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Sleep smart—optimizing sleep for declarative learning and memory

机译:睡眠智能-优化睡眠以进行声明式学习和记忆

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摘要

The last decade has witnessed a spurt of new publications documenting sleep's essential contribution to the brains ability to form lasting memories. For the declarative memory domain, slow wave sleep (the deepest sleep stage) has the greatest beneficial effect on the consolidation of memories acquired during preceding wakefulness. The finding that newly encoded memories become reactivated during subsequent sleep fostered the idea that reactivation leads to the strengthening and transformation of the memory trace. According to the active system consolidation account, trace reactivation leads to the redistribution of the transient memory representations from the hippocampus to the long-lasting knowledge networks of the cortex. Apart from consolidating previously learned information, sleep also facilitates the encoding of new memories after sleep, which probably relies on the renormalization of synaptic weights during sleep as suggested by the synaptic homeostasis theory. During wakefulness overshooting potentiation causes an imbalance in synaptic weights that is countered by synaptic downscaling during subsequent sleep. This review briefly introduces the basic concepts and central findings of the research on sleep and memory, and discusses implications of this lab-based work for everyday applications to make the best possible use of sleep's beneficial effect on learning and memory.
机译:在过去的十年中,见证了大量新出版物的出现,这些文献记录了睡眠对大脑形成持久性记忆的能力的重要贡献。对于声明性记忆域,慢波睡眠(最深的睡眠阶段)对在之前的清醒过程中获得的记忆的合并具有最大的有益影响。新编码的记忆在随后的睡眠过程中被重新激活的发现,催生了重新激活导致记忆轨迹增强和转换的想法。根据主动系统合并帐户,跟踪重新激活会导致瞬时记忆表示从海马重新分布到皮质的持久知识网络。除了巩固先前学习的信息外,睡眠还促进了睡眠后新记忆的编码,这可能依赖于突触稳态理论所建议的睡眠过程中突触权重的重新归一化。在清醒期间,过冲增强会导致突触重量失衡,这种失衡可以通过随后的睡眠过程中的突触缩小来抵消。这篇综述简要介绍了睡眠和记忆研究的基本概念和主要发现,并讨论了这项基于实验室的工作对日常应用的意义,以最大程度地利用睡眠对学习和记忆的有益作用。

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