The　brain　structures　are　key　indicators　to　represent　the　complexity　of　many　cognitive　functions,　e.g.,　visual　pathways　and　memory　circuits.　Inspired　by　the　topology　of　the　mouse　brain　provided　by　the　Allen　Brain　Institute,　whereby　213　brain　regions　are　linked　as　a　mesoscale　connectome,　we　propose　a　mouse-brain　topology　improved　evolutionary　neural　network　(MT-ENN).　The　MT-ENN　model　incorporates　parts　of　biologically　plausible　brain　structures　after　hierarchical　clustering,　and　then　is　tuned　by　the　evolutionary　learning　algorithm.　Two　benchmark　Open-AI　Mujoco　tasks　were　used　to　test　the　performance　of　the　proposed　algorithm,　and　the　experimental　results　showed　that　the　proposed　MT-ENN　was　not　only　sparser　(containing　only　61%　of　all　connections),　but　also　performed　better　than　other　algorithms,　including　the　ENN　using　a　random　network,　standard　long-short-term　memory　(LSTM),　and　multi-layer　perception　(MLP).　We　think　the　biologically　plausible　structures　might　contribute　more　to　the　further　development　of　artificial　neural　networks.　©　2022,　IFIP　International　Federation　for　Information　Processing.