实测连接组结论未必可靠：一项研究揭示调参方式如何左右"结构决定功能"判断

研究用合成真值、安慰剂零假设和三个 Drosophila 电路模型量化这些偏差的影响。部分结论在各种协议下都成立：分离的奖惩多巴胺通路改善学习，头方向拓扑结构是持续 bump 的必要条件。但蘑菇体的结论反转：真实 Kenyon 细胞输出连线在 P1 和 P2 下优于跨亚型恒等打乱 0.09，在 P3 下反而落后 0.09（95% 置信区间排除零，保留验证集确认）。关键变量是活跃 Kenyon 细胞中 alpha'beta' 细胞占比，P1 倾向选择低占比；按成像数据推算的约 44% 占比下，真实连线模型无法学习，说明是模型不足而非果蝇连线缺陷。

在 flyvis 中，P1 协议在 50 个成员中的 50 个里对镜像连接组的惩罚大于空间打乱组，尽管镜像连接组存在中位误差仅差 0.07 的已知解；该排名在十倍再训练后仍成立，但在五十倍时依赖所选 checkpoint。研究结论是：可靠的结构性主张需要匹配的优化、数据约束的生理参数，以及在此约束下仍能正常工作的模型。

When Does the Real Wiring Win? Testing connectome structure-function claims against unmeasured physiology

Connectome-constrained models are often used to argue that a measured wiring diagram outperforms shuffled or rewired wiring, and hence that structure shapes function. Such comparisons can confound wiring with how the models are tuned: a connectome fixes who connects to whom, not the excitability, gains or plasticity rates the model also needs, and modellers often tune these until the real-wiring model works. We separate three estimands: physiology tuned on the real wiring and transferred to nulls (P1), each wiring tuned on its own (P2), and P2 restricted to physiology consistent with independent data (P3). The biases involved are known in machine learning (under-tuned baselines, inherited weights, winner's curse); we measure what they do to biological conclusions, using synthetic ground truth, placebo nulls and three Drosophila circuit models. Some claims survive every protocol we test: segregated reward and punishment dopamine improves learning, and head-direction topography is required for a persistent bump. Others do not. In the mushroom body, the real Kenyon-cell output wiring beats a cross-subtype identity shuffle by 0.09 under P1 and P2 but loses to it by 0.09 under P3 (95% CIs exclude zero; held-out runs confirm). The verdict is set by the share of alpha'beta' cells among active Kenyon cells, which P1 selects low; at the share implied by imaging (about 44%) the real-wiring model fails to learn, exposing a model inadequacy rather than a deficit of the fly's wiring. In flyvis, P1 penalises a mirrored connectome, which has a constructed solution within 0.07 (median) of the real error, more than a spatially shuffled one in 50 of 50 members; the ranking survives ten-fold longer retraining but depends on the checkpoint at fifty-fold. A credible structural claim needs matched optimisation, data-constrained physiology and a model that still works there.

本条情报归属 AITOP「论文」核心追踪栏目。涉及核心实体与技术议题包括：连接组、Drosophila、计算神经科学、机器学习，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Yeyuan Liu），经 AITOP 多维评分管线结构化提炼。