A combinatorial neural code for long-term motor memory

Jae-Hyun Kim, Kayvon Daie and Nuo Li ()
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Jae-Hyun Kim: Duke University
Kayvon Daie: Allen Institute for Neural Dynamics
Nuo Li: Duke University

Nature, 2025, vol. 637, issue 8046, 663-672

Abstract: Abstract Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood1–8. 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.

Date: 2025
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DOI: 10.1038/s41586-024-08193-3

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