Consensus-Based Sorting of Neuronal Spike Waveforms.

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 ens

SUBMITTER: Fournier J 

PROVIDER: S-EPMC4990262 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature