The expansion of large-scale neural recording capabilities has provided new opportunities to examine multi-scale cortical network activity at single neuron resolution. At the same time, the growing scale and complexity of these datasets introduce new conceptual and technical challenges beyond what can be addressed using traditional analysis techniques. Here, we present the Similarity Networks (SIMNETS) analysis framework: an efficient and scalable pipeline designed to embed simultaneously recorded neurons into low dimensional maps according to the intrinsic relationship between their spike trains, making it possible to identify and visualize groups of neurons performing similar computations. The critical innovation is the use of pairwise spike train similarity (SSIM) matrices to capture the intrinsic relationship between the spike trains emitted by a neuron at different points in time (i.e., different experimental conditions), reflecting how the neuron responds to time-varying internal and external drives and making it possible to easily compare the information processing properties across neuronal populations. We use three publicly available neural population test datasets from the visual, motor, and hippocampal CA1 brain regions to validate the SIMNETS framework and demonstrate how it can be used to identify putative subnetworks (i.e., clusters of neurons with similar computational properties). Our analysis pipeline includes a novel statistical test designed to evaluate the likelihood of detecting spurious neuron clusters to validate network structure results. The SIMNETS framework provides a way to rapidly examine the computational structure of neuronal networks at multiple scales based on the intrinsic structure of single unit spike trains.