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 failure to correctly identify near-synchronous spikes in a pair of neurons leads to an artifact that can be observed directly. Figure 2 A shows the cross-correlation function (CCF) between recovered spike trains of an adjacent pair of ON parasol retinal ganglion cells (RGCs), which are known to exhibit some synchrony in their spiking. The cross-correlation function provides an estimate of the instantaneous spike rate of the second cell relative to the time of a spike in the first cell. The plot in Figure 2 A shows an increase in rate over the interval ms, which is typical for the timescale of synchrony in these cells [33]–[35]. But one can also see a pronounced CCF notch in the interval ms, which corresponds to the most highly synchronized spikes. This notch has been observed in extracellular but not intracellular recordings [34], [36], and its duration is matched to the interval over which the clustering failures identified in Figure 1 occur. These two facts suggest that the notch is an artifact that corresponds to spikes that the clustering method has failed to identify. |
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