Abstract

Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.

Data availability

Volume electron microscopy and segmentation data is available at https://www.microns-explorer.org/phase1. AIS synapse and PyC structural data are included in Supplemental Data. All other meshes and data tables are on Zenodo (DOI:10.5281/zenodo.5579388). All ChC calcium traces are also on Zenodo (DOI: 10.5281/zenodo.5725826). Analysis code and NEURON models are available at http://github.com/AllenInstitute/ChandelierL23.

The following data sets were generated

Article and author information

Author details

  1. Casey M Schneider-Mizell

    Neuronal circuit, Allen Institute for Brain Science, Seattle, United States
    For correspondence
    caseys@alleninstitute.org
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9477-3853
  2. Agnes L Bodor

    Neuronal circuit, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  3. Forrest Collman

    Neuronal circuit, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0280-7022
  4. Derrick Brittain

    Allen Institute for Brain Science, Seattle, WA, United States
    Competing interests
    No competing interests declared.
  5. Adam Bleckert

    Neuronal circuit, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  6. Sven Dorkenwald

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  7. Nicholas L Turner

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  8. Thomas Macrina

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    Thomas Macrina, discloses financial interests in Zetta AI LLC.
  9. Kisuk Lee

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  10. Ran Lu

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  11. Jingpeng Wu

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  12. Jun Zhuang

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  13. Anirban Nandi

    Allen Institute for Brain Science, Seattle, WA, United States
    Competing interests
    No competing interests declared.
  14. Brian Hu

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5866-8762
  15. JoAnn Buchanan

    Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  16. Marc M Takeno

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8384-7500
  17. Russel Torres

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  18. Gayathri Mahalingam

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  19. Daniel J Bumbarger

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  20. Yang Li

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  21. Thomas Chartrand

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7093-8681
  22. Nico Kemnitz

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  23. William M Silversmith

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  24. Dodam Ih

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  25. Jonathan Zung

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  26. Aleksandar Zlateski

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  27. Ignacio Tartavull

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  28. Sergiy Popovych

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  29. William Wong

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  30. Manuel Castro

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  31. Chris S Jordan

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
  32. Emmanouil Froudarakis

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3249-3845
  33. Lynne Becker

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  34. Shelby Suckow

    Allen Institute for Brain Science, Seattle, WA, United States
    Competing interests
    No competing interests declared.
  35. Jacob Reimer

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    Jacob Reimer, discloses financial interests in Vathes LLC.
  36. Andreas S Tolias

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    Andreas S Tolias, discloses financial interests in Vathes LLC.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4305-6376
  37. Costas A Anastassiou

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
  38. H Sebastian Seung

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    H Sebastian Seung, discloses financial interests in Zetta AI.
  39. R Clay Reid

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8697-6797
  40. Nuno Maçarico da Costa

    Neural Coding, Allen Institute for Brain Science, Seattle, United States
    For correspondence
    nunod@alleninstitute.org
    Competing interests
    No competing interests declared.

Funding

Intelligence Advanced Research Projects Agency (D16PC00003,D16PC00004,D16PC0005)

  • Casey M Schneider-Mizell
  • Agnes L Bodor
  • Forrest Collman
  • Derrick Brittain
  • Adam Bleckert
  • Sven Dorkenwald
  • Nicholas L Turner
  • Thomas Macrina
  • Kisuk Lee
  • Ran Lu
  • Jingpeng Wu
  • Jun Zhuang
  • Anirban Nandi
  • Brian Hu
  • JoAnn Buchanan
  • Marc M Takeno
  • Russel Torres
  • Gayathri Mahalingam
  • Daniel J Bumbarger
  • Yang Li
  • Thomas Chartrand
  • Nico Kemnitz
  • William M Silversmith
  • Dodam Ih
  • Jonathan Zung
  • Aleksandar Zlateski
  • Ignacio Tartavull
  • Sergiy Popovych
  • William Wong
  • Manuel Castro
  • Chris S Jordan
  • Emmanouil Froudarakis
  • Lynne Becker
  • Shelby Suckow
  • Jacob Reimer
  • Andreas S Tolias
  • Costas A Anastassiou
  • H Sebastian Seung
  • R Clay Reid
  • Nuno Maçarico da Costa

National Institute of Neurological Disorders and Stroke (U19 NS104648)

  • H Sebastian Seung

Army Research Office (W911NF-12-1-0594)

  • H Sebastian Seung

National Eye Institute (R01 EY027036)

  • H Sebastian Seung

National Institute of Mental Health (U01 MH114824)

  • H Sebastian Seung

National Institute of Neurological Disorders and Strokescience (R01 NS104926)

  • H Sebastian Seung

National Institute of Mental Health (RF1MH117815)

  • H Sebastian Seung

Mathers Foundation

  • H Sebastian Seung

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government.

