Department of Medicine.
Neurosciences Graduate Program, University of California, San Diego, California 92093.
J Neurosci. 2022 Jul 6;42(27):5330-5345. doi: 10.1523/JNEUROSCI.2044-21.2022. Epub 2022 May 25.
Relational memory, the ability to make and remember associations between objects, is an essential component of mammalian reasoning. In relational memory tasks, it has been shown that periods of offline processing, such as sleep, are critical to making indirect associations. To understand biophysical mechanisms behind the role of sleep in improving relational memory, we developed a model of the thalamocortical network to test how slow-wave sleep affects performance on an unordered relational memory task. First, the model was trained in the awake state on a paired associate inference task, in which the model learned to recall direct associations. After a period of subsequent slow-wave sleep, the model developed the ability to recall indirect associations. We found that replay, during sleep, of memory patterns learned in awake increased synaptic connectivity between neurons representing the item that was overlapping between tasks and neurons representing the unlinked items of the different tasks; this forms an attractor that enables indirect memory recall. Our study predicts that overlapping items between indirectly associated tasks are essential for relational memory, and sleep can reactivate pathways to and from overlapping items to the unlinked objects to strengthen these pathways and form new relational memories. Experimental studies have shown that some types of associative memory, such as transitive inference and relational memory, can improve after sleep. Still, it remains unknown what specific mechanisms are responsible for these sleep-related changes. In this new work, we addressed this problem by building a thalamocortical network model that can learn relational memory tasks and that can be simulated in awake or sleep states. We found that memory traces learned in awake were replayed during slow waves of NREM sleep and revealed that replay increased connections to and from overlapping memory items to form new relational memories. Our work discovered specific mechanisms behind the role of sleep in associative memory and made testable predictions about how sleep augments associative learning.
关系记忆，即建立和记住物体之间关联的能力，是哺乳动物推理的重要组成部分。在关系记忆任务中，已经表明离线处理（如睡眠）的时期对于进行间接联想至关重要。为了了解睡眠在改善关系记忆中的作用的生物物理机制，我们开发了一个丘脑-皮层网络模型，以测试慢波睡眠如何影响无序关系记忆任务的表现。首先，模型在配对联想推断任务中在清醒状态下进行训练，在该任务中，模型学会了回忆直接关联。在随后的慢波睡眠一段时间后，模型发展出了回忆间接关联的能力。我们发现，在睡眠期间对在清醒状态下学习的记忆模式的重放增加了表示任务之间重叠项的神经元与表示不同任务的未链接项的神经元之间的突触连接；这形成了一个吸引子，使间接记忆能够被召回。我们的研究预测，间接相关任务之间的重叠项对于关系记忆至关重要，睡眠可以重新激活从重叠项到未链接对象的路径，以加强这些路径并形成新的关系记忆。实验研究表明，一些类型的联想记忆，如传递推理和关系记忆，在睡眠后可以改善。然而，仍然不清楚是什么特定的机制导致了这些与睡眠相关的变化。在这项新工作中，我们通过构建一个可以学习关系记忆任务的丘脑-皮层网络模型来解决这个问题，该模型可以在清醒或睡眠状态下进行模拟。我们发现，在清醒状态下学习的记忆痕迹在 NREM 睡眠的慢波中被重放，并且发现重放增加了与重叠记忆项的连接，从而形成了新的关系记忆。我们的工作发现了睡眠在联想记忆中的作用背后的特定机制，并对睡眠如何增强联想学习做出了可测试的预测。
