Neuromorphic　computing　with　brain-like　functions　has　become　one　of　the　important　strategies　for　the　von　Neumann　bottleneck.　However,　current　artificial　neuromorphic　systems　are　mainly　based　on　volatile　or　non-volatile　synaptic　devices,　which　limit　the　flexibility　and　computational　efficiency　of　neuromorphic　computing　systems.　Here,　we　report　an　adaptive　immunomorphic　hardware　based　on　the　heterostructure　of　MXene-TiO2　complexes　and　organic　semiconductors.　The　hardware　has　photon-triggered　synaptic　plasticity　for　accurate　recognition　and　electrically　triggered　non-volatile　retention　for　effective　preservation　of　weight　values.　As　a　result,　the　retraining　time　and　power　consumption　of　the　hardware　can　be　reduced　by　95%　and　96%,　respectively.　Moreover,　the　array　system　expanded　to　5　×　5　can　extract　special　information　from　complex　signals　within　0.2　s,　enabling　feature　information　recognition.　This　work,　therefore,　provides　a　new　strategy　for　improving　the　efficiency　of　artificial　neuromorphic　computation　and　has　significant　application　prospects　in　intelligent　sensing　systems　and　edge　computing.　©　2022　The　Authors