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）在人工生命领域之外的应用日益广泛，理解其行为并构建合理解读其学习成果的途径变得尤为迫切。NCA的本质在于，其训练优势与可解释性不足之间形成平衡：我们能够设计涌现行为，但理解所学内容的能力有限。本文采用多种技术手段来撬开NCA的黑箱，并初步了解其所习得的功能。我们应用流形学习技术（主成分分析、密集自编码器与稀疏自编码器）以及拓扑数据分析技术（持续同调）来捕捉NCA的底层行为流形，取得了不同程度的成效。结果表明，当在宏观层面进行分析时（即将整个NCA状态视为单一数据点），其底层流形通常相当简单，能够被较好地捕捉与分析；而当在微观层面进行分析时（即将单个细胞的状态视为单一数据点），流形则高度复杂，需要借助更复杂的技术手段方能解读。