复杂神经突网络中物质传输的深度学习
Deep learning of material transport in complex neurite networks
摘要：
神经元在其分支的神经突网络中表现出复杂的几何形状，这对于单个神经元的功能是必不可少的，但也带来了在其整个神经突网络中运输各种必需物质以用于其生存和功能的挑战。虽然等几何分析（IGA）等数值方法已被用于通过求解偏微分方程（PDE）来模拟物质输运过程，但它们需要很长的计算时间和巨大的计算资源来确保精确的几何表示和解决方案，从而限制了它们的生物医学应用。在这里，我们提出了一个基于图神经网络（GNN）的深度学习模型来学习基于IGA的材料传输模拟，并在任何拓扑结构的神经突网络中提供快速的材料浓度预测。在给定输入边界条件和几何构型的情况下，训练好的模型可以预测输运过程中的动态浓度变化，平均误差小于10%，比IGA模拟快120~330倍。该模型的有效性证明在几个复杂的神经突网络。
英文摘要：
Neurons exhibit complex geometry in their branched networks of neurites which is essential to the function of individual neuron but also brings challenges to transport a wide variety of essential materials throughout their neurite networks for their survival and function. While numerical methods like isogeometric analysis (IGA) have been used for modeling the material transport process via solving partial differential equations (PDEs), they require long computation time and huge computation resources to ensure accurate geometry representation and solution, thus limit their biomedical application. Here we present a graph neural network (GNN)-based deep learning model to learn the IGA-based material transport simulation and provide fast material concentration prediction within neurite networks of any topology. Given input boundary conditions and geometry configurations, the well-trained model can predict the dynamical concentration change during the transport process with an average error less than 10% and 120~330 times faster compared to IGA simulations. The effectiveness of the proposed model is demonstrated within several complex neurite networks.
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