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 real-time manner, which manifests the comb-based PNP can make effective use of the ultra-broad bandwidth of photonics for parallel and nonlinear processing.

中文翻译：

---

用于并行和非线性处理的基于梳的光子神经种群

人们认为神经信息的表示和处理依赖于神经群体而不是单个神经元。在神经形态光子学中，非线性响应形式的光子神经元已在单个设备和时间节点中得到广泛研究。然而，为了构建光子神经群 (PNP)，考虑到物理复杂性和响应延迟，扩大和大规模互连的过程仍然具有挑战性。在这里，我们提出了一种通过载波耦合互连的基于梳状的 PNP，具有卓越的可扩展性。在基于梳子的 PNP 中，神经群体的两个独特属性在理论上和实验上得到了证明，包括非线性响应曲线和群体活动编码。