Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure

@article{Groberger2018UnsupervisedCO,
  title={Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure},
  author={Lukas Gro{\ss}berger and Francesco Paolo Battaglia and Martin A. Vinck},
  journal={PLoS Computational Biology},
  year={2018},
  volume={14}
}
Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based… 
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