Abstract: Place cells are neurons in the hippocampus that only fire if the animal is at a particular location in its environment, independent of its orientation. Head-direction cells, on the other hand, only fire if the animal looks in a particular direction, independent of its location. Primates have view-cells that only fire if the animal fixates a particular location at the wall, independent of the animal's position or orientation. Such 'oriospatial' cells are considered to be the basis for spatial navigation. We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural video sequences from a virtual environment simulation. The model receives visual input of 320x40 color pixels and processes it first with a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to be a good model for complex cells in the early visual system (Berkes and Wiskott, 2005, J of Vision, 5(6):579-602). This network extracts a distributed grid-like representation of position and orientation, somewhat similar to the well known grid cells found in entorhinal cortex. The distributed representation is then transcoded into a localized one by sparse coding, which yields the above-mentioned three types of oriospatial cells, i.e. cells that only encode location and/or orientation. The type of cells that develops depends solely on the relevant input statistics, i.e. the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis based on variational calculus that allows us to accurately predict the output of the top SFA layer.