Hejda Matěj, Zhang Weikang, Al-Taai Qusay Raghib Ali, Malysheva Ekaterina, Owen-Newns Dafydd, Figueiredo José M L, Romeira Bruno, Robertson Joshua, Dolores-Calzadilla Victor, Wasige Edward, Hurtado Antonio
Institute of Photonics, SUPA Dept of Physics, University of Strathclyde, Glasgow G11XQ, United Kingdom.
Hewlett-Packard Laboratories, Hewlett-Packard Enterprise, Machelen 1831, Belgium.
ACS Photonics. 2024 Oct 1;11(10):4279-4287. doi: 10.1021/acsphotonics.4c01199. eCollection 2024 Oct 16.
Interconnectivity between functional building blocks (such as neurons and synapses) represents a fundamental functionality for realizing neuromorphic systems. However, in the domain of neuromorphic photonics, synaptic interlinking and cascadability of spiking optical artificial neurons remains challenging and mostly unexplored in experiments. In this work, we report an optical synaptic link between optoelectronic spiking artificial neurons based upon resonant tunneling diodes (RTDs) that allows for cascadable spike propagation. First, deterministic spiking is triggered using multimodal (electrical and optical) inputs in RTD-based spiking artificial neurons, which are optoelectronic (OE) circuits incorporating either micron-scale RTDs or photosensitive nanopillar-based RTDs. Second, feedforward linking with dynamical weighting of optical spiking signals between pre- and postsynaptic RTD artificial neurons is demonstrated, including cascaded spike activation. By dynamically weighting the amplitude of optical spikes, it is shown how the cascaded spike activation probability in the postsynaptic RTD node directly follows the amplitude of the weighted optical spikes. This work therefore provides the first experimental demonstration of programmable synaptic optical link and spike cascading between multiple fast and efficient RTD OE spiking artificial neurons, therefore providing a key functionality for photonic-electronic spiking neural networks and light-enabled neuromorphic hardware.
功能构建块(如神经元和突触)之间的互连性是实现神经形态系统的一项基本功能。然而,在神经形态光子学领域,尖峰光学人工神经元的突触互连和级联能力仍然具有挑战性,并且在实验中大多未被探索。在这项工作中,我们报道了一种基于共振隧穿二极管(RTD)的光电尖峰人工神经元之间的光学突触连接,它允许级联的尖峰传播。首先,在基于RTD的尖峰人工神经元中使用多模态(电和光)输入触发确定性尖峰,这些神经元是包含微米级RTD或基于光敏纳米柱的RTD的光电(OE)电路。其次,展示了前突触和后突触RTD人工神经元之间具有光学尖峰信号动态加权的前馈连接,包括级联尖峰激活。通过动态加权光学尖峰的幅度,展示了后突触RTD节点中级联尖峰激活概率如何直接跟随加权光学尖峰的幅度。因此,这项工作首次通过实验证明了多个快速高效的RTD OE尖峰人工神经元之间可编程的突触光学连接和尖峰级联,从而为光子 - 电子尖峰神经网络和光驱动神经形态硬件提供了关键功能。