随着神经元细胞自动机（Neural Cellular Automata, NCAs）日益应用于人工生命领域之外的现实场景，深入理解其行为模式并构建恰当的解释路径变得尤为迫切。NCAs 的训练优势天然伴随着可解释性的缺失：我们能够设计出涌现行为，却难以理解其实际习得的内容。本文采用多种技术手段尝试打开 NCA 的‘黑箱’，以期获得对其学习内容的初步理解。我们结合流形学习技术（主成分分析、稠密与稀疏自编码器）及拓扑数据分析技术（持续同调），试图刻画 NCA 潜在的行为流形，效果各异。结果表明，在宏观尺度上（即以整个 NCA 状态作为一个数据点）进行分析时，其潜在流形通常较为简单，易于捕捉与分析；而在微观尺度上（即以单个细胞的状态作为一个数据点）进行分析时，该流形则高度复杂，需借助更复杂的技术方能有效解读。

As Neural Cellular Automata (NCAs) are increasingly applied outside of the toy models in Artificial Life, there is a pressing need to understand how they behave and to build appropriate routes to interpret what they have learnt. By their very nature, the benefits of training NCAs are balanced with a lack of interpretability: we can engineer emergent behaviour, but have limited ability to understand what has been learnt. In this paper, we apply a variety of techniques to pry open the NCA black box and glean some understanding of what it has learnt to do. We apply techniques from manifold learning (principal components analysis and both dense and sparse autoencoders) along with techniques from topological data analysis (persistent homology) to capture the NCA's underlying behavioural manifold, with varying success. Results show that when analysis is performed at a macroscopic level (i.e. taking the entire NCA state as a single data point), the underlying manifold is often quite simple and can be captured and analysed quite well. When analysis is performed at a microscopic level (i.e. taking the state of individual cells as a single data point), the manifold is highly complex and more complicated techniques are required in order to make sense of it.