  Mammals navigate novel environments and exhibit resilience to sparse
environmental sensory cues via place and grid cells, which encode position in
space. While the efficiency of grid cell coding has been extensively studied,
the computational role of place cells is less well understood. This gap arises
partially because spatial information measures have, until now, been limited to
single place cells. We derive and implement a higher-order spatial information
measure, allowing for the study of the emergence of multiple place cells in a
self-supervised manner. We show that emergent place cells have many desirable
features, including high-accuracy spatial decoding. This is the first work in
which higher-order spatial information measures that depend solely on place
cells' firing rates have been derived and which focuses on the emergence of
multiple place cells via self-supervised learning. By quantifying the spatial
information of multiple place cells, we enhance our understanding of place cell
formation and capabilities in recurrent neural networks, thereby improving the
potential navigation capabilities of artificial systems in novel environments
without objective location information.
