Skip to main content
Log in

Real-Time Linux Dynamic Clamp: A Fast and Flexible Way to Construct Virtual Ion Channels in Living Cells

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

We describe a system for real-time control of biological and other experiments. This device, based around the Real-Time Linux operating system, was tested specifically in the context of dynamic clamping, a demanding real-time task in which a computational system mimics the effects of nonlinear membrane conductances in living cells. The system is fast enough to represent dozens of nonlinear conductances in real time at clock rates well above 10 kHz. Conductances can be represented in deterministic form, or more accurately as discrete collections of stochastically gating ion channels. Tests were performed using a variety of complex models of nonlinear membrane mechanisms in excitable cells, including simulations of spatially extended excitable structures, and multiple interacting cells. Only in extreme cases does the computational load interfere with high-speed “hard” real-time processing (i.e., real-time processing that never falters). Freely available on the worldwide web, this experimental control system combines good performance, immense flexibility, low cost, and reasonable ease of use. It is easily adapted to any task involving real-time control, and excels in particular for applications requiring complex control algorithms that must operate at speeds over 1 kHz. © 2001 Biomedical Engineering Society.

PAC01: 8716Uv, 8239Jn, 8780Jg, 8719Nn

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  1. Acker, C. D. Synchronization of strongly coupled excitatory neurons: Relating biophysics to network behavior. M.S. Thesis, Boston University, Department of Biomedical Engineering, 2000.

    Google Scholar 

  2. Barabanov, M., and V. Yodaiken. Real-time linux. Linux J.34.:19–23., 1997.

    Google Scholar 

  3. Bartos, M., Y. Manor., F. Nadim., E. Marder., and M. P. Nusbaum. Coordination of fast and slow rhythmic neuronal circuits. J. Neurosci.19.:6650–6660., 1999.

    Google Scholar 

  4. Bertram, R., J. Previte., A. Sherman., T. A. Kinard., and L. S. Satin. The phantom burster model for pancreatic beta-cells. Biophys. J.79.:2880–2892., 2000.

    Google Scholar 

  5. Christini, D. J., K. M. Stein., S. M. Markowitz., and B. B. Lerman. Practical real-time computing system for biomedical experiment interface. Ann. Biomed. Eng.27.:180–186., 1999.

    Google Scholar 

  6. Eng, E., QT GUI toolkit. Linux J.31.:32–43., 1996.

    Google Scholar 

  7. Fox, R. J., Stochastic versions of the Hodgkin-Huxley equations. Biophys. J.72.:2068–2074., 1997.

    Google Scholar 

  8. Gramoll, S., J. Schmidt., and R. L. Calabrese. Stitching in the activity state of an interneuron that controls coordination of the hearts in the medicinal leech (Hirudo-medicinalis.). J. Exp. Biol.186.:157–171., 1994.

    Google Scholar 

  9. Harsch, A., and H. P. C. Robinson. Postsynaptic variability of firing in rat cortical neurons: The roles of input synchronization and synaptic NMDA receptor conductance. J. Neurosci.20.:6181–6192., 2000.

    Google Scholar 

  10. Hille, B. Ionic Channels of Excitable Membranes, 2nd ed. Sutherland, MA: Sinauer, 1992.

    Google Scholar 

  11. Hodgkin, A. L., and A. F. Huxley. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. (London).117.:500–544., 1952.

    Google Scholar 

  12. Hughes, S. W., D. W. Cope., and V. Crunelli. Dynamic clamp study of I-h modulation of burst firing and delta oscillations in thalamocortical neurons in vitro.. Neuroscience (Oxford).87.:541–550., 1998.

    Google Scholar 

  13. Jaeger, D., and J. M. Bower. Synaptic control of spiking in cerebellar Purkinje cells: Dynamic current clamp based on model conductances. J. Neurosci.19.:6090–6101., 1999.

    PubMed  Google Scholar 

  14. Ma, M., and J. Koester. The role of K+. currents in frequency-dependent spike broadening in Aplysia R20 neurons: A dynamic-clamp analysis. J. Neurosci.16.:4089–4101., 1996.

    Google Scholar 

  15. Reyes, A. D., E. W. Rubel., and W. J. Spain. In vitro. analysis of optimal stimuli for phase-locking and time-delayed modulation of firing in avian nucleus laminaris neurons. J. Neurosci.16.:993–1007., 1996.

    Google Scholar 

  16. Robinson, H. P. C., and N. Kawai. Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. J. Neurosci. Methods.49.:157–165., 1993.

    Google Scholar 

  17. Sakmann, B., and E. Neher. Single-Channel Recording, 2nd ed. New York, NY: Plenum, 1995.

    Google Scholar 

  18. Sharp, A. A., M. B. O'Neil., L. F. Abbott., and E. Marder. Dynamic clamp: Computer-generated conductances in real neurons. J. Neurophysiol.69.:992–995., 1993.

    Google Scholar 

  19. Sharp, A. A., M. B. O'Neil., L. F. Abbott., and E. Marder. The dynamic clamp: Artificial conductances in biological neurons. Trends Neurosci.16.:389–394., 1993.

    Google Scholar 

  20. Sharp, A. A., F. K. Skinner., and E. Marder. Mechanisms of oscillation in dynamic clamp constructed two-cell half-center circuits. J. Neurophysiol.76.:867–883., 1996.

    Google Scholar 

  21. Traub, R. D., R. K. S. Wong., R. Miles., and H. Michelson. A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J. Neurophysiol.66.:635–650., 1991.

    PubMed  Google Scholar 

  22. Weiss, T. Cellular Biophysics. Cambridge, MA: MIT Press, 1996.

    Google Scholar 

  23. White, J. A., and A. D. Dorval. Effects of channel noise in the hippocampal formation. Ann. Biomed. Eng.28.:s109., 2000.

    Google Scholar 

  24. White, J. A., J. S. Haas, and A. D. Dorval. Stochastic dynamic clamping as a method for studying the effects of biological noise sources. In: Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society, 1999, p. 880.

  25. White, J. A., R. Klink., A. Alonso., and A. R. Kay. Noise from voltage-gated ion channels may influence neuronal dynamics in the entorhinal cortex. J. Neurophysiol.80.:262–269., 1998.

    Google Scholar 

  26. White, J. A., J. T. Rubinstein., and A. R. Kay. Channel noise in neurons. Trends Neurosci.23.:131–137., 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dorval, A.D., Christini, D.J. & White, J.A. Real-Time Linux Dynamic Clamp: A Fast and Flexible Way to Construct Virtual Ion Channels in Living Cells. Annals of Biomedical Engineering 29, 897–907 (2001). https://doi.org/10.1114/1.1408929

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1114/1.1408929

Navigation