Elementary Cellular Automata (ECA) are a well-studied computational universe that is, despite its simple configurations, capable of impressive computational variety. Harvesting this computation in a useful way has historically shown itself to be difficult, but if combined with reservoir computing (RC), this becomes much more feasible. Furthermore, RC and ECA enable energy-efficient AI, making the combination a promising concept for Edge AI. In this work, we contrast ECA to substrates of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). They are, in comparison, the topological heterogeneous counterparts of ECA. This represents a step from ECA towards more biological-plausible substrates. We analyse these substrates by testing on an RC benchmark (5-bit memory), using Temporal Derrida plots to estimate the sensitivity and assess the defect collapse rate. We find that, counterintuitively, disordered topology does not necessarily mean disordered computation. There are countering computational "forces" of topology imperfections leading to a higher collapse rate (order) and yet, if accounted for, an increased sensitivity to the initial condition. These observations together suggest a shrinking critical range.

翻译：基本细胞自动机（ECA）是一个经过深入研究的计算宇宙，尽管其配置简单，却展现出令人印象深刻的计算多样性。历史上，以有效方式利用这种计算一直较为困难，但若与储备池计算（RC）结合，则可行性显著提高。此外，RC与ECA的结合能够实现高能效的人工智能，使其成为边缘人工智能领域极具前景的概念。在本研究中，我们将ECA与部分局部细胞自动机（PLCA）及同质同质随机布尔网络（HHRBN）的基底进行对比。相比之下，后者是ECA在拓扑结构上的异质对应物。这代表了从ECA向更具生物合理性的基底迈出的一步。我们通过在RC基准测试（5位记忆任务）上对这些基底进行验证，利用时间德里达图估计其敏感性并评估缺陷坍缩率。研究发现，与直觉相反，无序的拓扑结构并不必然导致无序的计算。拓扑缺陷会产生相互抗衡的计算“作用力”：一方面导致更高的坍缩率（有序性），另一方面若加以考量，则增强对初始条件的敏感性。这些观察共同表明临界范围正在缩小。