## 4 Experimental Results and Analysis
### Spatial Location Representation Map Construction Experiment  
The place cell properties obtained by the method in this paper were validated in a Hafting circular experimental environment. Figure 12 shows the firing response maps of the pose/place cells to the current location as acquired by RatSLAM, as well as by the competing Hebb learning networks and the method used in this paper at different times. In addition, the maps of their spatial location representations are generated after completing the environmental exploration. The RatSLAM method still did not perform well in the circular environment because the RatSLAM algorithm only integrated the rat's self-motion information to activate the pose cells in response to the rat's current location, without exhaustively considering the effect of the simple similarity boundaries on the firing pattern of the pose cells. At the same time, along with the increase in the environmental scene, the growth of the exploration time response leads to a gradual increase in the accumulated error, which renders the RatSLAM method unable to generate a single pose cell by which to respond accurately to the current location. Compared with the RatSLAM method, the competitive Hebb learning network can adjust the response values of the place cells via the connection weights between the place cells, which enables the system to maintain a better localization performance at the early stage of unknown environment explorations. However, as the exploration proceeds and the environment itself is characterized, the number of place cells gradually increases and the burden of adjusting the connection weights between the place cells increases, thus leading to the overlapping phenomenon of place cell responses, which then affects the accuracy of localization. In order to overcome the negative effects of environmental size and cumulative errors, this method introduces environmental boundary information to correct the place cell firing responses in real time, such that only one place cell responds to the current location at each individual time. By removing the interference of the other place cell firing responses, the accuracy of the location information estimation of this method is improved. Figure 11: The grid cell response maps obtained by the OI and CAN models, as well as those obtained by the method used in this paper at different times. (**a**) 5 min for the OI model to obtain results. (**b**) 5 min for the CAN model to obtain results. (**c**) 5 min for this paper’s method to obtain results. (**d**) 15 min for the OI model to obtain results. (**e**) 15 min for the CAN model to obtain results. (**f**) 15 min for this paper’s method to obtain results. (**g**) 30 min for the OI model to obtain results. (**h**) 30 min for the CAN model to obtain results. (**i**) 30 min for this paper’s method to obtain results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Exploration**} & \multicolumn{4}{c}{**Number of Activated**} & \multicolumn{4}{c}{**Grid Cell Fraction**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Grid Cells (pcs)**} & \multicolumn{2}{c}{**Grid Cell Fraction**} \\ \cline{2-7}  & **OI** & **CAN** & **SLRB** & **OI** & **CAN** & **SLRB** \\ \hline
5 & 171 & 160 & 128 & 0.71 & 0.75 & 0.74 \\
10 & 382 & 171 & 165 & 0.73 & 0.74 & 0.69 \\
15 & 410 & 290 & 195 & 0.68 & 0.79 & 0.79 \\
20 & 472 & 353 & 287 & 0.54 & 0.71 & 0.84 \\
25 & 524 & 427 & 354 & 0.51 & 0 Figure 13 shows a comparison of the experimental data that was generated by the three methods for constructing spatial location representation maps. From Figure 13a, it can be seen that the number of place cells activated by all three methods in the early stage of the environmental information exploration grew rapidly with time, but with the completion of the environmental learning in the rats, the growth of grid cells in this method entered a stable interval after 20 min. Furthermore, the final number was stabilized at about 300, which was reduced by about half when compared with the other two methods.