Postdoctoral Fellowship: SPRF: Integration of new information into existing knowledge with sleep

该奖项是NSF社会、行为和经济科学博士后研究奖学金（SPRF）计划的一部分。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在宾夕法尼亚大学的安娜夏皮罗和莎伦·汤普森-希尔博士的赞助下，这个博士后奖学金支持一位早期职业科学家研究睡眠如何支持我们整合相关记忆以建立新知识的方式。在我们的日常生活中，我们不断地受到新信息的轰炸。然而，这些信息通常在某种程度上与我们以前对世界的认识有关。例如，当你去动物园时，你可能会了解到一种你以前从未听说过的鸟类。了解这种新的鸟类不仅需要了解特定物种的独特之处，还需要将这些信息与您已经了解的其他鸟类联系起来。大脑如何整合新旧信息？文献已经指出了睡眠在整合过程中的可能作用，但这个想法缺乏直接的测试。本项目将结合联合收割机脑电图（EEG）、行为记忆评估和计算建模来揭示睡眠如何支持这种整合过程。具体来说，我们将测试一个假设，即为了将新的记忆与先前的知识整合在一起，大脑需要在睡眠期间以交错的方式重放这两种记忆。我们将首先通过运行一个多天的实验来经验性地测试这一点。在多个培训课程中，参与者将学习不同类别的新对象。然后，他们将在一到两周后返回进行最后一次会议，在此期间，他们将学习来自相同类别的新对象，随后在实验室中进行EEG记录。在小睡期间，一个被称为目标记忆再激活（TMR）的程序将用于提示新学习的对象（在小睡之前学习的对象）或新学习的和现有的知识（在小睡之前学习的对象和在之前的会话期间学习的对象）。醒来后，我们将评估参与者对每个物体的记忆以及他们对新旧物体的整合。这种方法将使我们能够测试新信息的整合依赖于新信息与先前学习的信息的交错激活的假设。我们还将在离线大脑皮层相互作用的神经网络模型中模拟任务（包括睡眠期间的TMR），提供不同形式的重放对记忆变化的影响的机械解释。总之，研究结果和由此产生的理论框架将推进我们对睡眠如何影响记忆整合的理解。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Drs. Anna Schapiro and Sharon Thompson-Schill at the University of Pennsylvania, this postdoctoral fellowship award supports an early career scientist investigating how sleep supports the way we integrate across related memories to build up new knowledge. In our daily lives we are constantly bombarded with new information. However, this information is typically related in some way to our previous knowledge of the world. For example, when you go to the zoo, you may learn about a new species of bird that you have never heard of before. Learning about this new bird requires not only learning the unique aspects of the particular species, but also relating this information to what you already know about other birds. How does the brain integrate new and old information? The literature has pointed to a possible role for sleep — a time when new and old information can be reactivated and synthesized — in this integration process, but this idea lacks direct tests.This project will combine electroencephalography (EEG), behavioral memory assessments, and computational modeling to uncover how sleep supports this integration process. Specifically, we will test the hypothesis that in order to integrate new memories with previous knowledge, the brain needs to replay both kinds of memory during sleep, in an interleaved fashion. We will first test this empirically by running a multi-day experiment. Over the course of multiple training sessions, participants will learn about novel objects from different categories. They will then return for a final session one to two weeks later, during which they will learn about new objects from the same categories and subsequently take a nap in the laboratory with EEG recording. During the nap, a procedure known as Targeted Memory Reactivation (TMR) will be used to cue either newly learned objects (the objects learned just prior to the nap) or both newly learned and existing knowledge (both the objects learned prior to the nap and the objects learned the during the previous sessions) during sleep. Upon waking, we will assess participants’ memory for each object as well as their integration of new and old objects. This approach will allow us to test the hypothesis that integration of new information relies on the interleaved activation of that new information with previously learned information. We will also simulate the task (including TMR during sleep) in a neural network model of offline hippocampal-cortical interactions, providing a mechanistic account of the impact of different forms of replay on memory change. Together, the findings and resulting theoretical framework will advance our understanding of how sleep influences memory integration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.