A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Fast, Scalable, Bayesian Spike Identification for Multi-Electrode Arrays
2011
PLoS ONE
We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes
doi:10.1371/journal.pone.0019884
pmid:21799725
pmcid:PMC3140468
fatcat:sls5iz3ld5bsdlt3x2ds675csu