  The discovery of place cells and other spatially modulated neurons in the
hippocampal complex of rodents has been crucial to elucidating the neural basis
of spatial cognition. More recently, the replay of neural sequences encoding
previously experienced trajectories has been observed during consummatory
behavior potentially with implications for quick memory consolidation and
behavioral planning. Several promising models for robotic navigation and
reinforcement learning have been proposed based on these and previous findings.
Most of these models, however, use carefully engineered neural networks and are
tested in simple environments. In this paper, we develop a self-organized model
incorporating place cells and replay, and demonstrate its utility for rapid
one-shot learning in non-trivial environments with obstacles.
