Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
This work addresses a fundamental mystery in neuroscience about how grid cells form, potentially impacting computational models of spatial navigation.
The researchers tackled the problem of understanding the mechanisms behind spatial neural representations like grid cells by training recurrent neural networks on navigation tasks, and found that grid-like patterns emerged along with other experimentally observed cell types, suggesting these are efficient solutions for spatial representation.
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.