EAGER: Identifying network dynamics promoting memory consolidation during sleep

虽然睡眠的确切生理功能仍然未知，但越来越多的证据表明它在巩固长期记忆方面起着重要作用。特别是，睡眠似乎促进了陈述性记忆的巩固，这需要一个功能完整的海马体，包括位置记忆。在啮齿类动物中，位置依赖的恐惧记忆会被睡眠促进，并被睡眠剥夺所破坏。睡眠不足也会破坏一些被认为与记忆巩固有关的生化和神经生理过程。这些研究使许多人认为，睡眠通过调节海马体内的神经网络动力学和突触可塑性来促进长期记忆的巩固。在实验研究中，co-PI最近发现了睡眠期间海马神经网络动力学的变化，这些变化是由位置依赖性恐惧学习引起的。 在计算建模研究中，co-PI已经表明乙酰胆碱（一种调节性化学物质，其水平在睡眠状态中变化）可以以类似的方式改变神经网络动力学。拟议的项目采取多学科，多尺度的方法来弥合实验和计算结果之间的差距，以确定乙酰胆碱对神经元的影响如何导致神经网络动力学的变化，以促进学习，最终导致学习和记忆行为。虽然重点是恐惧学习和记忆巩固，学习相关的和睡眠相关的大脑网络动力学的基础知识所提出的实验和计算将提供有价值的见解的机制，为所有类型的learning. Now，目前还不清楚如何睡眠相关的海马网络动力学的变化可能会促进上下文恐惧记忆巩固。该团队最近的实验研究表明，情境恐惧条件反射会产生持久的，睡眠依赖性的海马网络功能连接模式稳定性的增加。这些结果，再加上该团队最近的计算研究，描述了乙酰胆碱在网络范围活动和突触可塑性模式中的作用，导致了睡眠促进记忆巩固的假设，至少部分是通过动态改变海马神经网络活动模式在自然发生的快速眼球运动和慢波睡眠大脑状态。睡眠依赖性乙酰胆碱通过其对细胞兴奋性特性的影响在驱动网络活动的这些转变中起主要作用。 拟议的项目使用行为，生理和计算方法来解决缺失的链接，这些链接将显示假设的网络机制发生在大脑海马网络中，并参与恐惧学习和记忆。 将在清醒和睡眠状态下操纵海马乙酰胆碱水平，同时记录海马中的多单位活动，以量化恐惧和随后的睡眠或睡眠剥夺背景下的尖峰定时动态变化。该团队开发了一套量化措施来识别网络动态中与学习相关的变化。 此外，将在海马锥体细胞和抑制性中间神经元群体中测量乙酰胆碱诱导的影响网络动力学的细胞膜特性的变化。研究结果将被用于告知生物物理神经网络模型的细节，以确定乙酰胆碱介导的网络活动动态变化促进网络稳定性，结构变化和与学习和巩固相关的突触重组的特定动力学机制。

While the exact physiological function of sleep remains unknown, there is mounting evidence that it plays an important role in the consolidation of long-term memories. In particular, it appears that sleep promotes the consolidation of declarative memories that require a functionally intact hippocampus, including memories of place. In rodents, place-dependent fear memory is promoted by sleep and disrupted by sleep deprivation. Sleep deprivation also disrupts a number biochemical and neurophysiological processes that are thought to be involved in memory consolidation. These studies have led many to suggest that sleep promotes long-term memory consolidation by modulating neural network dynamics and synaptic plasticity within the hippocampus. In experimental studies, the co-PIs have recently identified changes in hippocampal neural network dynamics during sleep that are induced by place-dependent fear learning. In computational modeling studies, the co-PIs have shown that acetylcholine, a modulatory chemical whose levels vary across sleep states, can change neural network dynamics in a similar way. The proposed projects take a multidisciplinary, multi-scale approach to bridge the gap between experimental and computational results, to identify how effects of acetylcholine on neurons lead to changes in neural network dynamics to promote learning, ultimately leading to learning and memory behavior. While the focus is on fear learning and memory consolidation, the fundamental knowledge of learning-related and sleep-related brain network dynamics gained by the proposed experiments and computations will provide valuable insights into mechanisms for all types of learning.At present, it is unclear how sleep-related changes in hippocampal network dynamics might promote contextual fear memory consolidation. The team's recent experimental studies have shown that contextual fear conditioning produces long-lasting, sleep-dependent increases in the stability of hippocampal network functional connectivity patterns. These results, coupled with the team's recent computational studies describing a role for acetylcholine in network-wide activity and synaptic plasticity patterning, have led to the hypothesis that sleep promotes memory consolidation, at least in part, by dynamically shifting patterns in hippocampal neural network activity during naturally-occurring rapid eye movement and slow wave sleep brain states. Sleep-dependent acetylcholine has a primary role in driving these shifts in network activity through its effects on cellular excitability properties. The proposed projects use behavioral, physiological, and computational approaches to tackle the missing links that will show the hypothesized network mechanisms occur in brain hippocampal networks and participate in fear learning and memory. Hippocampal acetylcholine levels will be manipulated across wake and sleep states while recording multi-unit activity in hippocampus to quantify changes in spike timing dynamics in the context of fear and subsequent sleep or sleep deprivation. The team has developed a suite of quantitative measures to identify learning-related changes in network dynamics. In addition, acetylcholine-induced changes in cellular membrane properties that affect network dynamics will be measured in hippocampal pyramidal cell and inhibitory interneuron populations. The results will be used to inform details of biophysical neural network models to identify specific dynamical mechanisms by which acetylcholine-mediated changes in network activity dynamics promote network stability, structural changes and synaptic reorganization associated with learning and consolidation.