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A neural network account of memory replay and knowledge consolidation

View ORCID ProfileDaniel N. Barry, View ORCID ProfileBradley C. Love
doi: https://doi.org/10.1101/2021.05.25.445587
Daniel N. Barry
1Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H0AP, UK
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  • For correspondence: daniel.barry@ucl.ac.uk
Bradley C. Love
1Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H0AP, UK
2The Alan Turing Institute, 96 Euston Road, London, NW12DB, UK
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Abstract

Replay can consolidate memories through offline neural reactivation related to past experiences. Category knowledge is learned across multiple experiences, and its subsequent generalisation is promoted by consolidation and replay during rest and sleep. However, aspects of replay are difficult to determine from neuroimaging studies. We provided insights into category knowledge replay by simulating these processes in a neural network which approximated the roles of the human ventral visual stream and hippocampus. Generative replay, akin to imagining new category instances, facilitated generalisation to new experiences. Consolidation-related replay may therefore help to prepare us for the future as much as remember the past. Generative replay was more effective in later network layers functionally similar to the lateral occipital cortex than layers corresponding to early visual cortex, drawing a distinction between neural replay and its relevance to consolidation. Category replay was most beneficial for newly acquired knowledge, suggesting replay helps us adapt to changes in our environment. Finally, we present a novel mechanism for the observation that the brain selectively consolidates weaker information; a reinforcement learning process in which categories were replayed according to their contribution to network performance. This reinforces the idea of consolidation-related replay as an active rather than passive process.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/danielbarry1/replay.git

  • https://doi.org/10.6084/m9.figshare.14208470

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 27, 2022.
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A neural network account of memory replay and knowledge consolidation
Daniel N. Barry, Bradley C. Love
bioRxiv 2021.05.25.445587; doi: https://doi.org/10.1101/2021.05.25.445587
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A neural network account of memory replay and knowledge consolidation
Daniel N. Barry, Bradley C. Love
bioRxiv 2021.05.25.445587; doi: https://doi.org/10.1101/2021.05.25.445587

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