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.