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 datas. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to

SUBMITTER: Mackevicius EL 

PROVIDER: S-EPMC6363393 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature