发表机构
Innovamat Education; Universitat Rovira i Virgili; ICREA（Innovamat教育; 罗维拉-威尔吉利大学; ICREA）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过果蝇连接组与眼睛模型耦合，构建仅连接组模型，证明固定结构在可学习参数下支持多任务视觉，且生物布线在匹配成本下准确率更高，揭示结构与几何共同决定高效视觉计算。
AI 中文摘要
理解测量到的突触布线在多大程度上决定计算仍是一个核心挑战。在此，我们将经过校对读取的成年黑腹果蝇连接组与其眼睛的解剖学忠实模型相结合。视觉信息在眼睛模型中输入，然后传递到连接组，最后从以肯雅细胞为中心的线性解码器读取。这创建了一个仅连接组模型，其中解剖学图结构和眼睛几何形状固定，仅标量突触增益和神经元阈值可被学习。该模型支持多任务视觉，包括颜色辨别、形状分类以及遵循近似数字感知的比率依赖缩放特征的数值辨别。为了测试在布线经济性下精确连接是否重要，我们将生物图与随机化集成体进行比较，这些集成体逐渐保留生物突触约束。在匹配的布线成本下，生物网络始终产生更高的准确率，而约束较少的重新布线以布线成本膨胀为代价超越它。这些发现表明，测量到的连接性和眼睛几何形状共同为视觉计算设定了高效的工作点。
英文摘要
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.
