  Self-localization during navigation with noisy sensors in an ambiguous world
is computationally challenging, yet animals and humans excel at it. In
robotics, Simultaneous Location and Mapping (SLAM) algorithms solve this
problem though joint sequential probabilistic inference of their own
coordinates and those of external spatial landmarks. We generate the first
neural solution to the SLAM problem by training recurrent LSTM networks to
perform a set of hard 2D navigation tasks that include generalization to
completely novel trajectories and environments. The hidden unit representations
exhibit several key properties of hippocampal place cells, including stable
tuning curves that remap between environments. Our result is also a proof of
concept for end-to-end-learning of a SLAM algorithm using recurrent networks,
and a demonstration of why this approach may have some advantages for robotic
SLAM.
