皮质树突的矢量化指示信号，这一成果由麻省理工学院Mark T. Harnett团队经过不懈努力而取得。该项研究成果发表在2026年2月25日出版的《自然》上。

在这里，该研究组主题的神经反馈脑机接口任务与实验者定义的奖励功能，以测试树突矢量化的指导性信号。研究组训练小鼠调节脾后皮层第5层锥体神经元的两个空间混合群体（每个群体有5个神经元）的活动，使视觉光栅向目标方向旋转，同时研究组记录了体细胞和相应的远端根尖树突的GCaMP活动。该课题组研究人员观察到体细胞和树突信号的相对大小可以通过周围网络的活动来预测，并且包含有关任务相关变量的信息，这些变量可以作为指导性信号，包括奖励和错误。这些假定的教学信号的信号取决于单个神经元在任务中的诱导作用，并预测了学习过程中整体活动的变化。

此外，有针对性的光遗传干扰这些信号会破坏学习。这些结果证明了大脑中有一个矢量化的指向性信号，通过皮质树突的半独立计算实现，揭示了解决大脑信用分配的潜在机制。

据了解，教学信号的矢量化是几乎所有现代机器学习算法的关键要素，包括反向传播、目标传播和强化学习。矢量化允许一个可扩展的和计算效率的解决方案，信贷分配问题，通过剪裁指导信号到单个神经元。最近的理论模型表明，神经回路可以在细胞水平上通过处理不同树突隔室中的前馈和反馈信息流来实现单相矢量化学习。这提出了一个令人信服但未经检验的假设，即大脑皮层回路如何解决大脑中的信用分配问题。

附：英文原文

Title: Vectorized instructive signals in cortical dendrites

Author: Francioni, Valerio, Tang, Vincent D., Toloza, Enrique H. S., Ding, Zilan, Brown, Norma J., Harnett, Mark T.

Issue&Volume: 2026-02-25

Abstract: Vectorization of teaching signals is a key element of almost all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments1,2,3,4,5. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. Here we used a neurofeedback brain–computer interface task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites. We trained mice to modulate the activity of two spatially intermingled populations (four or five neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic and dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. Furthermore, targeted optogenetic perturbation of these signals disrupted learning. These results demonstrate a vectorized instructive signal in the brain, implemented via semi-independent computation in cortical dendrites, unveiling a potential mechanism for solving credit assignment in the brain.

DOI: 10.1038/s41586-026-10190-7

Source: https://www.nature.com/articles/s41586-026-10190-7