A combinatorial neural code for long-term motor memory

Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood<sup>1-8</sup>. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of the lifespan of a mouse and show that learned actions are stably retained in combination with context, which protects existing memories from erasure during new motor learning. We established a continual learning paradigm in which mice learned to perform directional licking in different task contexts while we tracked motor cortex activity for up to six months using two-photon imaging. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories instead of modifying existing representations. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. Continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning.

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Publication Details

Journal
Nature
Published
2024-11-13
DOI
https://doi.org/10.1038/s41586-024-08193-3
Citations
22
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
7.64
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article

A combinatorial neural code for long-term motor memory

Kayvon Daie, Jae‐Hyun Kim, Nuo Li
22 citations
Nature
Neural Networks and Applications
7.64
article

A combinatorial neural code for long-term motor memory

Kayvon Daie, Jae‐Hyun Kim, Nuo Li
article en
22 citations

Abstract

Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood<sup>1-8</sup>. Moreover, it is unknown how existing motor memories are maintained as new motor skills are continuously acquired. Here we tracked neural representation of learned actions throughout a significant portion of the lifespan of a mouse and show that learned actions are stably retained in combination with context, which protects existing memories from erasure during new motor learning. We established a continual learning paradigm in which mice learned to perform directional licking in different task contexts while we tracked motor cortex activity for up to six months using two-photon imaging. Within the same task context, activity driving directional licking was stable over time with little representational drift. When learning new task contexts, new preparatory activity emerged to drive the same licking actions. Learning created parallel new motor memories instead of modifying existing representations. Re-learning to make the same actions in the previous task context re-activated the previous preparatory activity, even months later. Continual learning of new task contexts kept creating new preparatory activity patterns. Context-specific memories, as we observed in the motor system, may provide a solution for stable memory storage throughout continual learning.

NatureVol. 637(8046)
Duke University (US), Baylor College of Medicine (US), Allen Institute (US), Allen Institute for Neural Dynamics (US)
Affordable and clean energy
Openalex Percentile: Top 2%
Neural Networks and Applications
7.64
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