The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT
Open accessAbstract
Our ability to recognize objects despite large changes in position, size, and context is achieved through computations that are thought to increase both the shape selectivity and the tolerance (“invariance”) of the visual representation at successive stages of the ventral pathway [visual cortical areas V1, V2, and V4 and inferior temporal cortex (IT)]. However, these ideas have proven difficult to test. Here, we consider how well population activity patterns at two stages of the ventral stream (V4 and IT) discriminate between, and generalize across, different images. We found that both V4 and IT encode natural images with similar fidelity, whereas the IT population is much more sensitive to controlled, statistical scrambling of those images. Scrambling sensitivity was proportional to receptive field (RF) size in both V4 and IT, suggesting that, on average, the number of visual feature conjunctions implemented by a V4 or IT neuron is directly related to its RF size. We also found that the IT population could better discriminate between objects across changes in position, scale, and context, thus directly demonstrating a V4-to-IT gain in tolerance. This tolerance gain could be accounted for by both a decrease in single-unit sensitivity to identity-preserving transformations (e.g., an increase in RF size) and an increase in the maintenance of rank-order object selectivity within the RF. These results demonstrate that, as visual information travels from V4 to IT, the population representation is reformatted to become more selective for feature conjunctions and more tolerant to identity preserving transformations, and they reveal the single-unit response properties that underlie that reformatting.
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Cites
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The fusiform face area: a module in human extrastriate cortex specialized for face perception
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A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
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Baker, C. I., Behrmann, M., & Olson, C. R. (2002) · Nature Neuroscience · doi:10.1038/nn960 · Cited by 293
Cited by
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924
Self-citationHow does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790
Self-citationUsing goal-driven deep learning models to understand sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621
Self-citationWagemans, J., Elder, J. H., Kubovy, M., Palmer, S. E., Peterson, M. A., Singh, M., & von der Heydt, R. (2012) · Psychological Bulletin · doi:10.1037/a0029333 · Cited by 1,539
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204
Metadata
- Authors
- Rust, N. C., & DiCarlo, J. J.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2010-09-29
- Volume
- 30
- Issue
- 39
- Pages
- 12978-12995
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W1990037101
- Cited by
- 387
- References
- 6
- FWCI
- 8.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
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