Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; International Research Center for Neurointelligence, Institutes for Advanced Study, University of Tokyo, Tokyo 113-0033, Japan.
School of Systems Science, Beijing Normal University, Beijing 100875, China; Peng Cheng Laboratory, Shenzhen 518055, China.
Prog Neurobiol. 2022 Apr;211:102228. doi: 10.1016/j.pneurobio.2022.102228. Epub 2022 Jan 25.
The geometric information of space, such as environment boundaries, is represented heterogeneously across brain regions. The computational mechanisms of encoding the spatial layout of environments remain to be determined. Here, we postulate a conjunctive encoding theory to illustrate the construct of cognitive maps from geometric perception. The theory naturally describes a spectrum of cell types including experimentally observed boundary vector cells, border cells, "annulus" and "bulls-eye" cells as special examples. In a similar way, inspired by the integration of egocentric and allocentric information as found in the postrhinal cortex, the theory also predicts a new cell type, named geometry cell. Geometry cells encode the geometric layout of the local space relative to the environment center, independent of the animal's positions and headings within the local space. The predicted geometry cell provides pure allocentric high-level representations of local scenes to support the quick formation of cognitive map representations capturing the spatial layout of complex environments. The theory sheds new light on the neural mechanisms of spatial cognition and brain-inspired autonomous intelligent systems.
空间的几何信息,如环境边界,在大脑区域之间呈现出异质性。编码环境空间布局的计算机制仍有待确定。在这里,我们提出了一个联合编码理论,以说明从几何感知到认知地图的构建。该理论自然描述了一系列细胞类型,包括实验中观察到的边界向量细胞、边界细胞、“环”和“靶心”细胞等特殊例子。同样地,受后穹窿皮层中发现的自我中心和客体中心信息整合的启发,该理论还预测了一种新的细胞类型,称为几何细胞。几何细胞相对于环境中心编码局部空间的几何布局,与动物在局部空间内的位置和朝向无关。预测的几何细胞提供了局部场景的纯客体中心的高级表示,以支持快速形成捕获复杂环境空间布局的认知地图表示。该理论为空间认知和基于大脑的自主智能系统的神经机制提供了新的视角。