An exact axon order test showed no clear gain beyond count controls in one newborn mouse source table
We tested a structural prediction of ordered motor axon activity. We used one released newborn mouse graph with 20 axons, 229 target rows, and 1,848 links. The export differs from the article totals, so the scope is this source table. An exact subset method fitted a line order on 114 targets. The score counts missing axons between the first and last input to a target. We averaged the score across all tied training optima. The mean score on 115 separate test targets was 5.943. Two control chains kept every target count and every axon count separately in training and test data. Each of 998 controls repeated the exact fit. Their mean score was 6.134. The observed decrease was 3.1%. Of the controls, 134 scored at least as well. The lower-tail Monte Carlo fraction was 0.135. Thus, this test did not establish added line order beyond the specified counts. The result does not measure recruitment or exclude weak structure. We provide a small exact tool for up to 20 axons, a method proof, figures, control scores, source provenance, and repeat instructions. Direct small-case checks passed. A replay from a fresh source download reproduced all numerical result files.
connectome · neuromuscular junction · synapse removal · motor development · axon order · fixed counts · exact optimization · reproducible analysis
References
5- usesdoi:10.1038/s41593-026-02344-7
We analyze the released Figure 6c source ZIP and test a structural prediction of ordered activity.
- usesAn order learned from 148 targets gave 80% test agreement beyond count controls in one six-axon mouse table sci:sha256:ac8d45daa6e486c18434493f9e3edac4d3a42c301e40b6b088009527d7875049
We use its source audit and extend its separate-target order test to the newborn export. Its main result used a different six-axon table.
- usesdoi:10.1016/j.ejor.2013.01.028
The gap objective is an established matrix order problem. We provide an independent exact implementation with test averaging across tied training optima.
- usesdoi:10.1038/ncomms5114
We implement Curveball row trades to keep all row and column counts.
- supportsdoi:10.1103/PhysRevE.91.042812
This work supports the uniform stationary reference for count-preserving trades, including failed and identity trades.
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