Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo
Open accessAbstract
In the visual cortex, inhibitory neurons alter the computations performed by target cells via combination of two fundamental operations, division and subtraction. The origins of these operations have been variously ascribed to differences in neuron classes, synapse location or receptor conductances. Here, by utilizing specific visual stimuli and single optogenetic probe pulses, we show that the function of parvalbumin-expressing and somatostatin-expressing neurons in mice in vivo is governed by the overlap of response timing between these neurons and their targets. In particular, somatostatin-expressing neurons respond at longer latencies to small visual stimuli compared with their target neurons and provide subtractive inhibition. With large visual stimuli, however, they respond at short latencies coincident with their target cells and switch to provide divisive inhibition. These results indicate that inhibition mediated by these neurons is a dynamic property of cortical circuits rather than an immutable property of neuronal classes.
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?
In vivo, because excitatory, inhibitory, and neuromodulatory loads vary with context, different or even the same interneuron types participate differently in cellular computations.
“It is therefore likely that in in vivo conditions, where spatiotemporal patterns of excitatory, inhibitory and neuromodulatory loads vary with context, different or even the same IN types will participate differently in cellular computations (El-Boustani and Sur, 2014).”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as El-Boustani and Sur, 2014
Results from optogenetic studies disagree about the specific role of each interneuron group in tuning.
“However, the results from these studies do not agree on the specific role of each interneuron group.”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as El-Boustani and Sur, 2014
PV and Sst inhibitory neurons play distinct roles in tuning excitatory neurons, producing either divisive or subtractive inhibition.
“These studies demonstrated that PV and Sst inhibitory neurons play distinct roles in the tuning of excitatory neurons producing either divisive or subtractive inhibition.”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as El-Boustani and Sur, 2014
Whether inhibition is subtractive or divisive is not a fixed property but a dynamic function depending on interneuron response modes and the nature of visual stimuli.
“the study by El-Boustani and Sur (El-Boustani and Sur, 2014) provides compelling evidence that whether inhibition is subtractive or divisive is not a fixed property, but rather a dynamic function that depends on the response modes of INs and the nature of visual stimuli.”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as El-Boustani and Sur, 2014
Some studies found that Sst interneurons sharpen orientation tuning, while activating PV interneurons has a minor effect on tuning but changes response gain.
“Some studies found that Sst INs sharpen orientation tuning (Wilson et al., 2012), while activation of PV INs was found to have a minor effect on tuning but changed response gain”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as El-Boustani and Sur, 2014
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
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
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
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906 · see citing works
Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689 · see citing works
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 · see citing works
Feature-based attention influences motion processing gain in macaque visual cortex
Treue, S., & Trujillo, J. C. M. (1999) · Nature · doi:10.1038/21176 · Cited by 1,465 · see citing works
The Normalization Model of Attention
Reynolds, J. H., & Heeger, D. J. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.002 · Cited by 1,457 · see citing works
Pfeffer, C. K., Xue, M., He, M., Huang, Z. J., & Scanziani, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3446 · Cited by 1,353 · see citing works
Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4
McAdams, C. J., & Maunsell, J. H. R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-01-00431.1999 · Cited by 1,214 · see citing works
Cortical interneurons that specialize in disinhibitory control
Pi, H.-J., Hangya, B., Kvitsiani, D., Sanders, J. I., Huang, Z. J., & Kepecs, A. (2013) · Nature · doi:10.1038/nature12676 · Cited by 1,136 · see citing works
Theory of orientation tuning in visual cortex.
Ben-Yishai, R., Bar‐Or, R. L., & Sompolinsky, H. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.9.3844 · Cited by 1,091 · see citing works
Gain Modulation from Background Synaptic Input
Chance, F. S., Abbott, L. F., & Reyes, A. D. (2002) · Neuron · doi:10.1016/s0896-6273(02)00820-6 · Cited by 1,012 · see citing works
A Cortical Circuit for Gain Control by Behavioral State
Fu, Y., Tucciarone, J. M., Espinosa, J. S., Sheng, N., Darcy, D. P., Nicoll, R. A., Huang, Z. J., & Stryker, M. P. (2014) · Cell · doi:10.1016/j.cell.2014.01.050 · Cited by 937 · cited 1× · see citing works
“However, we do not exclude the possibility that changes in brain state, such as during locomotion 43,45 , could alter the function of various inhibitory cell-types.”
