The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
How Noise Contributes to Contrast Invariance of Orientation Tuning in Cat Visual Cortex
Open accessAbstract
The width of the orientation tuning curves of the spike response of neurons in V1 is invariant to contrast. This property constrains the possible mechanisms underlying orientation selectivity. It has been suggested that noise circumvents the iceberg effect that would prevent contrast invariance in the purely feedforward mechanism. Here we investigate systematically how noise contributes to the contrast invariance of orientation tuning curves in V1. We study three models of increasing complexity: a simple threshold-linear firing rate model, a leaky integrate-and-fire model, and a conductance-based model. We show that the noise transmutes the threshold nonlinearity of the input-output relationships into an approximate power law without a threshold within some firing rate range. This implies that, under certain conditions which are derived here, the tuning of the neuron output is approximately contrast invariant. In particular we show that this mechanism for contrast invariance requires that the neuron firing rate must not be too large and that increasing or lowering the contrast too much destroys this invariance. We also show that if this mechanism operates in V1, the spike response, R, and average voltage response V of the neurons in V1 should vary with the contrast, C, according to R(C)gamma proportional to V(C)gamma. The exponent gamma can be estimated from the amount by which the spike tuning curve is sharpened with respect to the voltage tuning curves of the neurons. This prediction does not depend on the specifics of the model and can be tested experimentally.
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
“These fluctuations may contribute to contrast-invariant tuning in neurons of the visual cortex of cats and rodents10,13,63,64.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [64]
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
This paper’s reference list could not be split into single entries.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835
Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses
Arieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. (1996) · Science · doi:10.1126/science.273.5283.1868 · Cited by 1,660
Introduction to Theoretical Neurobiology
Tuckwell, H. C. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511623202 · Cited by 1,174
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
Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex
Carandini, M., Heeger, D. J., & Movshon, J. A. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-21-08621.1997 · Cited by 926
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
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 715
Visual input evokes transient and strong shunting inhibition in visual cortical neurons
Graham, L. J., Monier, C., & Frégnac, Y. (1998) · Nature · doi:10.1038/30735 · Cited by 669
Neural Mechanisms of Orientation Selectivity in the Visual Cortex
Ferster, D., & Miller, K. D. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.441 · Cited by 665
Cited by
Sorted by citations
Items 101–157 of 157
Sub-threshold neuronal activity and the dynamical regime of cerebral cortex
Amsalem, O., Inagaki, H., Yu, J., Svoboda, K., & Darshan, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51390-x · Cited by 14
Accurate multiplication with noisy spiking neurons
Nezis, P., & van Rossum, M. C. W. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/034005 · Cited by 15
Development of orientation tuning in simple cells of primary visual cortex
Moore, B. D., & Freeman, R. D. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00719.2011 · Cited by 13
Republished: Dynamics of Stochastic Integrate-and-Fire Networks
Ocker, G. K. (2023) · Physical Review X · doi:10.1103/physrevx.13.041047 · Cited by 13
Lin, I.-C., Xing, D., & Shapley, R. M. (2012) · Journal of Computational Neuroscience · doi:10.1007/s10827-012-0401-0 · Cited by 12
Synchrony, connectivity, and functional similarity in auditory midbrain local circuits
Atencio, C. A., Shen, V., & Schreiner, C. E. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.08.024 · Cited by 12
Single-cell optogenetics reveals attenuation-by-suppression in visual cortical neuronsPreprint
LaFosse, P. K., Zhou, Z., O’Rawe, J. F., Friedman, N. G., Scott, V. M., Deng, Y., & Histed, M. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.13.557650 · Cited by 11
Cogno, S. G., & Mato, G. (2015) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00042 · Cited by 10
