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Elife. 2019 Feb 5;8. pii: e38471. doi: 10.7554/eLife.38471.

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

Author information

McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, United States.
Neurosciences Program, Stanford University, Stanford, United States.
School of Life Sciences and Technology, ShanghaiTech University, Shanghai, China.
Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, United States.
Department of Ophthamology and Vision Science, University of California, Davis, Davis, United States.
Contributed equally


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 datas. 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.


Zebra finch; matrix factorization; neuroscience; rat; sequence; unsupervised

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