ArticleeLife2019
Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- Automatic phenotyping of emergency department patients with incidental hepatic steatosis: A machine learning clustering analysis.The American journal of emergency medicine · 2026Article
- Goal-directed hippocampal theta sweeps during memory-guided navigation.Nature neuroscience · 2026Article
- Replay of procedural memory is independent of the hippocampus.Nature neuroscience · 2026Article
- Energy paradox in REM sleep: balancing supply and consumption in brain metabolism.Communications biology · 2026Article
- A likelihood-based method for identifying replay from spike sequences.Nature communications · 2026Article
- Intrinsic space-time couplings governing multi-scale cortical dynamics.bioRxiv : the preprint server for biology · 2026Article
- Causal and directional elements of global brain dynamics.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporally dynamic noradrenergic regulation of cortical networks.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal dynamics and substates underlie emotional signalling in facial movements.Scientific reports · 2026Article
- Neural barcoding representing cortical spatiotemporal dynamics based on continuous-time Markov chains.Cell reports methods · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Low-dimensional signatures of neuronal activity associated with long-term operant conditioning in Aplysia.Communications biology · 2025Article
- Precise Extraction of Neural Motifs Reveals Multiscale, Parallel Encoding Schemes in Auditory Cortex.bioRxiv : the preprint server for biology · 2025Article
- The medial prefrontal cortex encodes procedural rules as sequential neuronal activity dynamics.Molecular brain · 2025Article
- Microendoscopic calcium imaging in motor cortices of macaques during rest and movement.iScience · 2025Article
- Article
- Neurovascular coupling: a review of spontaneous neocortical dynamics linking neuronal activity to hemodynamics and what we have learned from the rodent brain.Journal of neurophysiology · 2025Review
- Article
- Rastermap: a discovery method for neural population recordings.Nature neuroscience · 2025Article
- Interpretable deep learning for deconvolutional analysis of neural signals.bioRxiv : the preprint server for biology · 2024Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
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 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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