Kobayashi Ryota, Shinomoto Shigeru
Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan; Mathematics and Informatics Center, The University of Tokyo, Tokyo 113-8656, Japan.
Graduate School of Biostudies, Kyoto University, Kyoto 606-8501, Japan; Research Organization of Open Innovation and Collaboration, Ritsumeikan University, Osaka 567-8570, Japan.
Neurosci Res. 2025 Jun;215:37-46. doi: 10.1016/j.neures.2024.07.006. Epub 2024 Aug 2.
This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of "neuronal connectivity" in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.
本文对过去二十年来从多个神经元的尖峰序列推断单突触连接的进展进行了简要综述。首先，我们解释了神经科学不同研究领域中“神经元连接性”的多种含义，例如结构连接性、单突触连接性和功能连接性。其中，我们重点关注从尖峰数据推断单突触连接性的方法。然后，我们基于两种主要方法，即基于相关性的方法和基于模型的方法，总结了推断方法。最后，我们描述了用于连接性推断的可用源代码以及未来的挑战。尽管推断永远不会完美，但由于持续的努力，近年来识别单突触连接的准确性有了显著提高。
