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原文摘要

Reservoir computing offers a way to study the brain's wiring before it is even trained. The recurrent network remains fixed, while only a linear readout is trained. The human connectome is found to perform better than randomly rewired versions of itself, suggesting that biological wiring is special. To understand the source of this advantage, a mathematical graph-theoretic decomposition is introduced. Rewiring a weighted network changes both the regions that are connected and the weights assigned to those connections. The observed advantage therefore combines an effect of wiring with an effect of weight placement. These effects are separated by averaging the wiring term over the null model's weight rule, as a single draw introduces about one z-unit of noise. Adult and infant connectomes are then considered separately. In adult Human Connectome Project graphs, weight placement explains most of the reported effect, leaving a small but consistent wiring effect. Across the full dHCP release, a large wiring effect is found at every measured gestational age at birth, while gestational age explains almost none of its variance. The connectomes are also compared with non-brain networks. A Watts-Strogatz graph matched in size, edge count, weights, and strength performs as well as or better than the connectome. The newborn connectome's advantage is therefore real across all measured gestational ages, but it is not unique to the brain. The same separation can be applied whenever a weighted network is compared with rewired nulls.