We present a constructivist neural network that closely models the performance of agrammatic aphasics on German participle inflection. The network constructs a modular architecture leading to a double dissociation between regular and irregular verbs, and lesioning the trained network accounts for data obtained from aphasic subjects [6]. We analyze the internal structure of the network with respect to the representation of regular and irregular verbs and argue that the constructivist neural network presents a more plausible account of verb inflection than a recently proposed dual-mechanism theory.