The ongoing growth of use-cases for artificial neural networks (ANNs) fuels the search for new, tailor-made ANN-optimized hardware. Neuromorphic (brain-like) computers are among the proposed highly promising solutions, with optical neuromorphic realizations recently receiving increasing research interest. Among these, photonic neuronal models based on vertical cavity surface emitting lasers (VCSELs) stand out due to their favourable properties, fast operation and mature technology. In this work, we experimentally demonstrate different strategies to encode information into ultrafast spiking events in a VCSEL-neuron. We evaluate how the strength of the input perturbations (stimuli) influences the spike activation time, allowing for spike latency input coding. Based on a study of refractory behaviour in the system, we demonstrate the capability of the VCSEL-neuron to perform reliable binary-to-spike information coding with spiking rates surpassing 1 GHz. We also report experimental...

中文翻译：

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用于神经形态光子系统的垂直腔表面发射激光器中基于峰值的信息编码

人工神经网络（ANN）用例的不断增长推动了对新的，量身定制的ANN优化硬件的搜索。神经形态（类脑）计算机是提出的极有前途的解决方案之一，近来光学神经形态实现受到越来越多的研究兴趣。其中，基于垂直腔表面发射激光器（VCSEL）的光子神经元模型因其良好的性能，快速的操作和成熟的技术而脱颖而出。在这项工作中，我们实验性地演示了在VCSEL神经元中将信息编码为超快尖峰事件的不同策略。我们评估输入扰动（刺激）的强度如何影响峰值激活时间，从而允许峰值潜伏期输入编码。基于对系统中耐火性能的研究，我们展示了VCSEL神经元以超过1 GHz的峰值速率执行可靠的二进制到峰值信息编码的能力。我们还报告了实验性...