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

doi:10.1109/URTC.2015.7563742

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 proceeding › Conference 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 language	English (US)
Title of host publication	2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467385596
DOIs	https://doi.org/10.1109/URTC.2015.7563742
State	Published - Sep 8 2016
Event	2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015 - Cambridge, United States Duration: Nov 7 2015 → Nov 8 2015

Publication series

Name	2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015

Other

Other	2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015
Country/Territory	United States
City	Cambridge
Period	11/7/15 → 11/8/15

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Engineering (miscellaneous)

Access to Document

10.1109/URTC.2015.7563742

Cite this

Tolias, L. K., Shastri, B. J., Nahmias, M. M., Tait, A. N., De Lima, T. F., & Prucnal, P. R. (2016). Spike train encoding of analog signals in a graphene fiber ring laser. In 2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015 Article 7563742 (2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URTC.2015.7563742

@inproceedings{865aa826872942f79fb170ab6113256d,

title = "Spike train encoding of analog signals in a graphene fiber ring laser",

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.",

author = "Tolias, {Leonidas K.} and Shastri, {Bhavin J.} and Nahmias, {Mitchell M.} and Tait, {Alexander N.} and {De Lima}, {Thomas Ferrieira} and Prucnal, {Paul R.}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015 ; Conference date: 07-11-2015 Through 08-11-2015",

year = "2016",

month = sep,

day = "8",

doi = "10.1109/URTC.2015.7563742",

language = "English (US)",

series = "2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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address = "United States",

}

Tolias, LK, Shastri, BJ, Nahmias, MM, Tait, AN, De Lima, TF & Prucnal, PR 2016, Spike train encoding of analog signals in a graphene fiber ring laser. in 2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015., 7563742, 2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015, Institute of Electrical and Electronics Engineers Inc., 2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015, Cambridge, United States, 11/7/15. https://doi.org/10.1109/URTC.2015.7563742

Spike train encoding of analog signals in a graphene fiber ring laser. / Tolias, Leonidas K.; Shastri, Bhavin J.; Nahmias, Mitchell M. et al.
2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7563742 (2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Spike train encoding of analog signals in a graphene fiber ring laser

AU - Tolias, Leonidas K.

AU - Shastri, Bhavin J.

AU - Nahmias, Mitchell M.

AU - Tait, Alexander N.

AU - De Lima, Thomas Ferrieira

AU - Prucnal, Paul R.

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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Tolias LK, Shastri BJ, Nahmias MM, Tait AN, De Lima TF, Prucnal PR. Spike train encoding of analog signals in a graphene fiber ring laser. In 2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7563742. (2015 IEEE MIT Undergraduate Research Technology Conference, URTC 2015). doi: 10.1109/URTC.2015.7563742

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

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