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.
Highlights
Action potentials, often referred to as ‘‘spikes’’, are the fundamental unit of communication in much of the nervous system
We introduce a greedy algorithm – ‘‘binary pursuit’’ – for obtaining the approximate maximum a posteriori (MAP) estimate of the spike trains given the voltage data under this model
The centroid of each cluster is identified as the spike waveform of a neuron, and all traces that fall within a cluster are labelled as spikes of the corresponding neuron
Summary
Often referred to as ‘‘spikes’’, are the fundamental unit of communication in much of the nervous system. The earliest methods, developed for single neurons recorded on single electrodes, rely on the basic strategy of matched filtering: the electrode waveform is compared against a temporally sliding template and a spike is identified whenever the two are found to match within some tolerance This methodology predates the era of digital computers, when the matching was done using handadjusted threshold triggers on an oscilloscope [7]. Much of the ‘‘background’’ noise in neural recordings is likely due to spikes of other cells [9]; if those spikes are large enough, any methodology based on template matching is likely to fail [10,11] Because it typically requires hand-adjustment of thresholding parameters, matched filtering is not practical for sorting multi-electrode data from large electrode arrays
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