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Retinal ganglion cells act largely as independent encoders

Author

Listed:
  • S. Nirenberg

    (University of California Los Angeles)

  • S. M. Carcieri

    (University of California Los Angeles)

  • A. L. Jacobs

    (University of California Los Angeles)

  • P. E. Latham

    (University of California Los Angeles)

Abstract

Correlated firing among neurons is widespread in the visual system. Neighbouring neurons, in areas from retina to cortex, tend to fire together more often than would be expected by chance. The importance of this correlated firing for encoding visual information is unclear and controversial1,2,3,4,5. Here we examine its importance in the retina. We present the retina with natural stimuli and record the responses of its output cells, the ganglion cells. We then use information theoretic techniques to measure the amount of information about the stimuli that can be obtained from the cells under two conditions: when their correlated firing is taken into account, and when their correlated firing is ignored. We find that more than 90% of the information about the stimuli can be obtained from the cells when their correlated firing is ignored. This indicates that ganglion cells act largely independently to encode information, which greatly simplifies the problem of decoding their activity.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:nature:v:411:y:2001:i:6838:d:10.1038_35079612
    DOI: 10.1038/35079612
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    Cited by:

    1. Benjamin L Walker & Katherine A Newhall, 2018. "Inferring information flow in spike-train data sets using a trial-shuffle method," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    2. 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.
    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. Einat Granot-Atedgi & Gašper Tkačik & Ronen Segev & Elad Schneidman, 2013. "Stimulus-dependent Maximum Entropy Models of Neural Population Codes," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-14, March.

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