Virtually every aspect of our everyday routine actions is embedded in a sequential context. In this talk I will present and analyze a dynamic neural field model of ordinal and timing properties of action sequences. The model extends previous mathematical results on the existence of multiple bumps of equal strength in dynamic fields to implement a working memory of sequential events in which varying levels of self-sustained peak activity are correlated with the relative position of each item in the sequence. Our ultimate goal is to validate the sequence model as part of an existing dynamic field architecture for human-robot interaction (HRI). |