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测试果蝇哈希算法在四个连接组上的表现，发现测量布线无一致检索优势，其优势源于每个活跃单元而非运算，无需连接组数据。
AI 中文摘要
Dasgupta、Stevens 和 Navlakha（2017）表明，果蝇嗅觉回路（建模为随机稀疏投影后接赢者通吃）是一种局部敏感哈希，优于经典 LSH。由于当时布线未知，投影是随机的。我们使用四个电子显微镜连接组（MaleCNS、hemibrain、FlyWire、BANC；四个动物，七个半球）对其进行测试。首先，2017 年的模式在其协议于 SIFT、MNIST 和气味混合物上的重新实现中成立（GloVe 在每种方法的短码下接近随机水平）：在短哈希长度下，果蝇哈希优于 k 个高斯投影（在 MNIST 上 k=4 时 AP@200 为 3.1 倍）。其次，与测试的实值高斯基线相比，该优势是每个活跃细胞而非每次运算的：在相同投影算术下，高斯投影在每个测试数据集和输入维度上检索更好。第三，在四个连接组中，测量的肾小球配对与保持度数的重连相比没有一致的检索优势：检索略低（中位数 -1.6%），且小的气味缺陷取决于如何处理缺失的气味响应。另外，在固定连接数下均衡肾小球扇出在模型的每个半球中改善了检索，而均衡每个细胞的输入则平均降低检索。然而，扇出分布在四个采样动物间相似（动物间 Spearman rho 中位数 = 0.86），结构突触计数并未抵消它，且其与气味调谐的关系较弱。在模型中，这种不均衡分配损害检索。对实践而言：测量布线相比保持度数的随机布线没有一致的检索优势，因此果蝇哈希无需连接组数据，且其优势是每个活跃单元的，这可能适合活跃单元而非算术为绑定成本的硬件，这一假设我们未测试。
英文摘要
Dasgupta, Stevens and Navlakha (2017) showed that the Drosophila olfactory circuit, modelled as a random sparse projection followed by winner-take-all, is a locality-sensitive hash that beats classical LSH. The projection was random because the wiring was unknown. We test it against four electron-microscopy connectomes (MaleCNS, hemibrain, FlyWire, BANC; four animals, seven hemispheres). First, the 2017 pattern holds in a reimplementation of its protocol on SIFT, MNIST and odour mixtures (GloVe is near chance at short codes for every method): the fly hash beats k Gaussian projections at short hash lengths (3.1x in AP@200 on MNIST at k = 4). Second, against the tested real-valued Gaussian baseline that advantage is per active cell, not per operation: Gaussian projections given the same projection arithmetic retrieve better on every dataset and input dimension tested. Third, across four connectomes the measured pairing of glomeruli gives no consistent retrieval advantage over degree-preserving rewiring: retrieval is slightly lower (median -1.6%), and the small odour deficits depend on how missing odour responses are treated. Separately, equalising glomerular fan-out at fixed connection count improves retrieval in the model in every hemisphere, while equalising inputs per cell lowers it on average. Yet the fan-out profile is similar across the four sampled animals (median between-animal Spearman rho = 0.86), structural synapse counts do not offset it, and its relation to odour tuning is weak. In the model that uneven allocation costs retrieval. For practice: measured wiring gives no consistent retrieval advantage over degree-preserving random wiring, so a fly hash needs no connectome data, and its advantage is per active unit, which may suit hardware where active units rather than arithmetic are the binding cost, a hypothesis we do not test.
Comments7 pages, 5 figures. Code, data pipeline and results: https://github.com/ssenge/FlyHash-Connectome
