Across the primate cortex, neurons that perform similar functions tend to be spatially grouped together. In high-level visual cortex, this widely observed biological rule manifests itself as a modular organization of neuronal clusters, each tuned to a specific object category. The tendency toward short connections is one of the most widely accepted views of why such an organization exists in the brains of many animals. Yet, how such a feat is implemented at the neural level remains unclear.Here, using artificial deep neural networks as test beds, we demonstrate that a topographical organization similar to that in the primary, intermediate, and high-level human visual cortex emerges when units in these models are laterally connected and their weight parameters are tuned by top-down credit assignment. Importantly, the emergence of the modular organization in the absence of explicit topography-inducing learning rules and objectives questions their necessity and suggests that local lateral connectivity alone may be sufficient for the formation of the topographic organization across the cortex.