A neuronal code for extended time in the hippocampal-entorhinal circuitry

项目摘要/摘要 海马体结构对于自传体记忆的形成和提取是至关重要的，自传体记忆包括 关于何时何地发生了什么的信息。对象、上下文和空间(什么、哪里)是已知的 在海马体中表示，最近描述了以秒为单位的时间(当事件发生时 以分钟为代表的是海马细胞的顺序激活。在更长的时间尺度上，时变的 神经编码以前已经被理论模型证明适合于估计记忆的新近 事件。在最近发表的结果和作为初步数据呈现的结果中，我们发现了一个新的、随时间变化的 海马体神经编码，可以表示事件发生多长时间之前，以小时和天为时间尺度。我们的 首次发现CA1细胞的神经元放电模式以单调的速率累积为特征 不同经验之间的差异是时间的函数。然而，我们证明了存储的信息并不 简单地在CA1区域恶化，但时间编码可以与可靠的编码共存 事件，如空间位置或上下文。我们还发现，CA3包含一个非常精确的代码 不随时间变化的背景和空间。新的初步数据显示，CA2的作用与CA3相反。CA2 单元格表示经过的时间，但没有关于上下文的信息。根据这些发现，我们假设 延长时间的神经元编码与CA1细胞群体中的空间和上下文信息相结合，以 对先前事件的新近进行记忆的引导行为。结合使用行为测试和 在清醒行为的啮齿动物中进行单单位记录，这一假说在三个目标上得到检验：(1)确定 在海马CA2神经网络中产生延长时间间隔的神经元编码，(2)确定 来自海马CA3区的输入是维持大脑中稳定的记忆编码成分所必需的。 CA1神经网络，以及(3)确定在海马CA1中是否存在时变的神经编码 而CA2亚区与记忆“多久前”有关。前两个目标解决了如何不同 子区域对神经编码的时变和稳定分量有贡献。除了定义函数之外 CA2用于时间编码以及CA3在实现稳定的记忆表征方面的功能，我们还会问 内嗅皮层可以在扩展的尺度上表示时间，因此是时间编码结束的大脑区域 可以找到很大范围的鳞片。在第三个目标中，我们将使用一个行为任务，在这个任务中，已经显示出老鼠 记住延长的时间段中的“多久以前”。我们将测量Place细胞中活动模式的相似性 两个时间点之间的种群并确定神经元放电模式的变化是否对应于 RAT对已用时间的估计。综上所述，这些研究对于理解神经网络将是重要的 支持发作的脑结构中长期记忆稳定性和时间事件编码的机制 记忆。了解记忆处理的关键机制将指导修复脑内神经回路功能障碍的努力 精神、神经和神经退行性疾病。

PROJECT SUMMARY/ABSTRACT The hippocampal formation is critical for the formation and retrieval of autobiographical memories, which consist of information about what happened when and where. It is known how objects, context and space (what, where) are represented in the hippocampus and it has recently been described that time (when events occur) on a scale of seconds to minutes is represented by the sequential activation of hippocampal cells. On a longer time scale, a time-varying neural code has previously been shown by theoretical models to be suitable for estimating the recency of a remembered event. In recently published results and results presented as preliminary data, we identify a novel, time-varying hippocampal neural code that can represent how long ago an event occurred on a time scale of hours and days. Our data first identified that the neuronal firing patterns of CA1 cells are characterized by a monotonic accumulation of rate differences as a function of time between experiences. However, we demonstrate that stored information does not simply deteriorate in the CA1 area, but that the code for time can co-exist with reliable coding for other aspects of an event, such as the spatial location or the context. We also found that CA3 contains an exquisitely precise code for context and space that does not vary over time. New preliminary data show an effect in CA2 that is opposite of CA3. CA2 cells represent elapsed time but no information about context. Based on these findings, we hypothesize that the neuronal code for extended time is combined with spatial and contextual information in the CA1 cell population to guide behavior in which the recency of a previous event is remembered. Using a combination of behavioral testing and single-unit recordings in awake behaving rodents, this hypothesis is tested in three aims that: (1) determine whether the neuronal code for extended time intervals is generated in the hippocampal CA2 neural network, (2) determine whether the input from the hippocampal CA3 subregion is necessary for maintaining the stable memory coding component in the CA1 neural network over hours and days, and (3) determine whether a time-varying neural code in hippocampal CA1 and CA2 subregions is correlated with remembering "how long ago". The first two aims address how different subregions contribute to the time-varying and stable components of the neural code. In addition to defining the function of CA2 for temporal coding and the function of CA3 in enabling stable memory representations, we will also ask whether the entorhinal cortex can represent time on an extended scale and is thus a brain region in which temporal coding over a large range of scales can be found. In the third aim, we will use a behavioral task in which it has been shown that rats remember "how long ago" over extended time periods. We will measure the similarity of activity patterns in place cell populations between two time points and determine whether the change in neuronal firing patterns corresponds to the rat's estimate of elapsed time. Taken together, these studies will be important for understanding the neural network mechanisms for long-term memory stability and temporal event coding in the brain structures that support episodic memory. Understanding the key mechanisms for memory processing will guide efforts to repair circuit dysfunction in psychiatric, neurological, and neurodegenerative diseases.