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Segregation of object and background motion in the retina

Author

Listed:
  • Bence P. Ölveczky

    (Massachusetts Institute of Technology
    Harvard University)

  • Stephen A. Baccus

    (Harvard University)

  • Markus Meister

    (Harvard University)

Abstract

An important task in vision is to detect objects moving within a stationary scene. During normal viewing this is complicated by the presence of eye movements that continually scan the image across the retina, even during fixation. To detect moving objects, the brain must distinguish local motion within the scene from the global retinal image drift due to fixational eye movements. We have found that this process begins in the retina: a subset of retinal ganglion cells responds to motion in the receptive field centre, but only if the wider surround moves with a different trajectory. This selectivity for differential motion is independent of direction, and can be explained by a model of retinal circuitry that invokes pooling over nonlinear interneurons. The suppression by global image motion is probably mediated by polyaxonal, wide-field amacrine cells with transient responses. We show how a population of ganglion cells selective for differential motion can rapidly flag moving objects, and even segregate multiple moving objects.

Suggested Citation

  • Bence P. Ölveczky & Stephen A. Baccus & Markus Meister, 2003. "Segregation of object and background motion in the retina," Nature, Nature, vol. 423(6938), pages 401-408, May.
    • Handle: RePEc:nat:nature:v:423:y:2003:i:6938:d:10.1038_nature01652
    DOI: 10.1038/nature01652
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    Citations

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    Cited by:

    1. 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.
    2. Andrew Jo & Sercan Deniz & Suraj Cherian & Jian Xu & Daiki Futagi & Steven H. DeVries & Yongling Zhu, 2023. "Modular interneuron circuits control motion sensitivity in the mouse retina," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. 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.
    4. Pei-Yu Huang & Bi-Yi Jiang & Hong-Ji Chen & Jia-Yi Xu & Kang Wang & Cheng-Yi Zhu & Xin-Yan Hu & Dong Li & Liang Zhen & Fei-Chi Zhou & Jing-Kai Qin & Cheng-Yan Xu, 2023. "Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Klaudia P. Szatko & Maria M. Korympidou & Yanli Ran & Philipp Berens & Deniz Dalkara & Timm Schubert & Thomas Euler & Katrin Franke, 2020. "Neural circuits in the mouse retina support color vision in the upper visual field," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    6. 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.
    7. David Swygart & Wan-Qing Yu & Shunsuke Takeuchi & Rachel O. L. Wong & Gregory W. Schwartz, 2024. "A presynaptic source drives differing levels of surround suppression in two mouse retinal ganglion cell types," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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