Abstract
Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.
Highlights
Recent developments in electrode array fabrication promise to provide neuroscientists access to extracellular signals from hundreds to thousands of neurons simultaneously [1,2,3,4]
Based on the pairwise probability of points being assigned to the same cluster over successive runs of the K-means clustering, we identified a set of core clusters (Fig 1C), corresponding to groups of points which were more consistently clustered together
We developed a new approach to cluster extracellular spike waveforms, which identifies the most consensual partition of spike waveforms among an ensemble of clustering solutions obtained with a standard K-means clustering
Summary
Recent developments in electrode array fabrication promise to provide neuroscientists access to extracellular signals from hundreds to thousands of neurons simultaneously [1,2,3,4]. One related long-standing challenge concerns the computational separation, or sorting, of electrical signals from neuronal populations. Spike sorting consists of clustering waveforms of action potential recorded extracellularly (based on timing, shape and amplitude) so as to assign each.
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