CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep

在大量神经元的单位记录中，神经元的快速压缩顺序放电 人们发现，休息和早睡可以重演在活跃的清醒体验中首次观察到的模式。这些 非凡的模式引起了科学界和其他领域的广泛兴趣。数列 重播现在被认为在记忆力的长期稳定和储存中起着关键作用 重要信息。然而，尽管人们普遍承认顺序性的重要性 结构，因此对重播与之进行比较的空背景知之甚少。 具体地说，就像在晚睡中看到的那样，明显的“不重放”的尖峰模式仅仅是噪音吗？ 因为重放通常是通过与固定的已知模板进行比较来评估的，所以大多数方法可以 仅确定与模板的相似度是否超过预期 随机的尖峰列车。但这些方法不能评估其他模式是否仍保留在 不重要的事件。最近，DIBA和Kemere实验室成功合作，精确地解决了 这个问题。我们开发了基于隐马尔可夫模型(HMM)的方法来发现时间 以无监督、无模板的方式在神经元的棘波序列中构建。在这项建议中，我们的目标是 进一步改进这些方法，并评估海马区棘波串的隐藏结构 睡眠中的神经元数量。在我们的第二个具体目标中，我们将使用HMM来确定两者 海马棘波中的协同活动集合(“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，我们将探索睡眠重放的本质 此外，通过在长时间的迷宫之前将动物暴露在多种新颖和熟悉的迷宫环境中 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，以 检查这些模式从早睡到晚睡的因果相互作用和回响。总而言之，我们的 该提案旨在为回放中的“噪音”结构提供迄今最有力的描述 事件。 相关性(请参阅说明)： 这项研究将为评估睡眠在大脑和大脑中重组信息的作用提供一个机会。 帮助确定睡眠期间的关键时间窗口和神经元活动，这些都是特别重要的 用于信息存储和稳定。我们关于性质和目的的假设和推论 从医院和休息室轮班的时间长短来看，睡眠状况隐含地为各种公共政策提供了信息 工人们，早上开始公立学校的上课时间。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