An emergent model of orientation selectivity in cat visual cortical simple cells
Somers, D., Nelson, S., & Sur, M. (1995) · The Journal of Neuroscience · doi:10.1523/jneurosci.15-08-05448.1995 · Cited by 860 · see citing works
Self-citationA 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
Cellular mechanisms of brain state-dependent gain modulation in visual cortex
Polack, P.-O., Friedman, J., & Golshani, P. (2013) · Nature Neuroscience · doi:10.1038/nn.3464 · Cited by 628 · see citing works
PyNN: a common interface for neuronal network simulators
Davison, A. P. (2008) · Frontiers in Neuroinformatics · doi:10.3389/neuro.11.011.2008 · Cited by 622 · 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
Shunting Inhibition Modulates Neuronal Gain during Synaptic Excitation
Mitchell, S. J., & Silver, R. A. (2003) · Neuron · doi:10.1016/s0896-6273(03)00200-9 · Cited by 602 · 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-citationInhibition dominates sensory responses in the awake cortex
Haider, B., Häusser, M., & Carandini, M. (2012) · Nature · doi:10.1038/nature11665 · Cited by 541 · see citing works
Silver, R. A. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2864 · Cited by 481 · see citing works
Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons
Vogels, T. P., & Abbott, L. F. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.3508-05.2005 · Cited by 466 · 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
Rapid Neocortical Dynamics: Cellular and Network Mechanisms
Haider, B., & McCormick, D. A. (2009) · Neuron · doi:10.1016/j.neuron.2009.04.008 · Cited by 441 · see citing works
Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex
Gentet, L. J., Kremer, Y., Taniguchi, H., Huang, Z. J., Staiger, J. F., & Petersen, C. C. H. (2012) · Nature Neuroscience · doi:10.1038/nn.3051 · Cited by 438 · see citing works
The Contribution of Noise to Contrast Invariance of Orientation Tuning in Cat Visual Cortex
Anderson, J. S., Lampl, I., Gillespie, D. C., & Ferster, D. (2000) · Science · doi:10.1126/science.290.5498.1968 · Cited by 422 · see citing works
Salinas, E., & Thier, P. (2000) · Neuron · doi:10.1016/s0896-6273(00)00004-0 · Cited by 400 · see citing works
Nusser, Z., Sieghart, W., Benke, D., Fritschy, J., & Somogyi, P. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.21.11939 · Cited by 386 · see citing works
Neocortical Somatostatin-Expressing GABAergic Interneurons Disinhibit the Thalamorecipient Layer 4
Xu, H., Jeong, H.-Y., Tremblay, R., & Rudy, B. (2013) · Neuron · doi:10.1016/j.neuron.2012.11.004 · Cited by 366 · see citing works
A normalization model of multisensory integration
Ohshiro, T., Angelaki, D. E., & DeAngelis, G. C. (2011) · Nature Neuroscience · doi:10.1038/nn.2815 · Cited by 320 · see citing works
Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates
Holt, G. R., & Koch, C. (1997) · Neural Computation · doi:10.1162/neco.1997.9.5.1001 · Cited by 250 · see citing works
Local diversity and fine-scale organization of receptive fields in mouse visual cortex
Bonin, V., Histed, M. H., Yurgenson, S., & Reid, R. C. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.2974-11.2011 · Cited by 247 · see citing works
Synaptic Computation and Sensory Processing in Neocortical Layer 2/3
Petersen, C. C. H., & Crochet, S. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.020 · Cited by 239 · 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
GABA<sub>A</sub>Inhibition Controls Response Gain in Visual Cortex
Katzner, S., Busse, L., & Carandini, M. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.5753-10.2011 · Cited by 201 · see citing works
