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Network anatomy and in vivo physiology of visual cortical neurons

Author

Listed:
  • Davi D. Bock

    (Harvard Medical School
    The Center for Brain Science, Harvard University)

  • Wei-Chung Allen Lee

    (Harvard Medical School
    The Center for Brain Science, Harvard University)

  • Aaron M. Kerlin

    (Harvard Medical School)

  • Mark L. Andermann

    (Harvard Medical School)

  • Greg Hood

    (National Resource for Biomedical Supercomputing, Pittsburgh Supercomputing Center, Carnegie Mellon University)

  • Arthur W. Wetzel

    (National Resource for Biomedical Supercomputing, Pittsburgh Supercomputing Center, Carnegie Mellon University)

  • Sergey Yurgenson

    (Harvard Medical School)

  • Edward R. Soucy

    (The Center for Brain Science, Harvard University)

  • Hyon Suk Kim

    (Harvard Medical School
    The Center for Brain Science, Harvard University)

  • R. Clay Reid

    (Harvard Medical School
    The Center for Brain Science, Harvard University)

Abstract

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron's function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property—the preferred stimulus orientation—of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons’ local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

Suggested Citation

  • Davi D. Bock & Wei-Chung Allen Lee & Aaron M. Kerlin & Mark L. Andermann & Greg Hood & Arthur W. Wetzel & Sergey Yurgenson & Edward R. Soucy & Hyon Suk Kim & R. Clay Reid, 2011. "Network anatomy and in vivo physiology of visual cortical neurons," Nature, Nature, vol. 471(7337), pages 177-182, March.
  • Handle: RePEc:nat:nature:v:471:y:2011:i:7337:d:10.1038_nature09802
    DOI: 10.1038/nature09802
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    Citations

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    Cited by:

    1. Shang, Ke-ke & Small, Michael & Yan, Wei-sheng, 2017. "Link direction for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 767-776.
    2. 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.
    3. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    4. Sergiy Popovych & Thomas Macrina & Nico Kemnitz & Manuel Castro & Barak Nehoran & Zhen Jia & J. Alexander Bae & Eric Mitchell & Shang Mu & Eric T. Trautman & Stephan Saalfeld & Kai Li & H. Sebastian S, 2024. "Petascale pipeline for precise alignment of images from serial section electron microscopy," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    5. 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.
    6. 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.
    7. Ryan C Williamson & Benjamin R Cowley & Ashok Litwin-Kumar & Brent Doiron & Adam Kohn & Matthew A Smith & Byron M Yu, 2016. "Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-27, December.
    8. Volker Pernice & Benjamin Staude & Stefano Cardanobile & Stefan Rotter, 2011. "How Structure Determines Correlations in Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    9. Tianshuo Qiu & Qiang An & Jianqi Wang & Jiafu Wang & Cheng-Wei Qiu & Shiyong Li & Hao Lv & Ming Cai & Jianyi Wang & Lin Cong & Shaobo Qu, 2024. "Vision-driven metasurfaces for perception enhancement," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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