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Visuomotor integration is associated with zero time-lag synchronization among cortical areas

Author

Listed:
  • Pieter R. Roelfsema

    (Max-Planck-linstitute for Brain Research
    The Netherlands Ophthalmic Research Institute, and University of Amsterdam)

  • Andreas K. Engel

    (Max-Planck-linstitute for Brain Research)

  • Peter König

    (The Neurosciences Institute)

  • Wolf Singer

    (Max-Planck-linstitute for Brain Research)

Abstract

INFORMATION processing in the cerebral cortex invariably involves the activation of millions of neurons that are widely distributed over its various areas. These distributed activity patterns need to be integrated into coherent representational states. A candidate mechanism for the integration and coordination of neuronal activity between different brain regions is synchronization on a fine temporal scale1–3. In the visual cortex, synchronization occurs selectively between the responses of neurons that represent related features2–5 and that need to be integrated for the generation of coherent percepts; neurons in other areas of the cerebral cortex also synchronize their discharges6–10. However, little is known about the patterns and the behavioural correlates of synchrony among widely separated cortical regions. Here we report that synchronization occurs between areas of the visual and parietal cortex, and between areas of the parietal and motor cortex, in the awake cat. When cats responded to a sudden change of a visual pattern, neuronal activity in cortical areas exhibited synchrony without time lags; this synchrony was particularly strong between areas subserving related functions. During reward and inter-trial episodes, zero-time-lag synchrony was lost and replaced by interactions exhibiting large and unsystematic time lags.

Suggested Citation

  • Pieter R. Roelfsema & Andreas K. Engel & Peter König & Wolf Singer, 1997. "Visuomotor integration is associated with zero time-lag synchronization among cortical areas," Nature, Nature, vol. 385(6612), pages 157-161, January.
    • Handle: RePEc:nat:nature:v:385:y:1997:i:6612:d:10.1038_385157a0
    DOI: 10.1038/385157a0
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    Cited by:

    1. Deng, Bin & Deng, Yun & Yu, Haitao & Guo, Xinmeng & Wang, Jiang, 2016. "Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs," Chaos, Solitons & Fractals, Elsevier, vol. 82(C), pages 48-59.
    2. Wang, Jing & Ye, Weijie & Liu, Shenquan & Lu, Bo & Jiang, Xiaofang, 2016. "Qualitative and quantitative aspects of synchronization in coupled CA1 pyramidal neurons," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 32-38.
    3. Erfan Zabeh & Nicholas C. Foley & Joshua Jacobs & Jacqueline P. Gottlieb, 2023. "Beta traveling waves in monkey frontal and parietal areas encode recent reward history," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Danique Jeurissen & Anne F. Ham & Amparo Gilhuis & Paolo Papale & Pieter R. Roelfsema & Matthew W. Self, 2024. "Border-ownership tuning determines the connectivity between V4 and V1 in the macaque visual system," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Wu, Yong & Ding, Qianming & Huang, Weifang & Hu, Xueyan & Ye, Zhiqiu & Jia, Ya, 2024. "Dynamic modulation of external excitation enhance synchronization in complex neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    6. Cao, Haoyu & Yang, Zhiyin & Liu, Zonghua, 2023. "Remote synchronization in multi-layered community networks with star-like topology," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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