Spike　timing-dependent　plasticity　(STDP)　plays　an　important　role　in　sculpting　neural　circuits　to　store　information　in　the　hippocampus,　since　motor　learning　and　memory　are　thought　to　be　closely　linked　with　this　type　of　synaptic　plasticity.　We　built　a　computational　model　to　study　the　potential　learning　rule　by　linearly　changing　the　synaptic　weight　and　number　of　the　synapses　involved.　The　main　findings　are　the　following:　(i)　changes　in　the　synaptic　weight　and　number　of　synapses　can　lead　to　different　long-term　changes　in　the　synaptic　efficacy;　(ii)　the　first　spike　pair　of　two　neurons　exerts　a　great　influence　on　the　subsequent　spike　pair;　a　pre-post　spiking　pair　reinforces　the　subsequent　paired　spiking,　while　a　post-pre　spiking　pair　depresses　this　paired　spiking;　(iii)　when　the　synaptic　weight　and　synaptic　number　change,　the　interval　in　the　first　spiking　pair　is　reduced,　which　directly　influences　the　first　spiking　pair,　and　(iv)　when　a　stellate　neuron　is　stimulated　weakly　or　the　capacitance　of　a　CA1　pyramidal　neuron　is　decreased,　LTP　is　produced　more　easily　than　LTD;　in　the　opposite　case,　LTD　is　produced　more　readily;　an　increase　of　the　synaptic　number　can　promote　activation　of　CA1　pyramidal　neurons.