Reproducing　the　spatial　cognition　of　animals　using　computational　models　that　make　agents　navigate　autonomously　has　attracted　much　attention.　Many　biologically　inspired　models　for　spatial　cognition　focus　mainly　on　the　simulation　of　the　hippocampus　and　only　consider　the　effect　of　external　environmental　information　(i.e.,　exogenous　information)　on　the　hippocampal　coding.　However,　neurophysiological　studies　have　shown　that　the　striatum,　which　is　closely　related　to　the　hippocampus,　also　plays　an　important　role　in　spatial　cognition　and　that　information　inside　animals　(i.e.,　endogenous　information)　also　affects　the　encoding　of　the　hippocampus.　Inspired　by　the　progress　made　in　neurophysiological　studies,　we　propose　a　new　spatial　cognitive　model　that　consists　of　analogies　between　the　hippocampus　and　striatum.　This　model　takes　into　consideration　how　both　exogenous　and　endogenous　information　affects　coding　by　the　environment.　We　carried　out　a　series　of　navigation　experiments　that　simulated　a　water　maze　and　compared　our　model　with　other　models.　Our　model　is　self-adaptable　and　robust　and　has　better　performance　in　navigation　path　length.　We　also　discuss　the　possible　reasons　for　the　results　and　how　our　findings　may　help　us　understand　real　mechanisms　in　the　spatial　cognition　of　animals.　©　2021,　Institute　of　Automation,　Chinese　Academy　of　Sciences　and　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.