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Journal of Computational Neuroscience

, Volume 8, Issue 3, pp 209–226 | Cite as

Additional Efficient Computation of Branched Nerve Equations: Adaptive Time Step and Ideal Voltage Clamp

  • Lyle J. Borg-Graham
Article

Abstract

Various improvements are described for the simulation of biophysically and anatomically detailed compartmental models of single neurons and networks of neurons. These include adaptive time-step integration and a reordering of the circuit matrix to allow ideal voltage clamp of arbitrary nodes. We demonstrate how the adaptive time-step method can give equivalent accuracy as a fixed time-step method for typical current clamp simulation protocols, with about a 2.5 reduction in runtime. The ideal voltage clamp method is shown to be more stable than the nonideal case, in particular when used with the adaptive time-step method. Simulation results are presented using the Surf-Hippo Neuron Simulation System, a public domain object-oriented simulator written in Lisp.

numerical methods compartmental models neuron simulation 

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References

  1. Bhalla US, Bilitch DH, Bower JM (1992) Rallpacks: A set of benchmarks for neuronal simulators. Trends In Neurosci. 15(11). Available from ftp://genesis.bbb.caltech.edu/pub/genesis.Google Scholar
  2. Borg-Graham L (1998) The Surf-Hippo neuron simulation system. http://www.cnrs-gif.fr/iaf/iaf9/surf-hippo.html, v2.8.Google Scholar
  3. Bower JM, Beeman D (1994) The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. TELOS/Springer-Verlag.Google Scholar
  4. Desoer CA, Kuh ES (1969) Basic Circuit Theory. McGraw-Hill.Google Scholar
  5. Hines M (1984) Efficient computation of branched nerve equations. Int. J. Bio-Med. Comput. 15:69-76.Google Scholar
  6. Hines M, Carnevale NT (1995) Computer simulation methods for neurons. In: M Arbib, ed. The Handbook of Brain Theory and Neural Networks. MIT Press.Google Scholar
  7. Hines H, Carnevale NT (1997) The NEURON simulation environment. Neural Comput. 9:1179-1209.Google Scholar
  8. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiol. 117:500-544.Google Scholar
  9. Mascagni M, Sherman AS (1998) Numerical methods for neuronal modeling. In: C Koch, I Segev, eds. Methods in Neuronal Modeling, Ch. 13. MIT Press/Bradford Books 2nd edition.Google Scholar
  10. Rall W (1964) Theoretical significance of dendritic trees for neuronal input-output relations. In: RF Reiss, eds. Neural Theory and Modelling, Stanford University Press, pp. 73-79.Google Scholar
  11. Segev I, Burke RE, HinesM(1998) Compartmental models of complex neurons. In: C Koch, I Segev, eds. Methods in Neuronal Modeling, MIT Press/Bradford Books, 2nd edition. Ch. 3, pp. 63-96.Google Scholar
  12. Vlach J, Singhal K (1983) Computer Methods for Circuit Analysis and Design. Van Nostrand Reinhold.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Lyle J. Borg-Graham
    • 1
  1. 1.Unité de Neurosciences Integratiues et ComputationellesInstitut Alfred Fessard-CNRSGif-sur-YvetteFrance

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