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.

翻译：随着神经细胞自动机（NCA）越来越多地被应用于人工生命中的玩具模型之外，理解其行为并构建合理解释所学内容的路径变得迫在眉睫。就其本质而言，训练NCA的益处往往以缺乏可解释性为代价：我们能够设计涌现行为，但对其学习内容的理解能力十分有限。本文应用多种技术以撬开NCA的黑箱，并窥探其所习得的能力。我们采用流形学习技术（主成分分析、稠密自动编码器和稀疏自动编码器）以及拓扑数据分析技术（持久同调）来捕捉NCA的底层行为流形，并取得了不同程度的成功。结果表明，当在宏观层面（即将整个NCA状态作为一个数据点）进行分析时，底层流形通常相当简单，且能被较好地捕捉和分析。而当在微观层面（即将单个细胞的状态作为一个数据点）进行分析时，流形高度复杂，需要借助更复杂的技术才能对其进行理解。