TY  - JOUR
A1  - Fournier, Julien
A1  - Mueller, Christian M.
A1  - Shein-Idelson, Mark
A1  - Hemberger, Mike
A1  - Laurent, Gilles
T1  - Consensus-based sorting of neuronal spike waveforms
T2  - PLoS One
N2  - 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.
Y1  - 2016
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/41340
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-413402
SN  - 1932-6203
N1  - Copyright: © 2016 Fournier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 11
IS  - (8): e0160494
SP  - 1
EP  - 24
PB  - PLoS
CY  - Lawrence, Kan.
ER  -