斯蒂芬·沃尔弗拉姆（Stephen Wolfram）在其2003年开创性著作《一种新科学》（A New Kind Of Science）中宣称，以细胞自动机（Cellular Automata, CA）形式存在的简单递归程序，有望取代当前广泛使用的数学形式化方法（如微分方程），从而提升对复杂系统的建模能力。二十多年后，尽管细胞自动机在科学应用中仍未取得实质性突破，近期研究却展现出将沃尔弗拉姆思想与可学习的人工神经网络（Artificial Neural Networks）相结合的新路径：所谓神经元细胞自动机（Neural Cellular Automata, NCA）能够从数据样本中学习CA的复杂更新规则，从而建模具有自组织特性的复杂生成系统。本文旨在综述现有NCA相关工作，并提供一套统一的模块化框架与符号体系，以及基于开源库NCAtorch的参考实现。

Stephen Wolfram proclaimed in his 2003 seminal work "A New Kind Of Science" that simple recursive programs in the form of Cellular Automata (CA) are a promising approach to replace currently used mathematical formalizations, e.g. differential equations, to improve the modeling of complex systems. Over two decades later, while Cellular Automata have still been waiting for a substantial breakthrough in scientific applications, recent research showed new and promising approaches which combine Wolfram's ideas with learnable Artificial Neural Networks: So-called Neural Cellular Automata (NCA) are able to learn the complex update rules of CA from data samples, allowing them to model complex, self-organizing generative systems. The aim of this paper is to review the existing work on NCA and provide a unified modular framework and notation, as well as a reference implementation in the open-source library NCAtorch.