Learning Cellular Automaton Dynamics with Neural Networks

We have trained networks of E - II units with short-range connections tosimulate simple cellular automata that exhibit complex or chaotic behaviour. Three levels of learning are possible (in decreasing orderof difficulty): learning the underlying automaton rule, learning asymptotic dynamical behaviour, and learning to extrapolate thetraining history. The levels of learning achieved with and without weight sharing for different automata provide new insight into their dynamics.