The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.
Slow Cortical Dynamics and the Accumulation of Information over Long Timescales
Open accessAbstract
Making sense of the world requires us to process information over multiple timescales. We sought to identify brain regions that accumulate information over short and long timescales and to characterize the distinguishing features of their dynamics. We recorded electrocorticographic (ECoG) signals from individuals watching intact and scrambled movies. Within sensory regions, fluctuations of high-frequency (64-200 Hz) power reliably tracked instantaneous low-level properties of the intact and scrambled movies. Within higher order regions, the power fluctuations were more reliable for the intact movie than the scrambled movie, indicating that these regions accumulate information over relatively long time periods (several seconds or longer). Slow (<0.1 Hz) fluctuations of high-frequency power with time courses locked to the movies were observed throughout the cortex. Slow fluctuations were relatively larger in regions that accumulated information over longer time periods, suggesting a connection between slow neuronal population dynamics and temporally extended information processing.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“this topography has previously been mapped either by temporally scrambling the stimulus at different timescales to see which regions’ responses are disrupted (Hasson, Yang, Vallines, Heeger, & Rubin, 2008; Honey et al., 2012; Lerner, Honey, Silbert, & Hasson, 2011) or by examining the power spectrum of intrinsic dynamics within each region (Honey et al., 2012; Murray et al., 2014; Stephens et al.,”
From Discovering Event Structure in Continuous Narrative Perception and Memory (2017) · cites this paper as Honey et al., 2012
In the citing paper
“The topographical hierarchy of processing timescales along the cortical surface (2) has been supported and replicated across methodologies, including single-unit analysis (4), electrocorticography analysis (7), fMRI analysis (5, 6, 25), magnetoencephalography analysis (1), computational models (3), and resting state functional connectivity (30, 31).”
From Amplification of local changes along the timescale processing hierarchy (2017) · cites this paper as [7]
In the citing paper
“Previous work has suggested that long-TRW areas have intrinsically slower neural dynamics compared with short-TRW areas, and that these slower cortical dynamics may be related to an area’s ability to accumulate information over time (7, 8).”
From Amplification of local changes along the timescale processing hierarchy (2017) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Testing for nonlinearity in time series: the method of surrogate data
Theiler, J. P., Eubank, S., Longtin, A., Galdrikian, B., & Farmer, J. D. (1992) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(92)90102-s · Cited by 3,759
Kleiner, M., Brainard, D. H., & Pelli, D. G. (2007) · MPG.PuRe (Max Planck Society) · Cited by 3,602
High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2006) · Science · doi:10.1126/science.1128115 · Cited by 2,650
Research works printed but not linked: 1
- Weisskoff, 1993, Power spectrum analysis of functionally-weighted MR data: what's in the noise, Proc. Int. Soc. Magn. Reson. Med., 1, 7
Cited by
Sorted by citations
Items 501–533 of 533
Temporal signatures of thought—neurodynamics distinguish on- and off-task thoughts
Long, Z., Fu, X., Chen, Q., Zhang, J., & Northoff, G. (2026) · Communications Biology · doi:10.1038/s42003-026-09715-7
Wei, X., Zhou, X., Liu, X., Zang, Y., & Yu, Q. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108830
Luo, J., Zeng, X., Xiong, Y., Xu, Y., Zhou, C., Wang, Y., Yao, D., & Guo, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.19.712967
Effective Connectivity Identifies Divergent Cerebro-Cerebellar Network Organization in Schizophrenia
Pearson, K., Aberizk, K., An, C., Hodges, G., van Erp, T. G. M., Calhoun, V. D., & Turner, J. A. (2026) · The Cerebellum · doi:10.1007/s12311-026-01983-7
Learning long- and short-term dynamics for human attention prediction using large video models
Moradi, M., Moradi, M., Borji, A., Salanitri, F. P., Bellitto, G., Rundo, F., Palazzo, S., & Spampinato, C. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104740
Connectivity and Processing in the Macaque Cerebral Cortex
Gariel, M.-A. (2017) · theses.fr (ABES) · doi:10.70675/f0800cc4zde67z408bzbb7fz331bc22fbfbd
Perceiving regularity in sequences : behavioural, neural and computational signatures
Maheu, M. (2019) · theses.fr (ABES) · doi:10.70675/613538edz4e03z4a21za592z9859bfee3cff
Behavioral and functional imaging analyses of face and voice integration in gender perception
Abbatecola, C. (2018) · theses.fr (ABES) · doi:10.70675/d7ba7bacz21bbz4d9azb86bz0fb95421eee2
Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm
Christiano, O. R., & Michelmann, S. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1218
Neural, acoustic and information dynamics (mutually) constrain speech perception
giroud, J. (2021) · theses.fr (ABES) · doi:10.70675/e7d68cbcz88a1z4e9dz8918z60a356de42bb
Acquah, T., & Shafie, K. (2026) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2026.110772
Acoustic and Sublexical Rhythms
Meyer, L., & Strauß, A. (2026) · Cambridge University Press eBooks · doi:10.1017/9781009295888.010
Northoff, G., Çatal, Y., & Abbasi, S. (2026) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2025.0005
Li, G., Jin, Z., Wang, X.-J., & Li, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.28.728604
Chang, H.-J., Çatal, Y., Wolman, A., Ibáñez, A. M., Smith, D., Su, I.-W., Cheng, K.-Y., & Northoff, G. (2026) · Computer Speech & Language · doi:10.1016/j.csl.2026.102013
A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions
Wang, M. B., G’Sell, M., Castellano, J. F., Richardson, R. M., & Ghuman, A. S. (2026) · Nature Communications · doi:10.1038/s41467-026-73347-y
Infra-slow brain fluctuations: A taxonomy of mechanistic hypotheses
Mäki-Marttunen, V. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106825
Decoding Chronic Pain States from Distributed Intracranial RecordingsPreprint
Saal, J., Khambhati, A. N., Chang, E. F., & Shirvalkar, P. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732555
Dynamics of thoughts—novel candidate marker for differential-diagnosis of mental disorders?
