Dynamical systems are capable of performing computation in a reservoir
computing paradigm. This paper presents a general representation of these
systems as an artificial neural network (ANN). Initially, we implement the
simplest dynamical system, a cellular automaton. The mathematical fundamentals
behind an ANN are maintained, but the weights of the connections and the
activation function are adjusted to work as an update rule in the context of
cellular automata. The advantages of such implementation are its usage on
specialized and optimized deep learning libraries, the capabilities to
generalize it to other types of networks and the possibility to evolve cellular
automata and other dynamical systems in terms of connectivity, update and
learning rules. Our implementation of cellular automata constitutes an initial
step towards a general framework for dynamical systems. It aims to evolve such
systems to optimize their usage in reservoir computing and to model physical
computing substrates.Comment: 5 pages, 3 figures, accepted workshop paper at Workshop on Novel
Substrates and Models for the Emergence of Developmental, Learning and
Cognitive Capabilities at IEEE ICDL-EPIROB 201
