In　the　cerebellum,　long-term　depression　(LTD)　plays　a　key　function　in　sculpting　neuronal　circuits　to　store　information,　since　motor　learning　and　memory　are　thought　to　be　associated　with　such　long-term　changes　in　synaptic　efficacy.　To　better　understand　the　principles　of　transmission　of　information　in　the　cerebellum,　we,　in　our　model,　distinguished　different　types　of　neurons　(type　1-　and　type　2-like)　to　examine　the　neuronal　excitability　and　analyze　the　interspike　interval　(ISI)　bifurcation　phenomenon　in　these　units,　and　then　built　a　Purkinje　cell　circuit　to　study　the　impact　of　external　stimulation　on　LTD　in　this　circuit.　According　to　the　results　of　computational　analysis,　both　climbing　fiber-Purkinje　cell　and　granule　cell-Purkinje　cell　circuits　were　found　to　manifest　LTD;　the　external　stimuli　would　influence　LTD　by　changing　both　depression　time　and　depression　intensity.　All　of　the　simulated　results　showed　that　LTD　is　a　very　significant　factor　in　the　Purkinje　circuit　networks.　Finally,　to　deliver　the　learning　regularities,　we　simulated　spike　timing-dependent　plasticity　(STDP)　by　increasing　the　CaP　conductance.