arXiv：cs.LG· Eudald Correig-Fraga, Roger Guimer\`a, Marta Sales-Pardo·· 4 小时前AI 评分34
果蝇视觉系统研究：仅凭结构即可支持高效视觉计算
Structure alone supports efficient visual computation in the Drosophila visual system
AI 导读
研究人员将成年果蝇连接组与解剖学精确的眼部模型耦合，构建了仅依赖连接组的模型——解剖图和眼几何固定，仅可学习标量突触增益和神经元阈值。该模型支持颜色辨别、形状分类和数值辨别等多任务视觉，其中数值辨别呈现近似数量感知的比率依赖缩放特征。在匹配布线成本下，生物网络准确率始终高于随机化系综，表明实测连接与眼几何共同为视觉计算设定了高效工作点。
正文
Abstract:Understanding the extent to which measured synaptic wiring determines computation remains a central challenge. Here, we couple the proofread adult Drosophila melanogaster connectome to an anatomically faithful model of its eye. Visual information is inputted in the eye model, then passed to the connectome, and finally read from a Kenyon-cell-centered linear decoder. This creates a connectome-only model in which the anatomical graph and eye geometry are fixed and only scalar synaptic gains and neuronal thresholds may be learned. The model supports multitask vision, including color discrimination, shape classification, and numerical discrimination that follows a ratio-dependent scaling characteristic of approximate number perception. To test whether precise connectivity is consequential under wiring economy, we compare the biological graph to randomized ensembles that increasingly preserve biological synaptic constraints. At matched wiring cost, the biological network consistently yields higher accuracy, whereas less constrained rewiring surpasses it at the cost of inflated wiring. These findings indicate that the measured connectivity and eye geometry jointly set efficient operating points for visual computation.
| Subjects: | Neurons and Cognition (q-bio.NC); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10023 [q-bio.NC] |
| (or arXiv:2610.10023v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10023 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Roger Guimera [view email]
[v1]
Wed, 7 Oct 2026 13:10:12 UTC (5,631 KB)
来源：arXiv：cs.LG · arxiv.org