The　biologically　discovered　intrinsic　plasticity　(IP)　learning　rule,　which　changes　the　intrinsic　excitability　of　an　individual　neuron　by　adaptively　turning　the　firing　threshold,　has　been　shown　to　be　crucial　for　efficient　information　processing.　However,　this　learning　rule　needs　extra　time　for　updating　operations　at　each　step,　causing　extra　energy　consumption　and　reducing　the　computational　efficiency.　The　event-driven　or　spike-based　coding　strategy　of　spiking　neural　networks　(SNNs),　i.e.,　neurons　will　only　be　active　if　driven　by　continuous　spiking　trains,　employs　all-or-none　pulses　(spikes)　to　transmit　information,　contributing　to　sparseness　in　neuron　activations.　In　this　article,　we　propose　two　event-driven　IP　learning　rules,　namely,　input-driven　and　self-driven　IP,　based　on　basic　IP　learning.　Input-driven　means　that　IP　updating　occurs　only　when　the　neuron　receives　spiking　inputs　from　its　presynaptic　neurons,　whereas　self-driven　means　that　IP　updating　only　occurs　when　the　neuron　generates　a　spike.　A　spiking　convolutional　neural　network　(SCNN)　is　developed　based　on　the　ANN2SNN　conversion　method,　i.e.,　converting　a　well-trained　rate-based　artificial　neural　network　to　an　SNN　via　directly　mapping　the　connection　weights.　By　comparing　the　computational　performance　of　SCNNs　with　different　IP　rules　on　the　recognition　of　MNIST,　FashionMNIST,　Cifar10,　and　SVHN　datasets,　we　demonstrate　that　the　two　event-based　IP　rules　can　remarkably　reduce　IP　updating　operations,　contributing　to　sparse　computations　and　accelerating　the　recognition　process.　This　work　may　give　insights　into　the　modeling　of　brain-inspired　SNNs　for　low-power　applications.