A Simple Method to Simultaneously Track the Numbers of Expressed Channel Proteins in a Neuron

  • A. Aldo Faisal
  • Jeremy E. Niven
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4216)


Neurons express particular combinations of ion channels that confer specific membrane properties. Although many ion channels have been characterized the functional implications of particular combinations and the regulatory mechanisms controlling their expression are often difficult to assess in vivo and remain unclear. We introduce a method, Reverse Channel Identification (RCI), which enables the numbers and mixture of active ion channels to be determined. We devised a current-clamp stimulus that allows each channels characteristics to be determined. We test our method on simulated data from a computational model of squid giant axons and from fly photoreceptors to identify both the numbers of ion channels and their specific ratios. Our simulations suggest that RCI is a robust method that will allow identification of ion channel number and mixture in vivo.


Virtual Channel Single Channel Conductance Channel Density Squid Giant Axon Channel Open Probability 
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  1. 1.
    Marder, E., Prinz, A.A.: Modeling stability in neuron and network function: the role of activity in homeostasis. Bioessays 24(12), 1145–1154 (2002)CrossRefGoogle Scholar
  2. 2.
    Zhang, W., Linden, D.J.: The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nat. Rev. Neurosci. 4(11), 885–900 (2003)CrossRefGoogle Scholar
  3. 3.
    Weckstrom, M., Hardie, R., Laughlin, S.: Voltage-activated potassium channels in blowfly photoreceptors and their role in light adaptation. J. Physiol. 440, 635–657 (1991)Google Scholar
  4. 4.
    Stemmler, M., Koch, C.: How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nature Neurosci. 2(6), 521–527 (1999)CrossRefGoogle Scholar
  5. 5.
    McAnelly, M.L., Zakon, H.H.: Coregulation of voltage-dependent kinetics of na(+) and k(+) currents in electric organ. J. Neurosci. 20(9), 3408–3414 (2000)Google Scholar
  6. 6.
    Brickley, S.G., Revilla, V., Cull-Candy, S.G., Wisden, W., Farrant, M.: Adaptive regulation of neuronal excitability by a voltage-independent potassium conductance. Nature 409(6816), 88–92 (2001)CrossRefGoogle Scholar
  7. 7.
    Niven, J.E., Vahasoyrinki, M., Juusola, M.: Shaker K(+)-channels are predicted to reduce the metabolic cost of neural information in Drosophila photoreceptors. Proc. Biol. Sci. 270 (Suppl. 1), 58–61 (2003)CrossRefGoogle Scholar
  8. 8.
    Aizenman, C.D., Akerman, C.J., Jensen, K.R., Cline, H.T.: Visually driven regulation of intrinsic neuronal excitability improves stimulus detection in vivo. Neuron 39(5), 831–842 (2003)CrossRefGoogle Scholar
  9. 9.
    Niven, J.E.: Channelling evolution: canalization and the nervous system. PLoS Biol. 2(1), E19 (2004)CrossRefGoogle Scholar
  10. 10.
    Vahasoyrinki, M., Niven, J.E., Hardie, R.C., Weckstrom, M., Juusola, M.: Robustness of neural coding in Drosophila photoreceptors in the absence of slow delayed rectifier K+ channels. J. Neurosci. 26(10), 2652–2660 (2006)CrossRefGoogle Scholar
  11. 11.
    Attwell, D., Laughlin, S.B.: An energy budget for signalling the the grey matter of the brain. J. Cereb. Blood Flow and Metabolism 21, 1133–1145 (2001)CrossRefGoogle Scholar
  12. 12.
    Laughlin, S.B.: Energy as a constraint on the coding and processing of sensory information. Curr. Opin. Neurobiol. 11(4), 475–480 (2001)CrossRefGoogle Scholar
  13. 13.
    Niven, J.E., Vahasoyrinki, M., Kauranen, M., Hardie, R.C., Juusola, M., Weckstrom, M.: The contribution of Shaker K+ channels to the information capacity of Drosophila photoreceptors. Nature 421(6923), 630–634 (2003)CrossRefGoogle Scholar
  14. 14.
    Faisal, A., Laughlin, S., White, J.: How reliable is the connectivity in cortical neural networks? In: Wunsch, D. (ed.) Proceedings of the IEEE Intl. Joint. Conf. Neural Networks 2002. INNS, pp. 1–6 (2002)Google Scholar
  15. 15.
    Faisal, A.A., White, J.A., Laughlin, S.B.: Ion-channel noise places limits on the miniaturization of the brain’s wiring. Curr. Biol. 15(12), 1143–1149 (2005)CrossRefGoogle Scholar
  16. 16.
    Koch, C.: Biophysics of computation. In: Computational neuroscience, Oxford University Press, Oxford (1999)Google Scholar
  17. 17.
    Hodgkin, A., Huxley, A.: Quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)Google Scholar
  18. 18.
    Hille, B.: Ion channels of excitable membranes. 3rd edn. Sinauer Associates, Sunderland, MA, p. 814 (2001)Google Scholar
  19. 19.
    Jones, E.M., Gray-Keller, M., Fettiplace, R.: The role of ca2+-activated k+ channel spliced variants in the tonotopic organization of the turtle cochlea. J. Physiol. 518, 653–665 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Aldo Faisal
    • 1
  • Jeremy E. Niven
    • 2
  1. 1.Dept. of ZoologyCambridge UniversityCambridgeUK
  2. 2.Smithsonian Tropical Research Inst.Ancón Panamá

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