Proceedings of the Annual Meeting of the Cognitive Science Society

Sequential Cascaded Networks are recturent higher order connectionist networks which are used, like fiuoite state automata, to recognize lansuages. Such networks may be viewed as discrete dynamical systems (Dynamical Recognizers) wnose states are points inside a multi-dimensional hypercube, whose transitions are defined not by a list of rules, out by a parameterized non-linear function, and whose acceptance decision is defined by a threshold applied to one dimension. Learning proceeds by the ad^tation of weight parameters under error-driven feedback from performance on a teacher-suppbed set of exemplars. The weights give rise to a landscape where input tokens cause transitions between attractive points or regions, and induction in this framework corresponds to the clustering, splitting and joining of these regions. Usually, the resulting landscape settles into a finite set of attractive regions, and is isomorphic to a classical finite-state automaton. Occasionally, however, the landscape contains a "Strange Attractor" (e.g fig 3g), to which there is no direct analogy ia finite automata theory.