Different　types　of　dynamics　and　plasticity　principles　found　through　natural　neural　networks　have　been　well-applied　on　Spiking　neural　networks　(SNNs)　because　of　their　biologically-plausible　efficient　and　robust　computations　compared　to　their　counterpart　deep　neural　networks　(DNNs).　Here,　we　further　propose　a　special　Neuronal-plasticity　and　Reward-propagation　improved　Recurrent　SNN　(NRR-SNN).　The　historically-related　adaptive　threshold　with　two　channels　is　highlighted　as　important　neuronal　plasticity　for　increasing　the　neuronal　dynamics,　and　then　global　labels　instead　of　errors　are　used　as　a　reward　for　the　paralleling　gradient　propagation.　Besides,　a　recurrent　loop　with　proper　sparseness　is　designed　for　robust　computation.　Higher　accuracy　and　stronger　robust　computation　are　achieved　on　two　sequential　datasets　(i.e.,　TIDigits　and　TIMIT　datasets),　which　to　some　extent,　shows　the　power　of　the　proposed　NRR-SNN　with　biologically-plausible　improvements.