Journal of Computational Neuroscience

, Volume 23, Issue 1, pp 39–58 | Cite as

Using extracellular action potential recordings to constrain compartmental models

  • Carl GoldEmail author
  • Darrell A. Henze
  • Christof Koch


We investigate the use of extracellular action potential (EAP) recordings for biophysically faithful compartmental models. We ask whether constraining a model to fit the EAP is superior to matching the intracellular action potential (IAP). In agreement with previous studies, we find that the IAP method under-constrains the parameters. As a result, significantly different sets of parameters can have virtually identical IAP’s. In contrast, the EAP method results in a much tighter constraint. We find that the distinguishing characteristics of the waveform—but not its amplitude- resulting from the distribution of active conductances are fairly invariant to changes of electrode position and detailed cellular morphology. Based on these results, we conclude that EAP recordings are an excellent source of data for the purpose of constraining compartmental models.


Compartmental model Extracellular recording Model constraint CA1 Pyramidal neuron Neuron simulation 


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  1. Bédard C, Kröeger H, Destexhe A (2006) Model of low-pass filtering of local field potentials in brain tissue. Phys. Rev. E 73: 051911.CrossRefGoogle Scholar
  2. Borg-Graham LJ (1999) Interpretations of data and mechanisms for hippocampal pyramidal cell models. Cerebral Cortex 13: 19–138.Google Scholar
  3. Buzsáki G, Kandel A (1998) Somadendritic backpropagation of action potentials in cortical pyramidal cells of the awake rat. J. Neurophysiol 79(3): 1587–1591.PubMedGoogle Scholar
  4. Colbert CM, Magee JC, Hoffman DA, Johnston D (1997) Slow recovery from inactivation of Na+ channels underlies the activity-dependent attenuation of dendritic action potentials in hippocampal CA1 pyramidal neurons. Neuroscience 17: 6512–6521.PubMedGoogle Scholar
  5. Colbert CM, Pan E (2002) Ion channel properties underlying the axonal action potential initiation in pyramidal neurons. Nat. Neurosci. 5(6): 533–538.PubMedCrossRefGoogle Scholar
  6. De Nó RL (1947) Action potential of the motoneurons of the hypoglossus nucleus. J. Cell Comp. Phsiol. 29: 207–287.CrossRefGoogle Scholar
  7. Forsyth DA, Ponce J (2003) Computer Vision: A Modern Approach. Prentice Hall, Saddle River, NJ.Google Scholar
  8. Gold C, Henze DA, Koch C, Buzsáki G (2006a) On the original of the extracellular action potential waveform: a modeling study. J. Neurophys. 95: 3113–3128.CrossRefGoogle Scholar
  9. Gold C, Girardin C, Martin K, Koch C (2006b) Unpublished experiments.Google Scholar
  10. Hayes RD, Byrne JH, Cox SJ, Baxter DA (2005) Estimation of single-neuron model parameters from spike train data. Neurocomputing 66: 517–529.CrossRefGoogle Scholar
  11. Henze DA, Borhegyi Z, Csicsvari J, Mamiya A, Harris K, Buzsáki G (2000) Intracellular features predicted by extracellular recordings in the hippocampus in vivo. J. Neurophysi. 83: 390–400.Google Scholar
  12. Hines ML, Carnevale NT (1997) The neuron simulation environment. Neural Comput. 9: 1179–1209.PubMedCrossRefGoogle Scholar
  13. Hines ML, Carnevale NT (2001) Neuron: A tool for neuroscientists. The Neuroscientist 7: 123–135.PubMedGoogle Scholar
  14. Hoffman DA, Magee JC, Colbert CM, Johnston D (1997) K+ channel regulation of signal propagation in dendrites of hippocampal pyramidal neurons. Nat. Neurosci. 387: 869–875.Google Scholar
