Penn AI Seminar feat. Konrad Kording (PSOM, SEAS)

How Brains and Machines Solve the Binding Problem

Despite decades of research, we still do not know how the brain integrates the many features of an object into a coherent whole, or whether artificial systems perform similar binding. In our first study, we find that large self-supervised vision transformers spontaneously develop a low-dimensional “same-object” representation that predicts whether two image patches belong to the same object with over 90 % accuracy. Removing this signal disrupts segmentation, showing that object binding naturally emerges in deep networks trained on natural images. In our second study, we develop a mechanistic model of attention and binding that captures core neurobiological phenomena such as selective focus and inhibition of return. Together, these results suggest a shared computational principle: binding arises from structured interactions between distrib