Journal of Computational Neuroscience

, Volume 11, Issue 2, pp 95–110 | Cite as

Recovering Quasi-Active Properties of Dendritic Neurons from Dual Potential Recordings

  • Steven J. Cox
  • Boyce E. Griffith


We develop the theory and accompanying algorithm for the recovery of a dendritic neuron's cytoplasmic resistivity, membrane capacitance, leakage conductance, and two maximal channel conductances from weighted averages of simultaneous recordings of somatic and dendritic potential following a somatic current stimulus. We test our results on two model systems with distinct, though prescribed, channel kinetics and branching patterns.

identification parameter estimation moment methods symbolic solution Laplace transform 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agmon-Snir H (1995) A novel theoretical approach to the analysis of dendritic transients. Biophys. J. 69:1633-1656.Google Scholar
  2. Cox SJ (1998) A new method for extracting cable parameters from input impedance data. Math. Biosci. 153:1-12.Google Scholar
  3. Cox SJ, Ji L (2000) Identification of the cable parameters in the somatic shunt model. Biol. Cybernetics 83(2):151-159.Google Scholar
  4. Cox SJ, Ji L (2001) Discerning ionic currents and their kinetics from input impedance data. Bulletin of Math. Bio. 63(5):909-932.Google Scholar
  5. Fromherz P, Müller CO (1994) Cable properties of a straight neurite of a leech probed by a voltage-sensitive dye. Proc. Natl. Acad. Sci. USA 91:4604-4608.Google Scholar
  6. Isakov V (1998) Inverse Problems for Partial Differential Equations. Springer, New York.Google Scholar
  7. Johnston D, Magee JC, Colbert CM, Christie BR (1996) Active properties of neuronal dendrites. Annual Review of Neuroscience 19:165-186.Google Scholar
  8. Koch C (1999) Biophysics of Computation. Oxford University Press, Oxford.Google Scholar
  9. Mainen ZF, Sejnowski TJ (1998) Modeling active dendritic processes in pyramidal neurons. In: Koch C, Seger I, eds. Methods in Neuronal Modeling (2nd ed.). MIT Press, Cambridge, MA. pp. 171-210.Google Scholar
  10. Rall W, Burke RE, Holmes WR, Jack JJB, Redman SJ, Segev I (1992) Matching dendritic neuron models to experimental data. Physiol. Rev. 172:S159-S186.Google Scholar
  11. Shen GY, Chen WR, Midtgaard J, Shepherd GM, Hines ML (1999) Computational analysis of action potential initiation in mitral cell soma and dendrites based on dual patch recordings. J. Neurophysiol. 82:3006-3020.Google Scholar
  12. Surkis A, Peskin CS, Tranchina D, Leonard CS (1998) Recovery of cable properties through active and passive modeling of subthreshold membrane responses from laterodorsal tegmental neurons. J. Neurophysiol. 80(5):2593-2607.Google Scholar
  13. Vanier MC, Bower JM (1999) A comparative survey of automated parameter search methods for compartmental neural models. J. of Comput. Neurosci. 7(2):149-171.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Steven J. Cox
    • 1
  • Boyce E. Griffith
    • 1
  1. 1.Department of Computational and Applied MathematicsRice UniversityHouston

Personalised recommendations