance requires jointly optimizing neural representations and behavior, a theory called Active Efficient Coding. Here we test the plausibility of this theory by proposing a computational model of the development of binocular vision. The model explains the development of accurate binocular vision under healthy conditions. In the case of refractive errors, however, the model develops an amblyopia-like state and predicts conditions for successful treatment.