Skip to main content

A Control Approach to a Biophysical Neuron Model

  • Conference paper
Artificial Neural Networks – ICANN 2007 (ICANN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4668))

Included in the following conference series:

  • 2674 Accesses

Abstract

In this paper we present a neuron model based on the description of biophysical mechanisms combined with a regulatory mechanism from control theory. The aim of this work is to provide a neuron model that is capable of describing the main features of biological neurons such as maintaining an equilibrium potential using the NaK-ATPase and the generation of action potentials as well as to provide an estimation of the energy consumption of a single cell in a) quiescent mode (or equilibrium state) and b) firing state, when excited by other neurons. The same mechanism has also been used to model the synaptic excitation used in the simulated system.

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

Access this chapter

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. Koch, C., Segev, I.: Methods in Neuronal Modeling (1998)

    Google Scholar 

  2. Laughlin, S.B., de Ruyter van Steveninck, R.R., Anderson, J.C.: The metabolic cost of neural information. Nature Neuroscience 1, 36–41 (1998)

    Article  Google Scholar 

  3. MacGregor, R., Lewis, E.: Neural Modeling (1977)

    Google Scholar 

  4. Daut, J.: The living cell as an energy-transducing machine. A minimal model of myocardial metabolism. Biochimica et Biophysica Acta (BBA) - Reviews on Bioenergetics 895, 41–62 (1987)

    Article  Google Scholar 

  5. Destexhe, A., Mainen, Z.F., Sejnowski, T.J.: An efficient Method for Computing Synaptic Conductances Based on a Kinetic Model of Receptor Binding. Neural Computation 6, 14–18 (1994)

    Google Scholar 

  6. Föllinger, O.: Regelungstechnik (1994)

    Google Scholar 

  7. Hodgkin, A., Huxley, A.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Phys. 117, 500–544 (1952)

    Google Scholar 

  8. Destexhe, A.: Conductance-based integrate-and-fire models. Neural Computation 9, 503–514 (1997)

    Article  Google Scholar 

  9. Chapeau-Blondeau, F., Chambet, N.: Synapse Models for Neural Networks: From Ion Channel Kinetics to Multiplicative Coefficient w ij . Neural Computation 7, 713–734 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Joaquim Marques de Sá Luís A. Alexandre Włodzisław Duch Danilo Mandic

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kaulmann, T., Löffler, A., Rückert, U. (2007). A Control Approach to a Biophysical Neuron Model. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-74690-4_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74689-8

  • Online ISBN: 978-3-540-74690-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics