Active Electrode

Active Electrode Compensation

The Active Electrode Compensation (AEC) method for high-resolution intracellular recordings
In collaboration with Romain Brette (formerly postdoc in my group, now at Ecole Normale Supérieure, Paris) and Thierry Bal (ICN), we have developed a new method for performing high-resolution dynamic-clamp and voltage-clamp recordings [1,2,3,4]. This method takes advantage of the real-time feedback between a computer and the recorded neuron. This real-time feedback can be used to design a new type of recording paradigm, which we call “Active Electrode Compensation” (AEC), and which consists of a real-time computer-controlled compensation of the electrode artefacts and bias which currently limit recording precision. The essential idea behind the method is to represent the electrode by an arbitrarily complex linear circuit [2], extract the properties of this circuit for each particular recording, and actively compensate for the effect of the electrode by subtracting the voltage drop through this circuit from the recording. The voltage across the electrode is modeled as the convolution of the injected current and a kernel which characterizes the electrode. We have tested this method in dynamic-clamp experiments, and in particular using conductance pulses at variable frequency and amplitude, for which the analytic solution is known [1]. The AEC recordings show a spectacular improvement. The method can also be used to inject noise in real neurons, which is critical because the high-frequency components of the noise can induce particularly strong artefacts. We can demonstrate that these artefacts are almost completely absent in AEC recordings, and the frequency of dynamic-clamp experiments can be raised up to 20 KHz [1].

The AEC method was also tested in vivo [3], in collaboration with Cyril Monier and Yves Frégnac (ICN). The method could be successfully tested in vivo, we succeeded in performing continuous dynamic-clamp experiments and injection of artificial synaptic conductances in neurons of cat primary visual cortex in vivo (see details in [3]). These different aspects were also reviewed in a recent book chapter with Romain Brette [5].

One of the most important application of the AEC method is that it can be used to perform voltage-clamp experiments. This application was explored recently in vitro [6], where we could show that voltage-clamp experiments using different paradigms, ranging from the controlled injection of conductances up to conductance patterns evoked by electric stimulation in the slice. We could demonstrate that the AEC method provides a single-electrode voltage-clamp access for sharp electrodes, with is more precise than discontinuous methods [6].

1] Brette, R., Piwkowska, Z., Rudolph-Lilith, M., Bal, T. and Destexhe, A. High-resolution intracellular recordings using a real-time interaction between the neuron and a computational model of the electrode. ArXiv preprint,, 2007. [2] Brette, R., Piwkowska, Z., Rudolph, M., Bal, T. and Destexhe, A. A nonparametric electrode model for intracellular recording. Neurocomputing 70: 1597-1601, 2007 (see abstract) [3] Brette, R., Piwkowska, Z., Monier, C., Rudolph-Lilith, M., Fournier, J., Levy, M., Frégnac, Y., Bal, T. and Destexhe, A. High-resolution intracellular recordings using a real-time computational model of the electrode. Neuron 59: 379-391, 2008 (see abstract) [4] Brette R, Piwkowska Z, Monier C, Gomez Gonzalez JF, Fr\’egnac Y, Bal T and Destexhe A. Dynamic clamp with high resistance electrodes using active electrode compensation in vitro and in vivo. In: Dynamic-Clamp: From Principles to Applications, Springer, New York, pp. 347-382, 2009. [5] Brette, R. and Destexhe, A. Intracellular recording. In: Handbook of Neural Activity Measurement, Edited by Brette R. and Destexhe A., Cambridge University Press, Cambridge, UK, pp. 44-91, 2012 (see abstract) [6] Gomez-Gonzalez, J.F., Destexhe, A. and Bal, T. Application of active electrode compensation to perform continuous voltage-clamp recordings with sharp microelectrodes. J. Neural Engineering 11: 056028, 2014 (see abstract)

Alain Destexhe