  Grid cells are believed to play an important role in both spatial and
non-spatial cognition tasks. A recent study observed the emergence of grid
cells in an LSTM for path integration. The connection between biological and
artificial neural networks underlying the seemingly similarity, as well as the
application domain of grid cells in deep neural networks (DNNs), expect further
exploration. This work demonstrated that grid cells could be replicated in
either pure vision based or vision guided path integration DNNs for navigation
under a proper setting of training parameters. We also show that grid-like
behaviors arise in feedforward DNNs for non-spatial tasks. Our findings support
that the grid coding is an effective representation for both biological and
artificial networks.
