In　this　study,　a　modular　design　methodology　inherited　from　cognitive　neuroscience　and　neurophysiology　is　proposed　to　develop　artificial　neural　networks,　aiming　to　realize　the　powerful　capability　of　brain-divide　and　conquer-when　tackling　complex　problems.　First,　a　density-based　brain-like　partition　method　is　developed　to　construct　the　modular　architecture,　with　a　highly　connected　center　in　each　sub-network　as　the　human　brain.　The　whole　task　is　also　divided　into　different　sub-tasks　at　this　stage.　Then,　a　compact　radial　basis　function　(RBF)　network　with　fast　learning　speed　and　desirable　generalization　performance　is　applied　as　the　sub-network　to　solve　the　corresponding　task.　On　the　one　hand,　the　modular　structure　helps　to　improve　the　ability　of　neural　networks　on　complex　problems　by　implementing　divide　and　conquer.　On　the　other　hand,　sub-networks　with　considerable　ability　could　guarantee　the　parsimonious　and　generalization　of　the　entire　neural　network.　Finally,　the　novel　modular　RBF　(NM-RBF)　network　is　evaluated　through　multiple　benchmark　numerical　experiments,　and　results　demonstrate　that　the　NM-RBF　network　is　capable　of　constructing　a　relative　compact　architecture　during　a　short　learning　process　with　achievable　satisfactory　generalization　performance,　showing　its　effectiveness　and　outperformance.