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 are a well-studied computational universe that is%2C despite its simple configurations%2C capable of impressive computational variety. Harvesting this computation in a useful way has historically shown itself to be difficult%2C but if combined with reservoir computing (RC)%2C this becomes much more feasible. Furthermore%2C RC and ECA enable energy-efficient AI%2C making the combination a promising concept for Edge AI. In this work%2C we contrast ECA to substrates of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). They are%2C in comparison%2C 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)%2C using Temporal Derrida plots to estimate the sensitivity and assess the defect collapse rate. We find that%2C counterintuitively%2C 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%2C if accounted for%2C an increased sensitivity to the initial condition. These observations together suggest a shrinking critical range.?quality=80&w=800)
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