Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data

Ander Arriandiaga , Eva Portillo, Josafath Espinosa-Ramos , Nikola Kasabov

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
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This article proposes a new spike encoding and decoding algorithm for analog data. The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression. Two benchmark data sets are used to illustrate the method: stock index time series and human voice data. Applications of the method for spiking neural network (SNN) modeling and neuromorphic implementations are discussed. The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.

Original languageEnglish
Article number8901148
Pages (from-to)3920-3931
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Early online date14 Nov 2019
Publication statusPublished (in print/issue) - 6 Oct 2020

Bibliographical note

Funding Information:
This work was supported in part by UPV/EHU PPGA19/48.
(Corresponding author: Ander Arriandiaga.) A. Arriandiaga and E. Portillo are with the Department of Automatic Control and Systems Engineering, Faculty of Engineering, University of the Basque Country, 48080 Bilbao, Spain (e-mail: ander.arriandiaga@ehu.eus).

Funding Information:
Dr. Portillo received a grant funded by the Basque Government and a prize for outstanding doctoral thesis in 2010.

Publisher Copyright:
© 2012 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Analog data
  • data compression
  • spike encoding
  • spike series decoding
  • spiking neural networks (SNNs)
  • streaming data


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