To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a context, the access to existing neural models and data outstandingly facilitates the work of computational neuroscientists and fosters its novelty, as the scientific community grows wider and neural models progressively increase in type, size, and number. Nonetheless, even when accessibility is guaranteed, data and models are rarely reused since it is difficult to retrieve, extract and/or understand relevant information and scientists are often required to download and modify individual files, perform neural data analysis, optimize model parameters, and run simulations, on their own and with their own resources. While focusing on the construction of biophysically and morphologically accurate models of hippocampal cells, we have created an online resource, the Build section of the Hippocampus Hub -a scientific portal for research on the hippocampus- that gathers data and models from different online open repositories and allows their collection as the first step of a single cell model building workflow. Interoperability of tools and data is the key feature of the work we are presenting. Through a simple click-and-collect procedure, like filling the shopping cart of an online store, researchers can intuitively select the files of interest (i.e., electrophysiological recordings, neural morphology, and model components), and get started with the construction of a data-driven hippocampal neuron model. Such a workflow importantly includes a model optimization process, which leverages high performance computing resources transparently granted to the users, and a framework for running simulations of the optimized model, both available through the EBRAINS Hodgkin-Huxley Neuron Builder online tool.

中文翻译：

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通过海马中心构建海马神经元模型的在线互操作资源

为了建立脑细胞、回路和区域的生物物理详细模型，越来越多地采用数据驱动的方法。这有助于获得尽可能忠实地再现实验记录的神经动力学的模拟活动，并将模型转变为有用的框架，用于根据控制神经细胞性质的原理进行预测。在这样的背景下，随着科学界的扩大以及神经模型的类型、大小和数量的逐渐增加，对现有神经模型和数据的访问极大地促进了计算神经科学家的工作并培养了其新颖性。尽管如此，即使保证了可访问性，数据和模型也很少被重用，因为很难检索、提取和/或理解相关信息，并且科学家经常需要下载和修改单个文件、执行神经数据分析、优化模型参数和自行并使用自己的资源运行模拟。在专注于构建海马细胞生物物理和形态学精确模型的同时，我们创建了一个在线资源，即海马中心的构建部分——海马研究的科学门户——从不同的在线开放存储库收集数据和模型，允许将它们的收集作为单细胞模型构建工作流程的第一步。工具和数据的互操作性是我们所展示的工作的关键特征。通过简单的点击收集程序，就像填充在线商店的购物车一样，研究人员可以直观地选择感兴趣的文件（即电生理记录、神经形态和模型组件），并开始构建数据驱动的海马神经元模型。这样的工作流程重要地包括一个模型优化过程，该过程利用透明地授予用户的高性能计算资源，以及一个用于运行优化模型模拟的框架，这两个过程都可以通过 EBRAINS Hodgkin-Huxley Neuron Builder 在线工具获得。