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Spontaneously emerging cortical representations of visual attributes

Author

Listed:
  • Tal Kenet

    (The Weizmann Institute of Science
    University of California San Francisco)

  • Dmitri Bibitchkov

    (The Weizmann Institute of Science)

  • Misha Tsodyks

    (The Weizmann Institute of Science)

  • Amiram Grinvald

    (The Weizmann Institute of Science)

  • Amos Arieli

    (The Weizmann Institute of Science)

Abstract

Spontaneous cortical activity—ongoing activity in the absence of intentional sensory input—has been studied extensively1, using methods ranging from EEG (electroencephalography)2,3,4, through voltage sensitive dye imaging5,6,7, down to recordings from single neurons8,9. Ongoing cortical activity has been shown to play a critical role in development10,11,12,13,14, and must also be essential for processing sensory perception, because it modulates stimulus-evoked activity5,15,16, and is correlated with behaviour17. Yet its role in the processing of external information and its relationship to internal representations of sensory attributes remains unknown. Using voltage sensitive dye imaging, we previously established a close link between ongoing activity in the visual cortex of anaesthetized cats and the spontaneous firing of a single neuron6. Here we report that such activity encompasses a set of dynamically switching cortical states, many of which correspond closely to orientation maps. When such an orientation state emerged spontaneously, it spanned several hypercolumns and was often followed by a state corresponding to a proximal orientation. We suggest that dynamically switching cortical states could represent the brain's internal context, and therefore reflect or influence memory, perception and behaviour.

Suggested Citation

  • Tal Kenet & Dmitri Bibitchkov & Misha Tsodyks & Amiram Grinvald & Amos Arieli, 2003. "Spontaneously emerging cortical representations of visual attributes," Nature, Nature, vol. 425(6961), pages 954-956, October.
    • Handle: RePEc:nat:nature:v:425:y:2003:i:6961:d:10.1038_nature02078
    DOI: 10.1038/nature02078
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    Citations

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    Cited by:

    1. Lars Buesing & Johannes Bill & Bernhard Nessler & Wolfgang Maass, 2011. "Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-22, November.
    2. Haleigh N. Mulholland & Matthias Kaschube & Gordon B. Smith, 2024. "Self-organization of modular activity in immature cortical networks," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. 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.
    4. Rong J. B. Zhu & Xue-Xin Wei, 2023. "Unsupervised approach to decomposing neural tuning variability," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    5. Dejan Pecevski & Lars Buesing & Wolfgang Maass, 2011. "Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-25, December.
    6. Roberto F Galán, 2008. "On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-10, May.
    7. Matteo Carandini, 2004. "Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex," PLOS Biology, Public Library of Science, vol. 2(9), pages 1-1, August.
    8. Dimitri Yatsenko & Krešimir Josić & Alexander S Ecker & Emmanouil Froudarakis & R James Cotton & Andreas S Tolias, 2015. "Improved Estimation and Interpretation of Correlations in Neural Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-28, March.
    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. Stefan Habenschuss & Zeno Jonke & Wolfgang Maass, 2013. "Stochastic Computations in Cortical Microcircuit Models," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-28, November.
    11. 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.
    12. Mite Mijalkov & Joana B Pereira & Giovanni Volpe, 2020. "Delayed correlations improve the reconstruction of the brain connectome," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
    13. Bershadskii, A. & Ikegaya, Y., 2011. "Chaotic neuron clock," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 342-347.
    14. Lior Matityahu & Naomi Gilin & Gideon A. Sarpong & Yara Atamna & Lior Tiroshi & Nicolas X. Tritsch & Jeffery R. Wickens & Joshua A. Goldberg, 2023. "Acetylcholine waves and dopamine release in the striatum," Nature Communications, Nature, vol. 14(1), pages 1-23, December.

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