Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays.
|Title||Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Hilgen G, Sorbaro M, Pirmoradian S, Muthmann J-O, Kepiro IEdit, Ullo S, Ramirez CJuarez, Encinas APuente, Maccione A, Berdondini L, Murino V, Sona D, Zanacchi FCella, Sernagor E, Hennig MHelge|
|Date Published||2017 Mar 07|
We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode 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 parallelized 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, optogenetic 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.
|Alternate Journal||Cell Rep|
|Grant List||BB/H023569/1 / / Biotechnology and Biological Sciences Research Council / United Kingdom |
BB/I001042/1 / / Biotechnology and Biological Sciences Research Council / United Kingdom