An automated online positioning system and simulation environment for multi-electrodes in extracellular recordings

Abstract

Extracellular recordings are a key tool to record the activity of neurons in vivo. Especially in the case of experiments with behaving animals, however, the tedious procedure of electrode placement can take a considerable amount of expensive and restricted experimental time. Furthermore, due to tissue drifts and other sources of variability in the recording setup, the position of the electrodes with respect to the recorded neurons can change causing low recording quality. The contributions of this work are threefold. We introduce a quality measure for the recording position of the electrode which should be maximized during recordings and is especially suitable for the use of multi-electrodes. An automated positioning system based on this quality measure is proposed. The system is able to find favorable recording positions and adapts the electrode position smoothly to changes of the neuron positions. Finally, we evaluate the system using a new simulator for extracellular recordings based on realistically reconstructed 3D neurons.

Computationally efficient simulation of extracellular recordings with multielectrode arrays

“…Another alternative approach would be to pre-calculate the LSA on a grid of measurement points and interpolate the waveforms for off-grid measurement points, as discussed in Section 1 (Franke et al, 2010b). However, interpolating such a high number of waveforms from a grid of 42.875 measurement points would be significantly more demanding than calculating the matrix multiplication of Eq.…”

“…Therefore, despite their computational simplicity, they are not suitable for simulating recordings with multiple and/or positionally electrodes. A possible solution to this limitation is to first employ a compartment model to calculate spike waveforms on a three dimensional grid of measurement points surrounding the neuron and then to interpolate the resulting waveforms to obtain waveforms in measurement points not lying on the simulation grid (Franke et al, 2010b). Despite the increased level of realism introduced with this approach, it requires extensive waveform interpolation and may therefore not be suitable for simulating very large populations of neurons.…”

LFPy – multimodal modeling of extracellular neuronal recordings in Python

“…This numerical scheme has been applied to predictions of the local field potential (LFP), that is, the low-frequency part of the extracellular potential Lindén et al, 2010Lindén et al, , 2011Gratiy et al, 2011;Makarova et al, 2011;Schomburg et al, 2012;Łęski et al, 2013;Reimann et al, 2013;Martín-Vázquez et al, 2013Głąbska et al, 2014;Mazzoni et al, 2015;Tomsett et al, 2015;Sinha and Narayanan, 2015;Taxidis et al, 2015;Hagen et al, 2016;Głąbska et al, 2016;Ness et al, 2016Ness et al, , 2018Hagen et al, 2017;McColgan et al, 2017), as well as extracellular spike waveforms which carry more power at high frequencies (Holt and Koch, 1999;Gold et al, 2006Gold et al, , 2007Franke et al, 2010;Schomburg et al, 2012;Thorbergsson et al, 2012;Reimann et al, 2013;Ness et al, 2015;Hagen et al, 2015;Miceli et al, 2017;Cserpán et al, 2017;Luo et al, 2018;Buccino et al, 2018Buccino et al, , 2019.…”

Towards reliable spike-train recordings from thousands of neurons with multielectrodes

“…Model neurons with appropriate firing statistics can then be placed at appropriate positions around the electrode to mimic the system of interest. Effects of electrode drift can be taken into account [45] and noise added at wish [38]. A particularly interesting prospect is to combine synthetic test data with experimental data [10]: here model data from one or several neurons, for which the ground-truth spike times are exactly known, is added to experimental recordings incorporating the noise situation at hand.…”