Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex
Open accessAbstract
Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.
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
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?
Because rodent visual cortex lacks orientation columns, the summed excitatory input from nearby pyramidal neurons to interneurons is broad.
“Rodent visual cortex, however, lacks orientation columns, thus the sum of excitatory inputs from nearby pyramidal neurons to interneurons will be broad (Hofer et al., 2011).”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Hofer et al., 2011
The Introduction says that neurons with different orientation preferences are locally intermixed in mouse V1, and the Discussion reports that broadly tuned PV/FS interneurons sample inputs from nearby pyramidal cells with diverse orientation preferences. This supports the proposed relationship, but the paper’s direct findings concern mouse PV/FS interneurons, not interneurons throughout rodent visual cortex. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn mouse visual cortex, fast-spiking neurons are more broadly selective for orientation, direction and spatial frequency than pyramidal cells.
“in mouse visual cortex, selectivity to visual stimuli such as orientation, direction, and spatial frequency are broader for FS neurons than for pyramidal cells”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Hofer et al., 2011
The Results (‘Visual responses of PV interneurons’; page unavailable) report broader orientation tuning in most PV neurons than in pyramidal cells, and the Discussion identifies most PV neurons as fast-spiking. Our copy does not establish the same comparison for direction or spatial-frequency selectivity: its direction measurements are used to calculate orientation tuning, and the searched passages contain no spatial-frequency comparison. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Brainard, D. H. (1997) · Spatial Vision · doi:10.1163/156856897x00357 · Cited by 19,308 · see citing works
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907 · see citing works
Self-citationInterneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954 · see citing works
Two networks of electrically coupled inhibitory neurons in neocortex
Gibson, J. R., Beierlein, M., & Connors, B. W. (1999) · Nature · doi:10.1038/47035 · Cited by 1,404 · see citing works
In vivo two-photon calcium imaging of neuronal networks
Stosiek, C., Garaschuk, O., Holthoff, K., & Konnerth, A. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1232232100 · Cited by 1,313 · see citing works
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237 · see citing works
Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex
Ohki, K., Chung, S., Ch'ng, Y. H., Kara, P., & Reid, R. C. (2005) · Nature · doi:10.1038/nature03274 · Cited by 1,191 · see citing works
Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex
Gilbert, C., & Wiesel, T. (1989) · Journal of Neuroscience · doi:10.1523/jneurosci.09-07-02432.1989 · Cited by 1,162 · see citing works
The Asynchronous State in Cortical Circuits
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106 · see citing works
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 · see citing works
A network of fast-spiking cells in the neocortex connected by electrical synapses
Galarreta, M., & Hestrin, S. (1999) · Nature · doi:10.1038/47029 · Cited by 960 · see citing works
Functional specificity of local synaptic connections in neocortical networks
Ko, H., Hofer, S. B., Pichler, B., Buchanan, K. A., Sjöström, P. J., & Mrsic‐Flogel, T. D. (2011) · Nature · doi:10.1038/nature09880 · Cited by 861 · see citing works
Self-citationNetwork 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 851 · see citing works
Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice
Poulet, J. F. A., & Petersen, C. C. H. (2008) · Nature · doi:10.1038/nature07150 · Cited by 796 · see citing works
Sulforhodamine 101 as a specific marker of astroglia in the neocortex in vivo
Nimmerjahn, A., Kirchhoff, F., Kerr, J. N. D., & Helmchen, F. (2004) · Nature Methods · doi:10.1038/nmeth706 · Cited by 784 · see citing works
Sillito, A. M. (1975) · The Journal of Physiology · doi:10.1113/jphysiol.1975.sp011056 · Cited by 742 · see citing works
Linking Spontaneous Activity of Single Cortical Neurons and the Underlying Functional Architecture
Tsodyks, M. V., Kenet, T., Grinvald, A., & Arieli, A. (1999) · Science · doi:10.1126/science.286.5446.1943 · Cited by 702 · see citing works
Decorrelated neuronal firing in cortical microcircuits
Ecker, A. S., Berens, P., Keliris, G. A., Bethge, M., Logothetis, N. K., & Tolias, A. S. (2010) · Science · doi:10.1126/science.1179867 · Cited by 640 · see citing works
Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex
Kerlin, A. M., Andermann, M. L., Berezovskii, V. K., & Reid, R. C. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.002 · Cited by 613 · see citing works
Excitatory cortical neurons form fine-scale functional networks
Yoshimura, Y., Dantzker, J. L. M., & Callaway, E. M. (2005) · Nature · doi:10.1038/nature03252 · Cited by 601 · see citing works
Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations
Luczak, A., Barthó, P., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.014 · Cited by 593 · see citing works
Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex
Smith, M. A., & Kohn, A. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2929-08.2008 · Cited by 581 · see citing works
Pyramidal cell communication within local networks in layer 2/3 of rat neocortex.
