Response Selectivity Is Correlated to Dendritic Structure in Parvalbumin-Expressing Inhibitory Neurons in Visual Cortex
Open accessAbstract
Inhibitory neurons have been shown to perform a variety of functions within brain circuits, including shaping response functions in target cells. Still, how the properties of specific inhibitory neuron classes relate to their local circuits remains unclear. To better understand the distribution and origins of orientation selectivity in inhibitory neurons expressing the calcium binding protein parvalbumin (PV) in the mouse primary visual cortex, we labeled PV(+) neurons with red fluorescent protein (RFP) and targeted them for cell-attached electrophysiological recordings. PV(+) neurons could be broadly tuned or sharply tuned for orientation but tended to be more broadly tuned than unlabeled neurons on average. The dendritic morphology of PV(+) cells, revealed by intracellular labeling, was strongly correlated with tuning: highly tuned PV(+) neurons had shorter dendrites that branched nearer to the soma and had smaller dendritic fields overall, whereas broadly tuned PV(+) neurons had longer dendrites that branched farther from the soma, producing larger dendritic fields. High-speed two-photon calcium imaging of visual responses showed that the orientation preferences of highly tuned PV(+) neurons resembled the preferred orientations of neighboring cells. These results suggest that the diversity of the local neighborhood and the nature of dendritic sampling may both contribute to the response selectivity of PV(+) neurons.
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?
The complexity and extent of the dendritic arbor of supragranular PV interneurons in visual cortex correlate with their selectivity to visual stimuli.
“the complexity and extent of the dendritic arbor of supragranular PV INs in visual cortex correlate with their selectivity to visual stimuli”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Runyan and Sur, 2013
The Results section, “Morphology of PV+ neurons: dendritic extent, but not complexity, correlates with orientation selectivity” (page not provided), reports that measures of dendritic extent correlated with orientation selectivity. It explicitly reports that neither measure of dendritic complexity correlated significantly, so the claim's complexity component is not supported. (LLM, conf=0.92)
Experimental — accuracy not yet measuredFeature selectivity may simply reflect the availability of inputs within a cell's dendritic domain.
“Therefore, feature selectivity would simply be a reflection of input availability within a cell’s dendritic domain (Runyan and Sur, 2013).”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Runyan and Sur, 2013
The Introduction (page unavailable) suggests that dendritic arbors may influence orientation selectivity in PV-positive visual-cortex neurons by limiting the number and diversity of local inputs. The Discussion (page unavailable) likewise says local input sampling could produce high selectivity. This supports the proposed mechanism, but not the citing sentence’s stronger assertion that feature selectivity generally would simply reflect input availability. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOrientation selectivity of PV neurons is inversely correlated with the extent of their dendritic arbor, suggesting that PV interneuron tuning may relate to the spatial extent over which they sample excitatory inputs.
“the orientation selectivity of PV neurons was later shown to be inversely correlated with the extent of their dendritic arbor, suggesting that tuning of PV INs could be simply related to the spatial extent to which they can sample excitatory inputs (Runyan and Sur, 2013).”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Runyan and Sur, 2013
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
Imaging Neuronal Subsets in Transgenic Mice Expressing Multiple Spectral Variants of GFP
Feng, G., Mellor, R. H., Bernstein, M., Keller-Peck, C., Nguyen, Q. T., Wallace, M., Nerbonne, J. M., Lichtman, J. W., & Sanes, J. R. (2000) · Neuron · doi:10.1016/s0896-6273(00)00084-2 · Cited by 3,042 · see citing works
Interneurons 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
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Cardin, J. A., Carlén, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.-H., & Moore, C. I. (2009) · Nature · doi:10.1038/nature08002 · Cited by 2,603 · see citing works
How Inhibition Shapes Cortical Activity
Isaacson, J. S., & Scanziani, M. (2011) · Neuron · doi:10.1016/j.neuron.2011.09.027 · Cited by 1,678 · see citing works
Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex
Wehr, M., & Zador, A. M. (2003) · Nature · doi:10.1038/nature02116 · Cited by 1,363 · see citing works
Enforcement of Temporal Fidelity in Pyramidal Cells by Somatic Feed-Forward Inhibition
