ReviewCurrent opinion in neurobiology2021
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity.
Review in Current opinion in neurobiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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Who cites it
105 citing papers in PubMed.
- Behavioral demands organize a decision process into distinct yet coordinated neural representations in parietal cortex.bioRxiv : the preprint server for biology · 2026Article
- Artificial neural manifolds.Nature communications · 2026Article
- Neuropixels 1536 Channel Quad Base probe reveals brain-wide communication underlying flexible sensorimotor sequences.bioRxiv : the preprint server for biology · 2026Article
- Developmental tuning of functional manifold dimensionality across the human brain.bioRxiv : the preprint server for biology · 2026Article
- Hippocampal ripples initiate cortical dimensionality expansion for memory retrieval.Nature communications · 2026Article
- Article
- Benchmarking criteria to determine latent linear dimensionality in neural data.Scientific reports · 2026Article
- Live Spike Sorting of Large-scale Neural Recordings.bioRxiv : the preprint server for biology · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- Manifolds in the Medial Premotor Cortex During Switching From Attending to Tapping to a Metronome.Annals of the New York Academy of Sciences · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Closed-loop error damping in human BCI using pre-error motor cortex activity.bioRxiv : the preprint server for biology · 2026Article
- Review
- A complex network perspective on brain disease.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- Multi-Channel Neural Interface for Neural Recording and Neuromodulation.Small methods · 2026Review
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Quantifying the compressibility of the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The Geometric Signatures of Brain State Transitions: Recursive Informational Curvature Reveals Hidden Dynamics in Primate Cortex.Neuroscience insights · 2026Article
- Spark: modular spiking neural networks.Frontiers in artificial intelligence · 2026Article
45 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
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Abstract
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here, we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
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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.