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Dynamical systems are capable of performing computation in a reservoircomputing paradigm. This paper presents a general representation of thesesystems as an artificial neural network (ANN). Initially, we implement thesimplest dynamical system, a cellular automaton. The mathematical fundamentalsbehind an ANN are maintained, but the weights of the connections and theactivation function are adjusted to work as an update rule in the context ofcellular automata. The advantages of such implementation are its usage onspecialized and optimized deep learning libraries, the capabilities togeneralize it to other types of networks and the possibility to evolve cellularautomata and other dynamical systems in terms of connectivity, update andlearning rules. Our implementation of cellular automata constitutes an initialstep towards a general framework for dynamical systems. It aims to evolve suchsystems to optimize their usage in reservoir computing and to model physicalcomputing substrates.

A general representation of dynamical systems for reservoir computing