All candidates would do an excellent job of writing on the subject. Dr. Sharp may present an interesting historical account of the development of the procedure.
It looks like Boston and Atlanta are the only two places where this research is active!
No! There are lots of additional places: Bordeaux, Marseille, San Diego, New York, Chicago, etc etc.
This is a nice overview of the dynamic-clamp technique. I have several remarks about points that need improvement.
The first remark is that the paper should give a more detailed historical description of the dynamic-clamp. Below I quote references that should be mentioned, based on the introduction chapter by Piwkowska et al. in the "Dynamic-clamp" book edited by Destexhe & Bal (Springer, 2009). An early form of the dynamic-clamp technique, called the "Ersatz Nexus", has been described in cardiac physiology as early as 1979, in a PhD thesis studying the impact of electrical synapses (gap junctions) on the synchronization of clusters of cardiomyocytes in the chicken (Scott 1979). Note that I have myself a copy of this thesis and I do confirm that this is the very first published report of a technique which is essentially the same as the dynamic-clamp. Later studies of cardiac tissue re-introduced a technique named "coupling clamp" (Tan and Joyner 1990) for bi-directionally connecting two isolated myocytes by a virtual gap junction of chosen conductance. The injected current flowing through the virtual gap junction is calculated according to a driving force determined in real time and equal to the difference of membrane potential between the two cells (see Verheijck et al. 1998 for an example of application exploring the synchronization between two spontaneously active rabbit cardiac cells). An extension of this technique, named the model clamp by the authors, consists in coupling, through such a virtual gap junction, a real myocyte and a model myocyte simulated in real time (Wilders et al., 1996).
In neuroscience, the dynamic-clamp technique in its general form, with the general purpose of inserting into the membrane of a neuron any conductance the experimentalist might be interested in, has been introduced independently by Hugh Robinson (Robinson and Kawai 1993) and by a team led by Eve Marder and Larry Abbott, based on a collaboration with Gwendal Le Masson (Le Masson et al. 1992; Sharp et al. 1993; see also Le Masson, Le Masson and Moulins 1995). From the onset, based on the same principle of injecting a V$_m$-dependent current into a neuron, different implementations and applications were explored by the different groups: using digital systems, Robinson and Kawai injected synaptic inputs into cultured hippocampal neurons of the vertebrate CNS, while Sharp and colleagues studied various conductances and artificial networks in the stomato-gastric ganglion (STG) of decapod crustaceans (lobsters and crabs) nervous system. Le Masson and colleagues (Le Masson, Le Masson and Moulins 1995) developed an analog and a digital approach simultaneously for studies of the invertebrate preparation (and subsequently combined both approaches in a single study of mammalian thalamus networks, see Le Masson et al. 2002). Since this time, dynamic-clamp has been widely used in both vertebrate and invertebrate preparations.
The review should give more credit to Gwen Le Masson from Bordeaux - he was first author on the SFN abstract on dynamic-clamp in 1992 by the Marder-Abbott group, and I believe this is the very first publication of the technique in neuroscience. He was also first author of a Nature paper in 2000 based on the dynamic-clamp technique, which is certainly worth mentioning in the review.
Another remark is that the list of available systems should describe the "RT-Neuron", which enables running dynamic-clamp experiments from the Neuron simulator. This system is described in a chapter by Sadoc et al. in the 2009 dynamic-clamp book. Mike Hines (the author of Neuron) plans to include the RT module in the public distribution of Neuron, as an option. At the moment, this program runs only on the Windows operating system (Microsoft corp.), while the real-time is either given by a DSP acquisition board, or using commercial software to run real-time applications under Windows (see details in the Sadoc et al. chapter, and in particular in its appendix). A preliminary version of the RT-Neuron system was also described in a SFN abstract by LeFranc et al. This system was also developed by Gwen LeMasson.
Finally, the part on injection of "synaptic noise" in neurons missed a lot of references. A large number of studies studied this paradigm in different brain regions and using different methods. The conductance waveforms were generated either by the convolution of pre-synaptic spike trains with unitary synaptic conductances (Reyes et al., 1996; Jaeger and Bower 1999; Harsch and Robinson 2000; Gauck and Jaeger 2000; Chance et al., 2002; Suter and Jaeger 2004; de Polavieja et al. 2005; Zsiros and Hestrin, 2005; Dorval and White 2006; Tateno and Robinson 2006; Morita et al., 2008), or by effective stochastic models of synaptic bombardment or synaptic noise, without explicit representation of the pre-synaptic spike trains (Destexhe et al. 2001; Shu et al. 2003; McCormick et al. 2003; Fellous et al. 2003a, Fellous et al. 2003b; Wolfart et al. 2005; Hasenstaub et al. 2005; Shu et al. 2006; Desai and Walcott 2006; Piwkowska et al. 2008). The latter method's main advantage is to allow independent control of the mean and variance of conductances, which cannot be done using convoluted spike trains.
The review should mention these aspects
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