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Associating Living Cells and Computational Models: an Introduction to Dynamic Clamp Principles and its Applications

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Dynamic-Clamp

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 1))

Abstract

The dynamic-clamp electrophysiological technique allows the mimicking of the electrical effects of arbitrary ion channels, controlled by the experimentalist, activating and inactivating into the membrane of an intracellularly recorded biological cell. Dynamic clamp relies on the establishing of a loop between the injected current and the recorded membrane potential. In this introductory chapter, we first present the principles of the technique, starting by recalling the basis of the equivalent electrical circuit representation of a cellular membrane. We then briefly list some of the issues encountered in the practical implementation of the dynamic-clamp loop. Finally, we overview the numerous applications of the method to the study of neurons, other excitable cells and networks of cells: these include the manipulation of intrinsic ion channels and of single or multiple synaptic inputs to a cell, as well as the construction of whole hybrid networks in which the biological cell interacts with model cells simulated in real time using a digital or analog system. Many of the applications briefly presented here are the subject of the following chapters.

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Notes

  1. 1.

    Note that this equation assumes an “ideal” capacitance, in which the charges are re-equilibrated instantaneously. Cable models have been proposed based on “non-ideal” capacitances, which do affect the high-frequency response of the membrane (see Bedard and Destexhe 2008), but such effects will not be considered here.

  2. 2.

    In most cases, the difference in ionic concentrations – and, consequently, the E rev – is considered constant on the timescale of an electrophysiological experiment: it is continuously maintained by ATP-consuming pumps or ion co-transporters working against the concentration gradient. The instantaneous changes in ion numbers consecutive to ion flow through the channels are considered negligible compared to the total quantities of ions present inside or outside a cell, instantly flowing in to restore the ion concentration close to the membrane. This assumption does not hold in some cases, like intense firing activity modifying the equilibrium for K+ and Cl− ions in the hippocampus (McCarren and Alger 1985; Thompson and Gähwiler 1989a, b). Moreover, these ionic concentrations evolve on longer timescales: for example, during development in the mammalian central nervous system, the intracellular [Cl−] is progressively lowered due to the delayed expression of a chloride exporter, shifting the E rev of gamma-amino butyric acid (GABA)ergic, Cl-permeable synaptic receptor channels toward more negative values and transforming the function of GABAergic synapses from excitatory to inhibitory (e.g., review by Ben-Ari 2002).

  3. 3.

    This application requires, however, a very precise model of the channels existing in the recorded cell, in order to ensure that the injected current really cancels the biological current. It might not be obvious to detect a mismatch between the two, so that interpretation of such experiments is more difficult that when channels are added.

  4. 4.

    Such inhibition is sometimes called shunting (e.g., Vida et al. 2006): the term shunting is used in this specific case because the main source of inhibition of spikes is the clamp at E rev below spike threshold – as opposed to hyperpolarizing inhibition, which has the effect of actively pulling the V m below rest. However, as we have seen, all membrane conductances are shunting in the sense that they are tending to clamp the V m at their reversal potentials. The term is thus a little bit misleading at first because it seems to falsely imply that only shunting inhibition shunts, by virtue of some special property other conductances would not have.

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Acknowledgments

We thank Jose Gomez and Charlotte Deleuze for comments on an earlier version of this chapter. Research supported by CNRS, ANR, ACI, HFSP, and the European Community (FACETS grant FP6 15879). Z.P. gratefully acknowledges the support of the FRM.

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  1. Unité de Neurosciences Intégratives et Computationnelles (UNIC), CNRS, 91 198 Gif-sur-Yvette, France

    Zuzanna Piwkowska

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  1. Zuzanna Piwkowska
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Correspondence to Zuzanna Piwkowska .

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    Piwkowska, Z., Destexhe, A., Bal, T. (2009). Associating Living Cells and Computational Models: an Introduction to Dynamic Clamp Principles and its Applications. In: Bal, T., Destexhe, A. (eds) Dynamic-Clamp. Springer Series in Computational Neuroscience, vol 1. Springer, New York, NY. https://doi.org/10.1007/978-0-387-89279-5_1

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