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

Jonathan W. Pillow, Jonathon Shlens, E. J. Chichilnisky, Eero Simoncelli

Research output: Contribution to journalArticle

Abstract

We 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.

Original languageEnglish (US)
Article numbere62123
JournalPLoS One
Volume8
Issue number5
DOIs
StatePublished - May 3 2013

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Sorting
Artifacts
sorting
Neurons
neurons
Electric potential
electrodes
Electrodes
Cluster Analysis
methodology
Sampling
retina
Primates
Geometry
Retina
sampling

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings. / Pillow, Jonathan W.; Shlens, Jonathon; Chichilnisky, E. J.; Simoncelli, Eero.

In: PLoS One, Vol. 8, No. 5, e62123, 03.05.2013.

Research output: Contribution to journalArticle

Pillow, Jonathan W. ; Shlens, Jonathon ; Chichilnisky, E. J. ; Simoncelli, Eero. / A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings. In: PLoS One. 2013 ; Vol. 8, No. 5.
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