Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.

中文翻译：

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基于网格单元格和位置单元格的大脑启发式自适应空间表示模型。

网格细胞和位置细胞是动物脑中重要的神经元。它们之间的信息传递为动物的空间表示和导航提供了基础，也为研究智能主体的自主导航机制提供了参考。网格单元是位置单元的重要信息源。监督学习和非监督学习模型可用于模拟从网格单元输入生成位置单元。但是，现有模型预设了网格单元的点火特性。在本文中，我们提出了网格单元和位置单元的联合生成模型。首先，具有不均匀分布的视觉位置单元通过前馈网络生成具有局部发射场的视觉网格单元。第二，视觉网格单元和自身运动信息生成联合网格单元，其发射场通过遗传算法扩展到整个空间。最后，视觉位置单元和联合网格单元通过监督模糊自适应共振理论（ART）网络生成具有均匀分布的联合位置单元。仿真结果表明，该模型具有较强的环境适应性，可为环境信息不均匀的情况下智能主体的空间表示模型和脑启发导航机制的研究提供参考。视觉位置单元和联合网格单元通过监督模糊自适应共振理论（ART）网络生成具有均匀分布的联合位置单元。仿真结果表明，该模型具有较强的环境适应性，可为环境信息不均匀的情况下智能主体的空间表示模型和脑启发导航机制的研究提供参考。视觉位置单元和联合网格单元通过监督模糊自适应共振理论（ART）网络生成具有均匀分布的联合位置单元。仿真结果表明，该模型具有较强的环境适应性，可为环境信息不均匀的情况下智能主体的空间表示模型和脑启发导航机制的研究提供参考。