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

Pillow JW, Shlens J, Chichilnisky EJ, Simoncelli EP - PLoS ONE (2013)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Perceptual Systems, Department of Psychology and Section of Neurobiology, The University of Texas at Austin, Austin, Texas, USA. pillow@mail.utexas.edu

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.

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Quantifying the robustness of spike sorting in the absence of ground truth through a prior sensitivity analysis for all parasol cells (). (A) The sensitivity of the spike rate to the prior distribution plotted against the spike sorting error rate in simulated data (see text for details). Note that both axes are plotted in logarithmic space. Dashed line is best fit line (). Gray box indicates spike rate sensitivities achieving  error. (B) Distribution of error rates across simulation. The solid blue line indicates 2% error rate in simulation. (C) The distribution of spike rate sensitivities from simulation (top) indicate that 8 cells contain spike rate sensitivities which imply an  error rate. Distribution of spike rate sensitivities calculated from real data suggest that 49 cells contain  error rates.
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pone-0062123-g007: Quantifying the robustness of spike sorting in the absence of ground truth through a prior sensitivity analysis for all parasol cells (). (A) The sensitivity of the spike rate to the prior distribution plotted against the spike sorting error rate in simulated data (see text for details). Note that both axes are plotted in logarithmic space. Dashed line is best fit line (). Gray box indicates spike rate sensitivities achieving error. (B) Distribution of error rates across simulation. The solid blue line indicates 2% error rate in simulation. (C) The distribution of spike rate sensitivities from simulation (top) indicate that 8 cells contain spike rate sensitivities which imply an error rate. Distribution of spike rate sensitivities calculated from real data suggest that 49 cells contain error rates.

Mentions: To make use of this relationship in spike sorting, we need to estimate the relationship between the sensitivity and the error rate. We simulated 120 seconds of electrode data using the generative model of Eq. 1 for 293 neurons, and estimated the spikes of each neuron using binary pursuit. We recomputed these estimates while varying the prior of each neuron individually. Figure 7 A shows a scatter plot of the relationship between the sensitivity (quantified as the derivative of the spike count with respect to the threshold for each neuron), and the spike sorting error rate in the simulated data. The data are reasonably well fit by a power law (straight line fit on a log-log plot, ).


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

Pillow JW, Shlens J, Chichilnisky EJ, Simoncelli EP - PLoS ONE (2013)

Quantifying the robustness of spike sorting in the absence of ground truth through a prior sensitivity analysis for all parasol cells (). (A) The sensitivity of the spike rate to the prior distribution plotted against the spike sorting error rate in simulated data (see text for details). Note that both axes are plotted in logarithmic space. Dashed line is best fit line (). Gray box indicates spike rate sensitivities achieving  error. (B) Distribution of error rates across simulation. The solid blue line indicates 2% error rate in simulation. (C) The distribution of spike rate sensitivities from simulation (top) indicate that 8 cells contain spike rate sensitivities which imply an  error rate. Distribution of spike rate sensitivities calculated from real data suggest that 49 cells contain  error rates.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3643981&req=5

pone-0062123-g007: Quantifying the robustness of spike sorting in the absence of ground truth through a prior sensitivity analysis for all parasol cells (). (A) The sensitivity of the spike rate to the prior distribution plotted against the spike sorting error rate in simulated data (see text for details). Note that both axes are plotted in logarithmic space. Dashed line is best fit line (). Gray box indicates spike rate sensitivities achieving error. (B) Distribution of error rates across simulation. The solid blue line indicates 2% error rate in simulation. (C) The distribution of spike rate sensitivities from simulation (top) indicate that 8 cells contain spike rate sensitivities which imply an error rate. Distribution of spike rate sensitivities calculated from real data suggest that 49 cells contain error rates.
Mentions: To make use of this relationship in spike sorting, we need to estimate the relationship between the sensitivity and the error rate. We simulated 120 seconds of electrode data using the generative model of Eq. 1 for 293 neurons, and estimated the spikes of each neuron using binary pursuit. We recomputed these estimates while varying the prior of each neuron individually. Figure 7 A shows a scatter plot of the relationship between the sensitivity (quantified as the derivative of the spike count with respect to the threshold for each neuron), and the spike sorting error rate in the simulated data. The data are reasonably well fit by a power law (straight line fit on a log-log plot, ).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Perceptual Systems, Department of Psychology and Section of Neurobiology, The University of Texas at Austin, Austin, Texas, USA. pillow@mail.utexas.edu

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.

Show MeSH
Related in: MedlinePlus