Higher-Order Spatial Information for Self-Supervised Place Cell Learning

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 ari...

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Bibliographic Details
Published in:arXiv.org 2024-06
Main Authors: Deighton, Jared, Mackey, Wyatt, Schizas, Ioannis, Boothe, David L, Maroulas, Vasileios
Format: Article
Language:English
Subjects:
Decoding
Position measurement
Position sensing
Recurrent neural networks
Self-supervised learning
Spatial data
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Summary: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.
ISSN:2331-8422