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 行动适应需要迅速在我们的环境中拾取结构（例如，您是城市的布局 首次访问）并存储获得的知识以进行有效的未来使用（有效导航 随后的访问）。海马的主导理论集中在其编码个人的能力上 经验的快照，但我们和其他人发现证据表明，这对于寻找结构也至关重要 经验（了解同一遥远建筑的不同观点之间的关系）。这 这种基本学习形式的机制尚未建立。我们已经开发了一个神经网络 海马的模型实例化了以下理论，即它的一个子场可以快速使用 分布式表示形式，一种强大的表示神经元种群的形式 响应环境的多个相关特征。该项目的第一个目的是测试预测 该模型在需要集成的范式中使用高分辨率功能磁共振成像（fMRI） 跨经验的信息。结果将阐明我们如何学习新颖的基本机制 结构，在此过程的现有模型之间裁定，并告知进一步的模型开发。那里 关于新的海马表示最终的命运，也是竞争理论。一个观点假定 在睡眠期间，海马重播了最新信息，以构建长期分布式表示形式 新皮层。另一种观点声称记忆是直接和独立形成和合并的 海马和新皮层。该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 随着时间的流逝，海马和皮质表示的变化通过重新扫描参与者并跟踪变化。 在记忆中以一周的延迟。但是，随着时间的时间的所有观察到的大脑和行为变化可能是 由于时间的通用效果或睡眠期间的主动处理。因此，第三个目的是评估特定 睡眠对结构化信息巩固的因果贡献。我们将使用实时睡眠 脑电图（EEG）检测缓慢振荡的峰，当已知内源性重播已知 发生，并播放声音提示以使记忆重新激活。我们还将将我们的神经网络模型扩展到 检查最近规律性的离线海马重放如何塑造分布式表示形式 NeoCortex，提供了结构化信息离线合并的机理说明。我们期望这 工作将阐明解剖基板，并批评支持编码的表示形式的性质 和环境中新型结构的整合。具有强大的神经扎根模型 流程将有助于连接该领域的研究，并提供评估的框架 抑郁症和精神分裂症等心理健康障碍中出了什么问题，涉及深刻的 学习和睡眠的干扰。