标题： GALDS：一种基于图自编码器的潜在动力学代理模型用于预测神经突材料运输 显示英文标题

标题： GALDS: A Graph-Autoencoder-based Latent Dynamics Surrogate model to predict neurite material transport

摘要： 神经元在其树突网络中表现出复杂的几何结构，这些结构在信号传递和营养物质运输等过程中起着关键作用。 在网络中准确模拟材料传输对于理解这些生物现象至关重要，但由于涉及复杂的树状结构，这带来了显著的计算挑战。 传统方法耗时且资源需求高，但神经元树的固有特性，主要由具有稳态抛物线速度分布和分叉的管道组成，为计算优化提供了机会。 为解决这些挑战，我们提出了一种基于图自编码器的潜在动力学代理模型（GALDS），该模型专门用于简化神经树中的材料传输模拟。 GALDS使用图自编码器对网络的几何结构、速度场和浓度分布进行潜在表示编码。 这些潜在空间表示随后被组合成一个全局图，然后通过训练好的图潜在空间系统动态模型预测潜在空间中的系统动力学，该模型受神经常微分方程（Neural ODEs）概念的启发。 自编码器的集成使得可以使用更小的图神经网络模型，并减少训练数据的需求。 此外，神经ODE组件有效缓解了递归神经网络中常见的误差累积问题。 GALDS模型的有效性通过八个未见过的几何结构和四个异常传输示例的结果得到验证，我们的方法在平均相对误差为3%、最大相对误差小于8%的情况下表现出比之前代理模型方法快10倍的速度提升。 显示英文摘要

摘要： Neurons exhibit intricate geometries within their neurite networks, which play a crucial role in processes such as signaling and nutrient transport. Accurate simulation of material transport in the networks is essential for understanding these biological phenomena but poses significant computational challenges because of the complex tree-like structures involved. Traditional approaches are time-intensive and resource-demanding, yet the inherent properties of neuron trees, which consists primarily of pipes with steady-state parabolic velocity profiles and bifurcations, provide opportunities for computational optimization. To address these challenges, we propose a Graph-Autoencoder-based Latent Dynamics Surrogate (GALDS) model, which is specifically designed to streamline the simulation of material transport in neural trees. GALDS employs a graph autoencoder to encode latent representations of the network's geometry, velocity fields, and concentration profiles. These latent space representations are then assembled into a global graph, which is subsequently used to predict system dynamics in the latent space via a trained graph latent space system dynamic model, inspired by the Neural Ordinary Differential Equations (Neural ODEs) concept. The integration of an autoencoder allows for the use of smaller graph neural network models with reduced training data requirements. Furthermore, the Neural ODE component effectively mitigates the issue of error accumulation commonly encountered in recurrent neural networks. The effectiveness of the GALDS model is demonstrated through results on eight unseen geometries and four abnormal transport examples, where our approach achieves mean relative error of 3% with maximum relative error <8% and demonstrates a 10-fold speed improvement compared to previous surrogate model approaches.