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

ABSTRACT: 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 clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern "noise" spiking are present. Our method handles efficiently the additional information from in

SUBMITTER: Grossberger L 

PROVIDER: S-EPMC6051652 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature