Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 05, 2019
- Source ID
- 10.7554/elife.38471
Entities
People
- Alex H Williams
- Andrew Bahle
- Emily L Mackevicius
- Mark S. Goldman
- Michale Fee
- Natalia I Denisenko
- Shijie Gu
Organizations
- G. Harold & Leila Y. Mathers Foundation
- Massachusetts Institute of Technology
- Michigan Department of Licensing and Regulatory Affairs
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- National Institute on Deafness and Other Communication Disorders
- Office of the Director
- ShanghaiTech University
- Simons Foundation
- Stanford University
- United States Department of Defense
- University of California