Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays

Abstract

Graphical Abstract Highlights d An automated spike sorting method for dense, large-scale recordings is presented d Efficient data representation enables sorting of thousands of channels d Automated unit selection through model-based quality control d Conventional spike sorting frequently fails under non-optimal signal conditions Correspondence m.hennig@ed.ac.uk In Brief Data volume and complexity make spike sorting for large-scale extracellular recordings computationally extremely challenging. Hilgen et al. introduce a method enabling analysis of recordings with thousands of channels and provide tools for automated quality control and unit selection. SUMMARY We present a method for automated spike sorting for recordings with high-density, large-scale multielec-trode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallel-ized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optoge-netic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.

Systematic Comparison of Retinal Organoid Differentiation from Human Pluripotent Stem Cells Reveals Stage Specific, Cell Line, and Methodological Differences

“…To reliably extract spikes from the raw traces we used a quantile-based event detection [43]. Single-unit spikes were sorted using an automated spike sorting method for dense, large-scale recordings [44]. Statistical significance (unpaired t test) and firing rate analyses were evaluated by using MATLAB (Mathworks, MA) and Prism (GraphPad, CA).…”

A Guide to Emerging Technologies for Large-Scale and Whole-Brain Optical Imaging of Neuronal Activity

“…However, insertion of electrodes is invasive and approaches for spike sorting work only within the immediate vicinity of the electrodes where the peak amplitudes are high. This, depending on the number of electrodes used, currently limits the effective number of neurons that can be interrogated to ~1,000 –2,000 neurons (Berenyi et al 2014, Hilgen et al 2017). …”

Recent progress in multi-electrode spike sorting methods

“…Nowadays, new devices with CMOS components now allow recordings from thousands electrodes simultaneously (Berdondini et al (2005); Fiscella et al (2012); Müller et al (2015); Hilgen et al (2016)), and it remains to be seen it these algorithms can scale up and process such a large amount of data. We need to be sure that the time spent on manual curation can remain small enough that we can get thousands of spike trains in a decent amount of time (see preliminary evidence that it might be the case by Yger et al (2016)).…”

“…tetrodes), methods that could be seen as adaptations of single electrode sorting worked very well (McNaughton et al, 1983; Harris et al, 2000; Gao et al, 2012), this is not the case with new devices designed with hundreds of electrodes all densely packed. CMOS-based devices with thousands of electrodes have been tested and are now frequently used (Berdondini et al (2005); Fiscella et al (2012); Müller et al (2015); Hilgen et al (2016)), calling for new algorithmic methods, largely different from the usual sorting methods.…”