Encyclopedia of Biophysics

2013 Edition
| Editors: Gordon C. K. Roberts

Dynamic Clamp: Synthetic Conductances and Their Influence on Membrane Potential

  • Hugh P. C. RobinsonEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-642-16712-6_368



Dynamic clamp refers to the use of a computer to stimulate neurons or other excitable cells with a signal (usually an electrical current) which is calculated in real-time using feedback of the membrane potential. Most often, this is done to inject conductance which mimics the electrical behavior of membrane ion channels, for example, synaptic or voltage-gated channels.


The earliest use of an artificial membrane conductance as an experimental tool for studying excitable cells is probably the simulation of a gap-junctional connection between cardiac muscle cells by an analog resistor, described by Tan & Joyner in 1990, an idea which also appears in the Ph.D. thesis by Scott in 1979 (see Goaillard and Marder 2006). Dynamic clamp, in its usual sense of using a freely programmable digital computer, in a feedback loop comprising analog-to-digital conversion, a...

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


  1. Berecki G, Zegers JG, Verkerk AO, Bhuiyan ZA, de Jonge B, Veldkamp MW, Wilders R, van Ginneken AC. HERG channel (dys)function revealed by dynamic action potential clamp technique. Biophys J. 2005;88:566–78.PubMedCentralPubMedGoogle Scholar
  2. Bettencourt JC, Lillis KP, Stupin LR, White JA. Effects of imperfect dynamic clamp: computational and experimental results. J Neurosci Methods. 2008;169:282–9.PubMedCentralPubMedGoogle Scholar
  3. Butera RJ, McCarthy ML. Analysis of real-time numerical integration methods applied to dynamic clamp experiments. J Neural Eng. 2004;1:187–94.PubMedGoogle Scholar
  4. Destexhe A, Bal T, editors. Dynamic-clamp: from principles to applications. New York: Springer; 2009.Google Scholar
  5. Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ. Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience. 2001;107:13–24.PubMedCentralPubMedGoogle Scholar
  6. Dorval AD, Christini DJ, White JA. Real-time linux dynamic clamp: a fast and flexible way to construct virtual ion channels in living cells. Ann Biomed Eng. 2001;29:897–907.PubMedGoogle Scholar
  7. Goaillard JM, Marder E. Dynamic clamp analyses of cardiac, endocrine, and neural function. Physiology. 2006;21:197–207.PubMedGoogle Scholar
  8. Gouwens NW, Zeberg H, Tsumoto K, Tateno T, Aihara K, Robinson HPC. Synchronization of firing in cortical fast-spiking interneurons at gamma frequencies: a phase-resetting analysis. PLoS Comput Biol. 2010;6:e1000951.PubMedCentralPubMedGoogle Scholar
  9. Hasenstaub A, Shu Y, Haider B, Kraushaar U, Duque A, McCormick DA. Inhibitory postsynaptic potentials carry synchronized frequency information in active cortical networks. Neuron. 2005;47:423–35.PubMedGoogle Scholar
  10. Le Masson G, Renaud-Le Masson S, Debay D, Bal T. Feedback inhibition controls spike transfer in hybrid thalamic circuits. Nature. 2002;417:854–8.PubMedGoogle Scholar
  11. Lien C-C, Jonas P. Kv3 potassium conductance is necessary and kinetically optimized for high-frequency action potential generation in hippocampal interneurons. J Neurosci. 2003;23:2058–68.PubMedGoogle Scholar
  12. Morita K, Kalra R, Aihara K, Robinson HPC. Recurrent synaptic input and the timing of gamma-frequency-modulated firing of pyramidal cells during neocortical “UP” states. J Neurosci. 2008;28:1871–81.PubMedGoogle Scholar
  13. Netoff TI, Banks MI, Dorval AD, Acker CD, Haas JS, Kopell N, White JA. Synchronization in hybrid neuronal networks of the hippocampal formation. J Neurophysiol. 2005;93:1197–208.PubMedGoogle Scholar
  14. Nowotny T, Szucs A, Pinto RD, Selverston AI. StdpC: a modern dynamic clamp. J Neurosci Methods. 2006;158:287–99.PubMedGoogle Scholar
  15. Prinz AA, Abbott LF, Marder E. The dynamic clamp comes of age. Trends Neurosci. 2004;27:218–24.PubMedGoogle Scholar
  16. Raikov I, Preyer A, Butera RJ. MRCI: a flexible real-time dynamic clamp system for electrophysiology experiments. J Neurosci Methods. 2004;132:109–23.PubMedGoogle Scholar
  17. Robinson HPC. Kinetics of synaptic conductances in mammalian central neurons. Neurosci Res. 1991;16:VI.Google Scholar
  18. Robinson HPC. Analog circuits for injecting time-varying linear and nonlinear (NMDA-type) conductances into neurons. J Physiol. 1998;518P:9–10.Google Scholar
  19. Robinson HPC. A scriptable DSP-based system for dynamic conductance injection. J Neurosci Methods. 2008;169:271–81.PubMedGoogle Scholar
  20. Robinson HPC, Kawai N. Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. J Neurosci Methods. 1993;49:157–65.PubMedGoogle Scholar
  21. Sharp AA, O’Neil MB, Abbott LF, Marder E. Dynamic clamp: computer-generated conductances in real neurons. J Neurophysiol. 1993;69:992–5.PubMedGoogle Scholar
  22. Sohal VS, Zhang F, Yizhar O, Deisseroth K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature. 2009;459:698–702.PubMedGoogle Scholar
  23. Tateno T, Robinson HPC. The mechanism of ethanol action on midbrain dopaminergic neuron firing: a dynamic-clamp study of the role of Ih and GABAergic synaptic integration. J Neurophysiol. 2011;106:1901.PubMedGoogle Scholar
  24. Williams SR. Spatial compartmentalization and functional impact of conductance in pyramidal neurons. Nat Neurosci. 2004;7:961–7.PubMedGoogle Scholar

Copyright information

© European Biophysical Societies' Association (EBSA) 2013

Authors and Affiliations

  1. 1.Department of Physiology, Development and NeuroscienceUniversity of CambridgeCambridgeUK