  The entorhinal-hippocampal circuit plays a critical role in higher brain
functions, especially spatial cognition. Grid cells in the medial entorhinal
cortex (MEC) periodically fire with different grid spacing and orientation,
which makes a contribution that place cells in the hippocampus can uniquely
encode locations in an environment. But how sparse firing granule cells in the
dentate gyrus are formed from grid cells in the MEC remains to be determined.
Recently, the fruit fly olfactory circuit provides a variant algorithm (called
locality-sensitive hashing) to solve this problem. To investigate how the
sparse place firing generates in the dentate gyrus can help animals to break
the perception ambiguity during environment exploration, we build a
biologically relevant, computational model from grid cells to place cells. The
weight from grid cells to dentate gyrus granule cells is learned by competitive
Hebbian learning. We resorted to the robot system for demonstrating our
cognitive mapping model on the KITTI odometry benchmark dataset. The
experimental results show that our model is able to stably, robustly build a
coherent semi-metric topological map in the large-scale outdoor environment.
The experimental results suggest that the entorhinal-hippocampal circuit as a
variant locality-sensitive hashing algorithm is capable of generating sparse
encoding for easily distinguishing different locations in the environment. Our
experiments also provide theoretical supports that this analogous hashing
algorithm may be a general principle of computation in different brain regions
and species.
