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Context-dependent selectivity to natural images in the retina

Author

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  • Matías A. Goldin

    (Sorbonne Université, INSERM, CNRS)

  • Baptiste Lefebvre

    (Sorbonne Université, INSERM, CNRS
    CNRS, PSL University, Sorbonne University, and University of Paris)

  • Samuele Virgili

    (Sorbonne Université, INSERM, CNRS)

  • Mathieu Kim Pham Van Cang

    (Sorbonne Université, INSERM, CNRS
    Institut Pasteur, INSERM)

  • Alexander Ecker

    (University of Göttingen)

  • Thierry Mora

    (CNRS, PSL University, Sorbonne University, and University of Paris)

  • Ulisse Ferrari

    (Sorbonne Université, INSERM, CNRS)

  • Olivier Marre

    (Sorbonne Université, INSERM, CNRS)

Abstract

Retina ganglion cells extract specific features from natural scenes and send this information to the brain. In particular, they respond to local light increase (ON responses), and/or decrease (OFF). However, it is unclear if this ON-OFF selectivity, characterized with synthetic stimuli, is maintained under natural scene stimulation. Here we recorded ganglion cell responses to natural images slightly perturbed by random noise patterns to determine their selectivity during natural stimulation. The ON-OFF selectivity strongly depended on the specific image. A single ganglion cell can signal luminance increase for one image, and luminance decrease for another. Modeling and experiments showed that this resulted from the non-linear combination of different retinal pathways. Despite the versatility of the ON-OFF selectivity, a systematic analysis demonstrated that contrast was reliably encoded in these responses. Our perturbative approach uncovered the selectivity of retinal ganglion cells to more complex features than initially thought.

Suggested Citation

  • Matías A. Goldin & Baptiste Lefebvre & Samuele Virgili & Mathieu Kim Pham Van Cang & Alexander Ecker & Thierry Mora & Ulisse Ferrari & Olivier Marre, 2022. "Context-dependent selectivity to natural images in the retina," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33242-8
    DOI: 10.1038/s41467-022-33242-8
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    References listed on IDEAS

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    1. Tom Baden & Philipp Berens & Katrin Franke & Miroslav Román Rosón & Matthias Bethge & Thomas Euler, 2016. "The functional diversity of retinal ganglion cells in the mouse," Nature, Nature, vol. 529(7586), pages 345-350, January.
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    Cited by:

    1. Saad Idrees & Michael B. Manookin & Fred Rieke & Greg D. Field & Joel Zylberberg, 2024. "Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Franklin Leong & Babak Rahmani & Demetri Psaltis & Christophe Moser & Diego Ghezzi, 2024. "An actor-model framework for visual sensory encoding," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

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