Artificial　synapses,　basic　units　of　neuromorphic　hardware,　have　been　studied　to　emulate　synaptic　dynamics,　which　are　beneficial　for　realizing　high-quality　neural　networks.　Currently,　two-dimensional　(2D)　material　heterojunction　structures　are　widely　used　in　the　study　of　artificial　synapses;　however,　their　dynamic　weight-updating　characteristics　are　restricted　owing　to　their　high　nonlinearity　and　low　symmetricity.　In　this　study,　we　treated　h-BN　with　oxygen　plasma　to　form　a　charge-trapping　layer　(CTL),　and　we　prepared　2D　ReS2/CTL/h-BN　heterojunction　synapses.　The　device　achieves　a　large　memory　window　and　excellent　synaptic　performance　and　simulates　the　adaptive　behavior　of　the　human　eye　through　the　synergistic　modulation　of　the　optoelectronic　double　pulse.　The　mechanism　of　the　effect　of　trap　states　in　CTL　on　the　weight-updating　performance　was　analyzed,　and　the　device　was　further　optimized.　In　the　long-term　potentiation/depression　(LTP/D)　weight-updating　characteristic　of　the　device,　the　nonlinearity　was　reduced　to　0.63　and　symmetricity　reached　41.25,　which　is　superior　to　most　similar　devices　reported　to　date.　Therefore,　this　research　provides　insights　into　improving　the　LTP/D　weight-updating　performance　of　synaptic　devices.