上海交通大学智能微波光波融合创新中心（imLic）博士生马伯文的工作——Comb-based photonic neural population for parallel and nonlinear processing（利用梳状光子神经元群实现并行和非线性处理）的相关成果近期被Photonics Research期刊接收发表，该工作得到了国家重点研发计划（2019YFB2203700）、国家自然科学基金（61822508）的部分资助。该工作提出了一种基于光频梳的光子神经元群实现方法，成功模拟了生物神经元群的非线性分布式响应与群体活动编码特性。利用光域的宽带优势，可以高效地扩展光子神经元群规模，而无需增加硬件成本。实验结果表明，光子神经元群能够在模拟域直接对射频信号进行分布式编码，进而实现复杂射频模式的分类任务，并且具有硬件复杂度低、响应延迟小等优势。该工作对于光子神经拟态处理领域以及超快射频频谱监测等应用具有借鉴意义。
摘要: It is believed that neural information representation and processing relies on the neural population instead of a single neuron. In neuromorphic photonics, photonic neurons in the form of nonlinear responses have been extensively studied in single devices and temporal nodes. However, to construct a photonic neural population (PNP), the process of scaling up and massive interconnections remain challenging considering the physical complexity and response latency. Here, we propose a comb-based PNP interconnected by carrier coupling with superior scalability. Two unique properties of neural population are theoretically and experimentally demonstrated in the comb-based PNP, including nonlinear response curves and population activities coding. A classification task of three input patterns with dual radio-frequency (RF) tones is successfully implemented in a time-efficient manner, which manifests the comb-based PNP can make effective use of the ultra-broad bandwidth of photonics for parallel and nonlinear processing.