Based on the well-known Fabry–Prot approach, after taking into account the variation of bias current of the vertical-cavity semiconductor optical amplifier (VCSOA) according to the present synapse weight, we implement the optical spike timing dependent plasticity (STDP) with weight-dependent learning window in a VCSOA with double optical spike injections, and numerically investigate the corresponding weight-dependent STDP characteristics. The simulation results show that, the bias current of VCSOA has significant effect on the optical STDP curve. After introducing an adaptive variation of the bias current according to the present synapse weight, the optical weight-dependent STDP based on VCSOA can be realized. Moreover, the weight training based on the optical weight-dependent STDP can be effectively controlled by adjusting some typical external or intrinsic parameters and the excessive adjusting of synaptic weight is avoided, which can be used to balance the stability and competition among synapses and pave a way for the future large-scale energy efficient optical spiking neural networks based on the weight-dependent STDP learning mechanism.

基于众所周知的 Fabry-Prot 方法，在考虑垂直腔半导体光放大器 (VCSOA) 偏置电流根据当前突触权重的变化后，我们实现了光尖峰时序相关可塑性 (STDP)具有双光尖峰注入的 VCSOA 中的权重相关学习窗口，并数值研究相应的权重相关 STDP 特性。仿真结果表明，VCSOA的偏置电流对光STDP曲线有显着影响。根据当前突触权重引入偏置电流的自适应变化后，可以实现基于VCSOA的光学权重相关STDP。而且，