Holmgren, C., Harkany, T., Svennenfors, B., & Zilberter, Y. (2003) · The Journal of Physiology · doi:10.1113/jphysiol.2003.044784 · Cited by 573 · see citing works
Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque
Kohn, A., & Smith, M. A. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.5106-04.2005 · Cited by 521 · see citing works
Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression
Ozeki, H., Finn, I. M., Schaffer, E., Miller, K. D., & Ferster, D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.028 · Cited by 470 · see citing works
Fast nonnegative deconvolution for spike train inference from population calcium imaging
Vogelstein, J. T., Packer, A. M., Machado, T. A., Sippy, T., Babadi, B., Yuste, R., & Paninski, L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.01073.2009 · Cited by 449 · see citing works
Self-citationFunctional maps of neocortical local circuitry.
Thomson, A. M. (2007) · Frontiers in Neuroscience · doi:10.3389/neuro.01.1.1.002.2007 · Cited by 434 · see citing works
Membrane potential dynamics of GABAergic neurons in the barrel cortex of behaving mice
Gentet, L. J., Avermann, M., Matyas, F., Staiger, J. F., & Petersen, C. C. H. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.006 · Cited by 429 · cited 1× · see citing works
“On the other hand, other studies have reported that sensory stimuli or changes in brain state can alter the strength of neuronal correlations and network interaction”
BackgroundSmall modulation of ongoing cortical dynamics by sensory input during natural vision.
Fiser, J., Chiu, C., & Weliky, M. (2004) · Nature · doi:10.1038/nature02907 · Cited by 423 · see citing works
Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity
Yoshimura, Y., & Callaway, E. M. (2005) · Nature Neuroscience · doi:10.1038/nn1565 · Cited by 390 · see citing works
Rules of Connectivity between Geniculate Cells and Simple Cells in Cat Primary Visual Cortex
Alonso, J., Usrey, W. M., & Reid, R. C. (2001) · Journal of Neuroscience · doi:10.1523/jneurosci.21-11-04002.2001 · Cited by 385 · see citing works
Stimulus contrast modulates functional connectivity in visual cortex
Nauhaus, I., Busse, L., Carandini, M., & Ringach, D. L. (2008) · Nature Neuroscience · doi:10.1038/nn.2232 · Cited by 380 · see citing works
Internal Dynamics Determine the Cortical Response to Thalamic Stimulation
MacLean, J. N., Watson, B. O., Aaron, G. B., & Yuste, R. (2005) · Neuron · doi:10.1016/j.neuron.2005.09.035 · Cited by 373 · see citing works
Gonchar, Y. (2008) · Frontiers in Neuroanatomy · doi:10.3389/neuro.05.003.2007 · Cited by 369 · see citing works
Inhibition, Spike Threshold, and Stimulus Selectivity in Primary Visual Cortex
Priebe, N. J., & Ferster, D. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.005 · Cited by 359 · see citing works
Homeostatic Regulation of Eye-Specific Responses in Visual Cortex during Ocular Dominance Plasticity
Mrsic-Flogel, T. D., Hofer, S. B., Ohki, K., Reid, R. C., Bonhoeffer, T., & Hübener, M. (2007) · Neuron · doi:10.1016/j.neuron.2007.05.028 · Cited by 330 · see citing works
Self-citationSparse coding and high-order correlations in fine-scale cortical networks
Ohiorhenuan, I. E., Mechler, F., Purpura, K. P., Schmid, A., Hu, Q., & Victor, J. D. (2010) · Nature · doi:10.1038/nature09178 · Cited by 308 · see citing works
Functional connectivity between simple cells and complex cells in cat striate cortex
Alonso, J.-M., & Martinez, L. M. (1998) · Nature Neuroscience · doi:10.1038/1609 · Cited by 278 · see citing works
Runyan, C. A., Schummers, J., Van Wart, A., Kuhlman, S. J., Wilson, N. R., Huang, Z. J., & Sur, M. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.006 · Cited by 244 · see citing works
Sohya, K., Kameyama, K., Yanagawa, Y., Obata, K., & Tsumoto, T. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.4641-06.2007 · Cited by 227 · see citing works
Ma, W., Liu, B., Li, Y.-T., Huang, Z. J., Zhang, L. I., & Tao, H. W. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.3248-10.2010 · Cited by 212 · see citing works
