It has been found that in biological studies, the simple linear superposition mathematical model cannot be used to express the feature mapping relationship from multiple activated grid cells' grid fields to a single place cell's place field output in the hippocampus of the cerebral cortex of rodents. To solve this problem, people introduced the Gauss distribution activation function into the area. We in this paper use the localization properties of the function to deal with the linear superposition output of grid cells' input and the connection weights between grid cells and place cells, which filters out the low activation rate place fields. We then obtained a single place cell field which is consistent with biological studies. Compared to the existing competitive learning algorithm place cell model, independent component analysis method place cell model, Bayesian positon reconstruction method place cell model, our experimental results showed that the model on the neurophysiological basis can not only express the feature mapping relationship between multiple activated grid cells grid fields and a single place cell's place field output in the hippocampus of the cerebral cortex of rodents, but also make the algorithm simpler, the required grid cells input less and the accuracy rate of the output of a single place field higher.
Biological studies show that place cells are the main basis for rats to know their current location in space. Since grid cells are the main input source of place cells, a mapping model from grid cells to place cells needs to be constructed. To solve this problem, a neural network mapping model of back propagation error from grid cells to place cells is proposed in this paper, which can accurately express the location in a given region. According to the physiological characteristics of border cells’ specific discharge to the environment, the periodic resetting of the grid field phase by border cells is realized, and the position recognition in any space is completed by this model. In this paper, we designed a simulation experiment to compare the activity of the theoretical place cell plate, and then compared the time consumption of the competitive neural network model and the positioning error of RatSLAM pose cells plate. The experimental results showed that the proposed model could obtain a single place field, and the algorithm efficiency was improved by 85.94% compared with the competitive neural network model in the time-consuming experiment. In the localization experiment, the mean localization error was 41.35% lower than that of RatSLAM pose cells plate. Therefore, the location cognition model proposed in this paper can not only realize the efficient transfer of information between grid cells and place cells, but also realize the accurate location of its own location in any spatial area.