If I understand this correctly, they added a property to a NN of distance, and set the training to penalize increased distance between nodes to simulate a physical constraint, under these conditions 'hubs' emerged which facilitated connections across distance, the other observation was that "the same node might be able to encode multiple locations of a maze, rather than needing specialised nodes for encoding specific locations".
The work suggests that existing approaches to neural network architecture would benefit from more closely emulating the operation of the brain in this regard.
The work suggests that existing approaches to neural network architecture would benefit from more closely emulating the operation of the brain in this regard.