Pouille, F., & Scanziani, M. (2001) · Science · doi:10.1126/science.1060342 · Cited by 1,097 · 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 Developmental Switch in the Response of DRG Neurons to ETS Transcription Factor Signaling
Hippenmeyer, S., Pecho‐Vrieseling, E., Sigrist, M., Portmann, T., Laengle, C., Ladle, D. R., & Arber, S. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030159 · Cited by 1,059 · see citing works
Defined types of cortical interneurone structure space and spike timing in the hippocampus
Somogyi, P., & Klausberger, T. (2005) · The Journal of Physiology · doi:10.1113/jphysiol.2004.078915 · Cited by 927 · see citing works
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 851 · see citing works
Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition
Royer, S., Zemelman, B. V., Losonczy, A., Kim, J., Chance, F., Magee, J. C., & Buzsáki, G. (2012) · Nature Neuroscience · doi:10.1038/nn.3077 · Cited by 728 · see citing works
Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010) · Nature Biotechnology · doi:10.1038/nbt.1612 · Cited by 707 · see citing works
A neural circuit for spatial summation in visual cortex
Adesnik, H., Bruns, W., Taniguchi, H., Huang, Z. J., & Scanziani, M. (2012) · Nature · doi:10.1038/nature11526 · Cited by 702 · see citing works
Tuned Responses of Astrocytes and Their Influence on Hemodynamic Signals in the Visual Cortex
Schummers, J., Yu, H., & Sur, M. (2008) · Science · doi:10.1126/science.1156120 · Cited by 632 · see citing works
Self-citationBroadly 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
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
Activation of specific interneurons improves V1 feature selectivity and visual perception
Lee, S.-H., Kwan, A. C., Zhang, S., Phoumthipphavong, V., Flannery, J. G., Masmanidis, S. C., Taniguchi, H., Huang, Z. J., Zhang, F., Boyden, E. S., et al. (2012) · Nature · doi:10.1038/nature11312 · Cited by 593 · 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
Self-citationDense Inhibitory Connectivity in Neocortex
Fino, E., & Yuste, R. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.025 · Cited by 532 · see citing works
Packer, A. M., & Yuste, R. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3131-11.2011 · Cited by 506 · see citing works
Hofer, S. B., Ko, H., Pichler, B., Vogelstein, J., Ros, H., Zeng, H., Lein, E., Lesica, N. A., & Mrsic-Flogel, T. D. (2011) · Nature Neuroscience · doi:10.1038/nn.2876 · Cited by 470 · see citing works
Regulation of neuronal input transformations by tunable dendritic inhibition
Lovett-Barron, M., Turi, G. F., Kaifosh, P., Lee, P. H., Bolze, F., Sun, X.-H., Nicoud, J.-F., Zemelman, B. V., Sternson, S. M., & Losonczy, A. (2012) · Nature Neuroscience · doi:10.1038/nn.3024 · Cited by 417 · see citing works
Receptive fields of single cells and topography in mouse visual cortex
Dräger, U. C. (1975) · The Journal of Comparative Neurology · doi:10.1002/cne.901600302 · Cited by 415 · 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
Wang, Y. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.4.395 · Cited by 371 · see citing works
Blatow, M., Rozov, A., Katona, I., Hormuzdi, S. G., Meyer, A. H., Whittington, M. A., Caputi, A., & Monyer, H. (2003) · Neuron · doi:10.1016/s0896-6273(03)00300-3 · Cited by 316 · see citing works
Invariant computations in local cortical networks with balanced excitation and inhibition
Mariño, J., Schummers, J., Lyon, D. C., Schwabe, L., Beck, O., Wiesing, P., Obermayer, K., & Sur, M. (2005) · Nature Neuroscience · doi:10.1038/nn1391 · Cited by 308 · see citing works
Self-citationDu, J., Zhang, L., Weiser, M., Rudy, B., & McBain, C. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-02-00506.1996 · Cited by 246 · 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
Self-citationChow, A., Erişir, A., Farb, C. R., Nadal, M. S., Ozaita, A., Lau, D., Welker, E., & Rudy, B. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-21-09332.1999 · Cited by 238 · 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
Lund, J. S., & Lewis, D. A. (1993) · The Journal of Comparative Neurology · doi:10.1002/cne.903280209 · Cited by 207 · see citing works
Dombeck, D. A., Graziano, M. S. A., & Tank, D. W. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2985-09.2009 · Cited by 203 · see citing works
Kuhlman, S. J., & Huang, Z. J. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002005 · Cited by 171 · see citing works
A custom-made two-photon microscope and deconvolution system
Majewska, A. K., Yiu, G. C., & Yuste, R. (2000) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s004240000435 · Cited by 165 · see citing works
Liu, B., Li, P., Li, Y.-T., Sun, Y. J., Yanagawa, Y., Obata, K., Zhang, L. I., & Tao, H. W. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1915-09.2009 · Cited by 145 · see citing works
Synaptic Integration by V1 Neurons Depends on Location within the Orientation Map
