Artificial　neural　systems　demonstrate　outstanding　performance　by　processing　sensory　data　and　realtime　contexts　in　parallel,　far　surpassing　conventional　von　Neumann　computers　in　terms　of　energy　efficiency.　This　property　has　sparked　widespread　interest　within　the　field　of　artificial　intelligence.　In　recent　years,　research　on　electric-double-layer　synaptic　transistors　has　attracted　much　attention　due　to　their　similarity　in　ionic　motion　modulation　to　that　of　biological　synapses,　thereby　demonstrating　a　wealth　of　potential　application　scenarios.　In　this　paper,　we　demonstrate　a　solid-state　electrolyte-gated　transistor　that　uses　TaOx　with　a　unique　ionic　composition　as　the　insulating　layer　and　transparent　indium　tin　oxide　(ITO)　as　the　semiconductor　layer　to　prepare　artificial　synaptic　thin-film　transistors　(TFTs)　with　signal　transmission　and　self-learning　properties.　The　devices　exhibit　significant　memory　holding,　memory　bank　voltage　exceeds　6V　at　operating　voltages　below　10V.　The　shift　of　oxygen　Void　Sites　in　the　insulating　layer　in　synaptic　transistor,　entrained　by　electrical　forces　created　from　input　signals,　plays　a　crucial　role　in　simulating　synaptic　behavior.　In　addition,　the　device　successfully　simulates　the　enhancement　and　inhibition　of　synaptic　weights,　such　as　excitatory　postsynaptic　current　(EPSC),　inhibitory　response　(IPSC),　paired　pulse　facilitation　(PPF),　and　long-term　potentiation　(LTP).　©　2024　IEEE.