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 F. Battaglia and M. 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… Expand
Bayesian inference of neuronal assemblies
On the methods for reactivation and replay analysis
An educational guide for nanopore sequencing in the classroom
SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm

References

SHOWING 1-10 OF 106 REFERENCES
Robust Discovery of Temporal Structure in Multi-neuron Recordings Using Hopfield Networks
A combinatorial method for analyzing sequential firing patterns involving an arbitrary number of neurons based on relative time order.
Finding neural assemblies with frequent item set mining
...
1
2
3
4
5
...