To　build　neuromorphic　computing　networks　equivalent　to　the　human　brain,　single　artificial　synaptic　devices　should　exhibit　low　energy　consumption　down　to　femtojoules.　However,　most　existing　solutions　for　implementing　low-energy　synaptic　devices　based　on　an　Ohmic　contact　are　complex　in　structure　or　require　specific　materials,　which　hinder　the　further　development　of　artificial　neural　networks.　In　this　study,　a　Schottky-barrier-regulated　organic　synaptic　transistor　(SBROST)　was　reported.　The　device　performance　was　improved　by　introducing　the　Schottky　barrier　at　the　contact　interface　between　the　source　electrode　and　the　semiconductor,　thereby　considerably　reducing　the　energy　consumption　of　one　synaptic　event　compared　with　conventional　OSTs　with　an　Ohmic　contact.　The　SBROST　can　not　only　reduce　the　device’s　operating　voltage　and　current　but　also　possess　a　simple　structure　that　can　be　utilized　in　different　organic　synaptic　devices.　Furthermore,　high　recognition　accuracy　at　low　energy　consumption　can　be　achieved　by　the　SBROST.　After　100　epochs,　the　SBROST-based　handwritten　artificial　neural　network　exhibits　excellent　recognition　accuracy　(93.53%),　which　is　close　to　the　ideal　accuracy　(95.62%).　The　scheme　of　introducing　a　Schottky　barrier　into　synaptic　transistors　offers　a　new　perspective　for　constructing　brain-like　neural　computing　networks.　[Figure　not　available:　see　fulltext.]　©　2023,　Science　China　Press.