NCS-FO: Collaborative Research: Sleep's role in determining the fate of individual memories

确定认知，计算和神经机制，负责确定为什么有些记忆幸存下来，而另一些记忆褪色，是人类心灵和大脑研究人员面临的许多重大挑战之一。人们普遍认为睡眠在长期记忆中起着关键作用，但睡眠期间究竟发生了什么影响记忆的持久性仍然是未知的。该项目汇集了一组研究人员，他们将整合认知神经科学，认知心理学和计算机科学中的多个独立工作线，以研究最近形成的记忆表征在人睡觉时所经历的精确机制，以及这些机制如何决定哪些记忆存活，哪些记忆褪色。所提出的用于EEG和神经成像数据的尖端神经数据分析方法、基本人类记忆理论和神经网络建模的集成使得能够非侵入性地跟踪人类大脑中的个体记忆，因为它们在睡眠期间相互竞争并被修改。这项工作的潜在进展可能会影响教育，培训情况和公共卫生，促进新的策略的发展，以确保重要的记忆在最初的学习后存活下来。研究表明，记忆会争夺神经空间，因此重新激活一个特定的记忆可能会对其他相关的记忆造成“附带损害”。换句话说，访问一个存储器可以以稍后能够访问网络空间中的其他附近存储器为代价。 这项研究验证了一个假设，即重要性通过选择性地增强记忆的再激活来塑造睡眠期间的神经动力学;这种增强确保了重要记忆在睡眠期间胜过相关记忆，从而加强了重要记忆，削弱了不太重要的记忆。为了验证这一假设，在睡眠期间，通过播放声音线索来引发记忆之间的竞争，每个声音线索都与两个不同的图片-位置记忆相关联（在清醒期间）。多种连锁方法将跟踪睡眠期间的记忆竞争如何塑造记忆的持久性与褪色。神经网络模型将用于预测编码过程中的奖励反应如何影响睡眠中的竞争动态，以及这些竞争动态如何决定竞争记忆的最终命运。预测将通过使用fMRI测量与编码期间的奖励处理相关的神经活动，EEG测量睡眠期间的大脑活动，以及模式分类器从睡眠EEG数据解码记忆激活来进行测试。在睡眠中的竞争动力学的观察，然后将与后来的记忆表现和记忆变化的多变量功能磁共振成像措施。该项目有可能首次提供一个全面的“引擎盖下”的记忆生活，因为它是在睡眠中获得，处理，并最终回忆。将获得有关编码时奖励处理的差异如何影响睡眠回放动态以及睡眠回放动态如何影响随后的记忆表现和神经表征结构的基本知识。

Identifying the cognitive, computational and neural mechanisms responsible for determining why some memories survive when others fade is one of the many grand challenges facing researchers of the human mind and brain. It is widely understood that sleep plays a critical role in long-term remembering, yet what exactly happens during sleep to affect the persistence of memories remains largely unknown. This project brings together a team of researchers who will integrate multiple independent lines of work in cognitive neuroscience, cognitive psychology, and computer science in order to investigate the precise mechanisms undergone by recently-formed memory representations as a person sleeps and how these mechanisms determine which memories survive and which fade. The proposed integration of cutting-edge neural data analysis methods for EEG and neuroimaging data, basic human memory theory, and neural network modeling make possible the ability to non-invasively track individual memories in the human brain as they compete with each other and are modified during sleep. The potential advances from this work could impact education, training situations, and public health by facilitating the development of new strategies for ensuring that important memories survive after initial learning.Research suggests that memories compete for neural space such that reactivating one particular memory can exert "collateral damage" on other related memories. In other words, accessing one memory can come at the expense of later being able to access other nearby memories in the network space. The proposed studies test the hypothesis that importance shapes neural dynamics during sleep by selectively boosting memory reactivation; this boost ensures that important memories out-compete related memories during sleep, resulting in strengthening of important memories and weakening of less-important memories. To test this hypothesis, competition between memories will be elicited during sleep by playing sound cues, each of which was linked (during wake) to two different picture-location memories. Multiple interlocking approaches will track how memory competition during sleep shapes a memory's persistence versus fading. Neural network models will be used to generate predictions about how reward responses during encoding shape competitive dynamics during sleep, and how these competitive dynamics determine the eventual fates of competing memories. Predictions will be tested by using fMRI to measure neural activity associated with reward processing during encoding, EEG to measure brain activity during sleep, and pattern classifiers to decode memory activation from the sleep EEG data. Observations of competitive dynamics during sleep will then be related to later memory performance and to multivariate fMRI measures of memory change. The project has the potential to provide, for the first time, a comprehensive look "under the hood" at the life of a memory as it is acquired, processed during sleep, and eventually recalled. Pivotal knowledge will be gained about how variance in reward processing at encoding influences sleep replay dynamics, and about how sleep replay dynamics affect subsequent memory performance and the structure of neural representations.