The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Functional organization of excitatory synaptic strength in primary visual cortex
Open accessAbstract
The strength of synaptic connections fundamentally determines how neurons influence each other’s firing. Excitatory connection amplitudes between pairs of cortical neurons vary over two orders of magnitude, comprising only very few strong connections among many weaker ones. Although this highly skewed distribution of connection strengths is observed in diverse cortical areas, its functional significance remains unknown: it is not clear how connection strength relates to neuronal response properties, nor how strong and weak inputs contribute to information processing in local microcircuits. Here we reveal that the strength of connections between layer 2/3 (L2/3) pyramidal neurons in mouse primary visual cortex (V1) obeys a simple rule—the few strong connections occur between neurons with most correlated responses, while only weak connections link neurons with uncorrelated responses. Moreover, we show that strong and reciprocal connections occur between cells with similar spatial receptive field structure. Although weak connections far outnumber strong connections, each neuron receives the majority of its local excitation from a small number of strong inputs provided by the few neurons with similar responses to visual features. By dominating recurrent excitation, these infrequent yet powerful inputs disproportionately contribute to feature preference and selectivity. Therefore, our results show that the apparently complex organization of excitatory connection strength reflects the similarity of neuronal responses, and suggest that rare, strong connections mediate stimulus-specific response amplification in cortical microcircuits.
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.
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
Brainard, D. H. (1997) · Spatial Vision · doi:10.1163/156856897x00357 · Cited by 19,702
The VideoToolbox software for visual psychophysics: transforming numbers into movies
Pelli, D. G. (1997) · Spatial Vision · doi:10.1163/156856897x00366 · Cited by 11,098
Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S., & Chklovskii, D. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030068 · Cited by 1,512
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 1,312
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 1,086
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
The log-dynamic brain: how skewed distributions affect network operations
Buzsáki, G., & Mizuseki, K. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3687 · Cited by 981
Recurrent Excitation in Neocortical Circuits
Douglas, R. J., Koch, C., Mahowald, M., Martin, K. A. C., & Suarez, H. H. (1995) · Science · doi:10.1126/science.7638624 · Cited by 980
Markram, H., Lübke, J., Frotscher, M., Roth, A., & Sakmann, B. (1997) · The Journal of Physiology · doi:10.1113/jphysiol.1997.sp022031 · Cited by 960
The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex
Lefort, S., Tomm, C., Floyd Sarria, J.-C., & Petersen, C. C. H. (2009) · Neuron · doi:10.1016/j.neuron.2008.12.020 · Cited by 875
Cited by
Predictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836
Self-citationThalamic amplification of cortical connectivity sustains attentional control
Schmitt, L. I., Wimmer, R. D., Nakajima, M., Happ, M., Mofakham, S., & Halassa, M. M. (2017) · Nature · doi:10.1038/nature22073 · Cited by 663
Cortical layer-specific critical dynamics triggering perception
Marshel, J. H., Kim, Y. S., Machado, T. A., Quirin, S., Benson, B., Kadmon, J., Raja, C., Chibukhchyan, A., Ramakrishnan, C., Inoue, M., et al. (2019) · Science · doi:10.1126/science.aaw5202 · Cited by 652
High-dimensional geometry of population responses in visual cortex
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1346-5 · Cited by 594
Projections from neocortex mediate top-down control of memory retrieval
Rajasethupathy, P., Sankaran, S., Marshel, J. H., Kim, C. K., Ferenczi, E., Lee, S. Y., Berndt, A., Ramakrishnan, C., Jaffe, A., Lo, M., et al. (2015) · Nature · doi:10.1038/nature15389 · Cited by 509
Metadata
- Authors
- Cossell, L., Iacaruso, M. F., Muir, D. R., Houlton, R., Sader, E. N., Ko, H., Hofer, S. B., & Mrsic-Flogel, T. D.
- Venue
- Nature
- Published
- 2015-02
- Volume
- 518
- Issue
- 7539
- Pages
- 399-403
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W1973654985
- Cited by
- 527
- References
- 37
- FWCI
- 23.50
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Functional organization of excitatory synaptic strength in primary visual cortex. https://app.cochat.ai/papers/10.1038/nature14182
- The paper
- Cossell, L., Iacaruso, M. F., Muir, D. R., Houlton, R., Sader, E. N., Ko, H., Hofer, S. B., & Mrsic-Flogel, T. D. (2015). Functional organization of excitatory synaptic strength in primary visual cortex. Nature. https://doi.org/10.1038/nature14182
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.