Publications

Export 155 results:
1989
Schmajuk NA, DiCarlo JJ. Neural dynamics of hippocampal modulation of classical conditioning. 12th Symposium on Models of Behavior: Neural Network Models of Conditioning and Action. 1989.
1991
Schmajuk NA, DiCarlo JJ. A hippocampal theory of schizophrenia. Behavioral and Brain Sciences. 1991;14:47-49. doi:10.1017/S0140525X00065353.
Schmajuk NA, DiCarlo JJ. Neural dynamics of hippocampal modulation of classical conditioning. In: Commons M, Grossberg S, Staddon JER Neural Network Models of Conditioning and Action. Neural Network Models of Conditioning and Action. Hillsdale, NJ: Lawrence Erlbaum Association; 1991.
Schmajuk NA, DiCarlo JJ. A neural network approach to hippocampal function in classical conditioning. Behavioral Neuroscience. 1991;105(1):82 - 110. doi:10.1037/0735-7044.105.1.82. (3.46 MB)
1992
Schmajuk NA, DiCarlo JJ. Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review. 1992;99(2):268 - 305. doi:10.1037/0033-295X.99.2.268. (4.3 MB)
1998
DiCarlo JJ, Johnson KO, Hsiao SS. Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey. The Journal of Neuroscience. 1998;18(7):2626 - 2645. doi:10.1523/JNEUROSCI.18-07-02626.1998. (1.33 MB)
1999
DiCarlo JJ, Johnson KO. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.
DiCarlo JJ, Johnson KO. Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey. The Journal of Neuroscience. 1999;19(1):401 - 419. doi:10.1523/JNEUROSCI.19-01-00401.1999. (847.96 KB)
2006
de Beeck HPOp, Baker CI, DiCarlo JJ, Kanwisher NG. Discrimination Training Alters Object Representations in Human Extrastriate Cortex. Journal of Neuroscience. 2006;26(50):13025 - 13036. doi:10.1523/JNEUROSCI.2481-06.2006. (455.73 KB)
Li N, Cox DD, Zoccolan D, DiCarlo JJ. Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance. Society for Neuroscience. 2006.
de Beeck HPOp, Deutsch JA, Vanduffel W, Kanwisher N, DiCarlo JJ. A large-scale shape map in monkey inferior temporal cortex. Society for Neuroscience. 2006.
Kourtzi Z, DiCarlo JJ. Learning and neural plasticity in visual object recognition. Current Opinion in Neurobiology. 2006;16(2):152 - 158. doi:10.1016/j.conb.2006.03.012. (181.23 KB)
DiCarlo JJ. Making faces in the brain. Nature. 2006;442:644 - 644. doi:10.1038/nature05000. (121.91 KB)
Kreiman G, Hung CP, Kraskov A, Quiroga RQuian, Poggio T, DiCarlo JJ. Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex. Neuron. 2006;49(3):433 - 445. doi:10.1016/j.neuron.2005.12.019. (778.45 KB)
Zoccolan D, Kouh M, Poggio T, DiCarlo JJ. Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex. Gordon Conference: Sensation and the Natural Environment. 2006.
2007
de Beeck HPOp, Deutsch JA, Vanduffel W, Kanwisher NG, DiCarlo JJ. A Stable Topography of Selectivity for Unfamiliar Shape Classes in Monkey Inferior Temporal Cortex. Cerebral Cortex. 2007;18(7):1676 - 1694. doi:10.1093/cercor/bhm196. (1.58 MB)
Zoccolan D, Kouh M, Poggio T, DiCarlo JJ. Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex. Journal of Neuroscience. 2007;27(45):12292 - 12307. doi:10.1523/JNEUROSCI.1897-07.2007. (758.94 KB)
DiCarlo JJ, Cox DD. Untangling invariant object recognition. Trends in Cognitive Sciences. 2007;11(8):333 - 341. doi:10.1016/j.tics.2007.06.010. (1.48 MB)
2008
Rust NC, DiCarlo JJ. Concurrent increases in selectivity and tolerance produce constant sparseness across the ventral visual stream. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.
Cox DD, DiCarlo JJ. Does Learned Shape Selectivity in Inferior Temporal Cortex Automatically Generalize Across Retinal Position?. Journal of Neuroscience. 2008;28(40):10045 - 10055. doi:10.1523/JNEUROSCI.2142-08.2008. (8.59 MB)
Pinto N, DiCarlo JJ, Cox DD. Establishing Good Benchmarks and Baselines for Face Recognition. In: European Conference on Computer Vision-Faces in 'Real-Life' Images Workshop. European Conference on Computer Vision-Faces in 'Real-Life' Images Workshop. Marseille, France: EECV; 2008. (1.74 MB)
de Beeck HPOp, DiCarlo JJ, Goense JBM, et al. Fine-Scale Spatial Organization of Face and Object Selectivity in the Temporal Lobe: Do Functional Magnetic Resonance Imaging, Optical Imaging, and Electrophysiology Agree?. Journal of Neuroscience. 2008;28(46):11796 - 11801. doi:10.1523/JNEUROSCI.3799-08.2008.
Cox DD, Papanastassiou AM, Oreper D, Andken BB, DiCarlo JJ. High-Resolution Three-Dimensional Microelectrode Brain Mapping Using Stereo Microfocal X-ray Imaging. Journal of Neurophysiology. 2008;100(5):2966 - 2976. doi:10.1152/jn.90672.2008. (1.25 MB)
Cox DD, Pinto N, Doukhan D, Corda B, DiCarlo JJ. A high-throughput screening approach to discovering good forms of visual representation. Computation and Systems Neuroscience (COSYNE). 2008.
Rust NC, DiCarlo JJ. Increases in selectivity are offset by increases in tolerance ("invariance") to maintain sparseness across the ventral visual pathway. Society for Neuroscience. 2008:514.8. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=fc0d0a2d-b563-4b41-8311-0805f08bde8a&cKey=8a2c998e-bc76-4d92-96ac-ad5199da59bf&mKey=%7bAFEA068D-D012-4520-8E42-10E4D1AF7944%7d.
Rust NC, DiCarlo JJ. Inferior temporal cortex robustly signals encounters with new objects, but is not an online representation of the visual world. Society for Neuroscience. 2008:316.6. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=ee83e7f7-5aea-4ec8-a948-436658d20e37&cKey=fc64f0af-c81e-4b0e-b809-796349279531&mKey=%7bAFEA068D-D012-4520-8E42-10E4D1AF7944%7d.
Li N, DiCarlo JJ. Natural experience drives online learning of tolerant object representations in visual cortex. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.
Zoccolan D, Cox DD, Oertelt N, Radwam B, Tsang S, DiCarlo JJ. Is the rodent a valuable model system for studying invariant object recognition?. Computation and Systems Neuroscience (COSYNE). 2008. Available at: http://www.cosyne.org/c/images/8/8e/Cosyne_pf_new.pdf.
Li N, DiCarlo JJ. Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex. Science. 2008;321:1502 - 1507. doi:10.1126/science.1160028.
Li N, DiCarlo JJ. Unsupervised natural experience rapidly alters invariant object representation in visual cortex. Society for Neuroscience. 2008:316.5. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=ee83e7f7-5aea-4ec8-a948-436658d20e37&cKey=9a873eb3-f8d3-48b5-8df9-9f7b2ef0a3d9&mKey=%7bAFEA068D-D012-4520-8E42-10E4D1AF7944%7d.
Pinto N, Cox DD, Corda B, Doukhan D, DiCarlo JJ. Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition. Computation and Systems Neuroscience (COSYNE). 2008.
Pinto N, Cox DD, DiCarlo JJ. Why is Real-World Visual Object Recognition Hard?. Friston KJ. PLoS Computational Biology. 2008;4:e27. doi:10.1371/journal.pcbi.0040027. (1.93 MB)
2009
Rust NC, DiCarlo JJ. Balanced increases in selectivity and invariance produce constant sparseness across the ventral visual pathway. Journal of Vision. 2009;9(8):738 - 738. doi:10.1167/9.8.738.
Pinto N, Doukhan D, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation. Friston KJ. PLoS Computational Biology. 2009;5(11):e1000579. doi:10.1371/journal.pcbi.1000579. (538.96 KB) (141.46 KB)
Pinto N, DiCarlo JJ, Cox DD. How far can you get with a modern face recognition test set using only simple features?. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). Miami, FL: IEEE; 2009. doi:10.1109/CVPR.2009.5206605. (375.73 KB)
Zoccolan D, Oertelt N, DiCarlo JJ, Cox DD. A rodent model for the study of invariant visual object recognition. Proceedings of the National Academy of Sciences. 2009;106(21):8748 - 8753. doi:10.1073/pnas.0811583106. (730.6 KB)
Li N, DiCarlo JJ. The size invariance of neuronal object representations can be reshaped by temporally contiguous visual experience. Society for Neuroscience. 2009:306.10. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=8bb461de-0fd1-4f6a-9dfe-d62b65382083&cKey=507938c9-dc2c-4a47-a74f-601df562eddc&mKey=%7b081F7976-E4CD-4F3D-A0AF-E8387992A658%7d.
Papanastassiou AM, de Beeck HPOp, Andken BB, DiCarlo JJ. A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.
Pinto N, Cox DD, DiCarlo JJ. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.
Pinto N, Cox DD, DiCarlo JJ. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.
Pinto N, Cox DD, DiCarlo JJ. The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.
