Aiming　at　environmental　cognition　problem　of　mobile　robot,　inspired　by　the　activation　of　hippocampal　place　cells　in　particular　regions,　a　dynamic　growing　and　pruning　place　cells-based　cognitive　map　model　(DGPPCCMM)　is　established,　which　enables　robot　to　construct　the　cognitive　map　self-organizingly　by　interacting　with　the　environment　and　to　implement　environmental　cognition.　In　the　beginning,　cognitive　map　consists　of　the　activated　place　cell　responding　to　current　region;　With　the　interaction　with　the　environment,　the　responding　activated　place　cells　at　different　regions　are　gradually　obtained,　and　the　relationship　among　them　is　established,　thus　realizing　the　dynamic　growing　of　the　cognitive　map;　If　new　obstacles　are　discovered　in　the　visited　area,　the　cognitive　map　is　updated　using　dynamic　pruning　mechanism.　Besides,　a　sequence　planning　algorithm　of　place　cells　is　proposed　to　realize　robot　navigation,　which　uses　the　constructed　cognitive　map　as　input.　To　verify　the　correctness　and　validity　of　the　model,　the　classical　Tolman　detour　task　was　reproduced.　Results　show　that　the　model　can　enable　robot　to　construct　and　update　the　cognitive　map　dynamically　in　the　process　of　interacting　with　the　environment,　and　to　complete　the　reproduction　of　the　Tolman　detour　task　substantially.　In　addition,　comparative　experiments　with　occupancy　grids,　dynamic　window　approach　and　discussion　about　other　cognitive　map　models　are　carried　out,　and　results　show　the　advantages　of　the　proposed　methods　in　the　aspects　of　simplicity,　completeness　of　the　constructed　cognitive　maps　and　adaptability　to　dynamic　obstacles.　Copyright　©　2021　Acta　Automatica　Sinica.　All　rights　reserved.