Recent experimental evidence on morphological learning and processing has prompted a less deterministic and modular view of the interaction between stored word knowledge and on-line processing. Storing a word in the mental lexicon does not simply entail keeping a faithful memory image of that word in the most compact way. It also requires encoding and manipulating such image through topological structures that are optimally adapted to word production and comprehension. Temporal Self-Organizing Maps (THSOMs) are a novel model of artificial neural network that keeps time serial information through predictive activation chains of receptors encoding both spatial and temporal information of input stimuli. The impact of this model on issues of lexical organization and morphological processing is investigated in detail through a series of simulations shedding light on the dynamics between short-term memory (activation), long-term memory (learning) and morphological organization of stored word forms (topology).