The method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.
直接使用速度信息和角度信息驱动网格细胞吸引子对环境编码的方法，抗干扰能力较差且不具有仿生性。针对这一问题，本文提出一种基于感知速度与感知角度的网格野计算模型。其特点在于，通过对视觉流进行解码处理获得视觉速度，对速度细胞建模并解码获得本体速度，对视觉速度和本体速度进行融合求得感知速度信息；利用加入兴奋性连接的一维环状模型模拟头朝向细胞的放电机制，使机器人以仿生的方式获取当前的感知角度信息。最后，融合速度和角度两种感知信息实现对网格细胞吸引子模型的驱动。对所提模型进行实验验证，结果表明该模型可以实现网格细胞周期性六边形放电野模式以及精确的路径积分功能。研究成果为仿海马认知机制的机器人认知地图构建方法研究奠定了基础。.
Keywords: grid cell; grid field computing model; perceived angle; perceived speed; speed cell.