Towards a Complete Description of the Circuitry Underlying Memory replay.

 描述（由申请人提供）：大脑区域的功能是许多细胞类型的涌现特性。在对无脊椎动物“简单”网络的研究中，已经建立了理解网络所需的标准，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、机械的理解。我们认为，现在进行这种努力的时机已经成熟。具体来说，我们建议了解海马体中的CA 3网络如何产生尖波涟漪（SWR）。这些事件由于其认知功能而引起人们极大的兴趣：它们代表情景记忆序列的重放，并且是随后的记忆回忆所必需的，如在行为水平上所证明的。我们对SWR的理解将建立在以前建立海马细胞类型的工作上。然而，为了满足“理解”的标准，必须获得大量关于细胞的连通性和内在性质的额外信息。我们将使用最近开发的大规模电子和光学记录方法和个体发育来获得这些信息。此外，还将开发若干新的方法/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，以获得高通量的细胞类型的特定信息的连接的CA 3网络。我们还将构建海马CA 3区的第一个全尺寸计算模型，其中每个细胞和突触连接都被明确表示。这个严格的数据驱动的全尺寸模型将 提供了一个广泛适用的工具，综合实验结果和测试我们的能力，理解的原则，背后的SWR生成。

 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.