Zeng Taiping, Si Bailu, Li Xiaoli
International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo 113-0033, Japan.
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Curr Res Neurobiol. 2022 Mar 21;3:100035. doi: 10.1016/j.crneur.2022.100035. eCollection 2022.
The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus. In the path integration system, grid cell network and head direction (HD) cell network integrate movement and visual information, and form attractor states to represent the positions and head directions of the animal. In the cognitive map system, a topological map is constructed capturing the attractor states of the path integration system as nodes and the transitions between attractor states as links. On loop closure, when the animal revisits a familiar place, the topological map is calibrated to minimize odometry errors. The change of the topological map is mapped back to the path integration system, to correct the states of the grid cells and the HD cells. The proposed model was tested on iRat, a rat-like miniature robot, in a realistic maze. Experimental results showed that, after familiarization of the environment, both grid cells and HD cells develop globally coherent firing maps by map calibration and activity correction. These results demonstrate that the hippocampus and the entorhinal cortex work together to form globally coherent metric representations of the environment. The underlying mechanisms of the hippocampal-entorhinal circuit in capturing the structure of the environment from sequences of experience are critical for understanding episodic memory.
内嗅皮层中网格细胞的放电图被认为提供了一种高效的度量系统,能够在所有环境中支持空间推理。然而,网格细胞的空间表征是由局部环境线索决定,还是被组织成全局连贯的模式,仍未确定。我们提出了一个导航模型,该模型在内嗅皮层中包含一个路径积分系统,在海马体中包含一个认知地图系统。在路径积分系统中,网格细胞网络和头部方向(HD)细胞网络整合运动和视觉信息,并形成吸引子状态来表征动物的位置和头部方向。在认知地图系统中,构建一个拓扑地图,将路径积分系统的吸引子状态作为节点,将吸引子状态之间的转换作为链接。在闭环时,当动物重新访问一个熟悉的地方时,拓扑地图会进行校准,以最小化里程计误差。拓扑地图的变化会映射回路径积分系统,以校正网格细胞和HD细胞的状态。我们在类大鼠微型机器人iRat上的一个逼真迷宫中对所提出的模型进行了测试。实验结果表明,在熟悉环境后,网格细胞和HD细胞都通过地图校准和活动校正形成了全局连贯的放电图。这些结果表明,海马体和内嗅皮层共同作用,形成环境的全局连贯度量表征。海马体 - 内嗅皮层回路从经验序列中捕捉环境结构的潜在机制对于理解情景记忆至关重要。