Artificial　sensory　memory,　which　is　expected　to　collect,　integrate,　and　refine　massive　sensory　data　timely　for　dynamically　training　the　bioinspired　neural　network,　is　a　promising　candidate　to　achieve　novel　architectures　of　hardware　artificial　intelligence　to　mimic　neural　network.　Unfortunately,　the　reports　about　artificial　sensory　memory　are　very　limited　and　more　importantly,　there　are　still　many　unsolved　problems　in　previously　reported　artificial　sensory　memory　devices,　such　as　the　low　sensitivity　of　perception　receptors,　high　power　consumption,　and　realization　of　instantaneous　neuromorphic　computing.　Here,　we　propose　a　rapid-response,　high-sensitivity,　and　self-powered　artificial　sensory　memory,　which　is　integrated　with　a　triboelectric　nanogenerator　(TENG)　and　a　field　effect　synaptic　transistor,　and　is　able　to　achieve　real-time　neuromorphic　computing　with　a　TENG　matrix　for　the　first　time.　Typical　properties　of　sensory　memory　are　successfully　demonstrated,　such　as,　excitatory　postsynaptic　current　and　paired　pulse　facilitation,　followed　with　hierarchical　memorial　processes　from　sensory　memory　to　short-term　memory　and　to　long-term　memory.　Finally,　28　x　28　matrix　triboelectric　sensory　receptors　are　fabricated　to　connect　the　real-time　handwritten　image　with　large-scale　data　processing.　This　work　proposed　a　remarkable　self-powered　artificial　afferent　nerve　to　realize　rapid　and　high-sensitivity　response,　which　would　show　a　widespread　potential　in　low　consumption　artificial　neuromorphic　interface　such　as　human-robot　interaction,　edge　computing　and　neurorobotics.