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

Mackevicius, Emily L.; Bahle, Andrew H.; Williams, Alex H.; Gu, Shijie; Denisenko, Natalia I.; Goldman, Mark S.; Fee, Michale S.

doi:10.7554/elife.38471

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

Journal Article · 05 February 2019 · eLife

DOI: https://doi.org/10.7554/elife.38471 · OSTI ID:1628890

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Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); DOE/OSTI
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Stanford Univ., CA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); ShanghaiTech Univ. (China)
Univ. of California, Davis, CA (United States)

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.

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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); Simons Foundation; USDOD; USDOE Labor and Economic Growth

OSTI ID:: 1628890

Journal Information:: eLife, Journal Name: eLife Vol. 8; ISSN 2050-084X

Publisher:: eLife Sciences Publications, Ltd.Copyright Statement

Country of Publication:: United States

Language:: English

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