Artificial　neurons　have　received　extensive　attention　as　an　important　part　of　neuromorphic　computing.　Recently,　tremendous　efforts　have　been　made　on　the　memristor-based　neurons,　while　the　regulation　of　performance　of　such　neurons　and　its　underlining　mechanism　has　been　rarely　studied.　In　this　work,　we　propose　an　artificial　neuron　device　based　on　Ag/TaOx/Si,　which　exhibits　good　threshold　switching　characteristics　(on-off　ratio　above　10(5))　along　with　good　device　stability　and　cycling　stability.　The　Leaky　Integrate-and-Fire　(LIF)　neuron　model　is　successfully　simulated　without　additional　circuitry,　including　leaky　integrated　firing　and　refractory　periods.　In　addition,　the　effect　of　oxygen　vacancy　concentration　on　the　performance　of　artificial　neurons　is　investigated,　and　the　results　showed　that　an　increase　of　oxygen　vacancies　can　significantly　reduce　the　threshold　voltage　of　neuron　activation,　the　holding　voltage　and　the　probability　of　refractory　period.　This　work　provides　a　simple　and　effective　strategy　for　the　development　of　artificial　neurons　with　tunable　properties.