Toward Neuromorphic Photonic Networks of Ultrafast Spiking Laser Neurons
Toward Neuromorphic Photonic Networks of Ultrafast Spiking Laser Neurons
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DOI：
10.1109/jstqe.2019.2931215
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发表时间：
2020-01-01
影响因子：
4.9
通讯作者：
Hurtado, Antonio
中科院分区：
文献类型：
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作者：
Robertson, Joshua;Wade, Ewan;Hurtado, Antonio
我们报告超快人工激光神经元和他们的潜力，未来的神经形态（类脑）光子信息处理系统。我们介绍了我们最近和正在进行的活动，证明可控激励的尖峰信号在光学神经元的基础上垂直腔表面发射激光器（VCSEL神经元）。这些尖峰机制类似于生物神经元所表现出的尖峰机制，但速度为亚纳秒级（快7个数量级）。我们还描述了不同的方法，光学或电子激发技术的基础上，亚纳秒尖峰信号的VCSEL神经元的激活/抑制。我们报告了我们的工作，展示了VCSEL神经元之间的尖峰模式的通信，以实现未来的光学神经形态网络。此外，新发现表明，VCSEL-Neurons可以执行多种神经启发的锋电位处理任务。我们实验证明光子尖峰存储模块使用单个和相互耦合的VCSEL神经元。此外，据我们所知，首次通过实验证明了使用VCSEL-神经元系统对视网膜中神经元回路的超快仿真。我们的研究结果是用现成的VCSEL在1310和1550 nm的电信波长下工作。这使得我们的方法与当前的光网络和数据中心技术完全兼容，从而为未来的超快神经形态激光神经元网络提供了巨大的潜力，用于脑启发计算和人工智能的新范式。
We report on ultrafast artificial laser neurons and on their potentials for future neuromorphic (brainlike) photonic information processing systems. We introduce our recent and ongoing activities demonstrating controllable excitation of spiking signals in optical neurons based upon vertical-cavity surface emitting lasers (VCSEL-Neurons). These spiking regimes are analogous to those exhibited by biological neurons, but at sub-nanosecond speeds (>7 orders of magnitude faster). We also describe diverse approaches, based on optical or electronic excitation techniques, for the activation/inhibition of sub-ns spiking signals in VCSEL-Neurons. We report our work demonstrating the communication of spiking patterns between VCSEL-Neurons toward future implementations of optical neuromorphic networks. Furthermore, new findings show that VCSEL-Neurons can perform multiple neuro-inspired spike processing tasks. We experimentally demonstrate photonic spiking memory modules using single and mutually coupled VCSEL-Neurons. Additionally, the ultrafast emulation of neuronal circuits in the retina using VCSEL-Neuron systems is demonstrated experimentally for the first time to our knowledge. Our results are obtained with off-the-shelf VCSELs operating at the telecom wavelengths of 1310 and 1550 nm. This makes our approach fully compatible with current optical network and data center technologies, hence offering great potentials for future ultrafast neuromorphic laser-neuron networks for new paradigms in brain-inspired computing and artificial intelligence.