A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
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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.
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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
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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. Related in: MedlinePlus |
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Mentions: The black curves in the panels of Fig. 5 summarize the relative behavior of the two spike sorting methods. Figure 5 A shows that binary pursuit identifies more spikes for every cell in our population (N = 293 cells). Figure 5 B shows a comparison of the magnitude of the CCF artifact. The spike trains obtained using binary pursuit are seen to have little or no artifact. From these two plots, one might be tempted to believe that binary pursuit has solved the spike sorting problem. But further examination reveals a new problem: an increase in refractory-period violations, which provide another indicator of spike-sorting errors [4], [15], [24], [41]–[43]. We quantify these errors in terms of the “contamination rate” for each neuron, defined as the ratio of the frequency of occurrence of spikes within the refractory period ( ms) to the baseline frequency of spikes outside this window. (A contamination rate of 50% indicates that the rate of spikes detected during the refractory window is equal to half the rate of spikes detected outside this window). Figure 5 C shows a comparison of the contamination rate for spikes sorted by clustering and binary pursuit. We see that for a large proportion of the cells, binary pursuit has a significantly higher contamination rate than clustering, and thus some of the increase in spike rate seen in these cells is likely due to inclusion of erroneous spikes. |
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