A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles

@article{Groberger2018AND,
  title={A novel distance measure for the unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles},
  author={Lukas Gro{\ss}berger and F. Battaglia and M. Vinck},
  journal={bioRxiv},
  year={2018},
  pages={252791}
}
Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDistClust (Spike Pattern Optimal Transport Distance Clustering), for their detection from high-dimensional neural ensembles. SPOTDistClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDist). Then, it performs density-based… Expand
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