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

Emily L. Mackevicius, Andrew H. Bahle, Alex H. Williams, Shijie Gu, Natalia I. Denissenko, Mark S. Goldman, Michale S. Fee
doi: https://doi.org/10.1101/273128
Emily L. Mackevicius
1McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MIT
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  • For correspondence: elm@mit.edu fee@mit.edu msgoldman@ucdavis.edu
Andrew H. Bahle
1McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MIT
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Alex H. Williams
2Neurosciences Program, Stanford University, Stanford, CA, 94305
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Shijie Gu
1McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MIT
3School of Life Sciences and Technology, ShanghaiTech University
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Natalia I. Denissenko
1McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MIT
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Mark S. Goldman
4Center for Neuroscience, Department of Neurobiology, Physiology and Behavior, Department of Ophthamology and Vision Science, UC Davis
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  • For correspondence: elm@mit.edu fee@mit.edu msgoldman@ucdavis.edu
Michale S. Fee
1McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, MIT
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  • For correspondence: elm@mit.edu fee@mit.edu msgoldman@ucdavis.edu
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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 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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Posted December 23, 2018.
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Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Emily L. Mackevicius, Andrew H. Bahle, Alex H. Williams, Shijie Gu, Natalia I. Denissenko, Mark S. Goldman, Michale S. Fee
bioRxiv 273128; doi: https://doi.org/10.1101/273128
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Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Emily L. Mackevicius, Andrew H. Bahle, Alex H. Williams, Shijie Gu, Natalia I. Denissenko, Mark S. Goldman, Michale S. Fee
bioRxiv 273128; doi: https://doi.org/10.1101/273128

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