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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
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    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. 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.
    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. 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.

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