Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. He...

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Detalles Bibliográficos
Autores principales: Mackevicius, Emily L, Bahle, Andrew H, Williams, Alex H, Gu, Shijie, Denisenko, Natalia I, Goldman, Mark S, Fee, Michale S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363393/
https://www.ncbi.nlm.nih.gov/pubmed/30719973
http://dx.doi.org/10.7554/eLife.38471

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363393/
https://www.ncbi.nlm.nih.gov/pubmed/30719973
http://dx.doi.org/10.7554/eLife.38471

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