Cited by 130Neural dynamics and brain functionVisual perception and processing mechanismsNeuroscience and Neuropharmacology Research

Laminar dependence of neuronal correlations in visual cortex

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Smith, Matthew A., Jia, Xiaoxuan, Zandvakili, Amin, Kohn, Adam·2013·Journal of Neurophysiology

Abstract

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.

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Crossref · 10.1152/jn.00846.2012

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  • Gamma power is higher in superficial, feedforward, cortical layers, and alpha/beta power is higher in deep, feedback, cortical layers.

    “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]

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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
Published
2013-02-15
Volume
109
Issue
4
Pages
940-947
ISSN
  • 0022-3077 (ISSN-L)
  • 1522-1598
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
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

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