  15. Holt G (1998) A Critical Reexamination of Some Assumptions and Implications of Cable Theory in Neurobiology. PhD thesis, California Institute of Technology.Google Scholar
  16. Holt G, Koch C (1999) Electrical interactions via the extracellular potential near cell bodies. J. Comput. Neurosci. 6: 169–184.PubMedCrossRefGoogle Scholar
  17. Huys, QJM, Ahrens MB, Paninski l (2006) Efficient Estimation of Detailed Single-Neuron Models J Neurophys. 96: 872–890.CrossRefGoogle Scholar
  18. Jackson JD (1962) Classical Electrodynamics. Wiley, New York.Google Scholar
  19. Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J. Neurophysio. 94: 3730–3742.CrossRefGoogle Scholar
  20. Klee R, Ficker E, Heinemann U (1995) Comparison of voltage-dependent potassium currents in rat pyramidal neurons acutely isolated from hippocampal regions CA1, CA3. J. Neurophysiol. 74: 1982–1995.PubMedGoogle Scholar
  21. Koch C (1999) Biophysics of Computation. Oxford University Press, Oxford, UK.Google Scholar
  22. Koch C, Segev I (Eds.) (1999) Methods in Neuronal Modeling: From Ions to Networks. Bradford, Cambridge, Massachusetts.Google Scholar
  23. López-Aguado L, Ibarz JM, Herreras O (2001) Activity-dependent chanes of tissue resistivity in the CA1 region in vivo are layer specific: modulation of evoked potentials. Neuroscience 108(2): 249–262.PubMedCrossRefGoogle Scholar
  24. Magee JC, Johnston D (1995) Characterization of single voltage-gated Na, Ca 2 channels in apical dendrites of rat CA1 pyramidal neurons. J. Physiol. 487: 67–90.PubMedGoogle Scholar
  25. Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995) A model of spike initiation in neocortical pyramidal neurons. Neuron 15: 1427–1439.PubMedCrossRefGoogle Scholar
  26. Malmivuo J, Plonsey R (1995) Bioelectromagnetism. Oxford University Press, New York, Oxford.Google Scholar
  27. Migliore M, Sheperd GM (2002) Emerging rules for the distributions of active dendritic conductances. Nat. Rev. Neurosci. 3: 362–370.PubMedCrossRefGoogle Scholar
  28. Plonsey R (1969) Bioelectric Phenomena. McGraw-Hill, New York.Google Scholar
  29. Quiroga RQ, Nadasdy Z, Ben-Shaul Y (2004) Unsupervised spike sorting with wavelets and superparamagnetic clustering. Neur. Comput. 16: 1661–1687.CrossRefGoogle Scholar
  30. Rall W (1962) Electrophysiology of a dendritic neuron model. Biophysi J. 2: 145–167.Google Scholar
  31. Schwarz WM (1973) Intermediate Electromagnetic Theory. Robert E. Kreiger Publishing Company, New York.Google Scholar
  32. Spruston N, Johnston D (1992) Perforated patch-clamp analysis of the passive membrane properties of three classes of hippocampal neurons. J. Neurophysiol. 67(3): 508–529.PubMedGoogle Scholar
  33. Stuart G, Spruston N, Hausser M (2001) Dendrites. Oxford University Press, Oxford, U.K.Google Scholar
  34. Toledo-Rodriguez M, Blumenfeld B, Wu C, Luo J, Attali B, Goodman P, Markram H (2004) Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cerebral Cortex 14(12): 1310–1327.PubMedCrossRefGoogle Scholar
  35. Vanier MC, Bower JM (1999) A comparative survey of automated parameter search methods for compartmental models. J. Comput. Neurosci. 7(2): 149–171.PubMedCrossRefGoogle Scholar
  36. Varona P, Ibarz JM, Lopez-Aguado L, Herreras O (2000) Macroscopic and subcellular factors shaping population spikes. J. Neurophysiol. 83: 2192–2208.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Computation and Neural SystemsCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Department of Pain ResearchMerck Research LaboratoriesWest PointUSA

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