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Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
2019
eLife
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. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted
doi:10.7554/elife.38471
pmid:30719973
pmcid:PMC6363393
fatcat:bflnb73h6je6vmnnudmwwzcjtq