Topological Sensitivity in Connectome-Constrained Neural Networks

ArXi:2604.04033v1 Announce Type: cross Connectome-constrained neural networks are often evaluated against sparse random controls and then interpreted as evidence that biological graph topology improves learning efficiency. We revisit that claim in a controlled flyvis-based study using a Drosophila connectome, a naive self-loop-matched random graph, and a degree-preserving rewired null.