This　brief　presents　an　intrinsic　plasticity　(IP)-driven　neural-network-based　tracking　control　approach　for　a　class　of　nonlinear　uncertain　systems.　Inspired　by　the　neural　plasticity　mechanism　of　individual　neuron　in　nervous　systems,　a　learning　rule　referred　to　as　IP　is　employed　for　adjusting　the　radial　basis　functions　(RBFs),　resulting　in　a　neural　network　(NN)　with　both　weights　and　excitability　tuning,　based　on　which　neuroadaptive　tracking　control　algorithms　for　multiple-input-multiple-output　(MIMO)　uncertain　systems　are　derived.　Both　theoretical　analysis　and　numerical　simulation　confirm　the　effectiveness　of　the　proposed　method.