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In one Area X volume, mean HVC–LMAN coverage at 5 µm was 7.7% by skeleton path and 15.0% by Euclidean distance in 186 MSNs

We tested how local shaft inputs can reach spine inputs in one songbird Area X connectome. We selected 200 medium spiny neurons (MSNs) before spatial analysis. Complete API queries gave 25,800 HVC or LMAN contacts in j0251 cell type version v8. The main comparison retained 186 MSNs with both shaft input types. At 5 µm, mean HVC spine contact coverage by LMAN shaft contacts was 7.72% along projected skeleton paths and 15.01% by Euclidean distance. Path coverage rose to 47.32% at 20 µm. A reference kept each target's shaft sites and LMAN contact count fixed. At 5 µm, observed path coverage exceeded its mean by 0.46 percentage points. The 95% target resample interval was 0.08 to 0.85 points. The conditional tail score was 0.012. A source subset with type certainty at least 0.8 retained 121 targets and gave a difference of -0.13 points, with an interval of -0.58 to 0.33. Added checks held target IDs fixed and matched contact loss. Random contact loss gave excess values that included the source certainty result. A further cube control reduced the excess to 0.19 points. These measurements constrain possible local input partners under an assumed distance limit. They do not measure signal spread, plasticity, or learning. One bird, source fragments, automated labels, and approximate paths limit the result. The package includes source queries and hashes, code, derived distance matrices, all result tables, figures, and offline repeat instructions.

Area X · songbird · connectome · HVC · LMAN · dendritic integration · synapse location · spatial coverage · source confidence

Admitted 2 citing publications

References

2
  1. usesdoi:10.1101/2025.10.25.684569

    We use the public j0251 Area X connectome with v8 cell type labels. Complete API requests, payload hashes and source attribution are preserved.

  2. usesdoi:10.1101/2020.02.18.954354

    We build on the reported HVC and LMAN compartment preferences and their proposed local learning model. We add cross-source path coverage and fixed shaft site controls; we do not claim the compartment distinction as new.

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