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Attention modulates synchronized neuronal firing in primate somatosensory cortex

Author

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  • P. N. Steinmetz

    (Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University
    Computation and Neural Systems, California Institute of Technology)

  • A. Roy

    (Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University)

  • P. J. Fitzgerald

    (Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University)

  • S. S. Hsiao

    (Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University)

  • K. O. Johnson

    (Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University)

  • E. Niebur

    (Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University)

Abstract

A potentially powerful information processing strategy in the brain is to take advantage of the temporal structure of neuronal spike trains. An increase in synchrony within the neural representation of an object or location increases the efficacy of that neural representation at the next synaptic stage in the brain; thus, increasing synchrony is a candidate for the neural correlate of attentional selection1. We investigated the synchronous firing of pairs of neurons in the secondary somatosensory cortex (SII) of three monkeys trained to switch attention between a visual task and a tactile discrimination task. We found that most neuron pairs in SII cortex fired synchronously and, furthermore, that the degree of synchrony was affected by the monkey's attentional state. In the monkey performing the most difficult task, 35% of neuron pairs that fired synchronously changed their degree of synchrony when the monkey switched attention between the tactile and visual tasks. Synchrony increased in 80% and decreased in 20% of neuron pairs affected by attention.

Suggested Citation

  • P. N. Steinmetz & A. Roy & P. J. Fitzgerald & S. S. Hsiao & K. O. Johnson & E. Niebur, 2000. "Attention modulates synchronized neuronal firing in primate somatosensory cortex," Nature, Nature, vol. 404(6774), pages 187-190, March.
  • Handle: RePEc:nat:nature:v:404:y:2000:i:6774:d:10.1038_35004588
    DOI: 10.1038/35004588
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    Citations

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    Cited by:

    1. 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.
    2. Lu, Hongtao & van Leeuwen, C., 2006. "Synchronization of chaotic neural networks via output or state coupling," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 166-176.
    3. Qin, Ying-Mei & Che, Yan-Qiu & Zhao, Jia, 2018. "Effects of degree distributions on signal propagation in noisy feedforward neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 763-774.
    4. Li, Fan, 2020. "Effect of field coupling on the wave propagation in the neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    5. Polynikis, A. & di Bernardo, M. & Hogan, S.J., 2009. "Synchronizability of coupled PWL maps," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1353-1367.
    6. Adele Diederich & Annette Schomburg & Hans Colonius, 2012. "Saccadic Reaction Times to Audiovisual Stimuli Show Effects of Oscillatory Phase Reset," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-13, October.
    7. Zhou, Jin & Chen, Tianping & Xiang, Lan, 2006. "Robust synchronization of delayed neural networks based on adaptive control and parameters identification," Chaos, Solitons & Fractals, Elsevier, vol. 27(4), pages 905-913.
    8. Lu, Jun Guo & Chen, Guanrong, 2009. "Global asymptotical synchronization of chaotic neural networks by output feedback impulsive control: An LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2293-2300.
    9. Seyed-Ali Sadegh-Zadeh & Chandrasekhar Kambhampati & Darryl N. Davis, 2019. "Ionic Imbalances and Coupling in Synchronization of Responses in Neurons," J, MDPI, vol. 2(1), pages 1-24, January.
    10. Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja GrĂ¼n, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
    11. Helen Abadzi, 2006. "Efficient Learning for the Poor : Insights from the Frontier of Cognitive Neuroscience," World Bank Publications - Books, The World Bank Group, number 7023.

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