Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure.
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 ense...
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| Language: | English |
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Public Library of Science (PLoS)
2018-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006283&type=printable |
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| author | Lukas Grossberger Francesco P Battaglia Martin Vinck |
| author_facet | Lukas Grossberger Francesco P Battaglia Martin Vinck |
| author_sort | Lukas Grossberger |
| collection | DOAJ |
| description | 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 increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns. |
| format | Article |
| id | doaj-art-7c1ea13ae9bf4e9b910475eacd43836c |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2018-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-7c1ea13ae9bf4e9b910475eacd43836c2025-08-20T03:11:25ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-07-01147e100628310.1371/journal.pcbi.1006283Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure.Lukas GrossbergerFrancesco P BattagliaMartin VinckTemporally 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 increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006283&type=printable |
| spellingShingle | Lukas Grossberger Francesco P Battaglia Martin Vinck Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. PLoS Computational Biology |
| title | Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. |
| title_full | Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. |
| title_fullStr | Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. |
| title_full_unstemmed | Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. |
| title_short | Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. |
| title_sort | unsupervised clustering of temporal patterns in high dimensional neuronal ensembles using a novel dissimilarity measure |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1006283&type=printable |
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