The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Laminar dependence of neuronal correlations in visual cortex
Open accessAbstract
Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30–50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“This is consistent with gamma power being higher in the superficial, feedforward, cortical layers, and alpha/beta power being higher in the deep, feedback, cortical layers (26, 35–40).”
From Layer and rhythm specificity for predictive routing (2020) · cites this paper as [39]
Citations
Cites
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,537
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954
Society for Neuroscience 2007 abstracts
Wishart, T. M., Paterson, J. M., Short, D. M., Meredith, S., Robertson, K., Sutherland, C. D., Cousin, M. A., Dutia, M. B., & Gillingwater, T. H. (2007) · Cited by 2,094
Neural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743
Neuronal gamma-band synchronization as a fundamental process in cortical computation
Fries, P. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135603 · Cited by 1,713
Cellular and network mechanisms of rhythmic recurrent activity in neocortex
Sanchez-Vives, M. V., & McCormick, D. A. (2000) · Nature Neuroscience · doi:10.1038/79848 · Cited by 1,539
Spatial frequency selectivity of cells in macaque visual cortex
De Valois, R. L., Albrecht, D. G., & Thorell, L. G. (1982) · Vision Research · doi:10.1016/0042-6989(82)90113-4 · Cited by 1,489
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237
The Asynchronous State in Cortical Circuits
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106
Flagged first, then by citations
Research works printed but not linked: 2
- Maier A, 2010, Front Syst Neurosci, 4, 31
- Zandvakili A, 2010, Society for Neuroscience Abstracts
Cited by
Sorted by citations
Items 101–130 of 130
Dual counterstream architecture may support separation between vision and predictions
Marić, M., & Domijan, D. (2022) · Consciousness and Cognition · doi:10.1016/j.concog.2022.103375 · Cited by 7
Khateb, M., Schiller, J., & Schiller, Y. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.713397 · Cited by 5
The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding
Meyer, R., Ladenbauer, J., & Obermayer, K. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00034 · Cited by 5
Maksimov, A., Diesmann, M., & van Albada, S. J. (2018) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2018.00044 · Cited by 4
Adjusted regularization of cortical covariance
Vinci, G., Ventura, V., Smith, M. A., & Kass, R. E. (2018) · Journal of Computational Neuroscience · doi:10.1007/s10827-018-0692-x · Cited by 5
Self-citationMoore, B., Khang, S., & Francis, J. T. (2020) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2020.541920 · Cited by 5
Zhu, S., Xia, R., Chen, X., & Moore, T. (2024) · Frontiers in Neural Circuits · doi:10.3389/fncir.2024.1399571 · Cited by 5
Neural Oscillations and Multisensory Processing
Zuo, Y., & Wang, Z. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-981-99-7611-9_8 · Cited by 4
Size tuning of neural response variability in laminar circuits of macaque primary visual cortex
Nurminen, L., Bijanzadeh, M., & Angelucci, A. (2026) · eLife · doi:10.7554/elife.86334 · Cited by 5
Synaptic Plasticity in Correlated Balanced NetworksPreprint
Akil, A. E., Rosenbaum, R., & Josić́, K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.26.061515 · Cited by 7
Shi, Y.-L., Steinmetz, N. A., Moore, T., Boahen, K. A., & Engel, T. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.02.279893 · Cited by 5
Correlated variability and its attentional modulation depend on anatomical connectivity
Shah, S., Hembrook‐Short, J. R., Mock, V. L., & Briggs, F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2318841121 · Cited by 3
Baravalle, R., & Montani, F. (2020) · Entropy · doi:10.3390/e22040477 · Cited by 3
Kajikawa, Y., Mackey, C. A., & O’Connell, M. N. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae338 · Cited by 2
Wason, T. D. (2021) · Biosystems · doi:10.1016/j.biosystems.2021.104403 · Cited by 2
Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex
Zerlaut, Y., Zucca, S., Fellin, T., & Panzeri, S. (2022) · eNeuro · doi:10.1523/eneuro.0073-22.2022 · Cited by 2
A Statistical Model of Shared Variability in the Songbird Auditory SystemPreprint
Buesing, L., Calabrese, A., Cunningham, J. P., Woolley, S. M. N., & Paninski, L. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/113670 · Cited by 2
Chang, W., Langella, S., Seo, M. S., Huynh, K. M., Yap, P., Lin, W., & Giovanello, K. S. (2022) · Research Square · doi:10.21203/rs.3.rs-1789565/v1 · Cited by 2
Re-evaluating Laminar Specificity of Working Memory in Human Prefrontal CortexPreprint
Chaimow, D., Degutis, J. K., Haenelt, D., Trampel, R., Weiskopf, N., & Lorenz, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.31.635930 · Cited by 3
Commentary by Steven L. Bressler
Bressler, S. L. (2015) · Studies in systems, decision and control · doi:10.1007/978-3-319-24406-8_12 · Cited by 1
On the operation of visual cortical gamma in the light of frequency variation
Lowet, E. (2016) · Research Publications (Maastricht University) · doi:10.26481/dis.20160323el · Cited by 1
How strong are correlations in strongly recurrent neuronal networks?Preprint
Darshan, R., van Vreeswijk, C., & Hansel, D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/274480 · Cited by 2
Zou, L., Moreni, G., Pennartz, C. M. A., & Mejías, J. F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.05.621826 · Cited by 1
A ubiquitous spectrolaminar motif across independent studies, including Mackey et al.’s own dataPreprint
Major, A. J., Abdaltawab, A., Phillips, J. M., Wang, T., Lee, E., Lichtenfeld, M. J., Chandrasekaran, C., Saalmann, Y. B., Maier, A., Desimone, R., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.07.652644 · Cited by 1
Formation of structure in cortical networks through spike timing-dependent plasticity
Ocker, G. K. (2015) · D-Scholarship@Pitt (University of Pittsburgh)
Neuronal Synchronization of Different Time-scales in V1:
Tanaka, H. (2015) · The Brain & Neural Networks · doi:10.3902/jnns.22.181
Helmer, M. (2015) · doi:10.53846/goediss-5441
Mechanisms of Cortico-Cortical Communication
Kohn, A. (2023) · Oxford University Press eBooks · doi:10.1093/med/9780197676158.003.0040
Self-citationLayer-specific spatiotemporal dynamics of feedforward and feedback in human visual object perceptionPreprint
Carricarte, T., Xie, S., Singer, J., Trampel, R., Huber, L., Lu, Z., Kietzmann, T. C., Weiskopf, N., & Cichy, R. M. (2025) · eLife · doi:10.1101/2025.05.13.653501 · Cited by 1
State-Dependent Dissociation of Shared Input and Directed Information Flow in the Visual CortexPreprint
Xue, Y., Morton, M. P., Nandy, A. S., & Jadi, M. P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.22.701042
Metadata
- Authors
- Smith, M. A., Jia, X., Zandvakili, A., & Kohn, A.
- Affiliations
- University of Pittsburgh (US)
- Albert Einstein College of Medicine (US)
- Corresponding author
- Smith, Matthew A.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2013-02-15
- Volume
- 109
- Issue
- 4
- Pages
- 940-947
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC3569140
- OpenAlex
- W1987380439
- Cited by
- 129
- References
- 68
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 11 · 2023: 6 · 2022: 10 · 2021: 6 · 2020: 9 · 2019: 9 · 2018: 10 · 2017: 7 · 2016: 12 · 2015: 22 · 2014: 15 · 2013: 7
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Research to Prevent Blindness
- National Institutes of Health
- National Eye Institute (R01EY016774, EY018894, EY-016774, P30EY008098)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P30HD071593)
- 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
- neural correlations, laminar organization, primary visual cortex, V2, cortical synchronization, local field potentials, gamma power, population coding, macaque, cortical depth
- MeSH terms
- Action Potentials, Animals, Macaca fascicularis, Nerve Net, Neurons, Photic Stimulation, Visual Cortex, 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
