CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep

在大量神经元的单位记录中， 已经发现休息和早睡重现了在主动清醒体验中首次观察到的模式。这些 这些奇特的模式引起了科学界和其他领域的广泛兴趣。序列 重放现在被认为在记忆的长期稳定和存储中起关键作用。 重要信息然而，尽管普遍承认顺序的重要性， 结构，很少有人知道对重放比较的空背景。 具体来说，在晚睡中看到的“非重放”尖峰模式，仅仅是噪音吗？ 因为重放通常通过与固定的已知模板进行比较来评估，所以大多数方法可以 仅确定与模板的相似性是否超过预期 随机的尖峰脉冲序列但这些方法不能评估其他模式是否仍然存在， 不重要的事件。最近，迪巴和凯梅尔实验室成功地合作， 这个问题我们开发了基于隐马尔可夫模型（HMRM）的方法来揭示时间 结构的神经元的尖峰列车在无监督的无模板的方式。在本建议中，我们的目标是 进一步改进这些方法，并评估海马神经元内锋电位序列的隐藏结构， 睡眠中的神经元在我们的第二个具体目标中，我们将使用Hacker来确定 海马锋电位的共激活整体（“上下文”）和时间模式（“顺序”）结构 训练任务前和任务后的睡眠。在第三个具体目标中，我们将探讨睡眠回放的本质 此外，通过将动物暴露于多种新的和熟悉的迷宫环境， 睡吧在第四个具体目标中，我们将对神经元群体模式进行闭环破坏， 研究这些模式从早睡到晚睡的因果相互作用和反响。总之，我们的 该提案旨在提供最强的表征结构的“噪音”在重放日期 事件 相关性（参见说明）： 这项研究将为评估睡眠在重组大脑信息中的作用提供一个开放的平台， 有助于识别睡眠期间的关键时间窗和神经元活动， 用于信息存储和稳定。我们对自然和目的的假设和推断 从医院和救援人员轮班的持续时间， 工人，到公立学校的早晨开始时间。了解睡眠H的功能和机制

In unit recordings from large populations of neurons, fast compressed sequential firing of neurons during rest and early sleep have been found to replay patterns first observed in active awake experience. These remarkable patterns have sparked widespread interest in the scientific community and beyond. Sequence replay is now considered to play a critical role in the long-term stabilization and storage of mnemonically important information. However, despite the general acknowledgement of the importance of the sequential structure, very little is known about the null background against which replay is compared. Specifically, are apparently 'non-replaying' spike patterns, as seen in late sleep, just simply noise? Because replay is typically assessed by comparison against a fixed known template, most methods can only determine whether the resemblance to the template is more than what might be expected from random spike trains. But these methods cannot appraise whether other patterns remain in the nonsignificant events. Recently, the Diba and Kemere labs successfully collaborated to address precisely this issue. We developed methods based on hidden Markov models (HMMs) to uncover temporal structure in spike trains of neurons in an unsupervised template-free manner. In this proposal, we aim to further improve these methods and to evaluate the hidden structure of spike trains in hippocampal neuronal populations during sleep. In our second specific aim, we will use HMMs to determine both co-active ensemble ("contextual") and temporal patterns ("sequential") structure in hippocampal spike trains in both pre- and post-task sleep. In the third specific aim, we will probe the essence of sleep replay further, by exposing animals to multiple novel and familiar maze environments prior to long durations of sleep. In the fourth specific aim, we will perform closed-loop disruption of neuronal population patterns to examine the causal interplay and reverberation of these patterns from early to late sleep. In summary, our proposal is designed to provide strongest characterization to date of the structure of "noise" in replay events. RELEVANCE (See instructions): This study will provide an opening to evaluate the role of sleep in reorganizing information in the brain and help to identify critical time windows and neuronal activities during sleep which are particularly important for information storage and stabilization. Our assumptions and deductions about the nature and purpose of sleep implicitly inform all manner of public policy, from the durations of shifts for hospital and relief workers, to morning start times of public schools. Understanding the function and mechanisms of sleep H