Skip to main content
SpringerLink
Log in

A Framework for Application-Oriented Design of Large-Scale Neural Networks

  • Conference paper
Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6792))

Included in the following conference series:

  • 2315 Accesses

Abstract

Tools for simulations of neural networks exist aplenty. They range from simulators for detailed multi-compartment neurons, over packages for precise reconstruction of small biological networks, to simulators for large-scale networks with stochastic connectivity properties. However, no frameworks for constructing large-scale, dedicated networks exist. Based on the design principles used for our previous work, we introduce a C++ framework which is specifically tailored to simplify the construction of large networks with specific cognitive functionalities.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bergdoll, S., Koch, U.T.: BIOSIM — A biological neural network simulator for research and teaching, featuring interactive graphical user interface and learning capabilities. Neurocomputing 8, 93–112 (1995)

    Article  Google Scholar 

  2. Bower, J.M., Beeman, D.: The Book of GENESIS: Exploring Realistic Neural Models with the General Neural Simulation System. Spinger, Heidelberg (1998)

    Book  Google Scholar 

  3. Braitenberg, V., Schütz, A.: Anatomy of the Cortex. Springer, Heidelberg (1991)

    Book  Google Scholar 

  4. Gewaltig, M.-O., Diesmann, M.: Nest. Scholarpedia 2, 1430 (2007)

    Article  Google Scholar 

  5. Hebb, D.O.: The Organization of Behavior. Wiley, Chichester (1949)

    Google Scholar 

  6. Hines, M.L., Carnevale, T.N.: The neuron simulation environment. Neural Computation 9, 1179–1209 (1997)

    Article  Google Scholar 

  7. Markert, H., Knoblauch, A., Palm, G.: Modelling of syntactical processing in the cortex. BioSystems 89, 300–315 (2007)

    Article  Google Scholar 

  8. Palm, G.: Neural Assemblies: An Alternative Approach to Artificial Intelligence. Springer, Heidelberg (1982)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Neural Information Processing, Ulm University, 89069, Ulm, Germany

    David Bouchain, Florian Hauser & Günther Palm

Authors
  1. David Bouchain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Florian Hauser
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Günther Palm
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Timo Honkela  & Samuel Kaski  & 

  2. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK

    Mark Girolami

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bouchain, D., Hauser, F., Palm, G. (2011). A Framework for Application-Oriented Design of Large-Scale Neural Networks. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21738-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21737-1

  • Online ISBN: 978-3-642-21738-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation