## 1 Introduction
predicted the existence of a cell in the rat brain that responds to boundary information with a firing response and is able to use this response to correct for position errors in the position of the place cell [20]. In 2008, researchers discovered a new cell type in the rat internal olfactory cortex that fires when the animal approaches a wall or is separated by other obstacles; this new cell type was accordingly named boundary cells [21; 22]. In order to investigate the effect of boundary cells on the distribution and localization accuracy of grid cells, [PERSON] et al. replaced the circular environment with a hexagonal environment that was rich in environmental boundary information. Furthermore, the grid cell distribution was rearranged, and the localization accuracy was improved [23]. Based on this, a spatial location representation method (SLRB) incorporating boundary information was proposed, inspired by the mammalian spatial cognitive mechanism, which obtains the boundary cell response values through the mutual excitation and inhibition of direction-aware information, as well as the distance-aware information between the mobile robot and the environment boundary. This method obtains the boundary cell response values by mutual excitation and through the suppression of direction and distance-sensing information between the mobile robot and the environment boundary. The method then maps the boundary cell response values and the self-motion information of the mobile robot to the input layer of LAHN, and then the output layer of LAHN is mapped to the grid cell response values. The grid cell response values are used as the main input source of the place cells and the response values of the place cells are obtained through the competitive Hebb learning network. At the same time, when the mobile robot runs to the boundary cell excitation zone through the activated boundary cells to correct the location information of the place cells, the mobile robot then learns and remembers the location points in a specific space, as well as constructs a spatial location representation map that can accurately express the current spatial characteristics. A block diagram of the overall system structure is shown in Figure 1.  
The main innovations of this paper are as follows:  
1. Inspired by the mammalian spatial cognitive mechanism, a new boundary cell model is proposed to establish boundary cell activity states in multiple scenarios by the mutual excitation and the inhibition of the direction-aware and distance-aware information that is acquired by mobile robots. The boundary cell model proposed in this paper can encode the boundary information in the environment and supplement the lack of environmental boundary perceptual information with path integration. 2. The physiological phenomena indicate that the environmental boundary information can be used as the supplementary information of grid cells. The method in this paper maps the boundary cell response values to the input layer of LAHN, generates grid cells by LAHN learning rules, and uses the boundary cell response values to correct the grid cell distribution pattern, such that the grid cell firing response and distribution that is activated by the method are more consistent with the physiological characteristics. 3. According to the problem that the mobile robot runs for a long time in an unknown environment, when the mobile robot reaches the boundary cell excitation zone, the accumulated error caused by the long running time of the position cell is corrected by the activated boundary cells, such that only one place cell responds to the current position at each time in order to improve the location accuracy of the system.