Topological Sensitivity in Connectome-Constrained Neural Networks
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 ear...
| Authors | Nalin Dhiman |
|---|---|
| Primary category | q-bio.NC |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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