斯坦福大学医学院Thomas R. Clandinin研究团队取得进展。他们的研究报道了不频繁的强连接限制了神经元功能的连接组预测。2025年6月2日，国际知名学术期刊《细胞》发表了这一成果。

为了探索这些限制，小组表征了果蝇中43种细胞类型的视觉反应，并将其与连接组学预测进行了定量比较。研究组表明，这些预测对于某些响应属性是准确的，比如定向调谐，但对于其他属性，比如接受野大小，却令人惊讶地差。重要的是，强突触输入比偶然预期的功能同质性更强，并且对突触后反应产生不成比例的大影响。最后，该团队定量地定义了最能描述细胞类型之间功能差异的连接子集。他们的结果为提高连接组预测的准确性建立了一套强有力的约束条件。

据悉，电路是如何约束神经计算的最近的研究利用连接组数据集来预测多种物种大脑中细胞和回路的功能。然而，许多这些假设尚未与生理测量进行比较，模糊了基于连接体的功能预测的局限性。

附：英文原文

Title: Infrequent strong connections constrain connectomic predictions of neuronal function

Author: Timothy A. Currier, Thomas R. Clandinin

Issue&Volume: 2025-06-02

Abstract: How does circuit wiring constrain neural computation Recent work has leveraged connectomic datasets to predict the functions of cells and circuits in the brains of multiple species. However, many of these hypotheses have not been compared with physiological measurements, obscuring the limits of connectome-based functional predictions. To explore these limits, we characterized the visual responses of 43 cell types in the fruit fly and quantitatively compared them with connectomic predictions. We show that these predictions are accurate for some response properties, such as orientation tuning, but are surprisingly poor for other properties, such as receptive field size. Importantly, strong synaptic inputs are more functionally homogeneous than expected by chance and exert a disproportionately large influence on postsynaptic responses. Finally, we quantitatively define the subset of connections that best describe the functional differences between cell types. Our results establish a powerful set of constraints for improving the accuracy of connectomic predictions.

DOI: 10.1016/j.cell.2025.05.007

Source: https://www.cell.com/cell/abstract/S0092-8674(25)00518-5