A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

dc.creatorPillow, Jonathan W.en
dc.creatorShlens, Jonathonen
dc.creatorChichilnisky, E. J.en
dc.creatorSimoncelli, Eero P.en
dc.date.accessioned2013-05-24T14:57:59Zen
dc.date.available2013-05-24T14:57:59Zen
dc.date.issued2013-05-03en
dc.descriptionJonathan W. Pillow is with UT Austin, Jonathon Shlens is with the Salk Institute, E. J. Chichilnisky is with the Salk Institute, and Eero P. Simoncelli is with New York University.en
dc.description.abstractWe examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.en
dc.description.departmentPsychologyen
dc.description.sponsorshipThis work was supported by: Royal Society Society USA/Canada Research Fellowship (JWP) (http://royalsociety.org/grants/); Center for Perceptual Systems, startup funding (JP) (http://www.utexas.edu/cola/centers/cps/); Sloan Research Fellowship (JWP) (http://www.sloan.org/); Miller Institute for Basic Research in Science (JS) (http://millerinstitute.berkeley.edu/); National Eye Institute (NEI) grant EY018003 (EJC, EPS); National Institutes of Health (NIH) Grant EY017736 (EJC); and Howard Hughes Medical Institute (EPS) (http://www.hhmi.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.identifier.citationPillow JW, Shlens J, Chichilnisky EJ, Simoncelli EP (2013) A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings. PLoS ONE 8(5): e62123. doi:10.1371/journal.pone.0062123en
dc.identifier.doi10.1371/journal.pone.0062123en
dc.identifier.urihttp://hdl.handle.net/2152/20188en
dc.language.isoengen
dc.publisherPublic Library of Scienceen
dc.rightsAttribution 3.0 United Statesen
dc.rightsCC-BYen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/en
dc.subjectAlgorithmsen
dc.subjectCovarianceen
dc.subjectElectrode recordingen
dc.subjectGaussian noiseen
dc.subjectNeuronsen
dc.subjectOptimizationen
dc.subjectRetinal ganglion cellsen
dc.subjectStatistical noiseen
dc.titleA Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordingsen
dc.typeArticleen

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
journal.pone.0062123.pdf
Size:
967.49 KB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
admin_deposit_plosone.pdf
Size:
69.18 KB
Format:
Adobe Portable Document Format
Description:
Administrative Deposit