Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision.

Despite moving our eyes from one location to another, our perception of the world is stable-an aspect thought to rely on predictive computations that use efference copies to predict the upcoming foveal input. Are these complex computations and required connectivity scaffolds genetically encoded, or could they emerge from simpler principles? Here, we consider the organism's limited energy budget as a potential origin. We expose a recurrent neural network to sequences of fixation patches and saccadic efference copies, training the model to minimize energy consumption (preactivation). We show that targeted inhibitory predictive remapping emerges from this energy-efficiency optimization alone. Furthermore, this computation relies on the model's learned ability to re-code egocentric eye coordinates into an allocentric (image-centric) reference frame. Together, our findings suggest that both allocentric coding and predictive remapping can emerge from energy-efficiency constraints, demonstrating how complex neural computations can arise from simple physical principles.