May 15, 2013

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings

PloS One
Jonathan W PillowEero P Simoncelli

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 precisi...Continue Reading

  • References45
  • Citations34

References

  • References45
  • Citations34

Mentioned in this Paper

Middle Column
Entire Retina
Notch
Extracellular
Neurons
Action Potentials
Retinal Diseases
Electrical Activity of Brain
PRUNE gene
Morphologic Artifacts

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