More broadly, our work reaffirms the potential of utilising algorithms thought to be used by the brain as inspiration for machine learning architectures. The extensive previous neuroscience research into grid cells makes the agent’s interpretability – which is itself a major topic in AI research – significantly easier, by giving us clues about what to look for when trying to understand its internal representations. The work also showcases the potential of using artificial agents actively engaging in complex behaviours within realistic virtual environments to test theories of how the brain works.
Taking this principle further, a similar approach could be used to test theories concerning brain areas that are important for perceiving sound or controlling limbs, for example. In the future such networks may well provide a new way for scientists to conduct ‘experiments’, suggesting new theories and even complementing some of the work that is currently conducted in animals.
UPDATE 14.05.18: We’d encourage you to read The emergence of grid-like representations by training recurrent neural networks to perform spatial localization by Cueva and Wei, which was published contemporaneously at ICLR. While different in scope and findings, it shows interesting results. In brief, the authors found periodic firing that conformed to the shape of the enclosure, e.g rectangular grids in a square environment and triangular in a triangular environment (fig. 2 of Cueva and Wei). This differs from our study, where we found grid-like units whose firing pattern closely resembles rodent grid cells which typically show hexagonal firing patterns across different shaped environments (e.g. square and circular arena).
This work was done by Andrea Banino, Caswell Barry, Benigno Uria, Charles Blundell, Timothy Lillicrap, Piotr Mirowski, Alexander Pritzel, Martin Chadwick, Thomas Degris, Joseph Modayil, Greg Wayne, Hubert Soyer, Fabio Viola, Brian Zhang, Ross Goroshin, Neil Rabinowitz, Razvan Pascanu, Charlie Beattie, Stig Petersen, Amir Sadik, Stephen Gaffney, Helen King, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, and Dharshan Kumaran.
Read the Nature paper: [PDF]
Download the original paper (unformatted): [PDF]
Read Nobel Prize Laureate Edvard Moser’s review of the paper.