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Self-organization of modular activity in immature cortical networks

Author

Listed:
  • Haleigh N. Mulholland

    (University of Minnesota)

  • Matthias Kaschube

    (Frankfurt Institute for Advanced Studies
    Goethe University)

  • Gordon B. Smith

    (University of Minnesota
    University of Minnesota)

Abstract

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network’s intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity – which is reflected in the developing representations of visual orientation – strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48341-x
    DOI: 10.1038/s41467-024-48341-x
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    References listed on IDEAS

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