Through the use of biofidelic spiking neural network models (SNNs), this work offers mechanistic insights into the relationship between neocortical structure, dynamics, and computation. To set the stage, the topics of cortical computation, ANNs as applied to neuroscience, SNNs as applied to studying structure-function relationships in neocortex, and the challenges of model interpretability are introduced. In the first chapter, in the tradition of using SNNs to address structure-function questions, we ask why stable dynamics are possible in neocortex. Biofidelic SNNs were created through a grid search for architectures that yielded realistic spiking activity that was low-rate, asynchronous, and near-critical. The maintenance of this activity is linked to patterns of higher order coordination of synaptic activity, and this coordination takes the form of transitions in time between specific three-unit motifs. These motifs summarize the way spikes traverse the underlying synaptic topology. The second chapter turns its focus to computation, which occurs in neocortex on a substrate of stable activity that we studied in the first. Specifically, this chapter covers why computation becomes possible through specific synaptic changes and resulting dynamic changes that occur through learning. After training SNN models to perform an ethologically relevant task, models come to selectively adjust firing rates in response to the stimulus input. Excitatory and inhibitory connectivity between input and recurrent layers changed in accordance with this rate modulation. In particular, recurrent inhibitory units which were tuned to one input over the other strengthened their connections to recurrent units of the opposite tuning. We conclude by discussing the potential of task-trained SNNs for hypothesis generation and testing in future research on neocortical computation. Additional neocortical features that may be important for computation are surveyed, and questions of model interpretation are revisited in the context of these results.