The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Adult Visual Cortical Plasticity
Open accessAbstract
The visual cortex has the capacity for experience dependent change, or cortical plasticity, that is retained throughout life. Plasticity is invoked for encoding information during perceptual learning, by internally representing the regularities of the visual environment, which is useful for facilitating intermediate level vision - contour integration and surface segmentation. The same mechanisms have adaptive value for functional recovery after CNS damage, such as that associated with stroke or neurodegenerative disease. A common feature to plasticity in primary visual cortex (V1) is an association field that links contour elements across the visual field. The circuitry underlying the association field includes a plexus of long range horizontal connections formed by cortical pyramidal cells. These connections undergo rapid and exuberant sprouting and pruning in response to removal of sensory input, which can account for the topographic reorganization following retinal lesions. Similar alterations in cortical circuitry may be involved in perceptual learning, and the changes observed in V1 may be representative of how learned information is encoded throughout the cerebral cortex.
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Cites
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Cited by
Top-down influences on visual processing
Gilbert, C. D., & Li, W. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3476 · Cited by 1,224
Self-citationDevelopment and plasticity of the primary visual cortex
Espinosa, J. S., & Stryker, M. P. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.009 · Cited by 672
Conscious and Unconscious Memory Systems
Squire, L. R., & Dede, A. J. O. (2015) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a021667 · Cited by 366
Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision?
Kruger, N., Janssen, P., Kalkan, S., Lappe, M., Leonardis, A., Piater, J., Rodriguez-Sanchez, A. J., & Wiskott, L. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.272 · Cited by 362
Perceptual Learning: Toward a Comprehensive Theory
Watanabe, T., & Sasaki, Y. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015214 · Cited by 347
Metadata
- Authors
- Gilbert, C. D., & Li, W.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2012-07
- Volume
- 75
- Issue
- 2
- Pages
- 250-264
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2017742098
- Cited by
- 218
- References
- 22
- FWCI
- 8.14
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