The High-Conductance State of Cortical Networks
Kumar, A., Schrader, S., Aertsen, A., & Rotter, S. (2007) · Neural Computation · doi:10.1162/neco.2008.20.1.1 · Cited by 199 · see citing works
Separating Value from Choice: Delay Discounting Activity in the Lateral Intraparietal Area
Louie, K., & Glimcher, P. W. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.5742-09.2010 · Cited by 182 · see citing works
Drivers and modulators from push-pull and balanced synaptic input
Abbott, L. F., & Chance, F. S. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)49011-1 · Cited by 153 · 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
Multiplicative Gain Changes Are Induced by Excitation or Inhibition Alone
Murphy, B. K., & Miller, K. D. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-31-10040.2003 · Cited by 144 · see citing works
Kulik, Á., Vida, I., Fukazawa, Y., Guetg, N., Kasugai, Y., Marker, C. L., Rigato, F., Bettler, B., Wickman, K. D., Frotscher, M., et al. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.4178-05.2006 · Cited by 141 · see citing works
Arroyo, S., Bennett, C., Aziz, D., Brown, S. P., & Hestrin, S. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.0115-12.2012 · Cited by 138 · see citing works
Interneuron subtypes and orientation tuning
Lee, S., Kwan, A. C., & Dan, Y. (2014) · Nature · doi:10.1038/nature13128 · Cited by 125 · see citing works
Target-Specific Effects of Somatostatin-Expressing Interneurons on Neocortical Visual Processing
Cottam, J. C. H., Smith, S. L., & Häusser, M. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2624-13.2013 · Cited by 122 · see citing works
Subtractive and Divisive Inhibition: Effect of Voltage-Dependent Inhibitory Conductances and Noise
Doiron, B., Longtin, A., Berman, N. J., & Maler, L. (2001) · Neural Computation · doi:10.1162/089976601300014691 · Cited by 110 · see citing works
Neocortical Interneurons: From Diversity, Strength
Moore, C. I., Carlén, M., Knoblich, U., & Cardin, J. A. (2010) · Cell · doi:10.1016/j.cell.2010.07.005 · Cited by 104 · see citing works
Herman, A. M., Huang, L., Murphey, D. K., Garcia, I., & Arenkiel, B. R. (2014) · eLife · doi:10.7554/elife.01481 · Cited by 99 · see citing works
Cell Division Intersects with Cell Geometry
Moseley, J. B., & Nurse, P. (2010) · Cell · doi:10.1016/j.cell.2010.07.004 · Cited by 97 · see citing works
Function of inhibition in visual cortical processing
Alitto, H. J., & Dan, Y. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.02.012 · Cited by 57 · see citing works
Stimulus ensemble and cortical layer determine V1 spatial receptive fields
Yeh, C.-I., Xing, D., Williams, P. E., & Shapley, R. M. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907406106 · Cited by 55 · see citing works
Yger, P., El‐Boustani, S., Alain Destexhe, A., & Frégnac, Y. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0310-z · Cited by 49 · see citing works
Self-citationAtallah, B. V., Scanziani, M., & Carandini, M. (2014) · Nature · doi:10.1038/nature13129 · Cited by 34 · see citing works
El-Boustani, S., Wilson, N. R., Runyan, C. A., & Sur, M. (2014) · Nature · doi:10.1038/nature13130 · Cited by 24 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Katzner S., Busse L. & Carandini M. GABAA inhibition controls response gain in visual cortex. J. Neurosci. 31, 5931– 5941 (2011). 21508218 PMC3083851
- Diesmann M. & Gewaltig M.-O. in Forschung und wisschenschaftliches Rechnen, Beiträge zum Heinz-Billing-Preis 2001 Vol. 58 (eds Plesser, T. & Macho, V.) 43– 70 (2002).
