All facets of high-level cognition, including perception, language, memory, and action, involve relational information (e.g., What is where? and Who did what to whom?). In order to process relational information, the brain must be capable of supporting a dynamic, activation-based expression of such information as well as a persistent, structure-based encoding of such information. An important challenge for computational neuroscience is to model the brain's remarkable ability to express and seamlessly integrate both transient and persistent forms of relational information. A brief review of work done by the author and his collaborators on computational models that attempt to address this challenge using temporal synchrony, network structure, and synaptic plasticity is presented.