Stimulus feature selectivity in excitatory and inhibitory neurons in primary visual cortex
Cardin, J. A., Palmer, L. A., & Contreras, D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1692-07.2007 · Cited by 185 · see citing works
Functionally distinct inhibitory neurons at the first stage of visual cortical processing
Hirsch, J. A., Martinez, L. M., Pillai, C., Alonso, J.-M., Wang, Q., & Sommer, F. T. (2003) · Nature Neuroscience · doi:10.1038/nn1152 · Cited by 176 · see citing works
Efficient coding in heterogeneous neuronal populations
Chelaru, M. I., & Dragoi, V. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807744105 · Cited by 154 · see citing works
Intervening inhibition underlies simple-cell receptive field structure in visual cortex
Liu, B., Li, P., Sun, Y. J., Li, Y.-T., Zhang, L. I., & Tao, H. W. (2009) · Nature Neuroscience · doi:10.1038/nn.2443 · Cited by 122 · see citing works
Oswald, A.-M. M., Doiron, B., Rinzel, J., & Reyes, A. D. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1703-09.2009 · Cited by 114 · see citing works
Parvalbumin neurons in the forebrain as revealed by parvalbumin-Cre transgenic mice
Tanahira, C., Higo, S., Watanabe, K., Tomioka, R., Ebihara, S., Kaneko, T., & Tamamaki, N. (2009) · Neuroscience Research · doi:10.1016/j.neures.2008.12.007 · Cited by 109 · see citing works
Nowak, L. G., Sanchez-Vives, M. V., & McCormick, D. A. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm137 · Cited by 96 · see citing works
Relationship Between Spontaneous and Evoked Spike-Time Correlations in Primate Visual Cortex
Jermakowicz, W. J., Chen, X., Khaytin, I., Bonds, A. B., & Casagrande, V. A. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91207.2008 · Cited by 45 · see citing works
Ch'ng, Y. H. (2010) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2010.00020 · Cited by 40 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ultrasensitive fluorescent proteins for imaging neuronal activity
Chen, T.-W., Wardill, T. J., Sun, Y., Pulver, S. R., Renninger, S. L., Baohan, A., Schreiter, E. R., Kerr, R. A., Orger, M. B., Jayaraman, V., et al. (2013) · Nature · doi:10.1038/nature12354 · Cited by 6,474 · see citing works
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 · see citing works
Hu, H., Gan, J., & Jonas, P. (2014) · Science · doi:10.1126/science.1255263 · Cited by 1,197 · see citing works
Parvalbumin-expressing interneurons linearly transform cortical responses to visual stimuli
Atallah, B. V., Bruns, W., Carandini, M., & Scanziani, M. (2012) · Neuron · doi:10.1016/j.neuron.2011.12.013 · Cited by 612 · see citing works
Division and subtraction by distinct cortical inhibitory networks in vivo
Wilson, N. R., Runyan, C. A., Wang, F. L., & Sur, M. (2012) · Nature · doi:10.1038/nature11347 · Cited by 568 · see citing works
Experimental evidence for sparse firing in the neocortex
Barth, A. L., & Poulet, J. F. A. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.03.008 · Cited by 364 · see citing works
Cell-Type-Specific Activity in Prefrontal Cortex during Goal-Directed Behavior
Pinto, L., & Dan, Y. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.021 · Cited by 323 · see citing works
Visual stimuli recruit intrinsically generated cortical ensembles
Miller, J.-E. K., Ayzenshtat, I., Carrillo-Reid, L., & Yuste, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1406077111 · Cited by 288 · see citing works
Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex
Attinger, A., Wang, B., & Keller, G. B. (2017) · Cell · doi:10.1016/j.cell.2017.05.023 · Cited by 252 · see citing works
Cortical circuit activity underlying sleep slow oscillations and spindles
Niethard, N., Ngo, H.-V. V., Ehrlich, I., & Born, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805517115 · Cited by 245 · see citing works
Inhibitory Interneurons Regulate Temporal Precision and Correlations in Cortical Circuits
Cardin, J. A. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.07.015 · Cited by 254 · see citing works
Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex
Seeman, S. C., Campagnola, L., Davoudian, P. A., Hoggarth, A., Hage, T. A., Bosma-Moody, A., Baker, C. A., Lee, J. H., Mihalas, S., Teeter, C., et al. (2018) · eLife · doi:10.7554/elife.37349 · Cited by 183 · see citing works
Self-citationGenetic Approaches to Neural Circuits in the Mouse
Josh Huang, Z., & Zeng, H. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170307 · Cited by 200 · see citing works
Self-citationA blanket of inhibition: functional inferences from dense inhibitory connectivity
Karnani, M. M., Agetsuma, M., & Yuste, R. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.12.015 · Cited by 188 · see citing works
Genetically encoded optical indicators for the analysis of neuronal circuits
Knöpfel, T. (2012) · Nature reviews. Neuroscience · doi:10.1038/nrn3293 · Cited by 205 · see citing works
Altered Cortical Ensembles in Mouse Models of Schizophrenia
Hamm, J. P., Peterka, D. S., Gogos, J. A., & Yuste, R. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.019 · Cited by 175 · see citing works
Cooperative subnetworks of molecularly similar interneurons in mouse neocortex
Karnani, M. M., Jackson, J., Ayzenshtat, I., Tucciarone, J., Manoocheri, K., Snider, W. G., & Yuste, R. (2016) · Neuron · doi:10.1016/j.neuron.2016.02.037 · Cited by 196 · see citing works
Neuronal circuits in barrel cortex for whisker sensory perception
Staiger, J. F., & Petersen, C. C. H. (2021) · Physiological Reviews · doi:10.1152/physrev.00019.2019 · Cited by 165 · see citing works
Sleep-stage-specific regulation of cortical excitation and inhibition
Niethard, N., Hasegawa, M., Itokazu, T., Oyanedel, C. N., Born, J., & Sato, T. R. (2016) · Current Biology · doi:10.1016/j.cub.2016.08.035 · Cited by 140 · see citing works
Complementary networks of cortical somatostatin interneurons enforce layer specific control
Naka, A., Veit, J., Shababo, B., Chance, R. K., Risso, D., Stafford, D., Snyder, B., Egladyous, A., Chu, D., Sridharan, S., et al. (2019) · eLife · doi:10.7554/elife.43696 · Cited by 117 · see citing works
Wingert, J. C., & Sorg, B. A. (2021) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2021.673210 · Cited by 129 · see citing works
Runyan, C. A., & Sur, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2196-12.2013 · Cited by 67 · see citing works
Rupert, D. D., & Shea, S. D. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.886629 · Cited by 58 · see citing works
- Authors
- Hofer, S. B., Ko, H., Pichler, B., Vogelstein, J., Ros, H., Zeng, H., Lein, E., Lesica, N. A., & Mrsic-Flogel, T. D.
- Affiliations
- University College London (GB)
- National Institute for Medical Research (GB)
- Johns Hopkins University (US)
- Allen Institute for Brain Science (US)
- Venue
- Nature Neuroscience
- Published
- 2011-07-17
- Volume
- 14
- Issue
- 8
- Pages
- 1045-1052
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC6370002
- OpenAlex
- W2061681507
- Cited by
- 470
- References
- 50
- Citations by year
- 2026: 14 · 2025: 22 · 2024: 34 · 2023: 19 · 2022: 31 · 2021: 29 · 2020: 38 · 2019: 28 · 2018: 42 · 2017: 30 · 2016: 40 · 2015: 38 · 2014: 32 · 2013: 40 · 2012: 26
- FWCI
- 17.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Funders
- Wellcome Trust (095074)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- visual cortex, excitation-inhibition balance, pyramidal neurons, parvalbumin interneurons, fast-spiking interneurons, neural correlations, synaptic connectivity, recurrent connectivity, feature selectivity, stimulus dependence, mouse visual cortex
- MeSH terms
- Analysis of Variance, Animals, Calcium, Evoked Potentials, Visual, Mice, Inbred C57BL, Mice, Transgenic, Neural Inhibition, Neurons, Organic Chemicals, Parvalbumins, Photic Stimulation, Reaction Time, Statistics as Topic, Synapses, Visual Cortex, Visual Pathways, Patch-Clamp Techniques, Excitatory Postsynaptic Potentials, 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