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Dynamic predictive coding by the retina

Author

Listed:
  • Toshihiko Hosoya

    (Harvard University
    RIKEN Brain Science Institute)

  • Stephen A. Baccus

    (Harvard University
    Stanford University)

  • Markus Meister

    (Harvard University)

Abstract

Retinal ganglion cells convey the visual image from the eye to the brain. They generally encode local differences in space and changes in time rather than the raw image intensity. This can be seen as a strategy of predictive coding, adapted through evolution to the average image statistics of the natural environment. Yet animals encounter many environments with visual statistics different from the average scene. Here we show that when this happens, the retina adjusts its processing dynamically. The spatio-temporal receptive fields of retinal ganglion cells change after a few seconds in a new environment. The changes are adaptive, in that the new receptive field improves predictive coding under the new image statistics. We show that a network model with plastic synapses can account for the large variety of observed adaptations.

Suggested Citation

  • Toshihiko Hosoya & Stephen A. Baccus & Markus Meister, 2005. "Dynamic predictive coding by the retina," Nature, Nature, vol. 436(7047), pages 71-77, July.
    • Handle: RePEc:nat:nature:v:436:y:2005:i:7047:d:10.1038_nature03689
    DOI: 10.1038/nature03689
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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. Miguel Maravall & Rasmus S Petersen & Adrienne L Fairhall & Ehsan Arabzadeh & Mathew E Diamond, 2007. "Shifts in Coding Properties and Maintenance of Information Transmission during Adaptation in Barrel Cortex," PLOS Biology, Public Library of Science, vol. 5(2), pages 1-12, January.
    3. Johnatan Aljadeff & Ronen Segev & Michael J Berry II & Tatyana O Sharpee, 2013. "Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-12, September.
    4. Matthias S Keil & Agata Lapedriza & David Masip & Jordi Vitria, 2008. "Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-5, July.
    5. Matthias S Keil, 2009. "“I Look in Your Eyes, Honey”: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing," PLOS Computational Biology, Public Library of Science, vol. 5(3), pages 1-13, March.
    6. Krishnamurthy V. Vemuru, 2022. "Implementation of the Canny Edge Detector Using a Spiking Neural Network," Future Internet, MDPI, vol. 14(12), pages 1-12, December.
    7. Marcus H C Howlett & Robert G Smith & Maarten Kamermans, 2017. "A novel mechanism of cone photoreceptor adaptation," PLOS Biology, Public Library of Science, vol. 15(4), pages 1-28, April.
    8. 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.
    9. Tristan G. Heintz & Antonio J. Hinojosa & Sina E. Dominiak & Leon Lagnado, 2022. "Opposite forms of adaptation in mouse visual cortex are controlled by distinct inhibitory microcircuits," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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