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Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice

Author

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  • James F. A. Poulet

    (Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland)

  • Carl C. H. Petersen

    (Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland)

Abstract

Neurocomputation: degrees of cortical processing Differences in synchronized activity in cortical neurons characterize different brain states, and are thought to be fundamental mechanisms of neural computation. James Poulet and Carl Petersen now show using dual whole-cell recordings from somatosensory barrel cortex in behaving mice, that the membrane potential of nearby neurons is highly correlated during quiet wakefulness but this correlation is reduced when the mice were actively whisking — a stereotypic back-and-forth movement of the whickers used to explore the environment. This suggests that internal brain states dynamically regulate cortical membrane potential synchrony during behaviour, defining different modes of cortical processing.

Suggested Citation

  • James F. A. Poulet & Carl C. H. Petersen, 2008. "Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice," Nature, Nature, vol. 454(7206), pages 881-885, August.
    • Handle: RePEc:nat:nature:v:454:y:2008:i:7206:d:10.1038_nature07150
    DOI: 10.1038/nature07150
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    Cited by:

    1. Ashok Litwin-Kumar & Anne-Marie M Oswald & Nathaniel N Urban & Brent Doiron, 2011. "Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-14, December.
    2. Audrey J Sederberg & Aurélie Pala & He J V Zheng & Biyu J He & Garrett B Stanley, 2019. "State-aware detection of sensory stimuli in the cortex of the awake mouse," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-24, May.
    3. Iris Reuveni & Sourav Ghosh & Edi Barkai, 2017. "Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-31, January.
    4. Adrián Ponce-Alvarez & Biyu J He & Patric Hagmann & Gustavo Deco, 2015. "Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-26, August.
    5. Moritz Helias & Moritz Deger & Stefan Rotter & Markus Diesmann, 2010. "Instantaneous Non-Linear Processing by Pulse-Coupled Threshold Units," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-10, September.
    6. Cheng Ly & Brent Doiron, 2009. "Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-Fire Neurons," PLOS Computational Biology, Public Library of Science, vol. 5(4), pages 1-12, April.
    7. Simone Carlo Surace & Jean-Pascal Pfister, 2015. "A Statistical Model for In Vivo Neuronal Dynamics," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-21, November.
    8. Yuki Bando & Michael Wenzel & Rafael Yuste, 2021. "Simultaneous two-photon imaging of action potentials and subthreshold inputs in vivo," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    9. Christian Donner & Klaus Obermayer & Hideaki Shimazaki, 2017. "Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-27, January.
    10. Moritz Helias & Tom Tetzlaff & Markus Diesmann, 2014. "The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-21, January.

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