We designed, prototyped, and experimentally demonstrated, for the first time to our knowledge, an optoelectronic spiking neuron inspired by the Izhikevich model incorporating both excitatory and inhibitory optical spiking inputs and producing optical spiking outputs accordingly. The optoelectronic neurons consist of three transistors acting as electrical spiking circuits, a vertical-cavity surface-emitting laser (VCSEL) for optical spiking outputs, and two photodetectors for excitatory and inhibitory optical spiking inputs. Additional inclusion of capacitors and resistors complete the Izhikevich-inspired optoelectronic neurons, which receive excitatory and inhibitory optical spikes as inputs from other optoelectronic neurons. We developed a detailed optoelectronic neuron model in Verilog-A and simulated the circuit-level operation of various cases with excitatory input and inhibitory input signals. The experimental results closely resemble the simulated results and demonstrate how the excitatory inputs trigger the optical spiking outputs while the inhibitory inputs suppress the outputs. Utilizing the simulated neuron model, we conducted simulations using fully connected (FC) and convolutional neural networks (CNN). The simulation results using MNIST handwritten digits recognition show 90% accuracy on unsupervised learning and 97% accuracy on a supervised modified FC neural network. We further designed a nanoscale optoelectronic neuron utilizing quantum impedance conversion where a 200 aJ/spike input can trigger the output from on-chip nanolasers with 10 fJ/spike. The nanoscale neuron can support a fanout of ~80 or overcome 19 dB excess optical loss while running at 10 GSpikes/second in the neural network, which corresponds to 100x throughput and 1000x energy-efficiency improvement compared to state-of-art electrical neuromorphic hardware such as Loihi and NeuroGrid.

中文翻译：

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具有Izhikevich启发的光电神经元，具有兴奋性和抑制性输入，可实现高效的光子激发神经网络

根据我们的知识，我们首次设计，制作了原型并通过实验证明了受Izhikevich模型启发的光电子加标神经元，该模型结合了兴奋性和抑制性光学加标输入，并相应地产生了光学加标输出。光电神经元由充当电脉冲电路的三个晶体管，用于光脉冲输出的垂直腔表面发射激光器（VCSEL）和用于激励和抑制光脉冲输入的两个光电探测器组成。额外包含的电容器和电阻器完善了受Izhikevich启发的光电神经元，该光电神经元接收来自其他光电神经元的兴奋性和抑制性光尖峰作为输入。我们在Verilog-A中开发了详细的光电神经元模型，并通过兴奋性输入和抑制性输入信号模拟了各种情况下的电路级操作。实验结果与模拟结果非常相似，并演示了兴奋性输入如何触发光尖峰输出，而抑制性输入则抑制输出。利用模拟的神经元模型，我们使用完全连接（FC）和卷积神经网络（CNN）进行了模拟。使用MNIST手写数字识别的仿真结果显示，在无监督学习中，准确性为90％，在有监督改进的FC神经网络上，则为97％。我们进一步设计了利用量子阻抗转换的纳米级光电神经元，其中200 aJ / spike的输入可以触发10 fJ / spike的片上纳米激光器的输出。