The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex
Open accessAbstract
We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.
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
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
Two-photon laser scanning fluorescence microscopy
Denk, W., Strickler, J. H., & Webb, W. W. (1990) · Science · doi:10.1126/science.2321027 · Cited by 9,488
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
Millisecond-timescale, genetically targeted optical control of neural activity
Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G., & Deisseroth, K. (2005) · Nature Neuroscience · doi:10.1038/nn1525 · Cited by 4,861
Selective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (1985) · Science · doi:10.1126/science.4023713 · Cited by 2,433
Selective Attention Gates Visual Processing in the Extrastriate Cortex
Moran, J., & Desimone, R. (2003) · Essential Sources in the Scientific Study of Consciousness · doi:10.7551/mitpress/2834.003.0024 · Cited by 2,144
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483 · cited 3×
“Moreover, while some neurons showed a correlation between their calcium signal and running speed, as expected from previous studies (Niell and Stryker, 2010; Keller et al., 2012; Saleemet al., 2013), there was no positive relationshipbetweenhowstronglycellsweremodulatedbyrunning and/or optic flow speed and their chang…”
BackgroundResultA toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing
Madisen, L., Mao, T., Koch, H., Zhuo, J.-M., Berenyi, A., Fujisawa, S., Hsu, Y.-W. A., Garcia, A. J., Gu, X., Zanella, S., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3078 · Cited by 1,398
Relative reward preference in primate orbitofrontal cortex
Tremblay, L., & Schultz, W. (1999) · Nature · doi:10.1038/19525 · Cited by 1,314
Cited by
Suite2p: beyond 10,000 neurons with standard two-photon microscopy
Pachitariu, M., Stringer, C., Dipoppa, M., Schröder, S., Rossi, L. F., Dalgleish, H., Carandini, M., & Harris, K. D. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/061507 · Cited by 1,247
Self-citationPredictive 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-citationCortical 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
Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex
Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N., & Harvey, C. D. (2017) · Cell · doi:10.1016/j.cell.2017.07.021 · Cited by 445
Experience-dependent spatial expectations in mouse visual cortex
Fiser, A., Mahringer, D., Oyibo, H. K., Petersen, A. V., Leinweber, M., & Keller, G. B. (2016) · Nature Neuroscience · doi:10.1038/nn.4385 · Cited by 332
Self-citation
Metadata
- Authors
- Poort, J., Khan, A. G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., Bauza, M., Sahani, M., Keller, G. B., Mrsic-Flogel, T. D., & Hofer, S. B.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 86
- Issue
- 6
- Pages
- 1478-1490
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W1538429890
- Cited by
- 403
- References
- 48
- FWCI
- 17.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex. https://app.cochat.ai/papers/10.1016/j.neuron.2015.05.037
- The paper
- Poort, J., Khan, A. G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., Bauza, M., Sahani, M., Keller, G. B., Mrsic-Flogel, T. D., & Hofer, S. B. (2015). Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex. Neuron. https://doi.org/10.1016/j.neuron.2015.05.037
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.
