2026年9月2日，美国哥伦比亚大学Salomon Z. Muller等科学家在《自然》（Nature）发表研究，通过连接组学解析了小脑样回路中感觉预测的细胞类型和突触连接机制。

许多形式的学习，例如学习环境模型或运动技能，依赖于广泛分布于细胞类型和网络阶段的突触可塑性。理解这种分布式可塑性如何发挥作用是神经科学中的一个核心挑战。在此，研究人员利用连接组学绘制了电鱼小脑样结构中多层持续学习（该学习可取消可预测的感觉反应）背后的细胞类型和突触连接。他们的分析揭示了抑制性和去抑制性感觉输入通路，这些通路满足指导突触可塑性的理论要求；网络阶段之间的结构化突触连接解决了信用分配问题；以及结构化的循环连接加速了感觉预测和取消。一个受电生理记录约束的计算模型展示了这种突触连接如何确保多个可塑性位点协同工作以克服各自的局限性，从而实现快速、准确且对噪声鲁棒的取消。总体而言，这些发现凸显了连接组学与细胞类型特异性生理记录和计算建模相结合在解读神经回路学习中的潜力。

附：英文原文

Title: Connectome analysis of a cerebellum-like circuit for sensory prediction

Author: Perks, Krista E., Petkova, Mariela D., Muller, Salomon Z., Genecin, Michael, Ghatare, Adishree, Schalek, Richard, Wu, Yuelong, Januszewski, Michal, Jain, Viren, Lichtman, Jeff W., Abbott, L. F., Sawtell, Nathaniel B.

Issue&Volume: 2026-09-02

Abstract: Many forms of learning, for example, learning a model of the environment or a motor skill, rely on synaptic plasticity that is widely distributed across cell types and network stages. Understanding how this distributed plasticity functions is a central challenge in neuroscience1,2,3,4,5. Here we use connectomics to map the cell types and synaptic connections underlying a form of multi-layer continual learning that cancels predictable sensory responses in a cerebellum-like structure in electric fish6,7. Our analysis shows inhibitory and disinhibitory sensory input pathways that fulfil theoretical requirements for instructing synaptic plasticity8,9, structured synaptic connectivity between network stages that solves a credit assignment problem and structured recurrent connectivity that accelerates sensory prediction and cancellation. A computational model constrained by electrophysiological recordings shows how this synaptic connectivity ensures that multiple sites of plasticity cooperate to overcome their individual limitations, resulting in cancellation that is fast, accurate and robust to noise. Overall, these findings highlight the potential of connectomics, in combination with cell-type-specific physiological recordings and computational modelling, for deciphering learning in neural circuits.

DOI: 10.1038/s41586-026-10690-6

Source: https://www.nature.com/articles/s41586-026-10690-6