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Context-modular memory networks support high-capacity, flexible, and robust associative memories

View ORCID ProfileWilliam F. Podlaski, View ORCID ProfileEverton J. Agnes, View ORCID ProfileTim P. Vogels
doi: https://doi.org/10.1101/2020.01.08.898528
William F. Podlaski
Centre for Neural Circuits and Behaviour, University of Oxford, OX1 3SR Oxford, United Kingdom
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  • For correspondence: william.podlaski@gmail.com
Everton J. Agnes
Centre for Neural Circuits and Behaviour, University of Oxford, OX1 3SR Oxford, United Kingdom
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Tim P. Vogels
Centre for Neural Circuits and Behaviour, University of Oxford, OX1 3SR Oxford, United Kingdom
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Abstract

Context, such as behavioral state, is known to modulate memory formation and retrieval, but is usually ignored in associative memory models. Here, we propose several types of contextual modulation for associative memory networks that greatly increase their performance. In these networks, context inactivates specific neurons and connections, which modulates the effective connectivity of the network. Memories are stored only by the active components, thereby reducing interference from memories acquired in other contexts. Such networks exhibit several beneficial characteristics, including enhanced memory capacity, high robustness to noise, increased robustness to memory overloading, and better memory retention during continual learning. Furthermore, memories can be biased to have different relative strengths, or even gated on or off, according to contextual cues, providing a candidate model for cognitive control of memory and efficient memory search. An external context-encoding network can dynamically switch the memory network to a desired state, which we liken to experimentally observed contextual signals in prefrontal cortex and hippocampus. Overall, our work illustrates the benefits of organizing memory around context, and provides an important link between behavioral studies of memory and mechanistic details of neural circuits.

SIGNIFICANCE Memory is context dependent — both encoding and recall vary in effectiveness and speed depending on factors like location and brain state during a task. We apply this idea to a simple computational model of associative memory through contextual gating of neurons and synaptic connections. Intriguingly, this results in several advantages, including vastly enhanced memory capacity, better robustness, and flexible memory gating. Our model helps to explain (i) how gating and inhibition contribute to memory processes, (ii) how memory access dynamically changes over time, and (iii) how context representations, such as those observed in hippocampus and prefrontal cortex, may interact with and control memory processes.

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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 4.0 International license.
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Posted January 09, 2020.
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Context-modular memory networks support high-capacity, flexible, and robust associative memories
William F. Podlaski, Everton J. Agnes, Tim P. Vogels
bioRxiv 2020.01.08.898528; doi: https://doi.org/10.1101/2020.01.08.898528
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Context-modular memory networks support high-capacity, flexible, and robust associative memories
William F. Podlaski, Everton J. Agnes, Tim P. Vogels
bioRxiv 2020.01.08.898528; doi: https://doi.org/10.1101/2020.01.08.898528

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