The hippocampus plays an important role in representing space (for spatial navigation) and time (for episodic memory). Spatial representation of the environment is pivotal for navigation in rodents and primates. Two types of maps, topographical and topological, may be used for spatial representation. Rodent hippocampal place cells exhibit spatially-selective firing patterns in an environment that can be decoded to determine the animal’s location, heading, and past and future trajectory. We recorded ensembles of hippocampal neurons as rodents freely foraged in one and two-dimensional spatial environments, and we used a ``decode-to-uncover'' strategy to examine the temporally structured patterns embedded in the ensemble spiking activity in the absence of observed spatial correlates during rodent navigation. Specifically, the spatial environment was represented by a finite discrete state space. Trajectories across spatial locations (``states'') were associated with consistent hippocampal ensemble spiking patterns, which were characterized by a state transition matrix of a hidden Markov model. We incorporated informative structured priors and applied variational Bayesian inference. From the inferred state transition matrix, we derived a topology graph that defined the connectivity in the state space. In addition, we can conduct qualitative and quantitative assessment derived from the model. We also investigated the topographic versus topological contributions to spatial representation of hippocampal population codes. In contrast to a topographic code, our decoding analyses support the efficiency of topological coding in the presence of sparse sample size and fuzzy space mapping. Finally, we present some discussions about (i) philosophical questions how the the brain interpret the world (from a reductionist perspective) using ensemble spikes alone; (ii) how this work is implied to analysis of other hippocampal-neocortical recordings and sleep-associated ensemble spike data.