Organic　photoelectric　neuromorphic　devices　that　mimic　the　brain　are　widely　explored　for　advanced　perceptual　computing.　However,　current　individual　neuromorphic　synaptic　devices　mainly　focus　on　utilizing　linear　models　to　process　optoelectronic　signals,　which　means　that　there　is　a　lack　of　effective　response　to　nonlinear　structural　information　from　the　real　world,　severely　limiting　the　computational　efficiency　and　adaptability　of　networks　to　static　and　dynamic　information.　Here,　a　feedforward　photoadaptive　organic　neuromorphic　transistor　with　mixed-weight　plasticity　is　reported.　By　introducing　the　potential　of　the　space　charge　to　couple　gate　potential,　photoexcitation,　and　photoinhibition　occur　successively　in　the　channel　under　the　interference　of　constant　light　intensity,　which　enables　the　device　to　transform　from　a　linear　model　to　a　nonlinear　model.　As　a　result,　the　device　exhibits　a　dynamic　range　of　over　100　dB,　exceeding　the　currently　reported　similar　neuromorphic　synaptic　devices.　Further,　the　device　achieves　adaptive　tone　mapping　within　5　s　for　static　information　and　achieves　over　90%　robustness　recognition　accuracy　for　dynamic　information.　Therefore,　this　work　provides　a　new　strategy　for　developing　advanced　neuromorphic　devices　and　has　great　potential　in　the　fields　of　intelligent　driving　and　brain-like　computing.　This　work　proposes　a　feedforward　adaptive　organic　neuromorphic　transistor　with　mixed-weight　plasticity　(MP-ONH)　that　transitions　from　linear　mode　to　nonlinear　mode　based　on　light　intensity.　It　achieves　a　dynamic　range　of　100　dB　for　light　intensity,　surpassing　current　similar　synaptic　devices.　It　also　enables　adaptive　tone　mapping　in　5　s　for　static　information　and　achieves　over　90%　accuracy　in　robust　recognition　of　dynamic　information.image