Skip to main content
SpringerLink
Log in

Quaternion Spike Neural Networks

  • Conference paper
  • First Online:
Advances in Neural Networks – ISNN 2016 (ISNN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9719))

Included in the following conference series:

Abstract

This work presents a new type of Spike Neural Networks (SNN) developed in the quaternion algebra framework. This new neural structure based on SNN is developed using the quaternion algebra. The training algorithm was extended adjusting the weights according to the quaternion multiplication rule, which allows accurate results with a decreased network complexity with respect to the real SNN. The experimental part shows a good performance for robot manipulator control.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wolfgang, M.: Networks of spiking neurons: the third generation of neural network models. IEEE Trans. Neural Netw. 9, 1659–1671 (1997)

    Google Scholar 

  2. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New York (1999)

    MATH  Google Scholar 

  3. Wulfram, G., Werner, M.: Spiking Neuron Models, Single Neurons, Population, Plasticity. MIT Press, Cambridge (2002)

    MATH  Google Scholar 

  4. Van Rullen, R., Delorme, A., Gautrais, J., Thorpe, S.: (SpikeNET): a simulator for modeling large networks of integrate and fire neurons. Neurocomput. 22, 989–996 (1999)

    Google Scholar 

  5. Bohte, S., Kok, J., Han, L.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomput. 48, 17–37 (2002)

    Article  MATH  Google Scholar 

  6. Eduardo, B.C.: TGeometric Computing, for Wavelets Transforms, Robot Vision, Learning, Control and Action. Springer, London (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departamento de Control Automático, Centro de Investigación y de Estudios Avanzados, Unidad Guagalajara, Zapopan, Jalisco, Mexico

    Luis Lechuga-Gutiérrez & Eduardo Bayro-Corrochano

Authors
  1. Luis Lechuga-Gutiérrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eduardo Bayro-Corrochano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eduardo Bayro-Corrochano .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. Huazhong University of Science and Tech., Wuhan, Jiangsu, China

    Qingshan Liu

  3. Russian Academy of Sciences, SPIIRAS, St. Petersburg, Russia

    Andrey Ronzhin

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Lechuga-Gutiérrez, L., Bayro-Corrochano, E. (2016). Quaternion Spike Neural Networks. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_73

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-40663-3_73

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40662-6

  • Online ISBN: 978-3-319-40663-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation