We study the dynamical behavior of complex adaptive automata during unsupervised learning of periodic training sets. A new technique for their analysis is presented and applied to an adaptive network with distributed memory. We show that with general imput pattern sequences, the system can display behavior that ranges from convergence into simple fixed points and oscillations to chaotic wanderings. We also test the ability of the self-organized automaton to recognize spatial patterns, discriminate between them, and to elicit meaningful information out of noisy inputs. In this configuration we determine that the higher the ratio of excitation to inhibition, the broader the equivalence class into which patterns are put together.