Zhang Zhihui, Tang Fengzhen, Li Yiping, Feng Xisheng
The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China.
University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China.
Cogn Neurodyn. 2024 Aug;18(4):1861-1876. doi: 10.1007/s11571-023-10047-z. Epub 2024 Jan 2.
The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.
海马体-内嗅皮层回路被认为在动物的空间认知中发挥着重要作用。然而，该回路内信息流的机制及其对网格细胞模块功能的贡献仍是讨论的话题。主流理论认为，网格细胞主要受来自内侧内嗅皮层的自我运动输入的影响，位置细胞通过对网格细胞的视觉校准起次要作用。然而，最近的证据表明，自我运动输入和视觉线索可能共同促成网格状模式的形成。在本文中，我们基于一种空间转换机制引入了一种新颖的连续吸引子网络模型。这种机制能够在网格细胞模块内整合自我运动输入和视觉线索，协同驱动网格状模式的形成。从网络内单个神经元的角度来看，我们的模型成功地复制了网格放电模式。从网络内神经群体活动的角度来看，该网络能够形成并驱动激活波峰，这描述了网格细胞模块的特征，即路径整合。通过进一步的探索和实验，我们的模型在路径整合方面能够展现出显著的性能。这项研究为理解自我运动和视觉输入如何促成网格细胞模块内神经活动的机制提供了新的见解。此外，它为实现精确的路径整合提供了理论支持，这对各种需要空间导航和绘图的应用具有重要意义。