The accuracy of membrane potential reconstruction based on spiking receptive fields
Mohanty, D., Scholl, B., & Priebe, N. J. (2012) · Journal of Neurophysiology · doi:10.1152/jn.01176.2011 · Cited by 10
Kraynyukova, N., Renner, S., Born, G., Bauer, Y., Spacek, M. A., Tushev, G., Busse, L., & Tchumatchenko, T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207032119 · Cited by 10
Inhibitory synchrony as a mechanism for attentional gain modulationPreprint
Tiesinga, P. H. E., Fellous, J., Salinas, E., José, J. V., & B, T. J. S. A. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.q-bio/0503019 · Cited by 10
Clarifying the conceptual dimensions of representation in neuroscience
Pohl, S., Walker, E. Y., Barack, D. L., Lee, J., Denison, R. N., Block, N. J., Meyniel, F., & Ma, W. J. (2026) · Nature reviews. Neuroscience · doi:10.1038/s41583-026-01030-8 · Cited by 16
Santo, S. D., Dipoppa, M., Keller, A. J., Roth, M., Scanziani, M., & Miller, K. D. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.115088 · Cited by 13
Effects of contrast and contrast adaptation on static receptive field features in macaque area V1
Durand, J., Girard, P., Barone, P., Bullier, J., & Nowak, L. G. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00936.2011 · Cited by 8
Reuveni, I., Ghosh, S., & Barkai, E. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005306 · Cited by 9
Sadeh, S., & Rotter, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114237 · Cited by 7
Shi, X., Jin, Y., & Cang, J. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00163 · Cited by 7
Stimulus dependent transformations between synaptic and spiking receptive fields in auditory cortex
Kim, K. X., Atencio, C. A., & Schreiner, C. E. (2020) · Nature Communications · doi:10.1038/s41467-020-14835-7 · Cited by 7
Argaman, T., & Golomb, D. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.07.054 · Cited by 7
Stabilization of Memory States by Stochastic Facilitating Synapses
Miller, P. C. (2013) · The Journal of Mathematical Neuroscience · doi:10.1186/2190-8567-3-19 · Cited by 6
Stabilized Supralinear Network: Model of Layer 2/3 of the Primary Visual CortexPreprint
Obeid, D., & Miller, K. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.30.424892 · Cited by 6
A doubly stochastic renewal framework for partitioning spiking variability
Aghamohammadi, C., Chandrasekaran, C., & Engel, T. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63821-4 · Cited by 9
Chau, H. Y., Miller, K. D., & Palmigiano, A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2426758122 · Cited by 6
Learning shapes cortical dynamics to enhance integration of relevant sensory inputPreprint
Chadwick, A., Khan, A. G., Poort, J., Blot, A., Hofer, S. B., Mrsic‐Flogel, T. D., & Sahani, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.02.454726 · Cited by 4
Santo, S. D., Dipoppa, M., Keller, A. J., Roth, M., Scanziani, M., & Miller, K. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.27.493753 · Cited by 5
Moradi, F. (2004) · Biological Cybernetics · doi:10.1007/s00422-004-0499-x · Cited by 4
Timón, L. B., Ekelmans, P., Konrad, S., Nold, A., & Tchumatchenko, T. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013162 · Cited by 4
Combining cues in contour orientation discrimination
Popple, A. V., & Levi, D. M. (2004) · Vision Research · doi:10.1016/j.visres.2004.07.013 · Cited by 4
Inhibition facilitates direction selectivity in a noisy cortical environment
Sederberg, A., & Kaschube, M. (2014) · Journal of Computational Neuroscience · doi:10.1007/s10827-014-0538-0 · Cited by 3
Garg, A. R., Bhaumik, B., & Obermayer, K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30499-9_13 · Cited by 3
Neef, A., Hady, A. E., Nagpal, J. K., Bröking, K., Afshar, G., Schlüter, O. M., Geisel, T., Bamberg, E., Fleischmann, R., Stühmer, W., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.7125 · Cited by 3
Cortical direction selectivity increases from the input to the output layers of visual cortex
Dai, W., Wang, T., Li, Y., Yang, Y., Zhang, Y., Wu, Y., Zhou, T., Yu, H., Li, L., Wang, Y., et al. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3002947 · Cited by 2
Fortier, P. A. (2010) · Experimental Brain Research · doi:10.1007/s00221-010-2483-0 · Cited by 2
Hippocampal CA1 synaptic plasticity as a gamma transfer function
Tamura, H., Ikegaya, Y., & Shiosaka, S. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2006.01.004 · Cited by 2
Defining statistical perceptions with an empirical Bayesian approach
Tajima, S. (2013) · Physical Review E · doi:10.1103/physreve.87.042707 · Cited by 2
