How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning. Here, we leverage data released through the Machine Intelligence from Cortical Networks (MICrONS) program--a functional connectomics resource spanning multiple areas of mouse visual cortex, in which dense calcium imaging is co-registered with high-resolution electron microscopy reconstruction from the same animal--to build biologically grounded recurrent neural networks. Using neuronal spatial coordinates, anatomical connectivity, and function-derived relationships from nearly 12,000 coregistered excitatory neurons, we initialize recurrent weights and impose communication-aware spatial constraints during learning. Across three cognitive decision-making tasks, networks constrained by cortical structure and function consistently outperform baseline and partially constrained models. Functional weight initialization provides the largest gain, while real spatial embedding yields robust additional improvements across conditions. These biologically grounded networks also develop low-entropy, modular, and small-world organization, and retain strong performance even when recurrence is restricted to positive weights. Together, our results show that the machinery of cortex--its geometry, wiring, and functional structure--can be harnessed as a powerful inductive basis for building recurrent networks that learn more effectively while converging toward key organizational principles of biological computation.

翻译：皮层如何通过其连接模式与功能组织塑造循环计算，这一直是神经科学与机器学习领域的核心问题。本研究利用通过皮层网络机器智能（MICrONS）项目发布的数据——一个涵盖小鼠视觉皮层多个区域的功能连接组学资源，在同一动物体内将密集钙成像与高分辨率电子显微镜重建进行共配准——构建具有生物学基础的循环神经网络。基于近12,000个共配准兴奋性神经元的空间坐标、解剖连接与功能衍生关系，我们在学习过程中初始化循环权重并施加通信感知的空间约束。在三个认知决策任务中，受皮层结构与功能约束的网络持续优于基线模型及部分约束模型。功能权重初始化带来最大增益，而真实空间嵌入在多种条件下产生稳健的额外改进。这些具有生物学基础的网络还发展出低熵、模块化与小世界组织特性，且当循环权重被限制为正值时仍保持强劲性能。综合而言，我们的研究结果表明，皮层的机制——其几何结构、连接模式与功能架构——可作为构建循环网络的强大归纳基础，使网络在学习过程中更高效地收敛至生物计算的关键组织原则。