Hippocampal neurons encode physical variables^{1,2,3,4,5,6,7} such as space¹ or auditory frequency⁶ in cognitive maps⁸. In addition, functional magnetic resonance imaging studies in humans have shown that the hippocampus can also encode more abstract, learned variables^9,10,11. However, their integration into existing neural representations of physical variables^12,13 is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality^14,15,16. Nonlinear dimensionality reduction¹³ showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation—the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.

海马神经元编码物理变量^{1、2、3、4、5、6、7}，例如认知地图^{8中的空间1}或听觉频率⁶。此外，人类功能磁共振成像研究表明，海马体还可以编码更抽象的学习变量^9,10,11。^{然而，它们与物理变量12,13}的现有神经表征的整合是未知的。^{在这里，我们使用双光子钙成像表明，在虚拟现实14,15,16}中执行决策任务的小鼠中，背侧海马体中的单个神经元联合编码具有空间位置的累积证据。非线性降维¹³显示人口活动被大约四到六个潜在变量很好地描述，这表明神经活动被限制在低维流形上。在这个低维空间中，物理变量和抽象变量都以有序的方式联合映射，创建了一个我们展示的在小鼠之间相似的几何表示。联合认知图的存在表明海马体执行一般计算——创建特定于任务的低维流形，其中包含所学知识的几何表示。