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Contact totals for each cell fixed binary choice matching for 29 E-to-I contacts in one mouse PPC panel

We tested whether synapse allocation follows left-right trial preference after each cell keeps its contact total. We used one mouse posterior parietal cortex panel from Kuan et al. (2024). All six inhibitory targets that received an excitatory contact had a positive preference index. The 29 E-to-I contacts included eight from negative-index sources. Thus every table with the same cell totals had eight opposite-choice contacts. Complete enumeration confirmed this result in all 6,324 tables allowed by the measured pair set. Integer transport certificates gave the same limit. Continuous similarity still varied. An overlap-weighted conditional Poisson model gave primary directional probabilities of 0.6448 for E-to-I and 0.7438 for I-to-E. The I-to-E panel had 156 contacts in 550 possible pairs. Its connected-pair choice-density correlation was -0.2238, with secondary conditional probability 0.0138. This became 0.0275 within the two secondary tests or 0.0551 across all four tests. Contact allocation and pair density therefore gave different evidence. The exact E-to-I count limit follows from target preference coverage. It does not show that selective wiring is absent. The scope is one animal, inherited preference signs, and fixed cell totals. The density test also assumes the stated overlap model. The package includes new code, exact and sampled results, figures, source hashes, plans, checks, and repeat instructions.

connectome · structure and function · posterior parietal cortex · opponent inhibition · conditional inference · trial preference · synapse counts · data coverage

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References

4
  1. usesdoi:10.1038/s41586-024-07088-7

    We analyze the measured anatomical contacts, overlap and revised trial preference indices from the source study. The exact input is pinned to Git commit 42409f8138310a0cd94149100f21dd79d1b3cea7 and a SHA-256 hash.

  2. usesdoi:10.1214/aos/1030563990

    This work gives the established basis for conditional sampling with fixed sufficient statistics. Our code uses contact-token rotations and checks cycles needed for structural zeros.

  3. usesdoi:10.1214/20-BA1221

    We use rank-based split and folded R-hat checks for the saved Markov chains.

  4. usesdoi:10.1038/s41592-019-0686-2

    SciPy provides the transport optimization, correlations, and numerical functions. Exact installed versions are recorded.

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