Northoff, G., Hua, J., & Ge, X. (2026) · Journal of Psychiatry and Neuroscience · doi:10.1139/jpn-2026-0013
Neural mechanisms of time-forward predictions for naturalistic auditory tone sequences
Baumgarten, T. J., Koenig, L., Hardstone, R., Flinker, A., Devore, S., Friedman, D. J., Dugan, P. C., Devinsky, O., & He, B. J. (2026) · Nature Communications · doi:10.1038/s41467-026-75359-0
Self-citationMultiscale Temporal Processing Supports Sound Recognition under Causal ConstraintsPreprint
Esposito, M., Weidler, T., Ferreyra, C., Giordano, B. L., & Formisano, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.27.740874
The cognitive neuroscience of the self
Northoff, G., & Wolman, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29258-3.00031-8
Intrinsic neural timescales during working memory under 60-day simulated microgravityPreprint
Zhou, L., Wang, R., Liu, X., Wu, D., Liu, Z., Sun, X., Chen, B., & Wu, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10588590/v1
Northrop, T. J., Tolmie, A. K., & Wass, S. V. (2026) · Developmental Review · doi:10.1016/j.dr.2026.101290
Liquid neural networks for adaptive latent brain‐state modeling: A perspective
Zhang, Y., Yin, J., Ji, X., Ji, J., Lai, C. S., & Dong, Z. (2026) · Brain‐X · doi:10.1002/brx2.70053
Functional Objectives Shape the Temporal Organization of Memory in Delayed Sensorimotor SystemsPreprint
Yamaki, N., & Churiki, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7403122
Context-dependent abstraction of temporal relationships during naturalistic episodic memory
Ye, Q., Hu, Y., Ku, Y., Appiah, K., & Kwok, S. C. (2026) · iScience · doi:10.1016/j.isci.2026.117391
Phase dynamics shape thought dynamics – Oscillatory model of thought
Hua, J., Long, Z., Zhang, J., Ge, X., & Northoff, G. (2026) · Physics of Life Reviews · doi:10.1016/j.plrev.2026.09.008
Time after time: neural codes of speech comprehension
Gwilliams, L. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.09.003
Hierarchical coding of local and global positions in episodic memory for naturalistic sequences
Peng, X., Zhou, Y., Zhang, L., Li, T., Muhle-Karbe, P. S., & Xue, G. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1416
Functional Objectives Shape the Temporal Organization of Memory in Delayed Sensorimotor SystemsPreprint
Yamaki, N., & Churiki, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7601194
Parkes, L., Sydnor, V. J., Howell, A. M., France, J. M., Westbrook, A., & Larsen, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.21.760719
Metadata
- Authors
- Honey, C. J., Thesen, T., Donner, T. H., Silbert, L. J., Carlson, C. E., Devinsky, O., Doyle, W. K., Rubin, N., Heeger, D. J., & Hasson, U.
- Affiliations
- Princeton University (US)
- New York University (US)
- University of Amsterdam (NL)
- Corresponding author
- Honey, Christopher J.
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 76
- Issue
- 2
- Pages
- 423-434
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC3517908
- OpenAlex
- W2152009360
- Cited by
- 532
- References
- 73
- Citations by year
- 2026: 52 · 2025: 50 · 2024: 56 · 2023: 56 · 2022: 53 · 2021: 47 · 2020: 48 · 2019: 36 · 2018: 36 · 2017: 37 · 2016: 20 · 2015: 18 · 2014: 15 · 2013: 7
- FWCI
- 6.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH094480)
- National Institute on Drug Abuse (R21DA024423)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- information accumulation, electrocorticography, sensory processing, higher-order visual processing, film viewing, neural population dynamics, temporal integration
- MeSH terms
- Acoustic Stimulation, Adult, Brain Mapping, Cerebral Cortex, Electrodes, Electroencephalography, Female, Humans, Male, Mental Processes, Middle Aged, Photic Stimulation, Reaction Time, Spectrum Analysis, Time Factors, Reproducibility of Results, Epilepsy, Complex Partial, Young Adult, Brain Waves
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review