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. 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 Dynamics of first spike latency with and without A-type currents: for determining the solutions of SFA, which accurately predicts the distributed implications for cerebellar Purkinje cell spike firing dynamics representation of computer simulations. The architecture of the largely parameter-free model is inspired by the hierarchical organization of cortex. Our studies show that the model is capable of extracting cognitive information such as an animal's position from its complex visual perception, while becoming completely invariant to the animal's orientation.