IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v471y2011i7337d10.1038_nature09818.html
   My bibliography  Save this article

Wiring specificity in the direction-selectivity circuit of the retina

Author

Listed:
  • Kevin L. Briggman

    (Max Planck Institute for Medical Research)

  • Moritz Helmstaedter

    (Max Planck Institute for Medical Research)

  • Winfried Denk

    (Max Planck Institute for Medical Research)

Abstract

The proper connectivity between neurons is essential for the implementation of the algorithms used in neural computations, such as the detection of directed motion by the retina. The analysis of neuronal connectivity is possible with electron microscopy, but technological limitations have impeded the acquisition of high-resolution data on a large enough scale. Here we show, using serial block-face electron microscopy and two-photon calcium imaging, that the dendrites of mouse starburst amacrine cells make highly specific synapses with direction-selective ganglion cells depending on the ganglion cell’s preferred direction. Our findings indicate that a structural (wiring) asymmetry contributes to the computation of direction selectivity. The nature of this asymmetry supports some models of direction selectivity and rules out others. It also puts constraints on the developmental mechanisms behind the formation of synaptic connections. Our study demonstrates how otherwise intractable neurobiological questions can be addressed by combining functional imaging with the analysis of neuronal connectivity using large-scale electron microscopy.

Suggested Citation

  • Kevin L. Briggman & Moritz Helmstaedter & Winfried Denk, 2011. "Wiring specificity in the direction-selectivity circuit of the retina," Nature, Nature, vol. 471(7337), pages 183-188, March.
    • Handle: RePEc:nat:nature:v:471:y:2011:i:7337:d:10.1038_nature09818
    DOI: 10.1038/nature09818
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature09818
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature09818?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sichen Tao & Yuki Todo & Zheng Tang & Bin Li & Zhiming Zhang & Riku Inoue, 2022. "A Novel Artificial Visual System for Motion Direction Detection in Grayscale Images," Mathematics, MDPI, vol. 10(16), pages 1-32, August.
    2. Elishai Ezra-Tsur & Oren Amsalem & Lea Ankri & Pritish Patil & Idan Segev & Michal Rivlin-Etzion, 2021. "Realistic retinal modeling unravels the differential role of excitation and inhibition to starburst amacrine cells in direction selectivity," PLOS Computational Biology, Public Library of Science, vol. 17(12), pages 1-31, December.
    3. Stefano Recanatesi & Gabriel Koch Ocker & Michael A Buice & Eric Shea-Brown, 2019. "Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-29, July.
    4. Anna Kreshuk & Ullrich Koethe & Elizabeth Pax & Davi D Bock & Fred A Hamprecht, 2014. "Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    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. Brad Busse & Stephen Smith, 2013. "Automated Analysis of a Diverse Synapse Population," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-14, March.
    7. Umberto Esposito & Michele Giugliano & Mark van Rossum & Eleni Vasilaki, 2014. "Measuring Symmetry, Asymmetry and Randomness in Neural Network Connectivity," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-16, July.

    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:nature:v:471:y:2011:i:7337:d:10.1038_nature09818. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.