Spike train encoding of analog signals in a graphene fiber ring laser
Leonidas Tolias, Bhavin J. Shastri, Mitchell A. Nahmias, Alexander N. Tait, Thomas Ferreira de Lima, Paul R. Prucnal · 2015
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.