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Spike train encoding of analog signals in a graphene fiber ring laser

机译:石墨烯光纤环形激光器中模拟信号的尖峰序列编码

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Spiking neural networks (SNN) have inherent advantages over traditional computing architectures for many computational problems such as adaptive control, sensory processing, and pattern recognition. Recently, a graphene-based fiber laser has been shown that demonstrates all the key properties of spike processing: logic-level restoration, cascadability and input-output isolation, in one device[1]. Here, we show that this device is able to perform unique nonlinear operations on analog input signals, including the ability to convert those signals into spike train outputs. This represents a stepping stone towards practical implementations of laser devices that can perform spike-based operations on high frequency analog signals.
机译:尖峰神经网络(SNN)在许多计算问题(例如自适应控制,感觉处理和模式识别)上,具有优于传统计算体系结构的固有优势。最近,已显示出一种基于石墨烯的光纤激光器,该激光器可以在一个设备中演示尖峰处理的所有关键特性:逻辑级恢复,级联性和输入输出隔离。在这里,我们表明该设备能够对模拟输入信号执行独特的非线性运算,包括将那些信号转换为尖峰信号输出的能力。这代表了可以对高频模拟信号执行基于尖峰的操作的激光设备的实际实施的踏脚石。

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