ReviewNature neuroscience2026
Neural timescales from a computational perspective.
Review in Nature neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Comprehensive profiling of brain dynamics during anesthesia across phylogeny.Nature neuroscience · 2026Article
- Human cortical networks trade communication efficiency for computational reliability.Science advances · 2026Article
- Neuromorphic hierarchical modular reservoirs.Nature communications · 2026Article
- Emergent critical oscillations in motor cortex of Parkinson's patients.bioRxiv : the preprint server for biology · 2026Article
- iSTTC: A robust method for accurate estimation of intrinsic neural timescales from single-unit recordings.PLoS computational biology · 2026Article
- Neurons in auditory cortex integrate information within a constrained and context-invariant temporal window.Current biology : CB · 2025Article
- Network dynamics underlying activity-timescale differences between cortical regions.bioRxiv : the preprint server for biology · 2025Article
- Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information rate.bioRxiv : the preprint server for biology · 2025Article
- Brain-wide organization of intrinsic timescales at single-neuron resolution.bioRxiv : the preprint server for biology · 2025Article
- Hierarchical gradients of multiple timescales in the mammalian forebrain.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Neural activity fluctuates over a wide range of timescales within and across brain areas. Experimental observations suggest that diverse neural timescales reflect information in dynamic environments. However, the definitions and measurements of timescales derived from brain recordings vary across the literature. Moreover, these observations do not specify the mechanisms that underlie variations in timescales or whether specific timescales are necessary for neural computation and brain function. Here we synthesize three directions in which computational approaches can distill the broad set of empirical observations into quantitative and testable theories. We review (1) how different data analysis methods quantify timescales across distinct behavioral states and recording modalities; (2) how biophysical models provide mechanistic explanations for the emergence of diverse timescales; and (3) how task-performing networks and machine learning models uncover the functional relevance of neural timescales. This integrative computational perspective complements experimental investigations, providing a holistic view of how neural timescales reflect the relationships among brain structure, dynamics and behavior.
Indexed as
Identifiers
42393348What OpenQuestion holds
Registered trials
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.