Neuromorphic　computing　inspired　by　the　neural　systems　in　human　brain　will　overcome　the　issue　of　independent　information　processing　and　storage.　An　artificial　synaptic　device　as　a　basic　unit　of　a　neuromorphic　computing　system　can　perform　signal　processing　with　low　power　consumption,　and　exploring　different　types　of　synaptic　transistors　is　essential　to　provide　suitable　artificial　synaptic　devices　for　artificial　intelligence.　Hence,　for　the　first　time,　an　electret-based　synaptic　transistor　(EST)　is　presented,　which　successfully　shows　synaptic　behaviors　including　excitatory/inhibitory　postsynaptic　current,　paired-pulse　facilitation/　depression,　long-term　memory,　and　high-pass　filtering.　Moreover,　a　neuromorphic　computing　simulation　based　on　our　EST　is　performed　using　the　handwritten　artificial　neural　network,　which　exhibits　an　excellent　recognition　accuracy　(85.88%)　after　120　learning　epochs,　higher　than　most　reported　organic　synaptic　transistors　and　close　to　the　ideal　accuracy　(92.11%).　Such　a　novel　synaptic　device　enriches　the　diversity　of　synaptic　transistors,　laying　the　foundation　for　the　diversified　development　of　the　next　generation　of　neuromorphic　computing　systems.