Abstract: We present a model for the self-organized formation of hippocampal place cells based on unsupervised learning on natural visual stimuli. Our model consists of a hierarchy of Slow Feature Analysis (SFA) modules, which were recently shown to be a good model for the early visual system (Berkes & Wiskott, Journal of Vision 5(6):579). The system extracts a distributed representation of position, which is transcoded into a place field representation by sparse coding (ICA). We introduce a mathematical framework for determining the solutions of SFA, which accurately predicts the distributed representation of computer simulations.