中心硕士生段炼的工作——Weightless photonic spike processing of time-of-flight signals with delay learning（基于延时学习的飞行时间信号无权重光子尖峰处理）的相关成果近期被optics letters期刊接收，该工作得到了国家自然科学基金（T2225023, 62305213）的部分资助。
生物信息蕴含在精确的峰值时刻中，突触可仅调控尖峰延时完成声音定位、方向选择等功能。然而目前光学神经拟态需要协同处理时间和权重，完成编码、学习和判决等操作，处理效率较低。本文提出一种基于突触延时可塑性的光学尖峰处理方法，通过延时线调节光子尖峰的时刻来提取飞行时间信号的时序特征，在仅时间维度完成编码学习判决，提高处理时效性。
通过理论分析和数值仿真，在三维物体分类任务中，基于延时学习的无权重光子尖峰处理能够实现96.36%的准确率。进一步，在基于注入锁定激光器与可调谐光延时线的实验中，飞行时间信号的处理延时为58.66ns，相较于传统方案的处理延时降低约两个数量级。
摘要: Time-of-flight (ToF) signal processing has become increasingly crucial in depth perception applications. We propose a photonic spike processing method for ToF signals based on synaptic delay plasticity, which adjusts the spike timing of encoded signals to achieve low-latency processing without the need for weight loading and control. This method employs photonic neurons that directly encode optical pulses of ToF signals into temporal spike sequences, eliminating the necessity for a time-to-digital converter (TDC). We use tunable optical delay lines to emulate the photonic synaptic regulation of spike timing. In addition, we demonstrate the efficacy of a photonic spiking neural network that trains the synaptic delay parameters using the ModelNet dataset, achieving an accuracy of 96.36 %. In experiments, the processing delay for ToF signals is 58.66 ns, representing a reduction of two order of magnitude compared with traditional TDC-based methods. This approach facilitates applying synaptic diversity in photonic neuromorphic information processing.