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In\u2010sensor computing based on optoelectronic synapses allows efficient computation by directly sensing and processing optical signals. Herein, an optoelectronic synapse based on Au\/ZnO:N\/IGZO\/TiN structure is proposed. It shows uniform optical SET and electrical RESET behaviors, with various light\u2010tunable plasticity. Furthermore, a 4\u2010bit reservoir is experimentally implemented on the device, which is ideal to construct in\u2010sensor reservoir computing (RC) system. By converting spatiotemporal optical signals to higher dimensional feature space, in\u2010sensor RC has a great advantage in processing sequential visual information. Simulation results demonstrate that the in\u2010sensor RC system based on the proposed synapse achieves a considerable recognition accuracy (90.45%) for the MNIST dataset with very limited 36\u201030\u201010 perceptron network, and a 97.14% accuracy for human action classification from sequential vision data based on the Weizmann dataset. 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