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Decades of research on the neural code underlying spatial navigation haverevealed 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 representationsremain largely mysterious. As a new way to understand these neuralrepresentations, we trained recurrent neural networks (RNNs) to performnavigation tasks in 2D arenas based on velocity inputs. Surprisingly, we findthat grid-like spatial response patterns emerge in trained networks, along withunits that exhibit other spatial correlates, including border cells andband-like cells. All these different functional types of neurons have beenobserved experimentally. The order of the emergence of grid-like and bordercells is also consistent with observations from developmental studies.Together, our results suggest that grid cells, border cells and others asobserved in EC may be a natural solution for representing space efficientlygiven the predominant recurrent connections in the neural circuits.

Emergence of grid-like representations by training recurrent neural networks to perform spatial localization