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
Ferguson, B. R., & Gao, W.-J. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00037 · Cited by 594 · see citing works
Fee, C., Banasr, M., & Sibille, E. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.05.024 · Cited by 329 · see citing works
Mechanisms underlying gain modulation in the cortex
Ferguson, K. A., & Cardin, J. A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0253-y · Cited by 327 · see citing works
Circuits and Mechanisms for Surround Modulation in Visual Cortex
Angelucci, A., Bijanzadeh, M., Nurminen, L., Federer, F., Merlin, S., & Bressloff, P. C. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031418 · Cited by 268 · 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
Delevich, K., Tucciarone, J., Huang, Z. J., & Li, B. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.4565-14.2015 · Cited by 226 · see citing works
Somatostatin-Expressing Inhibitory Interneurons in Cortical Circuits
Yavorska, I., & Wehr, M. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00076 · Cited by 185 · see citing works
Inhibitory stabilization and cortical computation
Sadeh, S., & Clopath, C. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-00390-z · Cited by 183 · see citing works
Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes
Litwin-Kumar, A., Rosenbaum, R., & Doiron, B. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00732.2015 · Cited by 138 · see citing works
Cortical inhibitory interneurons control sensory processing
Wood, K. C., Blackwell, J. M., & Geffen, M. N. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.018 · Cited by 121 · see citing works
Inhibitory Actions Unified by Network Integration
Seybold, B. A., Phillips, E. A. K., Schreiner, C. E., & Hasenstaub, A. R. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.013 · Cited by 118 · see citing works
Rapid Rebalancing of Excitation and Inhibition by Cortical Circuitry
Moore, A. K., Weible, A. P., Balmer, T. S., Trussell, L. O., & Wehr, M. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.045 · Cited by 93 · see citing works
The State of the NIH BRAIN Initiative
Koroshetz, W. J., Gordon, J. A., Adams, A. B., Beckel-Mitchener, A. C., Churchill, J. D., Farber, G. K., Freund, M. P., Gnadt, J., Hsu, N. S., Langhals, N. B., et al. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.3174-17.2018 · Cited by 77 · see citing works
Control of response reliability by parvalbumin-expressing interneurons in visual cortex
Zhu, Y., Qiao, W., Liu, K., Zhong, H., & Yao, H. (2015) · Nature Communications · doi:10.1038/ncomms7802 · Cited by 71 · see citing works
Hertäg, L., & Sprekeler, H. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006999 · Cited by 68 · see citing works
Hafner, G., Witte, M., Guy, J., Subhashini, N., Fenno, L. E., Ramakrishnan, C., Kim, Y. S., Deisseroth, K. A., Callaway, E. M., Oberhuber, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.064 · Cited by 62 · see citing works
Spatial Correlations in Natural Scenes Modulate Response Reliability in Mouse Visual Cortex
Rikhye, R. V., & Sur, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1660-15.2015 · Cited by 55 · see citing works
Self-citationRupert, D. D., & Shea, S. D. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.886629 · Cited by 58 · see citing works
Large-Scale Fluorescence Calcium-Imaging Methods for Studies of Long-Term Memory in Behaving MammalsPreprint
Jercog, P. E., Rogerson, T., & Schnitzer, M. J. (2016) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a021824 · Cited by 53 · see citing works
GABA, not BOLD, reveals dissociable learning-dependent plasticity mechanisms in the human brain
Frangou, P., Correia, M., & Kourtzi, Z. (2018) · eLife · doi:10.7554/elife.35854 · Cited by 49 · see citing works
Alreja, A., Nemenman, I. M., & Rozell, C. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009642 · Cited by 40 · see citing works
Patterned perturbation of inhibition can reveal the dynamical structure of neural processing
Sadeh, S., & Claudia Clopath, A. (2020) · eLife · doi:10.7554/elife.52757 · Cited by 36 · see citing works
