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Editorial Focus
) address this problem by presenting a new method for the estimation of synaptic conductances from fluctuations in the membrane potential, proving again that the road less traveled may be the more interesting.
To fully appreciate the paper by Rudolph and colleagues (2004) one really should first read their earlier publication (Rudolph and Destexhe 2003) in which they derive, using stochastic calculus, an analytic expression for the steady-state distribution of Vm measured under conditions of intense network activity. They now take this analytic expression one step further, by deriving a simple expression linking the means and SDs of two subthreshold Vm distributions (measured at two different constant levels of current injected via the recording electrode in current-clamp mode) to the mean excitatory and inhibitory synaptic conductances and to their variances. A simple experiment to carry out: measure subthreshold activity in the high-conductance state, generate the amplitude histogram from the recordings, fit the histograms to a Gaussian distribution, obtain the means and SDs, plug them into the simple equation derived by Rudolph and colleagues and get the mean excitatory and inhibitory conductances. The method was tested on a series of numerical models of increasing complexity to show that it was reasonably accurate even in the face of nonlinear dendritic synaptic integration. It is important to note that the method is currently applicable to a mixture of AMPA and GABAA conductances with known reversal potentials. The effects of NMDA and GABAB have yet to be investigated. The authors also acknowledge that their method for estimating the leak conductance may be a potential source of error especially for in vivo recordings.
Using dynamic-clamp, Rudolph and colleagues (2004) apply their conductance estimation method to an excited slice preparation (Sanchez-Vives and McCormick 2000) and show that in the high-conductance state the estimated inhibitory synaptic conductance parameters were twice as large as those of excitatory conductance. This conductance ratio was then used to recreate high-conductance states using the dynamic-clamp configuration. Their results touch a sore point that most of us suppress when examining brain slices. Background synaptic activity in brain slices is low. This leads to a higher membrane resistance (Rm) and consequently to a slower membrane time constant (
) and longer passive space constant (
) as compared with that observed in vivo. Therefore synaptic integration in brain slices displaying low background synaptic activity is different from in vivo (Bernander et al. 1991; Destexhe and Pa
e 1999; Hô and Destexhe 2000; Pae et al. 1998). This problem raises several important questions. To what extent can we learn from results obtained in vitro about synaptic integration in vivo? Is it enough to scale membrane properties in cellular models obtained in vitro to reliably simulate in vivo properties? Isn't it more likely that increased synaptic activity will lead to nonlinear changes in synaptic integration? This can be the result of morphological characteristics of the dendritic tree, changes in the activation or inactivation states of voltage-gated ion channels, and modulation by metabotropic receptors activated by neurotransmitter spillover.
The paper by Rudolph et al. (2004) brings to the forefront important aspects of the investigation of synaptic integration. First, this new technique opens possibilities for investigations of synaptic integration both in vitro and in vivo. Personally, I am looking forward to the combination of this methodology with dendritic recordings of Vm so that we may learn how dendrites transform information under synaptic bombardment. Second, high-conductance states are fragile and respond badly to the standard pharmacological toolkit of the electrophysiologist. The proposed methodology should direct us to developing more tools that will be able to extract information from a highly noisy neuron. Finally, we who investigate synaptic integration in the quiescent slice preparation should start exploring the transformation of information in a neuron that is really doing what it is supposed to do, integrating many synaptic inputs simultaneously.
Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel
Address reprint requests and other correspondence to A. Korngreen (E-mail: korngra{at}mail.biu.ac.il).
REFERENCES
Bernander O, Douglas RJ, Martin KA, and Koch C. Synaptic background activity influences spatiotemporal integration in single pyramidal cells. Proc Natl Acad Sci USA 88: 11569–11573, 1991.
Destexhe A and Pae D. Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. J Neurophysiol 81: 1531–1547, 1999.
Hô N and Destexhe A. Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons. J Neurophysiol 84: 1488–1496, 2000.
Pae D, Shink E, Gaudreau H, Destexhe A, and Lang EJ. Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons In vivo. J Neurophysiol 79: 1450–1460, 1998.
Rudolph M and Destexhe A. Characterization of subthreshold voltage fluctuations in neuronal membranes. Neural Comput. 15: 2577–2618, 2003.
Rudolph M, Piwkowska Z, Badoual M, Bal T, and Destexhe A. A method to estimate synaptic conductances from membrane potential fluctuations. J Neurophysiol, 91: 2874–2886, 2004.
Sanchez-Vives MV and McCormick DA. Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nature Neurosci. 3: 1027–1034, 2000.[CrossRef][ISI][Medline]
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