Physiologically detailed models of neural networks are an important tool for studying how biophysical mechanisms impact neural information processing. An important, fundamental step in constructing such a model is determining where neurons are placed and how they connect to each other, based on known anatomical properties and constraints given by experimental data. Here we present an open-source software tool, pycabnn, that is dedicated to generating an anatomical model, which serves as the basis of a full network model. In pycabnn, we implemented efficient algorithms for generating physiologically realistic cell positions and for determining connectivity based on extended geometrical structures such as axonal and dendritic morphology. We demonstrate the capabilities and performance of pycabnn by using an example, a network model of the cerebellar granular layer, which requires generating more than half a million cells and computing their mutual connectivity. We show that pycabnn is efficient enough to carry out all the required tasks on a laptop computer within reasonable runtime, although it can also run in a parallel computing environment. Written purely in Python with limited external dependencies, pycabnn is easy to use and extend, and it can be a useful tool for computational neural network studies in the future.

中文翻译：

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Pycabnn：构建生理现实神经网络模型的解剖基础的高效且可扩展的软件

神经网络的生理详细模型是研究生物物理机制如何影响神经信息处理的重要工具。构建这样一个模型的一个重要的基本步骤是根据已知的解剖学特性和实验数据给出的约束，确定神经元的放置位置以及它们如何相互连接。在这里，我们展示了一个开源软件工具 pycabnn，它专门用于生成解剖模型，作为完整网络模型的基础。在 pycabnn 中，我们实施了有效的算法来生成生理上真实的细胞位置并根据扩展的几何结构（例如轴突和树突形态）确定连接性。我们通过一个例子展示了pycabnn的能力和性能，小脑颗粒层的网络模型，需要生成超过 50 万个细胞并计算它们的相互连接。我们表明 pycabnn 足够高效，可以在合理的运行时间内在膝上型计算机上执行所有必需的任务，尽管它也可以在并行计算环境中运行。pycabnn 完全用 Python 编写，外部依赖有限，易于使用和扩展，它可以成为未来计算神经网络研究的有用工具。