The nervous system underlies sensation and behavioral response, linking perception to action across diverse animal systems. Within a given species, a multitude of nested recurrent loops transform incoming sensory input into ecologically appropriate motor patterns. A key structure of interest is the mammalian neocortex, because neocortex is intimately associated with sophisticated, flexible response selection. Mammalian neocortex is characterized by stereotyped anatomical organization, suggesting the possibility of generic algorithms for processing information across sensory modalities and species. Ultimately, understanding information processing in the neocortex is paramount for progress in human technology, medicine, and philosophy. Connectivity is the substrate for spiking activity in neocortex. The relationship between connectivity and emergent activity is sometimes assumed to be straightforward, but in fact, given the state of current knowledge, knowing connectivity is not sufficient for predicting coordinated population responses. The complexity and diversity of synaptic mechanisms defy simple interpretations. Therefore, it is crucial to map population activity itself, and begin to delineate the reliable organizing principles that characterize the flow of activity through neocortical populations. Activity mapping is the key to eventual progress in understanding computation itself. In this thesis, I investigate whether activity mapping is practical under typical experimental constraints, and explore what activity mapping can tell us about the organization of neuronal firing. Building on those results, I then discover a surprising but intuitive relationship between a distributed connectivity scheme and higher-order correlations in population dynamics.