Skip to main content
SpringerLink
Log in

Exploiting Natural Asynchrony and Local Knowledge within Systemic Computation to Enable Generic Neural Structures

  • Conference paper
Natural Computing

Part of the book series: Proceedings in Information and Communications Technology ((PICT,volume 1))

Abstract

Bio-inspired processes are involved more and more in today’s technologies, yet their modelling and implementation tend to be taken away from their original concept because of the limitations of the classical computation paradigm. To address this, systemic computation (SC), a model of interacting systems with natural characteristics, followed by a modelling platform with a bio-inspired system implementation were introduced. In this paper, we investigate the impact of local knowledge and asynchronous computation: significant natural properties of biological neural networks (NN) and naturally handled by SC. We present here a bio-inspired model of artificial NN, focussing on agent interactions, and show that exploiting these built-in properties, which come for free, enables neural structure flexibility without reducing performance.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bentley, P.J.: Systemic computation: A Model of Interacting Systems with Natural Characteristics. Int.J. Parallel, Emergent and Distributed Systems 22, 103–121 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  2. Le Martelot, E., Bentley, P.J., Lotto, R.B.: A Systemic Computation Platform for the Modelling and Analysis of Processes with Natural Characteristics. In: Proc. of Genetic and Evolutionary Computation Conference, pp. 2809–2816 (2007)

    Google Scholar 

  3. Tang, H., Tan, K.C., Yi, Z.: Neural Networks: Computational Models and Applications. Springer, Heidelberg (2007)

    Book  MATH  Google Scholar 

  4. Kandel, E.R., Schwartz, J.H., Jessel, T.M.: Principles of Neural Science, 3rd edn., ch. 1,3. Elsevier, Amsterdam (1991)

    Google Scholar 

  5. Peterson, C., Anderson, J.R.: A Mean Field Theory Learning Algorithm for Neural Networks. Complex Systems 1, 995–1019 (1987)

    MATH  Google Scholar 

  6. Hinton, G.E.: Deterministic Boltzmann Learning Performs Steepest Descent in Weight-space. Neural computation 1, 143–150 (1990)

    Article  Google Scholar 

  7. Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Networks 10, 1659 (1997)

    Article  Google Scholar 

  8. O’Reilley, R.C.: Biologically Plausible Error-driven Learning using Local Activation Differences: The Generalized Recirculation Algorithm. Neural computation 8, 895–938 (1996)

    Article  Google Scholar 

  9. Yanling, Z., Bimin, D., Zhanrong, W.: Analysis and Study of Perceptron to Solve XOR Problem. In: Proc. of the 2nd Int. Workshop on Autonomous Decentralized System (2002)

    Google Scholar 

  10. Fisher, R.A., Marshall, M.: Iris Plants Database, UCI Machine Learning Repository (1988), http://www.ics.uci.edu/~mlearn/MLRepository.html

Download references

Author information

Authors and Affiliations

  1. Engineering Department, University College London, London, UK

    Erwan Le Martelot

  2. Computer Science Department, University College London, London, UK

    Peter J. Bentley

  3. Institute of Ophthalmology, University College London, London, UK

    Erwan Le Martelot & R. Beau Lotto

Authors
  1. Erwan Le Martelot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter J. Bentley
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Beau Lotto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Nagoya University, Japan

    Y. Suzuki

  2. The University of Tokyo, Japan

    M. Hagiya

  3. Osaka Electro-Communication University, Japan

    H. Umeo

  4. University of West England, Bristol, UK

    A. Adamatzky

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Tokyo

About this paper

Cite this paper

Le Martelot, E., Bentley, P.J., Lotto, R.B. (2009). Exploiting Natural Asynchrony and Local Knowledge within Systemic Computation to Enable Generic Neural Structures. In: Suzuki, Y., Hagiya, M., Umeo, H., Adamatzky, A. (eds) Natural Computing. Proceedings in Information and Communications Technology, vol 1. Springer, Tokyo. https://doi.org/10.1007/978-4-431-88981-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-4-431-88981-6_11

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-88980-9

  • Online ISBN: 978-4-431-88981-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation