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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Items 201–300 of 506
Bucher, E., Collins, J. M., King, A. E., Vickers, J. C., & Kirkcaldie, M. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02327-3 · Cited by 31
Common excitatory synaptic inputs to electrically connected cortical fast-spiking cell networks
Otsuka, T., & Kawaguchi, Y. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00071.2013 · Cited by 29
A Neural Model of Empathic States in Attachment-Based Psychotherapy
Cittern, D., & Edalat, A. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00006 · Cited by 30
The influence of cortical activity on perception depends on behavioral state and sensory context
Russell, L. E., Fişek, M., Yang, Z., Tan, L. P., Packer, A. M., Dalgleish, H. W. P., Chettih, S. N., Harvey, C. D., & Häusser, M. (2024) · Nature Communications · doi:10.1038/s41467-024-46484-5 · Cited by 59
Self-citationBringing Anatomical Information into Neuronal Network Models
van Albada, S. J., Morales-Gregorio, A., Dickscheid, T., Goulas, A., Bakker, R., Bludau, S., Palm, G., Hilgetag, C. C., & Diesmann, M. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-89439-9_9 · Cited by 29
Pafundo, D. E., Annan, C. A. P., Fulginiti, N. M., & Belforte, J. E. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab030 · Cited by 27
Panthi, S., & Leitch, B. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.688905 · Cited by 29
Hansen, M. B., Jespersen, S. N., Leigland, L. A., & Kroenke, C. D. (2013) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2013.00031 · Cited by 28
Stimulus-Selective Response Plasticity in Primary Visual Cortex: Progress and Puzzles
Montgomery, D. P., Hayden, D. J., Chaloner, F. A., Cooke, S. F., & Bear, M. F. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.815554 · Cited by 31
Optimizing interneuron circuits for compartment-specific feedback inhibition
Keijser, J., & Sprekeler, H. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009933 · Cited by 28
Mulholland, H. N., Hein, B., Kaschube, M., & Smith, G. B. (2021) · eLife · doi:10.7554/elife.72456 · Cited by 25
Net gain and loss: influence of natural rewards and drugs of abuse on perineuronal nets
Brown, T. E., & Sorg, B. A. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01337-x · Cited by 25
Inhibition in the Human Auditory Cortex
Inui, K., Nakagawa, K., Nishihara, M., Motomura, E., & Kakigi, R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155972 · Cited by 25
Modeling Inhibitory Interneurons in Efficient Sensory Coding Models
Zhu, M., & Rozell, C. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004353 · Cited by 25
PackIO and EphysViewer: software tools for acquisition and analysis of neuroscience dataPreprint
Watson, B. O., Yuste, R., & Packer, A. M. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/054080 · Cited by 25
Self-citationDzyubenko, E., Willig, K. I., Yin, D., Sardari, M., Tokmak, E., Labus, P., Schmermund, B., & Hermann, D. M. (2023) · Journal of Biomedical Science · doi:10.1186/s12929-023-00971-x · Cited by 24
The role of parvalbumin interneuron dysfunction across neurodegenerative dementias
Smeralda, C. L., Pandit, S., Turrini, S., Reilly, J., Palmisano, A., Sprugnoli, G., Hampel, H. J., Benussi, A., Borroni, B., Press, D. Z., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102509 · Cited by 26
Calcium-permeable AMPA receptors govern PV neuron feature selectivity
Hong, I., Kim, J., Hainmueller, T., Kim, D. W., Keijser, J., Johnson, R. C., Park, S. H., Limjunyawong, N., Yang, Z., Cheon, D., et al. (2024) · Nature · doi:10.1038/s41586-024-08027-2 · Cited by 26
Cocas, L. A., Fernández, G., Barch, M., Doll, J., Díaz, I., & Pleasure, S. J. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0375-15.2016 · Cited by 23
Kawaguchi, Y., Otsuka, T., Morishima, M., Ushimaru, M., & Kubota, Y. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00778.2018 · Cited by 25
Cortical parvalbumin neurons are responsible for homeostatic sleep rebound through CaMKII activation
Kon, K., Ode, K. L., Mano, T., Fujishima, H., Takahashi, R. R., Tone, D., Shimizu, C., Shiono, S., Yada, S., Matsuzawa, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50168-5 · Cited by 26
Hammock, E. A. D., & Levitt, P. R. (2012) · Neuroscience · doi:10.1016/j.neuroscience.2012.07.025 · Cited by 25
Hoffmann, J. H. O., Meyer, H. S., Schmitt, A. C., Straehle, J., Weitbrecht, T., Sakmann, B. J., & Helmstaedter, M. N. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv039 · Cited by 26
Zhang, X., Li, Z., Han, Z., Sultan, K. T., Huang, K., & Shi, S. (2017) · Nature Communications · doi:10.1038/ncomms16091 · Cited by 26
Inui, K., Takeuchi, N., Sugiyama, S., Motomura, E., & Nishihara, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190481 · Cited by 24
Two-Photon Optogenetic Mapping of Excitatory Synaptic Connectivity and Strength
Izquierdo-Serra, M., Hirtz, J. J., Shababo, B., & Yuste, R. (2018) · iScience · doi:10.1016/j.isci.2018.09.008 · Cited by 23
Self-citationLi, M., Cabrera-García, D., Salling, M. C., Au, E., Yang, G., & Harrison, N. L. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108501 · Cited by 25
Zaitsev, A. V., & Lewis, D. A. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12294 · Cited by 22
Vandenberg, A., Piekarski, D. J., Caporale, N., Munoz-Cuevas, F. J., & Wilbrecht, L. (2015) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00005 · Cited by 22
Romero-Sosa, J. L., Motanis, H., & Buonomano, D. V. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2830-20.2021 · Cited by 24
Armenta-Reséndiz, M., Assali, A., Tsvetkov, E., Cowan, C. W., & Lavín, A. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01371-9 · Cited by 25
Multi-scale spiking network model of human cerebral cortex
Pronold, J., van Meegen, A., Shimoura, R. O., Vollenbröker, H., Senden, M., Hilgetag, C. C., Bakker, R., & van Albada, S. J. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae409 · Cited by 26
Inhibition in Simple Cell Receptive Fields Is Broad and OFF-Subregion Biased
Taylor, M. M., Sedigh-Sarvestani, M., Vigeland, L. E., Palmer, L. A., & Contreras, D. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2099-17.2017 · Cited by 22
Dysbindin-1, BDNF, and GABAergic Transmission in Schizophrenia
Jun, R. A., Zhang, W., Beacher, N. J., Zhang, Y., Li, Y., & Lin, D. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.876749 · Cited by 23
Williams, S. B., & Hablitz, J. J. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00089 · Cited by 22
Xu, J., Calhoun, V. D., Worhunsky, P. D., Xiang, H., Li, J., Wall, J. T., Pearlson, G. D., & Potenza, M. N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117029 · Cited by 21
Micheva, K. D., Király, M., Perez, M. M., & Madison, D. V. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0871-21.2021 · Cited by 23
Li, X., Sun, H., Zhu, Y., Wang, F., Wang, X., Han, L., Cui, D., Luo, D., Zhai, Y., Zhuo, L., et al. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.1010152 · Cited by 25
Ebina, T., Sohya, K., Imayoshi, I., Yin, S., Kimura, R., Yanagawa, Y., Kameda, H., Hioki, H., Kaneko, T., & Tsumoto, T. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.10.057 · Cited by 20
Developmental Reduction of Asynchronous GABA Release from Neocortical Fast-Spiking Neurons
Jiang, M., Yang, M., Yin, L., Zhang, X. H., & Shu, Y. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht236 · Cited by 20
Ruggiero, A., Heim, L. R., Susman, L., Hreaky, D., Shapira, I., Katsenelson, M., Rosenblum, K., & Slutsky, I. (2024) · Neuron · doi:10.1016/j.neuron.2024.10.014 · Cited by 19
Circuit Mechanisms Underlying Epileptogenesis in a Mouse Model of Focal Cortical Malformation
Yang, W., Williams, A. J., & Sun, Q. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.029 · Cited by 17
Peng, Y., Tomás, F. J. B., Pfeiffer, P., Drangmeister, M., Schreiber, S., Vida, I., & Geiger, J. R. P. (2021) · Science Advances · doi:10.1126/sciadv.abg4693 · Cited by 20
Li, Y.-T., Liu, B.-H., Chou, X.-L., Zhang, L., & Tao, H. W. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5246-14.2015 · Cited by 19
Elabbady, L., Seshamani, S., Mu, S., Mahalingam, G., Schneider-Mizell, C. M., Bodor, Á. L., Bae, J. A., Brittain, D., Buchanan, J., Bumbarger, D. J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.20.499976 · Cited by 19
Triplett, M. A., Gajowa, M. A., Antin, B., Sadahiro, M., Adesnik, H., & Paninski, L. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02053-7 · Cited by 17
Xu, J. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.018 · Cited by 17
Motor Cortex Microcircuit Simulation Based on Brain Activity Mapping
Chadderdon, G. L., Mohan, A., Suter, B. A., Neymotin, S. A., Kerr, C. C., Francis, J. T., Shepherd, G. M., & Lytton, W. W. (2014) · Neural Computation · doi:10.1162/neco_a_00602 · Cited by 19
Thalamic microcircuits: presynaptic dendrites form two feedforward inhibitory pathways in thalamus
Crandall, S. R., & Cox, C. L. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00559.2012 · Cited by 18
Developmental time windows for axon growth influence neuronal network topology
Lim, S. I., & Kaiser, M. (2015) · Biological Cybernetics · doi:10.1007/s00422-014-0641-3 · Cited by 18
Cell-type specific function of GABAergic neurons in layers 2 and 3 of mouse barrel cortex
Petersen, C. C. H. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.10.004 · Cited by 19
Functional Logic of Layer 2/3 Inhibitory Connectivity in the Ferret Visual Cortex
Scholl, B., Wilson, D. E., Jaepel, J., & Fitzpatrick, D. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.004 · Cited by 18
Rogalla, M. M., & Hildebrandt, K. J. (2020) · eNeuro · doi:10.1523/eneuro.0511-19.2020 · Cited by 18
Sadeh, S., & Rotter, S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004045 · Cited by 18
Reward Timing and Its Expression by Inhibitory Interneurons in the Mouse Primary Visual Cortex
Monk, K. J., Allard, S., & Shuler, M. G. H. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa068 · Cited by 18
Organization of Cortical and Thalamic Input to Inhibitory Neurons in Mouse Motor Cortex
Okoro, S. U., Goz, R. U., Njeri, B. W., Harish, M., Ruff, C. F., Ross, S. E., Gerfen, C. R., & Hooks, B. M. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0950-22.2022 · Cited by 25
Foramen lacerum impingement of trigeminal nerve root as a rodent model for trigeminal neuralgia
Ding, W., Yang, L., Chen, Q., Hu, K., Liu, Y., Bao, E., Wang, C., Mao, J., & Shen, S. (2023) · JCI Insight · doi:10.1172/jci.insight.168046 · Cited by 17
Lagzi, F., & Fairhall, A. L. (2024) · Science Advances · doi:10.1126/sciadv.adi4350 · Cited by 27
Brain (Hyper)Excitability Revealed by Optimal Electrical Stimulation of GABAergic Interneurons
Wendling, F., Gerber, U. R., Cosandier‐Rimélé, D., Nica, A., de Montigny, J., Raineteau, O., Kalitzin, S., Lopes, F. H. D. S., & Benquet, P. (2016) · Brain stimulation · doi:10.1016/j.brs.2016.07.001 · Cited by 20
Senk, J., Hagen, E., van Albada, S. J., & Diesmann, M. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae405 · Cited by 21
Almeida, V. N. (2021) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2021.06.001 · Cited by 18
Baroni, F., Burkitt, A. N., & Grayden, D. B. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003574 · Cited by 16
Rostami, V., Rost, T., Schmitt, F. J., van Albada, S. J., Riehle, A., & Nawrot, M. P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.27.968339 · Cited by 15
Synaptic promiscuity in brain development
Wolterhoff, N., & Hiesinger, P. R. (2024) · Current Biology · doi:10.1016/j.cub.2023.12.037 · Cited by 18
Specific inhibition and disinhibition in the higher-order structure of a cortical connectome
Reimann, M., Santander, D. E., Ecker, A., & Muller, E. B. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae433 · Cited by 20
Chowdhury, T. G., Wable, G. S., Chen, Y.-W., Tateyama, K., Yu, I., Wang, J., Reyes, A. D., & Aoki, C. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy283 · Cited by 14
Harvey, M., Lau, D., Civillico, E. F., Rudy, B., & Contreras, D. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00102.2012 · Cited by 16
Bernardi, D., Doron, G., Brecht, M., & Lindner, B. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007831 · Cited by 16
Wingert, J. C., Ramos, J. D., Reynolds, S., Gonzalez, A. E., Rose, R. M., Hegarty, D. M., Aicher, S. A., Bailey, L. G., Brown, T. E., Abbas, A. I., et al. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0468-24.2024 · Cited by 16
Chaotic recurrent neural networks for brain modelling: A review
Mattera, A., Alfieri, V., Granato, G., & Baldassarre, G. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.107079 · Cited by 19
Jézéquel, J., Condomitti, G., Kroon, T., Hamid, F., Sanalidou, S., Garcés, T., Maeso, P. A., Balia, M., Hu, Z., Sahara, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59635-z · Cited by 13
Lukacs, I. P., Francavilla, R., Field, M., Hunter, E., Howarth, M., Horie, S., Plaha, P., Stacey, R. J., Livermore, L. J., Ansorge, O., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac195 · Cited by 14
Regulation of epileptiform discharges in rat neocortex by HCN channels
Albertson, A. J., Williams, S. B., & Hablitz, J. J. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00955.2012 · Cited by 17
Alterations in Prefrontal Cortical Circuitry and Cognitive Dysfunction in Schizophrenia
Lewis, D. A., & Glausier, J. R. (2016) · Nebraska Symposium on Motivation/The Nebraska symposium on motivation · doi:10.1007/978-3-319-30596-7_3 · Cited by 15
Sempere-Ferràndez, A., Martı́nez, S., & Geijo‐Barrientos, E. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01842-8 · Cited by 16
Bozzelli, P. L., Caccavano, A., Avdoshina, V., Mocchetti, I., Wu, J., & Conant, K. E. (2019) · Experimental Neurology · doi:10.1016/j.expneurol.2019.113077 · Cited by 15
Cortical Representation of Touch in Silico
Huang, C., Zeldenrust, F., & Celikel, T. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09576-5 · Cited by 15
Gauld, O. M., Packer, A. M., Russell, L. E., Dalgleish, H. W. P., Iuga, M., Sacadura, F., Roth, A., Clark, B. A., & Häusser, M. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.015 · Cited by 18
Self-citationMean-field analysis of orientation selectivity in inhibition-dominated networks of spiking neurons
Sadeh, S., Cardanobile, S., & Rotter, S. (2014) · SpringerPlus · doi:10.1186/2193-1801-3-148 · Cited by 14
Cell Type Specific Representation of Vibro-tactile Stimuli in the Mouse Primary Somatosensory Cortex