Ethics

Animal experimentation: All animal procedures were approved by the Institutional Animal Care and Use Committee at the Allen Institute for Brain Science (1503 and 1804) or Baylor College of Medicine (AN-4703).

Copyright

© 2021, Schneider-Mizell et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,954
    views
  • 712
    downloads
  • 66
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Casey M Schneider-Mizell
  2. Agnes L Bodor
  3. Forrest Collman
  4. Derrick Brittain
  5. Adam Bleckert
  6. Sven Dorkenwald
  7. Nicholas L Turner
  8. Thomas Macrina
  9. Kisuk Lee
  10. Ran Lu
  11. Jingpeng Wu
  12. Jun Zhuang
  13. Anirban Nandi
  14. Brian Hu
  15. JoAnn Buchanan
  16. Marc M Takeno
  17. Russel Torres
  18. Gayathri Mahalingam
  19. Daniel J Bumbarger
  20. Yang Li
  21. Thomas Chartrand
  22. Nico Kemnitz
  23. William M Silversmith
  24. Dodam Ih
  25. Jonathan Zung
  26. Aleksandar Zlateski
  27. Ignacio Tartavull
  28. Sergiy Popovych
  29. William Wong
  30. Manuel Castro
  31. Chris S Jordan
  32. Emmanouil Froudarakis
  33. Lynne Becker
  34. Shelby Suckow
  35. Jacob Reimer
  36. Andreas S Tolias
  37. Costas A Anastassiou
  38. H Sebastian Seung
  39. R Clay Reid
  40. Nuno Maçarico da Costa
(2021)
Structure and function of axo-axonic inhibition
eLife 10:e73783.
https://doi.org/10.7554/eLife.73783

Share this article

https://doi.org/10.7554/eLife.73783

Further reading

    1. Neuroscience
    John P Grogan, Matthias Raemaekers ... Sanjay G Manohar
    Research Article

    Motivation depends on dopamine, but might be modulated by acetylcholine which influences dopamine release in the striatum, and amplifies motivation in animal studies. A corresponding effect in humans would be important clinically, since anticholinergic drugs are frequently used in Parkinson’s disease, a condition that can also disrupt motivation. Reward and dopamine make us more ready to respond, as indexed by reaction times (RT), and move faster, sometimes termed vigour. These effects may be controlled by preparatory processes that can be tracked using electroencephalography (EEG). We measured vigour in a placebo-controlled, double-blinded study of trihexyphenidyl (THP), a muscarinic antagonist, with an incentivised eye movement task and EEG. Participants responded faster and with greater vigour when incentives were high, but THP blunted these motivational effects, suggesting that muscarinic receptors facilitate invigoration by reward. Preparatory EEG build-up (contingent negative variation [CNV]) was strengthened by high incentives and by muscarinic blockade, although THP reduced the incentive effect. The amplitude of preparatory activity predicted both vigour and RT, although over distinct scalp regions; frontal activity predicted vigour, whereas a larger, earlier, central component predicted RT. The incentivisation of RT was partly mediated by the CNV, though vigour was not. Moreover, the CNV mediated the drug’s effect on dampening incentives, suggesting that muscarinic receptors underlie the motivational influence on this preparatory activity. Taken together, these findings show that a muscarinic blocker impairs motivated action in healthy people, and that medial frontal preparatory neural activity mediates this for RT.

    1. Medicine
    2. Neuroscience
    LeYuan Gu, WeiHui Shao ... HongHai Zhang
    Research Article

    The advent of midazolam holds profound implications for modern clinical practice. The hypnotic and sedative effects of midazolam afford it broad clinical applicability. However, the specific mechanisms underlying the modulation of altered consciousness by midazolam remain elusive. Herein, using pharmacology, optogenetics, chemogenetics, fiber photometry, and gene knockdown, this in vivo research revealed the role of locus coeruleus (LC)-ventrolateral preoptic nucleus noradrenergic neural circuit in regulating midazolam-induced altered consciousness. This effect was mediated by α1 adrenergic receptors. Moreover, gamma-aminobutyric acid receptor type A (GABAA-R) represents a mechanistically crucial binding site in the LC for midazolam. These findings will provide novel insights into the neural circuit mechanisms underlying the recovery of consciousness after midazolam administration and will help guide the timing of clinical dosing and propose effective intervention targets for timely recovery from midazolam-induced loss of consciousness.