Signs and symbols relate to concepts and can be used to speak about objects, actions, and their features. Theories of semantic grounding address the question how the latter two, concepts and real‐world entities, come into play and interlink in symbol learning. Here, a neurobiological model is used to spell out concrete mechanisms of symbol grounding, which implicate the “association” of information about sign and referents and, at the same time, the extraction of semantic features and the formation of abstract representations best described as conjoined and disjoined feature sets that may or may not have a real‐life equivalent. The mechanistic semantic circuits carrying these feature sets are not static conceptual entries, but exhibit rich activation dynamics related to memory, prediction, and contextual modulation. Four key issues in specifying these activation dynamics will be highlighted: (a) the inner structure of semantic circuits, (b) mechanisms of semantic priming, (c) task specificity in semantic activation, and (d) context‐dependent semantic circuit activation in the processing of referential, existential, and universal statements. These linguistic‐semantic examples show that specific mechanisms are required to account for context‐dependent semantic function or conceptual “flexibility.” Static context‐independent concepts as such are insufficient to account for these different semantic functions. Whereas abstract amodal models of concepts did so far not spell out concrete mechanisms for context‐dependent semantic function, neuronal assembly mechanisms offer a workable perspective.