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Spike train encoding of analog signals in a graphene fiber ring laser
Ist Teil von
2015 IEEE MIT Undergraduate Research Technology Conference (URTC), 2015, p.1-3
Ort / Verlag
IEEE
Erscheinungsjahr
2015
Quelle
IEEE Xplore
Beschreibungen/Notizen
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.