ArticleNature neuroscience2024
Dimensionality reduction beyond neural subspaces with slice tensor component analysis.
Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Signatures of Covert Neuron Loss in the Local Field Potential of Motor Cortex.Journal of the American Heart Association · 2026Article
- Large-Scale Neural Recording Technologies for Investigating Circuit Dysfunction and Functional Biomarker Discovery in Animal Models of Alzheimer's Disease.International journal of molecular sciences · 2026Review
- Gut distension evokes rapid neural dynamics in vagal and hindbrain populations of larval zebrafish.iScience · 2026Article
- Benchmarking criteria to determine latent linear dimensionality in neural data.Scientific reports · 2026Article
- Large-scale electrophysiology at single-spike resolution.Nature reviews. Neuroscience · 2026Review
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Distinguishing task-evoked dynamic brain networks from intrinsic activity with tensor component analysis.Brain imaging and behavior · 2026Article
- Article
- Exploiting correlations across trials and behavioral sessions to improve neural decoding.Neuron · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- Technical development of two-photon optogenetic stimulation and its potential application to brain-machine interfaces.Neurophotonics · 2026Review
- Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification.bioRxiv : the preprint server for biology · 2025Article
- Exploiting correlations across trials and behavioral sessions to improve neural decoding.bioRxiv : the preprint server for biology · 2025Article
- Motor cortex flexibly deploys a high-dimensional repertoire of subskills.bioRxiv : the preprint server for biology · 2025Article
- Connectome-constrained ligand-receptor interaction analysis for understanding brain network communication.Nature communications · 2025Article
- Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes.bioRxiv : the preprint server for biology · 2025Article
- Article
- Article
- Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework.Frontiers in neuroscience · 2025Article
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Authors and funding
3 authors.
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Abstract
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.