Li N, Cox DD, Zoccolan D, DiCarlo JJ. What Response Properties Do Individual Neurons Need to Underlie Position and Clutter “Invariant” Object Recognition?. Journal of Neurophysiology. 2009;102(1):360 - 376. doi:10.1152/jn.90745.2008. (773.41 KB)
2010
DiCarlo JJ. Do we have a strategy for understanding how the visual system accomplishes object recognition?. In: Dickenson SJ, Leonardis A, Schiele B, Tarr MJ Object Categorization: Computer and Human Vision Perspectives. Object Categorization: Computer and Human Vision Perspectives. New York, NY, USA: Cambridge University Press; 2010.
Li N, DiCarlo JJ. Does the visual system use natural experience to construct size invariant object representations?. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00326.
Pinto N, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
Pinto N, Majaj NJ, Barhomi Y, Solomon EA, Cox DD, DiCarlo JJ. Human versus machine: comparing visual object recognition systems on a level playing field. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00283.
Rust NC, DiCarlo JJ. Selectivity and Tolerance ("Invariance") Both Increase as Visual Information Propagates from Cortical Area V4 to IT. Journal of Neuroscience. 2010;30(39):12978 - 12995. doi:10.1523/JNEUROSCI.0179-10.2010.
Issa EB, Papanastassiou AM, Andken BB, DiCarlo JJ. Towards large-scale, high resolution maps of object selectivity in inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2010. doi:10.3389/conf.fnins.2010.03.00154.
Li N, DiCarlo JJ. Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex. Neuron. 2010;67(6):1062 - 1075. doi:10.1016/j.neuron.2010.08.029.
Aparicio PL, Issa EB, DiCarlo JJ. What is the middle face patch?. Society for Neuroscience. 2010;40:581.8. Available at: https://www.abstractsonline.com/Plan/ViewAbstract.aspx?sKey=e08f5ff4-1ba9-4faf-a459-5c9d4be0a1bf&cKey=36fa0d7d-3e83-4910-be75-57d361ae9e58&mKey=%7bE5D5C83F-CE2D-4D71-9DD6-FC7231E090FB%7d.
2012
Rust NC, DiCarlo JJ. Balanced Increases in Selectivity and Tolerance Produce Constant Sparseness along the Ventral Visual Stream. Journal of Neuroscience. 2012;32(30):10170 - 10182. doi:10.1523/JNEUROSCI.6125-11.2012.
DiCarlo  J, Zoccolan D, Rust  C. How Does the Brain Solve Visual Object Recognition?. Neuron. 2012;73(3):415 - 434. doi:10.1016/j.neuron.2012.01.010.
Li N, DiCarlo JJ. Neuronal Learning of Invariant Object Representation in the Ventral Visual Stream Is Not Dependent on Reward. Journal of Neuroscience. 2012;32(19):6611 - 6620. doi:10.1523/JNEUROSCI.3786-11.2012.
Issa EB, DiCarlo JJ. Precedence of the Eye Region in Neural Processing of Faces. Journal of Neuroscience. 2012;32(47):16666 - 16682. doi:10.1523/JNEUROSCI.2391-12.2012.
Majaj NJ, Hong H, Solomon EA, DiCarlo JJ. A unified neuronal population code fully explains human object recognition. In: Computation and Systems Neuroscience (COSYNE). Computation and Systems Neuroscience (COSYNE). Salt Lake City, Utah, USA; 2012. doi:http://www.cosyne.org/c/index.php?title=Cosyne_12.
2013
Yamins DLK, Hong H, Cadieu CF, DiCarlo JJ. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream. In: Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems. Lake Tahoe, Nevada, United States.; 2013. doi:https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream.
Issa EB, Papanastassiou AM, DiCarlo JJ. Large-Scale, High-Resolution Neurophysiological Maps Underlying fMRI of Macaque Temporal Lobe. Journal of Neuroscience. 2013;33(38):15207 - 15219. doi:10.1523/JNEUROSCI.1248-13.2013.
Cadieu CF, Hong H, Yamins DLK, Pinto N, Majaj NJ, DiCarlo JJ. The Neural Representation Benchmark and its Evaluation on Brain and Machine. arXiv. 2013. doi:https://arxiv.org/abs/1301.3530.
Baldassi C, Alemi-Neissi A, Pagan M, DiCarlo JJ, Zecchina R, Zoccolan D. Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons. PLoS Computational Biology. 2013;9(8):e1003167. doi:10.1371/journal.pcbi.1003167.
2014
Cadieu CF, Hong H, Yamins DLK, et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. Bethge M. PLoS Computational Biology. 2014;10(12):e1003963. doi:10.1371/journal.pcbi.1003963.
Afraz A, Yamins DLK, DiCarlo JJ. Neural Mechanisms Underlying Visual Object Recognition. Cold Spring Harbor Symposia on Quantitative Biology. 2014;79:99 - 107. doi:10.1101/sqb.2014.79.024729.
Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences. 2014:8619 - 8624. doi:10.1073/pnas.1403112111.

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