The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.

中文翻译：

---

啮齿动物海马结构突触电生理学的综合知识库。

啮齿动物海马体的细胞和突触结构已在数千篇同行评审出版物中进行了描述。然而，对于该神经系统或任何其他神经系统，不存在人类或机器可读的突触电生理学数据公共目录。利用最先进的信息技术，我们开发了一个基于云的工具集，用于从有关突触电生理学的科学文献中识别经验证据，提取感兴趣的实验数据，并将每个条目链接到相关文本或图形摘录。通过挖掘 1,200 多篇已发表的期刊文章，我们确定了通过 90 种不同方法量化的 8 种不同信号模式，用于测量海马神经元的突触幅度、动力学和可塑性。我们设计了一种数据结构，既反映实验方式之间的差异，又保持实验方式之间现有的关系。此外，我们将每个带注释的实验映射到已识别的潜在连接，即特定的突触前和突触后神经元类型对。为了实现这一目标，我们利用了 Hippocampome.org，这是一个开放获取的知识库，涉及啮齿动物海马结构中形态学、电生理学和分子特征的神经元类型。具体来说，我们实现了一个计算管道，系统地将神经元类型属性转换为正式查询，以便找到所有兼容的潜在连接。通过这个系统，我们收集了近 40,000 个突触数据实体，覆盖了 Hippocampome.org 中 3,120 个潜在连接的 88%。根据液体连接电位校正膜电位显着减少了理论和实验反转电位之间的差异，从而能够将所有突触振幅准确转换为电导。该数据集允许对控制突触信号的一般规则进行大规模假设检验。为了说明这些应用，我们确认了突触测量及其协变量之间的几个预期相关性，同时提出了以前未报告的相关性。我们在 Hippocampome.org 上开源发布所有数据，以便进一步跨学科研究。