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

博士后奖学金：SPRF：将新信息整合到睡眠的现有知识中

• 批准号: 2313948
• 负责人: Brynn Sherman
• 金额: $ 16万
• 依托单位: Sherman, Brynn E
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313948&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; SPRF; Integration; new

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.

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、工业界或私营部门的科学职业培养有前途的早期职业博士级科学家。 SPRF 奖项涉及在知名科学家的赞助下进行为期两年的培训，并鼓励博士后研究员进行独立研究，旨在促进科学界各个领域的科学家的参与，包括来自代表性不足群体的科学家的参与。其研究项目和活动；博士后时期被认为是实现这一目标的重要科学问题。 -宾夕法尼亚大学的席尔，该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，我们不断受到新信息的轰炸。通常在某种程度上与例如，当您去动物园时，您可能会了解一种您以前从未听说过的新鸟类。了解这种新鸟类不仅需要了解该特定物种的独特方面。 ，而且还将这些信息与您已经了解的其他鸟类联系起来。文献指出了睡眠的可能作用——新旧信息可以被重新激活和合成。这个集成过程，但是这个想法缺乏直接的测试。这个项目将结合脑电图（EEG）、行为记忆评估和计算模型来揭示睡眠如何支持这一整合过程。具体来说，我们将测试这样一个假设：为了将新记忆与先前的知识整合起来，大脑需要在睡眠过程中重播这两种记忆。我们将首先通过进行为期多天的实验来进行实证测试，参与者将学习不同类别的新物体，然后他们将返回进行一到两次的最终课程。接下来的几周，在此期间他们将了解新的相同类别的物体，然后在实验室中进行脑电图记录小睡。 在小睡期间，将使用一种称为“目标记忆重新激活”（TMR）的程序来提示新学习的物体（小睡之前学习的物体）。或者在睡眠期间新学到的知识和现有知识（小睡前学到的物体和之前学习的物体）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检验以下假设：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，我们还将在离线海马皮质相互作用的神经网络模型中模拟任务（包括睡眠期间的 TMR），提供对新信息影响的机械解释。总之，这些发现和由此产生的理论框架将促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，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持。审查标准。