哈佛医学院Rachel I. Wilson小组宣布他们的最新研究提出了头部方向神经表征中的多模态线索整合与学习。该项研究成果发表在2025年7月23日出版的《自然—神经科学》上。

在此，该课题组研究了果蝇头部方向系统的这一过程，该系统具有环形吸引器和头部方向地形图的功能。利用群体钙成像和多模态虚拟现实环境，该研究组发现增加线索信息量可以提高编码精度，并产生更窄和更高的活动凸起。当线索冲突时，信息更丰富的线索会发挥更大的作用。熟悉的球杆被赋予更大的权重和主题，以指导不熟悉的球杆的重新映射。当线索信息较少时，它更容易被重新映射以应对线索冲突。所有这些结果都可以用一个具有可塑性感觉突触的吸引子模型来解释。他们的发现为大脑如何通过推理和学习来组装空间表征提供了一种机制解释。

据了解，导航需要他们考虑多个不同信息水平的空间线索，并学习它们的空间关系。

附：英文原文

Title: Multimodal cue integration and learning in a neural representation of head direction

Author: Basnak, Melanie A., Kutschireiter, Anna, Okubo, Tatsuo S., Chen, Albert, Gorelik, Pavel, Drugowitsch, Jan, Wilson, Rachel I.

Issue&Volume: 2025-07-23

Abstract: Navigation requires us to take account of multiple spatial cues with varying levels of informativeness and learn their spatial relationships. Here we investigate this process in the Drosophila head direction system, which functions as a ring attractor and a topographic map of head direction. Using population calcium imaging and multimodal virtual reality environments, we show that increasing cue informativeness improves encoding accuracy and produces a narrower and higher bump of activity. When cues conflict, the more informative cue exerts more weight. A familiar cue is weighted more heavily and used to guide the remapping of a less familiar cue. When a cue is less informative, it is remapped more readily in response to cue conflict. All these results can be explained by an attractor model with plastic sensory synapses. Our findings provide a mechanistic explanation for how the brain assembles spatial representations through inference and learning.

DOI: 10.1038/s41593-024-01823-z

Source: https://www.nature.com/articles/s41593-024-01823-z