Geometry of abstract learned knowledge in the hippocampus.
Geometry of abstract learned knowledge in the hippocampus.
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DOI：
10.1038/s41586-021-03652-7
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发表时间：
2021-07
期刊：
影响因子：
64.8
通讯作者：
中科院分区：
文献类型：
--
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海马神经元编码认知地图中的物理变量，如空间或听觉频率。此外，人类功能磁共振成像研究表明，海马体也可以编码更抽象的获得性变量。然而，它们与现有的物理变量的神经表示法的集成是未知的。利用双光子钙成像技术，我们证明了在虚拟现实中执行决策任务的小鼠背侧CA1区神经元联合编码累积的证据和空间位置。非线性降维表明，种群活动可以用4-6个潜在变量很好地描述，这表明神经活动被限制在一个低维流形上。在这个低维空间中，物理变量和抽象变量以有序的方式被联合映射，创建了一个几何表示，我们证明动物之间的几何表示是相似的。连体认知图的存在表明，海马体执行一般计算--创建由特定于任务的低维流形实例化的所学知识的几何表示。
Hippocampal neurons encode physical variables such as space or auditory frequency in cognitive maps. In addition, human fMRI studies have shown that the hippocampus can also encode more abstract, learned variables. However, their integration into existing neural representations of physical variables is unknown. Using 2-photon calcium imaging, we show that individual dorsal CA1 neurons jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality. Nonlinear dimensionality reduction showed that population activity was well described by ~4–6 latent variables, suggesting 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 fashion, creating a geometric representation that we demonstrate to be similar across animals. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation – to create geometric representations of learned knowledge instantiated by task-specific low-dimensional manifolds.
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