PT - JOURNAL ARTICLE AU - Mackevicius, Emily L. AU - Bahle, Andrew H. AU - Williams, Alex H. AU - Gu, Shijie AU - Denissenko, Natalia I. AU - Goldman, Mark S. AU - Fee, Michale S. TI - Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience AID - 10.1101/273128 DP - 2018 Jan 01 TA - bioRxiv PG - 273128 4099 - http://biorxiv.org/content/early/2018/12/23/273128.short 4100 - http://biorxiv.org/content/early/2018/12/23/273128.full AB - 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 patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.