Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Name
elife-38471-v3.pdf
Description
Published version
Size
4.59 MB
Format
Adobe PDF
Checksum (MD5)
bced1d7dd7d7d4f9bfa35b662d232d9a
Author(s)
Mackevicius, Emily Lambert
Bahle, Andrew H
Williams, Alex H
Gu, Shijie
Denissenko, Natalia
Goldman, Mark S
Fee, Michale S.
Date Issued
February 2019
Journal
Neuroscience
Publisher
eLife Sciences Publications, Ltd
Citation
Mackevicius, Emily L. et al. "Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience." Neuroscience 8 (February 2019): e38471 © 2019 The Authors
Version
Final published version
Abstract
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.
MIT Department
McGovern Institute for Brain Research at MIT
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.7554/elife.38471
