Characterising the representation of sensory stimuli in the brain is a fundamental scientific endeavor, which can illuminate principles of information coding. Most characterizations reduce the dimensionality of neural data by converting spike trains to firing rates or binned spike counts, applying explicitly named methods of "dimensionality reduction", or collapsing trial-to-trial variability. Characterisation of the full-dimensional geometry of timing-based representations may provide unexpected insights into how complex high-dimensional information is encoded. Recent research shows that the distribution of representations elicited over trials of a single stimulus can be geometrically characterized without the application of dimensionality reduction, maintaining the temporal spiking information of individual neurons in a cell assembly and illuminating rich geometric structure. We extend these results, showing that precise spike time patterns for larger cell assemblies are time-warped (i.e. stretched or compressed) on each trial. Moreover, by geometrically characterizing distributions of large spike time patterns, our analysis supports the hypothesis that the degree to which a spike time pattern is time-warped depends on the cortical area's background activity level on a single trial. Finally, we suggest that the proliferation of large electrophysiology datasets and the increasing concentration of "neural geometrists", creates ideal conditions for characterization of full-dimensional spike time representations, in complement to dimensionality reduction approaches.