  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.