Lee, B., Shin, D., Gross, S. P., & Cho, K. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.029 · Cited by 35 · see citing works
Zhong, P., Qin, L., & Yan, Z. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa027 · Cited by 34 · see citing works
Distinct roles of PV and Sst interneurons in visually induced gamma oscillations
Onorato, I., Τζάνου, Α., Schneider, M., Uran, C., Broggini, A. C., & Vinck, M. A. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115385 · Cited by 36 · see citing works
Wal, M. T., & Tiesinga, P. H. E. (2021) · Biological Cybernetics · doi:10.1007/s00422-021-00894-6 · Cited by 26 · see citing works
Lourenço, J., Koukouli, F., & Bacci, A. (2020) · Cortex · doi:10.1016/j.cortex.2020.08.015 · Cited by 22 · see citing works
van Heukelum, S., Mogavero, F., van de Wal, M. A. E., Geers, F. E., França, A. S. C., Buitelaar, J. K., Beckmann, C. F., Glennon, J. C., & Havenith, M. N. (2019) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2019.00809 · Cited by 18 · see citing works
Lateral inhibition in V1 controls neural and perceptual contrast sensitivity
Rosario, J. D., Coletta, S., Kim, S. H., Mobille, Z., Peelman, K., Williams, B., Otsuki, A. J., Valerio, A. D. C., Worden, K., Blanpain, L. T., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01888-4 · Cited by 19 · see citing works
Cascaded normalizations for spatial integration in the primary visual cortex of primates
Li, Y., Wang, T., Yang, Y., Dai, W., Wu, Y., Li, L., Han, C., Zhong, L., Li, L., Wang, G., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111221 · Cited by 17 · see citing works
The Impact of SST and PV Interneurons on Nonlinear Synaptic Integration in the Neocortex
Dorsett, C. R., Philpot, B. D., Smith, S. L., & Smith, I. T. (2021) · eNeuro · doi:10.1523/eneuro.0235-21.2021 · Cited by 16 · see citing works
Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks
Rutishauser, U., Slotine, J.-J. E., & Douglas, R. J. (2018) · Neural Computation · doi:10.1162/neco_a_01074 · Cited by 14 · see citing works
Dynamic Distortion of Orientation Representation after Learning in the Mouse Primary Visual Cortex
Corbo, J., McClure, J. P., Erkat, O. B., & Polack, P. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.2272-21.2022 · Cited by 13 · see citing works
Tan, R., Ma, R., Chu, F., Zhou, X., Wang, X., Yin, T., & Liu, Z. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3395641 · Cited by 13 · see citing works
Prince, L. Y., Tran, M. M., Grey, D., Saad, L., Chasiotis, H., Kwag, J., Kohl, M. M., & Richards, B. A. (2021) · Communications Biology · doi:10.1038/s42003-021-02437-y · Cited by 11 · see citing works
Allitt, B. J., Johnstone, V., Dell, K. L., Yan, E. B., & Rajan, R. (2017) · Cell Transplantation · doi:10.1177/0963689717714326 · Cited by 10 · see citing works
Ostenrath, A. M., Faturos, N., Çobanoğlu, Y. I. Ç., Serneels, B., Jeong, I., Dayanc, E. D., Enz, A., Hinrichsen, F., Mutlu, A. K., Bardenhewer, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62115-z · Cited by 8 · see citing works
Ingram, T. G. J., King, J. L., & Crowder, N. A. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2019.00040 · Cited by 8 · see citing works
Shapiro, J. T., Michaud, N. M., King, J. L., & Crowder, N. A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab269 · Cited by 8 · see citing works
Inhibition-augmented COSFIRE model of shape-selective neurons
Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2679458 · Cited by 8 · see citing works
Key role of neuronal diversity in structured reservoir computing
Thivierge, J., Giraud, É., Lynn, M. B., & Charbonneau, A. T. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0111131 · Cited by 7 · see citing works
Inhibition-augmented trainable COSFIRE filters for keypoint detection and object recognition
Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2016) · Machine Vision and Applications · doi:10.1007/s00138-016-0777-3 · Cited by 7 · see citing works
Functional Roles of Cortical Inhibitory Interneurons
Shin, H., & Adesnik, H. (2023) · doi:10.1093/med/9780197676158.003.0008 · Cited by 6 · see citing works
