Abstract: We present a model for the self-organized formation of hippocampal place cells and limbic head direction cells based on unsupervised learning on natural visual stimuli. The model is based on a hierarchy of Slow Feature Analysis (SFA) modules, which were recently shown to be a good model for complex cells in the early visual system. The system extracts a distributed representation of position and orientation, which is transcoded into a localized place field or head direction representation, respectively, by sparse coding (ICA). The model's output of orientationindependent or orientation-dependent place cell-type or position-independent head direction celltype solely depends on the animal's movement pattern.