Journal of Computational Neuroscience

, Volume 29, Issue 1–2, pp 49–62 | Cite as

Methods for predicting cortical UP and DOWN states from the phase of deep layer local field potentials

  • Aman B. Saleem
  • Paul Chadderton
  • John Apergis-Schoute
  • Kenneth D. Harris
  • Simon R. Schultz
Article

Abstract

During anesthesia, slow-wave sleep and quiet wakefulness, neuronal membrane potentials collectively switch between de- and hyperpolarized levels, the cortical UP and DOWN states. Previous studies have shown that these cortical UP/DOWN states affect the excitability of individual neurons in response to sensory stimuli, indicating that a significant amount of the trial-to-trial variability in neuronal responses can be attributed to ongoing fluctuations in network activity. However, as intracellular recordings are frequently not available, it is important to be able to estimate their occurrence purely from extracellular data. Here, we combine in vivo whole cell recordings from single neurons with multi-site extracellular microelectrode recordings, to quantify the performance of various approaches to predicting UP/DOWN states from the deep-layer local field potential (LFP). We find that UP/DOWN states in deep cortical layers of rat primary auditory cortex (A1) are predictable from the phase of LFP at low frequencies (< 4 Hz), and that the likelihood of a given state varies sinusoidally with the phase of LFP at these frequencies. We introduce a novel method of detecting cortical state by combining information concerning the phase of the LFP and ongoing multi-unit activity.

Keywords

UP and DOWN states LFP State dependent coding Neural coding Spontaneous activity Neural oscillations 

Supplementary material

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Suppl. Fig. S1

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High Resolution Image (EPS 1169 kb)
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Suppl. Material(DOC 40 kb)

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Aman B. Saleem
    • 1
  • Paul Chadderton
    • 2
    • 3
  • John Apergis-Schoute
    • 1
  • Kenneth D. Harris
    • 1
    • 3
  • Simon R. Schultz
    • 1
  1. 1.Department of BioengineeringImperial College LondonLondonUK
  2. 2.UCL Ear InstituteLondonUK
  3. 3.Center for Molecular and Behavioral NeuroscienceRutgers UniversityNewarkUSA

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