A single computational objective can produce specialization of streams in visual cortex.

Human visual cortex is organized into dorsal, lateral, and ventral streams. A long-standing hypothesis is that the functional organization into streams emerged to support distinct visual behaviors. Here, we compare neural network-based computational models against a massive fMRI dataset to investigate why visual streams emerge. We find that a self-supervised Topographic Deep Artificial Neural Network (TDANN), which encourages nearby units to respond similarly, better captures brain responses, as well as spatial segregation and functional differentiation across streams, than DANN models trained for stream-specific visual behaviors. These findings challenge the prevailing view that streams evolved to separately support different behaviors and suggest instead the possibility that functional organization can arise from a single principle: learning generally useful visual representations subject to local spatial constraints.