Laminar dependence of neuronal correlations in visual cortex

Matthew A. Smith, Xiaoxuan Jia, Amin Zandvakili, Adam Kohn

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)940-947
Number of pages8
JournalJournal of neurophysiology
Volume109
Issue number4
DOIs
StatePublished - Feb 15 2013

Keywords

  • Cortical layers
  • Neuronal correlation
  • Population coding
  • V1
  • Visual cortex

ASJC Scopus subject areas

  • General Neuroscience
  • Physiology

Fingerprint

Dive into the research topics of 'Laminar dependence of neuronal correlations in visual cortex'. Together they form a unique fingerprint.

Cite this