Over　the　years,　memristors　have　received　much　attention　as　strong　candidates　in　the　field　of　neuromorphology　and　have　presented　a　wide　range　of　application　scenarios.　In　order　to　ensure　the　effective　imitation　of　neurons　by　memristors　during　electroforming,　there　is　usually　some　variability　in　the　selection　of　electrode　materials　at　both　ends,　but　this　results　in　the　threshold　voltage　of　the　device　being　asymmetric.　However,　electrical　signals　within　the　range　of　neuron　threshold　voltage　can　be　used　to　carry　and　transmit　information,　the　asymmetry　of　threshold　voltage　often　leads　to　the　error　and　leakage　of　stored　information.　In　this　work,　we　present　a　system　consisting　of　an　Au/TaOx/Ta　memristor　(MTSM),　a　diode,　and　two　load　resistors.　We　have　developed　artificial　neurons　on　account　of　Au/TaOx/Ta　that　demonstrate　outstanding　power-on　and　power-off　efficiency,　with　a　conversion　ratio　above　105.　Furthermore,　these　artificial　neurons　exhibit　sustained　stability　over　multiple　cycle　tests,　indicating　their　potential　for　practical　use.　Furthermore,　we　found　that　the　size　of　the　load　resistance　can　affect　the　switching　of　individual　devices　and　thus　control　the　threshold　voltage　range　of　artificial　neurons.　Therefore,　this　paper　proposes　a　new　method　to　optimize　the　threshold　voltage　of　artificial　neurons　based　on　load　resistance.　This　optimized　strategy　can　be　used　to　adjust　the　setting　and　resetting　of　the　threshold　voltage　of　neurons　thus　providing　a　potential　platform　to　facilitate　the　development　of　electronic　devices.　　©　2023　IEEE.