  The Reservoir Computing (RC) paradigm utilizes a dynamical system, i.e., a
reservoir, and a linear classifier, i.e., a read-out layer, to process data
from sequential classification tasks. In this paper the usage of Cellular
Automata (CA) as a reservoir is investigated. The use of CA in RC has been
showing promising results. In this paper, selected state-of-the-art experiments
are reproduced. It is shown that some CA-rules perform better than others, and
the reservoir performance is improved by increasing the size of the CA
reservoir itself. In addition, the usage of parallel loosely coupled
CA-reservoirs, where each reservoir has a different CA-rule, is investigated.
The experiments performed on quasi-uniform CA reservoir provide valuable
insights in CA reservoir design. The results herein show that some rules do not
work well together, while other combinations work remarkably well. This
suggests that non-uniform CA could represent a powerful tool for novel CA
reservoir implementations.
