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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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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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