IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-52304-7.html
   My bibliography  Save this article

Fixational eye movements enhance the precision of visual information transmitted by the primate retina

Author

Listed:
  • Eric G. Wu

    (Stanford University)

  • Nora Brackbill

    (Stanford University)

  • Colleen Rhoades

    (Stanford University)

  • Alexandra Kling

    (Stanford University
    Stanford University
    Stanford University)

  • Alex R. Gogliettino

    (Stanford University
    Stanford University)

  • Nishal P. Shah

    (Stanford University
    Stanford University)

  • Alexander Sher

    (University of California, Santa Cruz)

  • Alan M. Litke

    (University of California, Santa Cruz)

  • Eero P. Simoncelli

    (Simons Foundation
    New York University
    New York University)

  • E. J. Chichilnisky

    (Stanford University
    Stanford University
    Stanford University)

Abstract

Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

Suggested Citation

  • Eric G. Wu & Nora Brackbill & Colleen Rhoades & Alexandra Kling & Alex R. Gogliettino & Nishal P. Shah & Alexander Sher & Alan M. Litke & Eero P. Simoncelli & E. J. Chichilnisky, 2024. "Fixational eye movements enhance the precision of visual information transmitted by the primate retina," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52304-7
    DOI: 10.1038/s41467-024-52304-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-52304-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-52304-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michele Rucci & Ramon Iovin & Martina Poletti & Fabrizio Santini, 2007. "Miniature eye movements enhance fine spatial detail," Nature, Nature, vol. 447(7146), pages 852-855, June.
    2. Ikuya Murakami & Patrick Cavanagh, 1998. "A jitter after-effect reveals motion-based stabilization of vision," Nature, Nature, vol. 395(6704), pages 798-801, October.
    3. Greg D. Field & Jeffrey L. Gauthier & Alexander Sher & Martin Greschner & Timothy A. Machado & Lauren H. Jepson & Jonathon Shlens & Deborah E. Gunning & Keith Mathieson & Wladyslaw Dabrowski & Liam Pa, 2010. "Functional connectivity in the retina at the resolution of photoreceptors," Nature, Nature, vol. 467(7316), pages 673-677, October.
    4. Xaq Pitkow & Haim Sompolinsky & Markus Meister, 2007. "A Neural Computation for Visual Acuity in the Presence of Eye Movements," PLOS Biology, Public Library of Science, vol. 5(12), pages 1-14, December.
    5. Jian K. Liu & Helene M. Schreyer & Arno Onken & Fernando Rozenblit & Mohammad H. Khani & Vidhyasankar Krishnamoorthy & Stefano Panzeri & Tim Gollisch, 2017. "Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization," Nature Communications, Nature, vol. 8(1), pages 1-14, December.
    6. S. Nirenberg & S. M. Carcieri & A. L. Jacobs & P. E. Latham, 2001. "Retinal ganglion cells act largely as independent encoders," Nature, Nature, vol. 411(6838), pages 698-701, June.
    7. 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.
    8. Nadav Ben-Shushan & Nimrod Shaham & Mati Joshua & Yoram Burak, 2022. "Fixational drift is driven by diffusive dynamics in central neural circuitry," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    9. Janis Intoy & Michele Rucci, 2020. "Finely tuned eye movements enhance visual acuity," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    10. Jonathan W. Pillow & Jonathon Shlens & Liam Paninski & Alexander Sher & Alan M. Litke & E. J. Chichilnisky & Eero P. Simoncelli, 2008. "Spatio-temporal correlations and visual signalling in a complete neuronal population," Nature, Nature, vol. 454(7207), pages 995-999, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nadav Ben-Shushan & Nimrod Shaham & Mati Joshua & Yoram Burak, 2022. "Fixational drift is driven by diffusive dynamics in central neural circuitry," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Niru Maheswaranathan & David B Kastner & Stephen A Baccus & Surya Ganguli, 2018. "Inferring hidden structure in multilayered neural circuits," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-30, August.
    3. James Trousdale & Yu Hu & Eric Shea-Brown & Krešimir Josić, 2012. "Impact of Network Structure and Cellular Response on Spike Time Correlations," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-15, March.
    4. Zhetuo Zhao & Ehud Ahissar & Jonathan D. Victor & Michele Rucci, 2023. "Inferring visual space from ultra-fine extra-retinal knowledge of gaze position," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Arne F Meyer & Jan-Philipp Diepenbrock & Max F K Happel & Frank W Ohl & Jörn Anemüller, 2014. "Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-15, April.
    6. Jonathan Rubin & Nachum Ulanovsky & Israel Nelken & Naftali Tishby, 2016. "The Representation of Prediction Error in Auditory Cortex," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-28, August.
    7. Noga Mosheiff & Haggai Agmon & Avraham Moriel & Yoram Burak, 2017. "An efficient coding theory for a dynamic trajectory predicts non-uniform allocation of entorhinal grid cells to modules," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-19, June.
    8. 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.
    9. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    10. Pengcheng Zhou & Shawn D Burton & Adam C Snyder & Matthew A Smith & Nathaniel N Urban & Robert E Kass, 2015. "Establishing a Statistical Link between Network Oscillations and Neural Synchrony," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-25, October.
    11. Yasser Roudi & Sheila Nirenberg & Peter E Latham, 2009. "Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    12. Noah C Benson & Jeremy R Manning & David H Brainard, 2014. "Unsupervised Learning of Cone Spectral Classes from Natural Images," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-13, June.
    13. Richard Naud & Wulfram Gerstner, 2012. "Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-14, October.
    14. Fanfan Li & Dingwei Li & Chuanqing Wang & Guolei Liu & Rui Wang & Huihui Ren & Yingjie Tang & Yan Wang & Yitong Chen & Kun Liang & Qi Huang & Mohamad Sawan & Min Qiu & Hong Wang & Bowen Zhu, 2024. "An artificial visual neuron with multiplexed rate and time-to-first-spike coding," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    15. Kenneth W. Latimer & David J. Freedman, 2023. "Low-dimensional encoding of decisions in parietal cortex reflects long-term training history," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    16. Braden A W Brinkman & Alison I Weber & Fred Rieke & Eric Shea-Brown, 2016. "How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-34, October.
    17. Jason S Prentice & Olivier Marre & Mark L Ioffe & Adrianna R Loback & Gašper Tkačik & Michael J Berry II, 2016. "Error-Robust Modes of the Retinal Population Code," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-32, November.
    18. Yanyun Ren & Xiaobo Bu & Ming Wang & Yue Gong & Junjie Wang & Yuyang Yang & Guijun Li & Meng Zhang & Ye Zhou & Su-Ting Han, 2022. "Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    19. Jan Humplik & Gašper Tkačik, 2017. "Probabilistic models for neural populations that naturally capture global coupling and criticality," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-26, September.
    20. Jacob L. Yates & Shanna H. Coop & Gabriel H. Sarch & Ruei-Jr Wu & Daniel A. Butts & Michele Rucci & Jude F. Mitchell, 2023. "Detailed characterization of neural selectivity in free viewing primates," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52304-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.