连接组到功能：用于储层计算的条件生成潜表示
Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing
- National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理全国重点实验室）
- School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
- School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
- College of Engineering, Peking University（北京大学工学院）
- Yuanpei College, Peking University（北京大学元培学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出条件生成潜框架，将连接组图编码为紧凑结构空间，以高AUC重建和生成连接组，捕获储层计算功能变异，揭示记忆与互惠连接、预测与谱特性的关联。
AI中文摘要：
连接组是神经元及其突触连接的图级图谱，为理解脑回路如何支持功能和计算提供了结构基础。然而，由于这些图是高维、稀疏且对局部结构变化敏感的，将连接组结构映射到计算仍然困难。现有方法通常依赖于手工设计的结构描述符或特定任务的预测器，这限制了它们以既具生成性又具功能意义的形式表示连接组的能力。我们提出了一种条件生成潜框架，该框架将连接组图编码到紧凑的结构空间中，同时利用可用的节点级条件来指导重建和生成。从这个空间中，模型可以以高达0.910的平均边重建AUC重建观察到的连接性，并生成新的候选连接组，从而实现对图结构和计算行为的统一分析。使用连接组衍生图作为循环计算基底，我们发现学习到的潜空间捕获了储层计算实验中的功能变异，交叉验证的$R^2$值高达约0.87。可解释性分析进一步揭示了任务特定的结构机制：在我们的示例中，记忆性能与互惠循环连接相关，而预测和分类则与循环网络的谱特性更密切相关。这些发现提出了一种将神经连接与计算联系起来的人工智能科学方法，并为研究不同结构机制如何塑造计算能力提供了生成性和可解释的基础。
英文摘要：
Connectomes, graph-level maps of neurons and their synaptic connections, provide a structural basis for understanding how brain circuits support function and computation. However, mapping connectome structure to computation remains difficult because these graphs are high-dimensional, sparse, and sensitive to local structural variation. Existing approaches often depend on hand-crafted structural descriptors or task-specific predictors, which limits their ability to represent connectomes in a form that is both generative and functionally meaningful. We propose a conditional generative latent framework that encodes connectome graphs into a compact structural space while using available node-level conditions to guide reconstruction and generation. From this space, the model can reconstruct observed connectivity with a mean edge-reconstruction AUC up to 0.910 and generate new candidate connectomes, enabling a unified analysis of graph structure and computational behavior. Using connectome-derived graphs as recurrent computational substrates, we found that the learned latent space captures functional variation across reservoir-computing experiments, with cross-validated $R^2$ values up to approximately 0.87. Interpretability analysis further revealed task-specific structural mechanisms: in our examples, memory performance is associated with reciprocal recurrent connectivity, whereas prediction and classification are more strongly associated with spectral properties of the recurrent network. These findings suggest an AI-for-science approach to linking neural connectivity to computation and provide a generative and interpretable basis for studying how distinct structural mechanisms shape computational capacity.
