Inspired　by　information　processing　in　biological　systems,　the　data　processing　efficiency　of　sensor　systems　combined　with　artificial　neuromorphic　devices　has　been　significantly　improved.　However,　the　current　reports　on　artificial　perception　systems　are　mainly　based　on　artificial　synapses,　while　little　attention　has　been　taken　on　the　artificial　neuron-based　perception　systems.　Here,　we　propose　a　self-powered　sensory　neuron　(SPSN)　composed　of　a　TaOx-based　device-level　artificial　neuron　and　a　high-sensitivity　triboelectric　nanogenerator　(TENG)　for　selfpowered　artificial　tactile　sensing　system.　The　SPSN　with　volatile　switching　and　a　high　on/off　ratio　of　105　can　mimic　the　leaky-integrate-and-fire　(LIF)　neuron　model　without　additional　circuitry　and　reset　operations,　while　multiple　stimulus　inputs　can　be　integrated　to　extract　characteristic　events　due　to　its　corresponding　firing　times　to　different　stimuli.　Moreover,　we　successfully　constructed　a　64　＇　64　neuron-based　artificial　sensing　array　system　and　extracted　the　pressing　trajectories　and　textures　by　the　dynamic　threshold　characteristics　of　sensory　neurons.　Compared　with　the　synapse-based　artificial　perception　system,　the　neuron-based　artificial　perception　system　provides　more　feature　extraction　layers　and　faster　data　processing　speed.　Our　results　provide　an　effective　strategy　for　building　next-generation　neuromorphic　perception　networks,　intelligent　human-computer　interaction　and　high-speed　feature　recognition.