RETRACTED: Dynamics of Stochastic Integrate-and-Fire Networks
Ocker, G. K. (2022) · Physical Review X · doi:10.1103/physrevx.12.041007 · Cited by 3
RetractedOn the Subspace Invariance of Population Responses
Tring, E., & Ringach, D. L. (2018) · PubMed · doi:10.48550/arxiv.1811.03251 · Cited by 2
Hedrick, M. S., Moon, I. J., Woo, J., & Won, J.-H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149128 · Cited by 1
Stabilized Supralinear Network Model of Responses to Surround Stimuli in Primary Visual Cortex
Obeid, D., & Miller, K. D. (2025) · eNeuro · doi:10.1523/eneuro.0459-24.2025 · Cited by 1
Raman, I. M., & Hopkins, C. D. (2025) · The Journal of Physiology · doi:10.1113/jp288299 · Cited by 1
Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticityPreprint
Ocker, G. K., & Buice, M. A. (2021) · arXiv (Cornell University) · doi:10.12751/nncn.bc2021.p121 · Cited by 1
Kim, K., Sonego, V., Strohmer, B., Chang, B., Liu, X., Yonza, A. K. I., Kucharz, K., Asiminas, A., Lind, B. L., Tao, L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.25.661479 · Cited by 1
The fluctuation-based regime of thalamocortical circuitryPreprint
Jennings, L., Hansel, D., & Priebe, N. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.20.689511 · Cited by 1
Self-citationWu, Y. K., Chau, H. Y., Santo, S. D., & Miller, K. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704473 · Cited by 1
Two Potential Mechanisms of Spatial Attention in Early Visual AreasPreprint
Persi, E. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0806.4161 · Cited by 1
Spike Pattern Structure Influences Efficacy Variability under STDP and Synaptic HomeostasisPreprint
Bi, Z., Zhou, C., & Zhou, H.-J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.04600
The mechanics of state-dependent neural
Litwin-Kumar, A., Rosenbaum, R., & Ocker, G. K. (2016)
Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells
Fortier, P. A. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.01.052
Analysis of the stabilized supralinear networkPreprint
Ahmadian, Y., Rubin, D. B., & Miller, K. D. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.6670
Reconciling contrast invariance and non-linear computation in cortical circuitsPreprint
Timón, L. B., Ekelmans, P., Konrad, S., Nold, A., & Tchumatchenko, T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.23.441165
Network mechanisms of working memory: the role of neuronal nonlinearitiesPreprint
Suarez-Perez, A., Harish, O., & Hansel, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.05091
Self-citationMiller, P. C. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733245602.793556628
Eppler, J.-B. (2022) · Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · doi:10.21248/gups.69221
Xià, J., Jasper, A. I., Kohn, A., & Miller, K. D. (2024) · iScience · doi:10.1016/j.isci.2024.110512
Metastability in networks of stochastic integrate-and-fire neurons
Paliwal, S., Ocker, G. K., & Brinkman, B. A. W. (2025) · Physical review. E · doi:10.1103/physreve.111.064402
Angelucci, A., Vafaei, A., Clark, A., Khadir, A., Dai, W., & Federer, F. (2025) · Research Square · doi:10.64898/2025.12.23.696300 · Cited by 2
Chau, H. Y., Oldenburg, I. A., Miller, K. D., & Palmigiano, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.27.746829
Metadata
- Authors
- Hansel, D., & van Vreeswijk, C.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- ORCID
- Hansel, D.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2002-06-15
- Volume
- 22
- Issue
- 12
- Pages
- 5118-5128
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6757721
- OpenAlex
- W2170697082
- Cited by
- 157
- References
- 28
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 5 · 2023: 5 · 2022: 9 · 2021: 9 · 2020: 4 · 2019: 3 · 2018: 4 · 2017: 4 · 2016: 11 · 2015: 9 · 2014: 4 · 2013: 7 · 2012: 10
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neuroscience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- National Science Foundation (9907949)
- Centre National de la Recherche Scientifique
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Keywords
- contrast invariance, orientation tuning, primary visual cortex, neural noise, orientation selectivity, leaky integrate-and-fire model, conductance-based models, threshold nonlinearity, power-law response, spike responses, voltage response, cat visual cortex
- MeSH terms
- Animals, Cats, Electric Conductivity, Kinetics, Membrane Potentials, Models, Neurological, Models, Theoretical, Synaptic Transmission, Neurons, Visual Cortex
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
