Learning novel structure across time and sleep

跨越时间和睡眠学习新颖的结构

• 批准号: 10657210
• 负责人: Anna C Schapiro
• 金额: $ 39.99万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657210
• 关键词: Anatomy; Area; Behavior; Brain; Cities; Cues; Distant; Electroencephalography; Environment; Functional Magnetic Resonance Imaging; Future; Hippocampus; Individual; Intervention; Knowledge acquisition; Laboratories; Learning; Memory; Mental Depression; Mental disorders; Modeling; Nature; Neocortex; Neural Network Simulation; Neurons; Participant; Pattern; Play; Population; Process; Research; Resolution; Scanning; Schizophrenia; Shapes; Sleep; Structure; Testing; Time; Visit; Work; adjudication; experience; model development; neocortical; neural; novel; predictive modeling; sound; theories

Project Summary Acting adaptively requires quickly picking up on structure in our environment (e.g., the layout of a city you are visiting for the first time) and storing the acquired knowledge for effective future use (efficient navigation on subsequent visits). Dominant theories of the hippocampus have focused on its ability to encode individual snapshots of experience, but we and others have found evidence that it is also crucial for finding structure across experiences (understanding the relationship between different views of the same distant building). The mechanisms of this essential form of learning have not been established. We have developed a neural network model of the hippocampus instantiating the theory that one of its subfields can quickly encode structure using distributed representations, a powerful form of representation in which populations of neurons become responsive to multiple related features of the environment. The first aim of this project is to test predictions of this model using high resolution functional magnetic resonance imaging (fMRI) in paradigms requiring integration of information across experiences. The results will clarify fundamental mechanisms of how we learn novel structure, adjudicating between existing models of this process, and informing further model development. There are also competing theories as to the eventual fate of new hippocampal representations. One view posits that during sleep, the hippocampus replays recent information to build longer-term distributed representations in neocortex. Another view claims that memories are directly and independently formed and consolidated within the hippocampus and neocortex. The second aim of this project is to test between these theories. We will assess changes in hippocampal and cortical representations over time by re-scanning participants and tracking changes in memory at a one-week delay. Any observed changes in the brain and behavior across time, however, may be due to generic effects of time or to active processing during sleep. The third aim is thus to assess the specific causal contributions of sleep to the consolidation of structured information. We will use real-time sleep electroencephalography (EEG) to detect the peaks of slow oscillations, when endogenous replay is known to occur, and play sound cues to bias memory reactivation. We will also expand our neural network model to examine how offline hippocampal replay of recent regularities can shape distributed representations in neocortex, providing a mechanistic account of offline consolidation of structured information. We expect that this work will clarify the anatomical substrates and, critically, the nature of the representations that support encoding and consolidation of novel structure in the environment. Having a robust, neurally grounded model of these processes will help connect research in this area across laboratories and provide a framework for evaluating what goes wrong in mental health disorders like depression and schizophrenia that involve profound disturbances in learning and sleep.

项目摘要 适应性行为需要快速了解我们环境中的结构（例如，你所在城市的布局 第一次访问）和存储所获得的知识以供将来有效使用（在 后续访问）。关于海马体的主要理论集中在它对个体信息进行编码的能力上， 但我们和其他人已经发现证据表明，它对于寻找跨文化的结构也至关重要。 体验（理解同一远处建筑物的不同视图之间的关系）。的 这一基本学习形式的机制尚未建立。我们开发了一个神经网络 海马体的模型实例化了它的一个子场可以使用 分布式表示，一种强大的表示形式，其中神经元群体成为 响应于环境的多个相关特征。该项目的第一个目标是测试预测， 该模型在需要整合的范例中使用高分辨率功能性磁共振成像（fMRI of information信息across横过experiences经验.研究结果将阐明我们如何学习新事物的基本机制 结构，在这一过程的现有模型之间进行裁决，并为进一步的模型开发提供信息。那里 对于新的海马表征的最终命运，也存在着相互竞争的理论。一种观点认为， 在睡眠期间，海马体重放最近的信息，以建立长期的分布式表征， 新皮层另一种观点认为，记忆是直接和独立地形成和巩固的， 海马体和新皮层。本项目的第二个目的是在这些理论之间进行测试。我们将评估 通过重新扫描参与者并跟踪变化， 在记忆中延迟一周。然而，任何观察到的大脑和行为随时间的变化， 这是由于时间的一般影响或睡眠期间的主动处理。第三个目标是评估具体的 睡眠对结构化信息整合的因果贡献。我们将使用实时睡眠 脑电图（EEG）检测缓慢振荡的峰值，当内源性重放已知时， 发生，并播放声音线索，以偏置记忆激活。我们还将扩展我们的神经网络模型， 研究离线海马体重放最近的海马体如何塑造分布式表征， 新皮层，提供了一个结构化信息的离线整合机制。我们期望这 这项工作将阐明解剖学基础，以及支持编码的表征的本质 和环境中新结构的巩固。有一个强大的，基于神经的模型， 这些过程将有助于将这一领域的研究跨实验室联系起来，并提供一个评估框架， 抑郁症和精神分裂症等精神疾病中的问题， 学习和睡眠障碍。