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Replay as structural inference in the hippocampal-entorhinal system

View ORCID ProfileTalfan Evans, View ORCID ProfileNeil Burgess
doi: https://doi.org/10.1101/2020.08.07.241547
Talfan Evans
1Institute of Cognitive Neuroscience, UCL
2Institute of Neurology, UCL
3CoMPLEX, UCL
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  • For correspondence: n.burgess@ucl.ac.uk
Neil Burgess
1Institute of Cognitive Neuroscience, UCL
2Institute of Neurology, UCL
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  • ORCID record for Neil Burgess
  • For correspondence: n.burgess@ucl.ac.uk
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Abstract

Model-based decision making relies on the construction of an accurate representation of the underlying state-space, and localization of one’s current state within it. One way to localize is to recognize the state with which incoming sensory observations have been previously associated. Another is to update a previous state estimate given a known transition. In practice, both strategies are subject to uncertainty and must be balanced with respect to their relative confidences; robust learning requires aligning the predictions of both models over historic observations. Here, we propose a dual-systems account of the hippocampal-entorhinal system, where sensory prediction errors between these models during online exploration of state space initiate offline probabilistic inference. Offline inference computes a metric embedding on grid cells of an associative place graph encoded in the recurrent connections between place cells, achieved by message passing between cells representing non-local states. We provide testable explanations for coordinated place and grid cell ‘replay’ as efficient message passing, and for distortions, partial rescaling and direction-dependent offsets in grid patterns as the confidence weighted balancing of model priors, and distortions to grid patterns as reflecting inhomogeneous sensory inputs across states.

Author Summary

  • Minimising prediction errors between transition and sensory input (observation) models predicts partial rescaling and direction-dependent offsets in grid cell firing patterns.

  • Inhomogeneous sensory inputs predict distortions of grid firing patterns during online localisation, and local changes of grid scale during offline inference.

  • Principled information propagation during offline inference predicts coordinated place and grid cell ‘replay’, where sequences propagate between structurally related features.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Some were having trouble downloading the original submission - we've reduced the file size in this "revision".

  • http://www.talfanevans.co.uk/Papers/evans_burgess_2020_bioArxiv/Video_1_Loop_closure.mp4

  • http://www.talfanevans.co.uk/Papers/evans_burgess_2020_bioArxiv/Video_2_Neural_mechanism.mp4

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 10, 2020.
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Replay as structural inference in the hippocampal-entorhinal system
Talfan Evans, Neil Burgess
bioRxiv 2020.08.07.241547; doi: https://doi.org/10.1101/2020.08.07.241547
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Replay as structural inference in the hippocampal-entorhinal system
Talfan Evans, Neil Burgess
bioRxiv 2020.08.07.241547; doi: https://doi.org/10.1101/2020.08.07.241547

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