Brain-like border ownership signals support prediction of natural videos.

To make sense of visual scenes, the brain must segment foreground from background. This is thought to be facilitated by neurons that signal border ownership (BOS), which indicate which side of a border in their receptive field is owned by an object. How these signals emerge without a teaching signal of what is foreground remains unclear. Here we find that many units in PredNet, a self-supervised deep neural network trained to predict future frames in natural videos, are selective for BOS. They share key properties with BOS neurons in the brain, including robustness to object transformations and hysteresis. Ablation revealed that BOS units contribute more to prediction than other units for videos with moving objects. Our findings suggest that BOS neurons might emerge due to an evolutionary or developmental pressure to predict future input in natural, complex dynamic environments, even without an explicit requirement to segment foreground from background.