eLife (Dec 2021)
Structure and function of axo-axonic inhibition
- 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
Affiliations
- Casey M Schneider-Mizell
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- Agnes L Bodor
- Allen Institute for Brain Sciences, Seattle, United States
- Forrest Collman
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- Derrick Brittain
- Allen Institute for Brain Sciences, Seattle, United States
- Adam Bleckert
- Allen Institute for Brain Sciences, Seattle, United States
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, United States; Computer Science Department, Princeton University, Princeton, United States
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, United States; Computer Science Department, Princeton University, Princeton, United States
- Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, United States; Computer Science Department, Princeton University, Princeton, United States
- Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, United States; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, United States
- Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Jun Zhuang
- Allen Institute for Brain Sciences, Seattle, United States
- Anirban Nandi
- Allen Institute for Brain Sciences, Seattle, United States
- Brian Hu
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- JoAnn Buchanan
- Allen Institute for Brain Sciences, Seattle, United States
- Marc M Takeno
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- Russel Torres
- Allen Institute for Brain Sciences, Seattle, United States
- Gayathri Mahalingam
- Allen Institute for Brain Sciences, Seattle, United States
- Daniel J Bumbarger
- Allen Institute for Brain Sciences, Seattle, United States
- Yang Li
- Allen Institute for Brain Sciences, Seattle, United States
- Thomas Chartrand
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- William M Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, United States; Computer Science Department, Princeton University, Princeton, United States
- William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, United States
- Emmanouil Froudarakis
- ORCiD
- Department of Neuroscience, Baylor College of Medicine, Houston, United States; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
- Lynne Becker
- Allen Institute for Brain Sciences, Seattle, United States
- Shelby Suckow
- Allen Institute for Brain Sciences, Seattle, United States
- Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, United States; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
- Andreas S Tolias
- ORCiD
- Department of Neuroscience, Baylor College of Medicine, Houston, United States; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States; Department of Electrical and Computer Engineering, Rice University, Houston, United States
- Costas A Anastassiou
- Allen Institute for Brain Sciences, Seattle, United States; Department of Neurology, University of British Columbia, Vancouver, Canada
- H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, United States; Computer Science Department, Princeton University, Princeton, United States
- R Clay Reid
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- Nuno Maçarico da Costa
- ORCiD
- Allen Institute for Brain Sciences, Seattle, United States
- DOI
- https://doi.org/10.7554/eLife.73783
- Journal volume & issue
-
Vol. 10
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.
Keywords