![]() However, in this condition, only one or two neurons emit spikes, and this simplifies drastically the spike sorting problem. Dual loose patch and extracellular recordings have been performed for culture of neurons or in cortical slices ( Anastassiou et al., 2015 Franke et al., 2015). In ground truth data, one neuron is cherry picked from among all the neurons recorded using an extracellular array using another technique, and simultaneousy recorded. A few algorithms have been designed to process large-scale recordings ( Marre et al., 2012 Pillow et al., 2013 Pachitariu et al., 2016 Leibig et al., 2016 Hilgen et al., 2017 Chung et al., 2017 Jun et al., 2017), but they have not been tested on real ‘ground truth’ data. Second, most algorithms do not scale up to hundreds or thousands of electrodes in vitro and in vivo, because their computation time would increase exponentially with the number of electrodes ( Rossant et al., 2016). First, many algorithms do not take into account that the spikes of a single neuron will evoke a voltage deflection on many electrodes. In particular, for thousands of electrodes, this problem is still largely unresolved.Ĭlassical spike sorting algorithms cannot process this new type of data for several reasons. However, for large-scale and dense recordings, it is still unclear how to extract the spike contributions from extracellular recordings. This process, called spike sorting, has received a lot of attention for recordings with a small number of electrodes (typically, a few tens of electrodes). To access the spiking activity of individual neurons, one needs to separate the waveform produced by each neuron and identify when it appears in the recording. However, this high resolution comes at a cost: each electrode receives the activity from many neurons. Thanks to this resolution, the spikes from a single neuron will be detected on several electrodes and produce extracellular waveforms with a characteristic spatio-temporal profile across the recording sites. Recently, newly developed microelectrode arrays (MEA) have allowed recording of local voltage from hundreds to thousands of extracellular sites separated only by tens of microns ( Berdondini et al., 2005 Fiscella et al., 2012 Lambacher et al., 2004), giving indirect access to large neural ensembles with a high spatial resolution. One of the most powerful and widespread techniques for neuronal population recording is extracellular electrophysiology. As local circuits represent information using large populations of neurons throughout the brain ( Buzsáki, 2010), technologies to record hundreds or thousands of them are therefore essential.
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