Modern neural recordings comprise thousands of neurons recorded at millisecond precision. An important step in analyzing these recordings is to identify neural ensembles – subsets of neurons that represent a subsystem of specific functionality. A famous example in the mammalian brain are grid cells, which are separated into ensembles of different spatial resolution. Recent work demonstrated that recordings from individual ensembles exhibit the clear topological signature of a torus, which, however, is obscured in combined recordings from multiple ensembles. Inspired by this observation, we introduce a topological ensemble detection algorithm that is capable of unsupervised identification of neural ensembles based on their topological signatures. This identification is achieved by optimizing a loss function that captures the assumed topological signature of the ensemble. To our knowledge, this is the first method that does not rely on external covariates and that leverages global features of the dataset to identify neural ensembles. This opens up exciting possibilities, e.g., searching for cell ensembles in prefrontal areas, which may represent cognitive maps on more conceptual spaces than grid cells.