The analysis of real‐world networks of neurons is biased by the current ability to measure just a subsample of the entire network. It is thus relevant to understand if the information gained in the subsamples can be extended to the global network to improve functional interpretations. Here we showed how average clustering coefficient (CC), average path length (PL), and small‐world propensity (SWP) scale when spatial sampling is applied to small‐world networks. This extraction mimics the measurement of physical neighbors by means of electrical and optical techniques, both used to study neuronal networks. We applied this method to in silico and in vivo data and we found that the analyzed properties scale with the size of the sampled network and the global network topology. By means of mathematical manipulations, the topology dependence was reduced during scaling. We highlighted the behaviors of the descriptors that, qualitatively, are shared by all the analyzed networks and that allowed an approximated prediction of those descriptors in the global graph using the subgraph information. In contrast, below a spatial threshold, any extrapolation failed; the subgraphs no longer contain enough information to make predictions. In conclusion, the size of the chosen subgraphs is critical to extend the findings to the global network.

中文翻译：

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在小世界网络中神经元空间二次采样的影响

当前仅测量整个网络的子样本的能力使对真实世界神经元网络的分析存在偏差。因此，有必要了解子样本中获得的信息是否可以扩展到全球网络以改善功能解释。在这里，我们展示了在将空间采样应用于小世界网络时，平均聚类系数（CC），平均路径长度（PL）和小世界倾向（SWP）的规模。该提取通过电学和光学技术模仿了物理邻居的测量，这两种技术都用于研究神经元网络。我们将此方法应用于计算机和体内数据，我们发现分析的属性随采样网络的大小和全局网络拓扑的变化而缩放。通过数学操作，在缩放过程中减少了拓扑依赖性。我们着重说明了描述符的行为，这些描述符在质量上被所有分析的网络共享，并且允许使用子图信息对全局图中的这些描述符进行近似预测。相反，在空间阈值以下，任何外推均会失败；子图不再包含足够的信息来进行预测。总之，所选子图的大小对于将发现扩展到全球网络至关重要。子图不再包含足够的信息来进行预测。总之，所选子图的大小对于将发现扩展到全球网络至关重要。子图不再包含足够的信息来进行预测。总之，所选子图的大小对于将发现扩展到全球网络至关重要。