The nervous system learns new associations while maintaining memories over long periods, exhibiting a balance between flexibility and stability. Recent experiments reveal that neuronal representations of learned sensorimotor tasks continually change over days and weeks, even after animals have achieved expert behavioral performance. How is learned information stored to allow consistent behavior despite ongoing changes in neuronal activity? What functions could ongoing reconfiguration serve? We highlight recent experimental evidence for such representational drift in sensorimotor systems, and discuss how this fits into a framework of distributed population codes. We identify recent theoretical work that suggests computational roles for drift and argue that the recurrent and distributed nature of sensorimotor representations permits drift while limiting disruptive effects. We propose that representational drift may create error signals between interconnected brain regions that can be used to keep neural codes consistent in the presence of continual change. These concepts suggest experimental and theoretical approaches to studying both learning and maintenance of distributed and adaptive population codes.

中文翻译：

---

代表性漂移的原因和后果。


神经系统在长期保持记忆的同时学习新的关联，表现出灵活性和稳定性之间的平衡。最近的实验表明，即使动物已经取得了专家的行为表现，习得的感觉运动任务的神经元表征也会在数天和数周内不断变化。尽管神经元活动不断发生变化，如何存储学到的信息以允许一致的行为？持续的重新配置可以起到什么作用？我们重点介绍了感觉运动系统中这种表征漂移的最新实验证据，并讨论了它如何适应分布式群体代码的框架。我们确定了最近的理论工作，这些理论工作表明了漂移的计算作用，并认为感觉运动表征的循环和分布式性质允许漂移，同时限制破坏性影响。我们认为表征漂移可能会在互连的大脑区域之间产生错误信号，这些信号可用于在持续变化的情况下保持神经代码的一致性。这些概念提出了研究分布式和适应性群体代码的学习和维护的实验和理论方法。