美国杜克大学Li Nuo课题组的一项最新研究，发现了长期运动记忆的组合神经代码。2024年11月13日出版的《自然》杂志发表了这项成果。

据悉，运动技能可以长时间稳定保留，但稳定记忆储存的神经机制仍然知之甚少。此外，当新的运动技能不断获得时，现有的运动记忆是如何维持的尚不清楚。

研究人员追踪了小鼠生命周期的大部分时间里习得动作的神经表征，并表明习得的动作在与环境的结合中被稳定地保留，这保护了在新的动作学习过程中现有的记忆不被抹去。该研究组建立了一个持续的学习范式，让老鼠在不同的任务环境中学习执行定向舔舐，同时该研究组跟踪运动皮层的活动长达六个月，主题是双光子成像。

在相同的任务背景下，驱动定向舔舐的活动随着时间的推移是稳定的，几乎没有代表性漂移。当学习新的任务情境时，新的准备活动出现以驱动相同的舔动作。学习创造了平行的新的运动记忆，而不是修改现有的表征。

甚至在几个月后，重新学习做出在前一个任务环境中相同的动作，会重新激活前一个任务的准备活动。对新任务背景的持续学习不断创造新的准备活动模式。正如该研究组在运动系统中观察到的那样，情境特定记忆可能在持续学习过程中，为稳定的记忆存储提供了一种解决方案。

附：英文原文

Title: A combinatorial neural code for long-term motor memory

Author: Kim, Jae-Hyun, Daie, Kayvon, Li, Nuo

Issue&Volume: 2024-11-13

Abstract: Motor skill repertoire can be stably retained over long periods, but the neural mechanism that underlies stable memory storage remains poorly understood1,2,3,4,5,6,7,8. 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.

DOI: 10.1038/s41586-024-08193-3

Source: https://www.nature.com/articles/s41586-024-08193-3