Dynamic Clamp

Abstract

The dynamic clamp technique uses the membrane voltage measured from an electrically excitable cell to solve computational ion channel models running in real time. These models comprise differential equations and ionic current calculations driven in part by the measured voltage. The current calculated from these simulations is then injected into the cell in a feedback configuration to create ionic currents that can simulate intrinsic ionic currents within single cells, as well as synaptic currents among cells to create small networks of cells. Applications include functional assessment of insertion or deletion of ion channels on membrane electrodynamics and studying interactions among coupled cells. Specific examples include studies of action potential propagation between cardiac myocytes and studying mechanisms of synchrony between coupled neurons.

Key Concepts:

  • The dynamic clamp is an electrophysiological technique that integrates the real‐time simulation of ion channel kinetics with electrophysiological experiments.

  • Simulated ion channels are driven by recorded membrane potentials and the calculated current is injected into a cell, altering the cell's membrane electrodynamics.

  • Applications include studies of ion channel mechanisms and dynamics of coupling of activity among cells.

  • The implementation of real‐time simulations required dedicated special purpose hardware and/or software suitable for closed‐loop tasks with low latencies (time delays).

  • Recently developed low‐cost embedded systems may emerge as the platform of choice for implementing this technique.

Keywords: electrophysiology; real‐time simulation; embedded system; cardiac; myocyte; neuron; real-time

Figure 1.

Block diagram of the dynamic clamp experimental setup. Standard electrophysiology techniques are used to obtain intracellular recordings from one or more cells, as shown on the left. The dynamic clamp is connected as shown on the right. The top right is an example of a hardware dynamic clamp system, whereas the bottom right is a software‐based system.

Figure 2.

Flowchart of the steps involved in a real‐time computation cycle.

Figure 3.

Examples of typical dynamic clamp configurations, green indicates the biological component whereas blue represents features implemented with the dynamic clamp. Current traces represent the amount of injected current. (a) Ionic current – an h‐type current is simulated and the resulting current is injected into a single neuron. (b) Chemical inhibitory synapse – two live neurons are connected with reciprocal inhibitory synapses triggered when the opposite neuron fires an action potential. (c) Electrical synapse – two live neurons are connected with an electrical synapse. The injected current is proportional to the voltage difference between the neurons. (d) Hybrid network – a live neuron is connected to a computational model neuron through an artificial synapse.

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References

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Robinson HP and Kawai N (1993) Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons. Journal of Neuroscience Methods 49: 157–165.

Sakurai A, Darghouth NR, Butera RJ and Katz PS (2006) Serotonergic enhancement of a 4‐AP‐sensitive current mediates the synaptic depression phase of spike timing‐dependent neuromodulation. Journal of Neuroscience 26: 2010–2021.

Sharp AA, Oneil MB, Abbott LF and Marder E (1993a) The dynamic clamp–artificial conductances in biological neurons. Trends in Neurosciences 16: 389–394.

Sharp AA, Oneil MB, Abbott LF and Marder E (1993b) Dynamic clamp – computer‐generated conductances in real neurons. Journal of Neurophysiology 69: 992–995.

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Wilders R, Kumar R, Joyner RW et al. (1996) Action potential conduction between a ventricular cell model and an isolated ventricular cell. Biophysical Journal 70: 281–295.

Further Reading

Butera R and McCarthy M (2004) Analysis of real‐time numerical integration methods applied to dynamic clamp experiments. Journal of Neural Engineering 1: 187–194.

Butera RJ, Wilson CG, DelNegro CA and Smith JC (2001) A methodology for achieving high‐speed rates for artificial conductance injection in electrically excitable biological cells. IEEE Transactions on Biomedical Engineering 48: 1460–1470.

Dorval AD, Christini DJ and White JA (2001) 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.

Goaillard JM and Marder E (2006) Dynamic clamp analyses of cardiac, endocrine, and neural function. Physiology 21: 197–207.

Le Masson G (1995) From conductances to neural network properties: analysis of simple circuits using the hybrid network method. Progress in Biophysics and Molecular Biology 64: 201–220.

Manor Y and Nadim F (2001) Frequency regulation demonstrated by coupling a model and a biological neuron. Neurocomputing 38: 269–278.

Pinto RD, Elson RC, Szucs A et al. (2001) Extended dynamic clamp: controlling up to four neurons using a single desktop computer and interface. Journal of Neuroscience Methods 108(1): 39–48.

Preyer AJ and Butera RJ (2005) Neuronal oscillators in Aplysia californica that demonstrate weak coupling in vitro. Physical Review Letters 95: 138103.

Prinz AA, Abbott LF and Marder E (2004) The dynamic clamp comes of age. Trends in Neurosciences 27: 218–224.

Raikov I, Preyer A and Butera RJ (2004) MRCI: a flexible real‐time dynamic clamp system for electrophysiology experiments. Journal of Neuroscience Methods 132: 109–123.

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How to Cite close
Preyer, Amanda, Norman, Sharon, and Butera, Robert J(Sep 2013) Dynamic Clamp. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0020293.pub2]