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

Leonidas K. Tolias, Bhavin J. Shastri, Mitchell M. Nahmias, Alexander N. Tait, Thomas Ferrieira De Lima, Paul R. Prucnal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467385596
DOIs
StatePublished - Sep 8 2016
Event2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015 - Cambridge, United States
Duration: Nov 7 2015Nov 8 2015

Publication series

Name2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015

Other

Other2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015
CountryUnited States
CityCambridge
Period11/7/1511/8/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Engineering (miscellaneous)

Fingerprint Dive into the research topics of 'Spike train encoding of analog signals in a graphene fiber ring laser'. Together they form a unique fingerprint.

Cite this