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Using extracellular action potential recordings to constrain compartmental models

Abstract

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.

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References

  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.

    Article  Google 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.

    PubMed  Google 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.

    PubMed  CAS  Google 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.

    PubMed  Article  CAS  Google Scholar 

  6. De Nó RL (1947) Action potential of the motoneurons of the hypoglossus nucleus. J. Cell Comp. Phsiol. 29: 207–287.

    Article  Google 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.

    Article  CAS  Google Scholar 

  9. Gold C, Girardin C, Martin K, Koch C (2006b) Unpublished experiments.

  10. Hayes RD, Byrne JH, Cox SJ, Baxter DA (2005) Estimation of single-neuron model parameters from spike train data. Neurocomputing 66: 517–529.

    Article  Google 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.

    PubMed  Article  CAS  Google Scholar 

  13. Hines ML, Carnevale NT (2001) Neuron: A tool for neuroscientists. The Neuroscientist 7: 123–135.

    PubMed  CAS  Google 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.

    CAS  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.

  16. Holt G, Koch C (1999) Electrical interactions via the extracellular potential near cell bodies. J. Comput. Neurosci. 6: 169–184.

    PubMed  Article  CAS  Google Scholar 

  17. Huys, QJM, Ahrens MB, Paninski l (2006) Efficient Estimation of Detailed Single-Neuron Models J Neurophys. 96: 872–890.

    Article  Google 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.

    Article  Google 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.

    PubMed  CAS  Google 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.

  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.

    PubMed  Article  Google 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.

    PubMed  CAS  Google Scholar 

  25. Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995) A model of spike initiation in neocortical pyramidal neurons. Neuron 15: 1427–1439.

    PubMed  Article  CAS  Google 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.

    PubMed  Article  CAS  Google 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.

    Article  Google Scholar 

  30. Rall W (1962) Electrophysiology of a dendritic neuron model. Biophysi J. 2: 145–167.

    CAS  Google Scholar 

  31. Schwarz WM (1973) Intermediate Electromagnetic Theory. Robert E. Kreiger Publishing Company, New York.

  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.

    PubMed  CAS  Google 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.

    PubMed  Article  Google 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.

    PubMed  Article  CAS  Google Scholar 

  36. Varona P, Ibarz JM, Lopez-Aguado L, Herreras O (2000) Macroscopic and subcellular factors shaping population spikes. J. Neurophysiol. 83: 2192–2208.

    PubMed  CAS  Google Scholar 

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Correspondence to Carl Gold.

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Action Editor: Alain Destexhe

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Gold, C., Henze, D.A. & Koch, C. Using extracellular action potential recordings to constrain compartmental models. J Comput Neurosci 23, 39–58 (2007). https://doi.org/10.1007/s10827-006-0018-2

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Keywords

  • Compartmental model
  • Extracellular recording
  • Model constraint
  • CA1
  • Pyramidal neuron
  • Neuron simulation