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Journal of Computational Neuroscience

, Volume 29, Issue 3, pp 567–579 | Cite as

Local field potentials indicate network state and account for neuronal response variability

  • Ryan C. KellyEmail author
  • Matthew A. Smith
  • Robert E. Kass
  • Tai Sing Lee
Article

Abstract

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell’s spiking activity relative to a behavior or stimulus, these network fluctuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

Keywords

Local field potential Correlation Network state Spontaneous activity Multielectrode array Decoding Population coding 

Notes

Acknowledgements

This work was supported by a National Science Foundation (NSF) Integrative Graduate Education and Research Traineeship to RCK (DGE-0549352), National Eye Institute (NEI) grants EY015958 and EY018894 to MAS, National Institute of Mental Health (NIMH) Grant MH64445 and NSF CISE IIS 0713206 to TSL, and NIMH grant MH064537 to REK. Data was collected by RCK, MAS and Adam Kohn in his laboratory as a part of a collaborative effort between the Kohn laboratory at Albert Einstein College of Medicine and the Lee laboratory at Carnegie Mellon University. We thank Adam Kohn for collaboration, and we are also grateful to Amin Zandvakili, Xiaoxuan Jia and Stephanie Wissig for assistance in data collection.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ryan C. Kelly
    • 1
    • 2
    Email author
  • Matthew A. Smith
    • 1
    • 3
  • Robert E. Kass
    • 1
    • 4
    • 5
  • Tai Sing Lee
    • 1
    • 2
  1. 1.Center for the Neural Basis of CognitionPittsburghUSA
  2. 2.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of NeuroscienceUniversity of PittsburghPittsburghUSA
  4. 4.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  5. 5.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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