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Anticipation of moving stimuli by the retina

Author

Listed:
  • Michael J. Berry

    (Harvard University)

  • Iman H. Brivanlou

    (Harvard University)

  • Thomas A. Jordan

    (Stanford University School of Medicine)

  • Markus Meister

    (Harvard University)

Abstract

A flash of light evokes neural activity in the brain with a delay of 30–100 milliseconds1, much of which is due to the slow process of visual transduction in photoreceptors2,3. A moving object can cover a considerable distance in this time, and should therefore be seen noticeably behind its actual location. As this conflicts with everyday experience, it has been suggested that the visual cortex uses the delayed visual data from the eye to extrapolate the trajectory of a moving object, so that it is perceived at its actual location4,5,6,7. Here we report that such anticipation of moving stimuli begins in the retina. A moving bar elicits a moving wave of spiking activity in the population of retinal ganglion cells. Rather than lagging behind the visual image, the population activity travels near the leading edge of the moving bar. This response is observed over a wide range of speeds and apparently compensates for the visual response latency. We show how this anticipation follows from known mechanisms of retinal processing.

Suggested Citation

  • Michael J. Berry & Iman H. Brivanlou & Thomas A. Jordan & Markus Meister, 1999. "Anticipation of moving stimuli by the retina," Nature, Nature, vol. 398(6725), pages 334-338, March.
    • Handle: RePEc:nat:nature:v:398:y:1999:i:6725:d:10.1038_18678
    DOI: 10.1038/18678
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    Citations

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    Cited by:

    1. Jian K Liu & Tim Gollisch, 2015. "Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-30, July.
    2. John A. Gaynes & Samuel A. Budoff & Michael J. Grybko & Joshua B. Hunt & Alon Poleg-Polsky, 2022. "Classical center-surround receptive fields facilitate novel object detection in retinal bipolar cells," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Roland W. Scholz, 2017. "Managing complexity: from visual perception to sustainable transitions—contributions of Brunswik’s Theory of Probabilistic Functionalism," Environment Systems and Decisions, Springer, vol. 37(4), pages 381-409, December.
    5. 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.
    6. Gabriel D Puccini & Maria V Sanchez-Vives & Albert Compte, 2007. "Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits," PLOS Computational Biology, Public Library of Science, vol. 3(5), pages 1-13, May.
    7. Mina A Khoei & Guillaume S Masson & Laurent U Perrinet, 2017. "The Flash-Lag Effect as a Motion-Based Predictive Shift," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-31, January.
    8. Andrea Pintimalli & Joseph Glicksohn & Fabio Marson & Tania Di Giuseppe & Tal Dotan Ben-Soussan, 2023. "Change in Time Perception Following the Place of Pre-Existence Technique," IJERPH, MDPI, vol. 20(4), pages 1-19, February.
    9. Weston Cox & Brian J Fischer, 2015. "Optimal Prediction of Moving Sound Source Direction in the Owl," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-20, July.

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