Large language models for artificial intelligence applications require energy-efficient computing. Neuromorphic photonics has the potential to reach significantly lower energy consumption in comparison with classical electronics. A recently proposed memlumor device uses photoluminescence output that carries information about its excitation history via the excited state dynamics of the material. Solution-processed metal halide perovskites can be used as efficient memlumors. We show that trapping of photogenerated charge carriers modulated by photoinduced dynamics of the trapping states themselves explains the memory response of perovskite memlumors on time scales from nanoseconds to minutes. The memlumor concept shifts the paradigm of the detrimental role of charge traps and their dynamics in metal halide perovskite semiconductors by enabling new applications based on these trap states. The appropriate control of defect dynamics in perovskites allows these materials to enter the field of energy-efficient photonic neuromorphic computing, which we illustrate by proposing several possible realizations of such systems.