Hayashi, A., Yoshida, T., & Ohki, K. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00109 · Cited by 14
Chen-Engerer, H.-J., Jaeger, S., Bondarenko, R., Sprengel, R., Hengerer, B., Rosenbrock, H., Mack, V., & Schuelert, N. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.06.027 · Cited by 13
Gao, Y.-Z., Liu, K., Wu, X., Shi, C., He, Q., Wu, H., Yang, J., Yao, H., & Ji, M. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04628-0 · Cited by 15
Ma, J., Mewes, J., Harris, K. T., Dore, T. M., Phillips, D. L., & Dreuw, A. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp05499d · Cited by 13
Adotevi, N. K., Su, A., Peiris, D., Hassan, M. J., & Leitch, B. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.05.024 · Cited by 13
Inhibitory control of synaptic signals preceding locomotion in mouse frontal cortex
Zhang, C.-L., Koukouli, F., Allegra, M., Ortiz, C., Kao, H.-L., Maskos, U., Changeux, J., & Schmidt‐Hieber, C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110035 · Cited by 13
Shapley, R. M., & Xing, D. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.09.005 · Cited by 13
Batabyal, T., Condron, B. G., & Acton, S. T. (2020) · Neuroinformatics · doi:10.1007/s12021-019-09450-x · Cited by 12
Inhibitory Units: An Organizing Nidus for Feature-Selective SubNetworks in Area V1
Palagina, G., Meyer, J. F., & Smirnakis, S. M. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2275-18.2019 · Cited by 12
Age- and movement-related modulation of cortical oscillations in a mouse model of presbycusis
Rumschlag, J. A., Lovelace, J. W., & Razak, K. A. (2020) · Hearing Research · doi:10.1016/j.heares.2020.108095 · Cited by 15
Serotonergic Modulation of the Excitation/Inhibition Balance in the Visual Cortex
Carlos-Lima, E., Higa, G. S. V., Viana, F. J. C., Tamais, A. M., Cruvinel, E., Borges, F. S., de Oliveira, J. F., Ulrich, H., & Pasquale, R. D. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms25010519 · Cited by 12
Martinez, C. A., Pantazopoulos, H., Gisabella, B., Stephens, E. T., Garteiser, J., & Arco, A. D. (2024) · Scientific Reports · doi:10.1038/s41598-024-57599-6 · Cited by 11
Cone, J. J., Mitchell, A. O., Parker, R. K., & Maunsell, J. H. R. (2024) · Current Biology · doi:10.1016/j.cub.2024.03.061 · Cited by 12
Kaleb, K., Pedrosa, V., & Clopath, C. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.109577 · Cited by 12
Perceptual Weighting of V1 Spikes Revealed by Optogenetic White Noise Stimulation
Day-Cooney, J. R., Cone, J. J., & Maunsell, J. H. R. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1736-21.2022 · Cited by 9
Chehrazi, P., Lee, K. K. Y., Lavertu-Jolin, M., Abbasnejad, Z., Carreño-Muñoz, M. I., Chattopadhyaya, B., & Cristo, G. D. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.04.019 · Cited by 16
Gainey, M. A., Wolfe, R., Pourzia, O., & Feldman, D. E. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148227 · Cited by 12
Self-organization of a doubly asynchronous irregular network state for spikes and bursts
Vercruysse, F., Naud, R., & Sprekeler, H. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009478 · Cited by 13
Possible Role of Parvalbumin Interneurons in Meditation and Psychiatric Illness
Thippaiah, S. M., Pradhan, B., Voyiaziakis, E., Shetty, R., Iyengar, S., Olson, C., & Tang, Y. (2022) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.21050136 · Cited by 12
Chapeton, J. I., Gala, R., & Stepanyants, A. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00074 · Cited by 11
Arzt, M., Sakmann, B. J., & Meyer, H. S. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx156 · Cited by 11
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review