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.　Copyright　©　2020　by　Editorial　Office　of　Journal　of　Biomedical　Engineering.