Context-dependent activation of V1 parvalbumin interneurons enhances visual discrimination
Kukovska, L., Wilmes, K. A., Homma, N. Y., Claudia Clopath, A., & Poort, J. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003518 · Cited by 5 · see citing works
Eriksson, D. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00058 · Cited by 4 · see citing works
Tran, M. M., Prince, L. Y., Gray, D., Saad, L., Chasiotis, H., Kwag, J., Kohl, M. M., & Richards, B. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/671248 · Cited by 4 · see citing works
Characterizing optogenetically mediated rebound effects in anaesthetized mouse primary visual cortex
Shapiro, J. T., Michaud, N. M., & Crowder, N. A. (2025) · The Journal of Physiology · doi:10.1113/jp287265 · Cited by 3 · see citing works
Boroujeni, K. B., Balaram, P., Tiesinga, P. H. E., & Womelsdorf, T. (2025) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2025.102873 · Cited by 4 · see citing works
Gwak, J., & Kwag, J. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5134765 · Cited by 3 · see citing works
Potter, C. T., Bassi, C. D., & Runyan, C. A. (2024) · iScience · doi:10.1016/j.isci.2024.110736 · Cited by 4 · see citing works
Somatostatin interneurons in auditory cortex contribute to perceptual categorizationPreprint
Chai, R., Zhang, Y., Xin, Y., Deng, L., & Xu, N.-L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.06.498950 · Cited by 4 · see citing works
Inhibition mediated by group III mGluRs regulates habenula activity and defensive behaviorsPreprint
Ostenrath, A. M., Faturos, N., Çobanoğlu, Y. I. Ç., Serneels, B., Jeong, I., Enz, A., Hinrichsen, F., Mutlu, A. K., Bardenhewer, R., Jetti, S. K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.11.612421 · Cited by 3 · see citing works
Shahidi, N., Rozenblit, F., Khani, M. H., Schreyer, H. M., Mietsch, M., Protti, D. A., & Gollisch, T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013031 · Cited by 2 · see citing works
Potter, C. T., Bassi, C. D., & Runyan, C. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.09.523298 · Cited by 2 · see citing works
Subtractive gating improves generalization in working memory tasks
Montero, M. L., Malhotra, G., Bowers, J. S., & Costa, R. P. (2019) · Conference on Cognitive Computational Neuroscience (CCN) · doi:10.32470/ccn.2019.1352-0
King, J. L. (2020) · DalSpace (Dalhousie University)
Somatostatine interneurons mediate dopamine-dependent sensory gating in the orbitofrontal cortexPreprint
Tripathi, A., Skold, T., Censoni, L., Lagerlöf, O., & Medini, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.14.643299
Qu, Z., Nocon, J. C., Sen, K., & Gritton, H. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.08.736456
- Authors
- El-Boustani, S., & Sur, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Sur, Mriganka
- ORCID
- Sur, Mriganka
- Venue
- Nature Communications
- Published
- 2014-12-11
- Volume
- 5
- Issue
- 1
- Pages
- 5689-5689
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC4268659
- OpenAlex
- W2006390041
- Cited by
- 61
- References
- 56
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 5 · 2020: 7 · 2019: 6 · 2018: 7 · 2017: 5 · 2016: 6 · 2015: 4
- FWCI
- 2.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Howard Hughes Medical Institute
- Simons Foundation
- National Institutes of Health (EY007023, NS090473)
- National Eye Institute (F32EY007023, EY007023, R01EY007023)
- National Institute of Neurological Disorders and Stroke (NS090473, U01NS090473)
- FP7 People: Marie-Curie Actions
- 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 › Visual perception and processing mechanisms
- Keywords
- divisive inhibition, parvalbumin interneurons, somatostatin interneurons, visual cortex, cortical circuits, response timing, optogenetic stimulation, inhibitory neurons
- MeSH terms
- Action Potentials, Animals, Electrodes, Implanted, Fluorescent Dyes, Mice, Transgenic, Neural Inhibition, Neurons, Parvalbumins, Photic Stimulation, Somatostatin, Stereotaxic Techniques, Time Factors, Visual Cortex, Visual Pathways, Biomarkers, Gene Expression, Mice, Optogenetics
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
