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Biophysical neural adaptation mechanisms enable artificial neural networks to capture dynamic retinal computation

Author

Listed:
  • Saad Idrees

    (York University
    York University)

  • Michael B. Manookin

    (University of Washington)

  • Fred Rieke

    (University of Washington)

  • Greg D. Field

    (University of California)

  • Joel Zylberberg

    (York University
    York University
    Canadian Institute for Advanced Research)

Abstract

Adaptation is a universal aspect of neural systems that changes circuit computations to match prevailing inputs. These changes facilitate efficient encoding of sensory inputs while avoiding saturation. Conventional artificial neural networks (ANNs) have limited adaptive capabilities, hindering their ability to reliably predict neural output under dynamic input conditions. Can embedding neural adaptive mechanisms in ANNs improve their performance? To answer this question, we develop a new deep learning model of the retina that incorporates the biophysics of photoreceptor adaptation at the front-end of conventional convolutional neural networks (CNNs). These conventional CNNs build on ’Deep Retina,’ a previously developed model of retinal ganglion cell (RGC) activity. CNNs that include this new photoreceptor layer outperform conventional CNN models at predicting male and female primate and rat RGC responses to naturalistic stimuli that include dynamic local intensity changes and large changes in the ambient illumination. These improved predictions result directly from adaptation within the phototransduction cascade. This research underscores the potential of embedding models of neural adaptation in ANNs and using them to determine how neural circuits manage the complexities of encoding natural inputs that are dynamic and span a large range of light levels.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50114-5
    DOI: 10.1038/s41467-024-50114-5
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    References listed on IDEAS

    as
    1. 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.
    2. Kiersten Ruda & Joel Zylberberg & Greg D. Field, 2020. "Ignoring correlated activity causes a failure of retinal population codes," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
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