Towards a Complete Description of the Circuitry Underlying Memory replay.

 描述(申请人提供)：大脑区域的功能是许多细胞类型的紧急性质。在对无脊椎动物“简单”网络的研究中，理解网络所需的标准已经确立，但还没有人试图对脊椎动物大脑中的任何网络提供如此全面的、机械性的理解。我们认为，现在作出这种努力的时机已经成熟。具体地说，我们建议理解海马体中的CA3网络如何产生尖锐的波纹(SWR)。这些事件之所以引起人们的极大兴趣，是因为它们具有认知功能：它们代表情节记忆序列的重播，是后续记忆回忆所必需的，正如在行为水平上所证明的那样。我们理解SWR的努力将建立在之前建立海马体细胞类型的工作基础上。然而，为了满足“理解”的标准，必须获得大量关于细胞连通性和内在属性的附加信息。我们将使用最近开发的大规模电和光记录方法以及个体发育来获取这些信息。此外，还将开发几种新的方法/工具。值得注意的是，我们建议优化一种新的突触定位光学方法，以获得关于CA3网络连接的高通量细胞类型特定信息。我们还将构建第一个完整的海马区CA3区域的计算模型，其中每个细胞和突触连接都是显式表示的。这一严格由数据驱动的全规模模型将 提供了一个广泛适用的工具，用于综合实验结果和测试我们理解SWR产生的原理的能力。

 DESCRIPTION (provided by applicant): The function of a brain region is an emergent property of many cell types. The criteria needed to understand a network have been established in studies of invertebrate "simple" networks, but there has not yet been an attempt to provide such a full, mechanistic understanding of any network in the vertebrate brain. We believe that the time is now ripe for such an effort. Specifically, we propose to understand how the CA3 network in the hippocampus generates sharp-wave-ripples (SWR). These events are of great interest because of their cognitive function: they represent replay of episodic memory sequences and are required for subsequent memory recall, as demonstrated at the behavioral level. Our efforts to understand the SWR will build on previous work establishing the cell types of the hippocampus. However, to meet the criteria for "understanding", a great deal of additional information about connectivity and intrinsic properties of cells must be obtained. We will use recently developed large-scale electrical and optical recording methods and ontogenetic to obtain this information. In addition, several new methods/tools will be developed. Notably, we propose to optimize a novel synapse localization optical method to obtain high-throughput cell type-specific information about the connective of the CA3 network. We will also construct the first full-scale computational model of the CA3 region of the hippocampus, in which every cell and synaptic connection is explicitly represented. This strictly data-driven, full-scale model will provide a widely applicable tool for synthesizing experimental results and testing our ability to understand the principles that underlie SWR generation.