探索神经信号生成模型中的潜在图学习，提升可解释性。
Latent Graph Learning in Generative Models of Neural Signals
- 基于神经环路仿真数据，分析生成模型权重的潜在图结构。
- 发现模型提取的共输入图与真实连接图高度一致。
- 为构建可解释的大规模神经数据基础模型提供新思路。
从神经信号中推断时间交互图和高阶结构，是构建系统神经科学生成模型的关键问题。大规模神经数据的基础模型代表了神经信号的共享潜在结构。然而，从基础模型中提取可解释的潜在图表示仍具挑战且未解决。本文研究神经信号生成模型中的潜在图学习。通过在具有已知真实连接的神经环路数值模拟数据上测试，评估多个关于模型权重解释的假设。发现提取的网络表示与底层有向图存在适度对齐，而共输入图表示则表现出强对齐。这些发现为在大规模神经数据基础模型构建中引入基于图的几何约束提供了方向。
原文摘要 · Abstract (English)
Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience. Foundation models for large-scale neural data represent shared latent structures of neural signals. However, extracting interpretable latent graph representations in foundation models remains challenging and unsolved. Here we explore latent graph learning in generative models of neural signals. By testing against numerical simulations of neural circuits with known ground-truth connectivity, we evaluate several hypotheses for explaining learned model weights. We discover modest alignment between extracted network representations and the underlying directed graphs and strong alignment in the co-input graph representations. These findings motivate paths towards incorporating graph-based geometric constraints in the construction of large-scale foundation models for neural data.
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