For an animal to successfully navigate its environment, it must form an accurate internal representation of its surroundings. Location-specific firing and co-firing of the hippocampal place cells play a crucial role in spatial cognition, which according to our recently published model is more akin to a subway map than a street map, i.e., is primarily topological. We tested our topological model of hippocampal activity, varying several parameters in computer simulations of rat trajectories in distinct test environments. Using a computational algorithm based on recently developed tools from Persistent Homology theory, we find that the patterns of neuronal co-firing can, in fact, convey topological information about the environment in a biologically realistic length of time. Notably, our simulations reveal a “learning region” —a sweet spot for spatial learning—that highlights the interplay between parameters in producing hippocampal states that are more or less adept at map formation. For example, within the learning region a smaller number of neurons firing can be compensated by adjustments in firing rate or place field size, but beyond a certain point map formation begins to fail. This notion of a learning region provides a coherent theoretical lens through which to view conditions that impair spatial learning by altering place cell firing rates or spatial specificity.