Uncovering the neural circuit and synaptic mechanisms underlying interval timing

项目总结/摘要 神经系统的许多功能，如学习和记忆，推断因果关系和预测 未来的结果，取决于大脑的能力，感知和形成记忆的时间持续时间 事件尽管在建立毫秒和昼夜节律的神经基础方面取得了进展， 计时超过小时，许多基本问题仍然是关于时间编码的中间尺度的时间间隔 计时（即秒到分钟）。为了研究间隔时间是如何在大脑中表现的， 行为方法（Heys和Dombeck，2018）和一种新的手术和光学方法，以实现大的， 在行为小鼠的MEC中的比例、细胞分辨率功能成像（Heys等人，2014）。通过组合 通过这些成像和行为方法，我发现了MEC中一个以前未知的神经元群体， 编码间隔时间，从而使单个时间编码神经元在特定时刻有规律地激活 在间隔计时任务中（Heys和Dombeck，2018）。然后，我确定MEC对于间隔时间至关重要 在学习期间（Heys等人，In Press）.这项先前的工作使我的实验室能够解决基本的 关于间隔计时的神经回路和突触机制的问题。我们将测试一本小说 概念框架，通过多阶段过程对时间进行编码。在这个模型的第一阶段， 神经时钟生成与上下文无关的逝去时间表示。在第二阶段，A 下游电路读出时钟，并将时间与经验的其他特征相关联，例如空间 上下文在我发表的文章的基础上，这项提议将检验前扣带皮层（ACC） 作为上游神经时钟，为内侧内嗅提供与上下文无关的时间信息， 皮层（MEC），这反过来又整合了时间与空间背景。为了测试这个模型，我会用我的切割- 边缘成像技术，记录和操纵突触和树突的神经活动， 在虚拟现实行为过程中，清醒行为小鼠中同时存在单个神经元（Heys et al. 2014） 允许精确控制时序行为的范例（Heys和Dombeck，2018）。通过这种方法，我们将 首先评估终止于MEC中的ACC轴突作为MEC中的神经时钟信号的能力。二是 将确定MEC是否通过整合突触输入来编码依赖于上下文的时间表示 与来自其他上游脑区的空间编码输入。第三，我们将识别突触， 树突状机制的细胞整合的上下文相关的时间表示在MEC。这 该提案将对细胞、系统和认知神经科学产生深远影响。由于间隔时间是 基本上所有主要大脑功能的基本组成部分，了解神经机制， 间隔时间，从突触到群体水平的神经编码，将提供一个基础，了解如何 大脑执行所有复杂的功能，这些功能依赖于以秒到分钟为尺度的时间编码。

Project Summary/Abstract Many functions of the nervous system, such as learning and memory, inferring cause and effect, and predicting future outcomes, depend upon the brain’s ability to perceive and form memories of the temporal duration of events. Despite progress in establishing the neural basis of timing on the scale of milliseconds and circadian timing over hours, many fundamental questions remain about time encoding on the intermediate scale of interval timing (i.e. seconds to minutes). To investigate how interval time is represented in the brain, I developed a novel behavioral approach (Heys and Dombeck, 2018) and a novel surgical and optical approach to enable large- scale, cellular-resolution functional imaging in MEC in the behaving mouse (Heys et al., 2014). By combining these imaging and behavioral methods, I discovered a previously unknown population of neurons in MEC that encode interval time, whereby individual time-encoding neurons become regularly activated at a specific moment during an interval timing task (Heys and Dombeck, 2018). I then established that MEC is critical for interval timing during learning (Heys et al., In Press). This previous work has positioned my laboratory to address fundamental questions regarding the neural circuit and synaptic mechanisms underlying interval timing. We will test a novel conceptual framework, whereby time is encoded through a multi-stage process. In the first stage of this model, a neural clock generates a context-independent representation of elapsed time. In the second stage, a downstream circuit reads out the clock and associates time with other features of experience, such as spatial context. Building upon my published, this proposal will test the hypothesis that the anterior cingulate cortex (ACC) serves as an upstream neural clock, providing context-independent temporal information to medial entorhinal cortex (MEC), which in turn integrates elapsed time with spatial context. To test this model, I will apply my cutting- edge imaging techniques to record and manipulate neural activity from synapses and dendrites, up to thousands of individual neurons simultaneously in awake behaving mice (Heys et al. 2014) during virtual-reality behavioral paradigms that allow precise control of timing behavior (Heys and Dombeck, 2018). Using this approach, we will first evaluate the ability of ACC axons terminating in MEC to serve as a neural clock signal in MEC. Second, we will determine whether MEC encodes a context-dependent representation of time by integrating synaptic input from ACC with spatial coding input from other upstream brain regions. Third, we will identify the synaptic and dendritic mechanisms underlying cellular integration of context-dependent representations of time in MEC. This proposal will have a far-reaching influence on cellular, systems and cognitive neuroscience. As interval timing is a fundamental component of essentially all major brain functions, understanding the neural mechanisms of interval timing, from synaptic to population level neural coding, will provide a basis for understanding how the brain performs all complex functions that depend upon encoding of time on the scale of seconds to minutes.