Title: Emergence of grid-like representations by training recurrent neural networks to perform spatial localization,
Abstract: Decades of research on the neural code underlying spatial navigation have
revealed a diverse set of neural response properties. The Entorhinal Cortex
(EC) of the mammalian brain contains a rich set of spatial correlates,
including grid cells which encode space using tessellating patterns. However,
the mechanisms and functional significance of these spatial representations
remain largely mysterious. As a new way to understand these neural
representations, we trained recurrent neural networks (RNNs) to perform
navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find
that grid-like spatial response patterns emerge in trained networks, along with
units that exhibit other spatial correlates, including border cells and
band-like cells. All these different functional types of neurons have been
observed experimentally. The order of the emergence of grid-like and border
cells is also consistent with observations from developmental studies.
Together, our results suggest that grid cells, border cells and others as
observed in EC may be a natural solution for representing space efficiently
given the predominant recurrent connections in the neural circuits.