In　this　paper,　a　spiking　growing　algorithm　(SGA)　is　proposed　for　optimizing　the　structure　of　radial　basis　function　(RBF)　neural　network.　Inspired　by　the　synchronous　behavior　of　spiking　neurons,　the　spiking　strength　(ss)　of　the　hidden　neurons　is　defined　as　the　criteria　of　SGA,　which　investigates　a　new　way　to　simulate　the　connections　between　hidden　and　output　neurons　of　RBF　neural　network.　This　SGA-based　RBF　(SGA-RBF)　neural　network　can　self-organize　the　hidden　neurons　online,　to　achieve　the　appropriate　network　efficiency.　Meanwhile,　to　ensure　the　accuracy　of　SGA-RBF　neural　network,　the　structure-adjusting　and　parameters-training　phases　are　performed　simultaneously.　Simulation　results　demonstrate　that　the　proposed　method　can　obtain　a　higher　precision　in　comparison　with　some　other　existing　methods.