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

• Main Authors: Mackevicius, Emily Lambert (Author), Bahle, Andrew H (Author), Williams, Alex H (Author), Gu, Shijie (Author), Denissenko, Natalia (Author), Goldman, Mark S (Author), Fee, Michale S. (Author)
• Other Authors: McGovern Institute for Brain Research at MIT (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
• Format: Article
• Language: English
• Published: eLife Sciences Publications, Ltd, 2020-07-29T22:05:56Z.
• Subjects: Article
• Online Access: Get fulltext

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.
NIH Office of the Director (Grant 5T32EB019940-03)
National Institute on Deafness and Other Communication Disorders (Grant R01-DC009183)
National Institute of Neurological Disorders and Stroke (Grant U19-NS104648)
National Institute of Mental Health (Grant R25 MH062204)