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

描述（由申请人提供）：海马结构对于自传体记忆的形成和检索至关重要，自传体记忆由关于何时何地发生的信息组成。人们知道物体、背景和空间（什么、在哪里）是如何在海马体中表示的，最近有人描述说，海马体细胞的顺序激活表示了秒到分钟尺度上的时间（事件发生时）。在更长的时间尺度上，理论模型已经证明了一种随时间变化的神经编码适合于估计记忆事件的近因。在最近发表的结果和作为初步数据呈现的结果中，我们确定了一种新的、随时间变化的海马神经代码，它可以代表事件在几小时和几天的时间尺度上发生了多久。我们的数据首先确定了CA1细胞的神经元放电模式的特点是作为经验之间的时间的函数的速率差的单调积累。然而，我们证明了存储的信息不会简单地在CA1区域恶化，而是时间代码可以与事件其他方面的可靠编码共存，例如 空间位置或背景。我们还发现，CA3包含了一个非常精确的上下文和空间代码，不会随着时间的推移而变化。新的初步数据显示，CA2的效果与CA3相反。CA2单元格表示经过的时间，但没有上下文信息。基于这些发现，我们假设，神经元代码的延长时间相结合的空间和上下文信息在CA1细胞群中，以指导行为，其中最近的一个先前的事件被记住。在清醒行为啮齿动物中使用行为测试和单个单元记录的组合，在三个目标中测试该假设：（1）确定在海马CA2神经网络中是否产生了延长时间间隔的神经元代码，（二）确定来自所述海马CA3亚区的输入对于在数小时内维持所述CA1神经网络中的所述稳定记忆编码组分是否是必需的，以及（3）确定海马CA1和CA2亚区中的时变神经代码是否与记忆“多久以前”相关。前两个目标是解决不同的子区域如何对神经代码的时变和稳定成分做出贡献。除了定义CA2的时间编码功能和CA3的稳定记忆表征功能之外，我们还将询问内嗅皮层是否可以在扩展的尺度上表示时间，因此是一个可以在大范围尺度上发现时间编码的大脑区域。我们将使用一种行为任务，在该任务中，已经显示大鼠在延长的时间段内记住“多久以前”。我们将测量位置细胞群体中活动模式的相似性， 两个时间点，并确定神经元放电模式的变化是否对应于大鼠对逝去时间的估计。总之，这些研究对于理解支持情景记忆的大脑结构中的长期记忆稳定性和时间事件编码的神经网络机制将是重要的。了解记忆处理的关键机制将指导修复精神病，神经学和神经退行性疾病中的电路功能障碍的努力。

DESCRIPTION (provided by applicant): 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.