The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Dense, Unspecific Connectivity of Neocortical Parvalbumin-Positive Interneurons: A Canonical Microcircuit for Inhibition?
Open accessAbstract
GABAergic interneurons play a major role in the function of the mammalian neocortex, but their circuit connectivity is still poorly understood. We used two-photon RuBi-Glutamate uncaging to optically map how the largest population of cortical interneurons, the parvalbumin-positive cells (PV+), are connected to pyramidal cells (PCs) in mouse neocortex. We found locally dense connectivity from PV+ interneurons onto PCs across cortical areas and layers. In many experiments, all nearby PV+ cells were connected to every local PC sampled. In agreement with this, we found no evidence for connection specificity, as PV+ interneurons contacted PC pairs similarly regardless of whether they were synaptically connected or not. We conclude that the microcircuit architecture for PV+ interneurons, and probably neocortical inhibition in general, is an unspecific, densely homogenous matrix covering all nearby pyramidal cells.
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In the citing paper
“Individual PV-INs contact nearly every local pyramidal neuron which enables them to synchronize networks [74].”
From Parvalbumin interneuron vulnerability and brain disorders (2021) · cites this paper as [74]
In the citing paper
“In addition, PV-INs connect to many principal neurons [74,75,87,88].”
From Parvalbumin interneuron vulnerability and brain disorders (2021) · cites this paper as [74]
In the citing paper
“Some subtypes, such as PVBCs and NGFCs, demonstrate very high rates of connectivity to PCs: connection probabilities in paired recordings are in the range of ~50–90% (204, 331, 876, 880).”
From Hippocampal GABAergic Inhibitory Interneurons (2017) · cites this paper as [876]
In the citing paper
“However, recent evidence suggests that certain forms of inhibition are promiscuous and less selective in downstream targeting than initially thought (331, 552, 876, 918).”
From Hippocampal GABAergic Inhibitory Interneurons (2017) · cites this paper as [876]
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Cites
CircStat : A MATLAB Toolbox for Circular Statistics
Berens, P. (2009) · Journal of Statistical Software · doi:10.18637/jss.v031.i10 · Cited by 3,099
NEURONAL CIRCUITS OF THE NEOCORTEX
Douglas, R. J., & Martin, K. A. C. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144152 · Cited by 1,886
Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure
Denk, W., & Horstmann, H. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020329 · Cited by 1,644
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
Interneuron Migration from Basal Forebrain to Neocortex: Dependence on Dlx Genes
Anderson, S. A., Eisenstat, D. D., Shi, L., & Rubenstein, J. L. R. (1997) · Science · doi:10.1126/science.278.5337.474 · Cited by 1,462
Monosynaptic Restriction of Transsynaptic Tracing from Single, Genetically Targeted Neurons
Wickersham, I. R., Lyon, D. C., Barnard, R. J. O., Mori, T., Finke, S., Conzelmann, K., Young, J. A. T., & Callaway, E. M. (2007) · Neuron · doi:10.1016/j.neuron.2007.01.033 · Cited by 1,176
Differential signaling via the same axon of neocortical pyramidal neurons
Markram, H., Wang, Y., & Tsodyks, M. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.9.5323 · Cited by 1,102
Highly selective receptive fields in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0623-08.2008 · Cited by 1,081
Network anatomy and in vivo physiology of visual cortical neurons
Bock, D. D., Lee, W.-C. A., Kerlin, A., Andermann, M. L., Hood, G., Wetzel, A. W., Yurgenson, S., Soucy, E. R., Kim, H. S., & Reid, R. C. (2011) · Nature · doi:10.1038/nature09802 · Cited by 867
Buhl, E. H., Halasy, K., & Somogyi, P. (1994) · Nature · doi:10.1038/368823a0 · Cited by 728
Research works printed but not linked: 1
- Mountcastle VB (1982) in Mindful brain, An organization principle for cerebral function: the unit module and the distributed system, ed Schmitt FO (MIT, Cambridge, MA), pp 1–50.
Cited by
Sorted by citations
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
Tremblay, R., Lee, S., & Rudy, B. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.033 · Cited by 2,033
How Inhibition Shapes Cortical Activity
Isaacson, J. S., & Scanziani, M. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.027 · Cited by 1,678
Reconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,557
Pfeffer, C. K., Xue, M., He, M., Huang, Z. J., & Scanziani, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3446 · Cited by 1,353
Fast-spiking, parvalbumin + GABAergic interneurons: From cellular design to microcircuit function
Hu, H., Gan, J., & Jonas, P. (2014) · Science · doi:10.1126/science.1255263 · Cited by 1,197
Metadata
- Authors
- Packer, A. M., & Yuste, R.
- Affiliations
- Howard Hughes Medical Institute (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2011-09-14
- Volume
- 31
- Issue
- 37
- Pages
- 13260-13271
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3178964
- OpenAlex
- W2014350866
- Cited by
- 506
- References
- 45
- Citations by year
- 2026: 15 · 2025: 29 · 2024: 53 · 2023: 31 · 2022: 36 · 2021: 43 · 2020: 40 · 2019: 34 · 2018: 30 · 2017: 35 · 2016: 35 · 2015: 35 · 2014: 26 · 2013: 42 · 2012: 16
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- parvalbumin interneurons, GABAergic interneurons, neocortex, pyramidal neurons, cortical connectivity, inhibitory microcircuits, dense connections, glutamate uncaging, two-photon calcium imaging, mouse cortex
- MeSH terms
- Animals, Brain Mapping, Female, Interneurons, Male, Mice, Transgenic, Neural Inhibition, Neural Pathways, Parvalbumins, Pyramidal Cells, Patch-Clamp Techniques, Microscopy, Confocal, Neocortex, Mice, In Vitro Techniques
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