Intrinsic　plasticity　(IP)　is　an　unsupervised,　self-adaptive,　local　learning　rule　that　was　first　found　in　biological　nerve　cells,　and　has　been　shown　to　be　able　to　maximize　neuronal　information　transmission　entropy.　In　this　article,　we　propose　a　soft-reset　leaky　integrate-and-fire　(LIF)　model,　a　spiking　neuron　model　based　on　widely　used　LIF　neurons,　with　a　new　IP　learning　rule　that　optimizes　the　neuronal　membrane　potential　state　to　be　exponentially　distributed.　Previous　studies　have　generally　used　such　as　spiking　neuron　expected　firing　rate　as　the　target　variable　to　maximize　output　spike　distribution.　In　contrast,　the　proposed　soft-reset　model　can　avoid　the　problem　that　conventional　LIF　neuronal　membrane　potential　is　not　fully　differentiable,　hence　the　proposed　IP　rule　can　directly　regulate　the　membrane　potential　as　an　auxiliary　＂output　signal＂　to　desired　distribution　to　maximize　its　information　entropy.　We　experimentally　evaluated　the　proposed　IP　rule　for　pattern　recognition　on　the　spiking　feed-forward　and　spiking　convolutional　neural　network　models.　Experimental　results　verified　that　the　proposed　IP　rule　can　effectively　improve　spiking　neural　network　computational　performance　in　terms　of　classification　accuracy,　spiking　inference　speed,　and　noise　robustness.