Schummers, J., Mariño, J., & Sur, M. (2002) · Neuron · doi:10.1016/s0896-6273(02)01012-7 · Cited by 136 · see citing works
Self-citationKrimer, L. S., Zaitsev, A. V., Czanner, G., Kröner, S., González‐Burgos, G., Povysheva, N. V., Iyengar, S., Barrionuevo, G., & Lewis, D. A. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00156.2005 · Cited by 135 · see citing works
Specificity and randomness in the visual cortex
Ohki, K., & Reid, R. C. (2007) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2007.07.007 · Cited by 131 · see citing works
Dendritic Branch Typing and Spine Expression Patterns in Cortical Nonpyramidal Cells
Kawaguchi, Y., Karube, F., & Kubota, Y. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhj015 · Cited by 125 · see citing works
Interneuron Diversity in Layers 2–3 of Monkey Prefrontal Cortex
Zaitsev, A. V., Povysheva, N. V., González‐Burgos, G., Rotaru, D. C., Fish, K. N., Krimer, L. S., & Lewis, D. A. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn198 · Cited by 123 · see citing works
Many specialists for suppressing cortical excitation
Burkhalter, A. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.026.2008 · Cited by 122 · see citing works
Functional and Structural Topography of Horizontal Inhibitory Connections in Cat Visual Cortex
Kisvárday, Z. F., & Eysel, U. T. (1993) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.1993.tb00226.x · Cited by 95 · see citing works
Gutiérrez, H., & Davies, A. M. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.02.002 · Cited by 82 · see citing works
Helmstaedter, M. N., Sakmann, B. J., & Feldmeyer, D. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn130 · Cited by 73 · see citing works
Vruwink, M., Schmidt, H. H. H. W., Weinberg, R. J., & Burette, A. (2001) · The Journal of Comparative Neurology · doi:10.1002/cne.1413 · Cited by 72 · see citing works
Zariwala, H. A., Madisen, L., Ahrens, K. F., Bernard, A., Lein, E. S., Jones, A. R., & Zeng, H. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00162 · Cited by 56 · see citing works
Subgroups of parvalbumin-expressing interneurons in layers 2/3 of the visual cortex
Helm, J. S., Akgül, G., & Wollmuth, L. P. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00782.2012 · Cited by 43 · see citing works
Yan, X., Baram, T. Z., Gerth, A., Schultz, L., & Ribak, C. E. (1998) · Experimental Brain Research · doi:10.1007/s002210050576 · Cited by 41 · see citing works
Han, Z. (1994) · Neuroscience Research · doi:10.1016/0168-0102(94)90013-2 · Cited by 16 · see citing works
Two-way communication with neural networks in vivo using focused light
Wilson, N. R., Schummers, J., Runyan, C. A., Yan, S. X., Chen, R. E., Deng, Y., & Sur, M. (2013) · Nature Protocols · doi:10.1038/nprot.2013.063 · Cited by 15 · see citing works
Self-citationHasenstaub, A. R., & Callaway, E. M. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.039 · Cited by 6 · see citing works
Flagged first, then by citations
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 · see citing works
Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation
Harris, J. A., Hirokawa, K. E., Sorensen, S. A., Gu, H., Mills, M., Ng, L. L., Bohn, P., Mortrud, M., Ouellette, B., Kidney, J., et al. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00076 · Cited by 485 · see citing works
Target-Specific Properties of Thalamocortical Synapses onto Layer 4 of Mouse Primary Visual Cortex
Kloc, M., & Maffei, A. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.2595-14.2014 · Cited by 99 · see citing works
Davis, M. F., Figueroa Velez, D. X., Guevarra, R. P., Yang, M. C., Habeeb, M., Carathedathu, M. C., & Gandhi, S. P. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.062 · Cited by 97 · see citing works
- Authors
- Runyan, C. A., & Sur, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- ORCID
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2013-07-10
- Volume
- 33
- Issue
- 28
- Pages
- 11724-11733
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3724550
- OpenAlex
- W2061469173
- Cited by
- 67
- References
- 54
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 2 · 2022: 4 · 2021: 9 · 2020: 7 · 2019: 2 · 2018: 9 · 2017: 4 · 2016: 9 · 2015: 3 · 2014: 6 · 2013: 2
- FWCI
- 3.66
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- Funders
- Simons Foundation
- National Institutes of Health (EY007023)
- National Eye Institute (F32EY007023, EY018041, EY007023, R01EY007023)
- National Institute of Neurological Disorders and Stroke (NS679512)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- parvalbumin, inhibitory interneurons, orientation selectivity, dendritic morphology, primary visual cortex, orientation tuning, two-photon calcium imaging, mouse visual cortex
- MeSH terms
- Animals, Dendrites, Female, Gene Expression Regulation, Male, Mice, Inbred C57BL, Neural Inhibition, Neurons, Parvalbumins, Photic Stimulation, Visual Cortex, Mice
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