According to the Strong Morphonotactic Hypothesis (SMH), speakers use morphonotactic consonant clusters as morphological boundary signals. Morphonotactic clusters are thereby assigned a morphological function in processing, which is assumed to facilitate processing and acquisition of complex consonantal structures. The aim of this paper is that of testing the SMH from a computational point of view, making use of a corpus-based model of neural network activation (PHACTS) trained on a German corpus. We evaluate whether PHACTS produces different representations for homophonous phonological sequences that either contain or do not contain a morphological boundary. The study provides a psycholinguistically plausible simulation of how the cognitive representation of (mor)phonotactic clusters emerges from corpus information about the phonotactics of a given language. © 2014 Elsevier Ltd.