We propose a theory of deterministic chaos for discrete systems, based on their representations in binary state spaces $ ��$, homeomorphic to the space of symbolic dynamics. This formalism is applied to neural networks and cellular automata; it is found that such systems cannot be viewed as chaotic when one uses the Hamming distance as the metric for the space. On the other hand, neural networks with memory can in principle provide examples of discrete chaos; numerical simulations show that the orbits on the attractor present topological transitivity and a dimensional phase space reduction. We compute this by extending the methodology of Grassberger and Procaccia to $ ��$. As an example, we consider an asymmetric neural network model with memory which has an attractor of dimension $D_a = 2$ for $N = 49$.

Essentially a new paper. postscript figures are from a scanner, they can be ghostviewed in in windows environment or printed. to appear in J. Phys. A: Math. Gen