[2604.04033] Topological Sensitivity in Connectome-Constrained Neural Networks

Quantitative Biology > Neurons and Cognition arXiv:2604.04033 (q-bio) [Submitted on 5 Apr 2026] Title:Topological Sensitivity in Connectome-Constrained Neural Networks Authors:Nalin Dhiman View a PDF of the paper titled Topological Sensitivity in Connectome-Constrained Neural Networks, by Nalin Dhiman View PDF HTML (experimental) Abstract: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. Under weak controls, in which both models were recovered from a connectome-trained checkpoint and the null matched only global graph counts, the connectome appeared substantially better in early loss, mean activity, and runtime. That picture changed under stricter controls. Training both graphs from a shared random initialization removed the early loss advantage, and replacing the naive null by a degree-preserving null removed the apparent activity advantage. A five-sample degree-preserving ensemble and a pre-training activity-scale diagnostic further strengthened this revised interpretation. We also report a descriptive mechanism analysis of the earlier weak-control comparison, but we treat it as behavioral characterization rather than proof of causal superiority. We show that previously report...