Publications

Export 129 results:
2020
Rajalingham R, Sorenson M, Azadi R, Bohn S, DiCarlo JJ, Afraz A. Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583. (2.64 MB)
Margalit E, Lee H, Marques T, DiCarlo JJ, Yamins DLK. Correlation-based spatial layout of deep neural network features generates ventral stream topography. Computation and Systems Neuroscience (COSYNE). 2020. Available at: http://cosyne.org/cosyne20/Cosyne2020_program_book.pdf.
Kar K, DiCarlo JJ. Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition. BioRxiv. 2020. doi:https://doi.org/10.1101/2020.05.10.086959.
Kar K, DiCarlo JJ. Fast Recurrent Processing Via Ventral Prefrontal Cortex is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron. 2020. doi:https://doi.org/10.1016/j.neuron.2020.09.035. (3.92 MB)
Rajalingham R, Kar K, Sanghavi S, Dehaene S, DiCarlo JJ. The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-17714-3.
Schrimpf M, Kubilius J, Lee MJ, N. Murty ARatan, Ajemian R, DiCarlo JJ. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron. 2020. doi:10.1016/j.neuron.2020.07.040. (1.04 MB)
Tremblay S, Acker L, Afraz A, et al. An Open Resource for Non-human Primate Optogenetics. Neuron. 2020. doi:10.1016/j.neuron.2020.09.027.
Dapello J, Marques T, Schrimpf M, Geiger F, Cox DD, DiCarlo JJ. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. bioRxiv. 2020. doi:10.1101/2020.06.16.154542. (2.48 MB)
Gan C, Schwartz J, Alter S, et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv. 2020. Available at: https://arxiv.org/abs/2007.04954. (7.06 MB)
Lee H, Margalit E, Jozwik KM, et al. Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network. bioRxiv. 2020. doi:https://doi.org/10.1101/2020.07.09.185116.
Jia X, Hong H, DiCarlo JJ. Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex. BioRxiv. 2020. doi:https://doi.org/10.1101/2020.01.13.900837.
Zhuang C, Yan S, Nayebi A, et al. Unsupervised Neural Network Models of the Ventral Visual Stream. bioRxiv. 2020. doi:10.1101/2020.06.16.155556. (2.7 MB)
Geiger F, Schrimpf M, Marques T, DiCarlo JJ. Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. bioRxiv. 2020. doi:10.1101/2020.06.08.140111. (1.29 MB)
2019
Kubilius J, Schrimpf M, Hong H, et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. In: Neural Information Processing Systems. Neural Information Processing Systems.; 2019. doi:https://papers.nips.cc/paper/9441-brain-like-object-recognition-with-high-performing-shallow-recurrent-anns.
Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience. 2019;22(6):974 - 983. doi:10.1038/s41593-019-0392-5.
Harris KD, Groh JM, DiCarlo JJ, et al. Funcitional Properties of Circuits, Cellular Populations, and Areas. In: Singer W, Sejnowski TJ, Rakic P The Neocortex.Vol 27. The Neocortex. Cambridge, MA: The MIT Press; 2019:223-265. doi:10.7551/mitpress/12593.001.0001. (1.06 MB)
Bashivan P, Kar K, DiCarlo JJ. Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.
Rajalingham R, DiCarlo JJ. Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits. Neuron. 2019;102(2):493 - 505.e5. doi:10.1016/j.neuron.2019.02.001.
Jozwik KM, Schrimpf M, Kanwisher N, DiCarlo JJ. To find better neural network models of human vision, find better neural network models of primate vision. BioRxiv. 2019. doi:https://doi.org/10.1101/688390.
2018
Schrimpf M, Kubilius J, Hong H, et al. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.
Kubilius J, Schrimpf M, Nayebi A, Bear D, Yamins DLK, DiCarlo JJ. CORnet: Modeling the Neural Mechanisms of Core Object Recognition. bioRxiv. 2018. doi:https://doi.org/10.1101/408385.
Batista AP, DiCarlo JJ. Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.
Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ. Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. bioRxiv. 2018. doi:https://doi.org/10.1101/354753.
Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. The Journal of Neuroscience. 2018;38(33):7255 - 7269. doi:10.1523/JNEUROSCI.0388-18.2018.
Rajalingham R, Issa EB, Bashivan P, Kar K, Schmidt K, DiCarlo JJ. Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. bioRxiv. 2018. doi:https://doi.org/10.1101/240614.
Ohayon S, Caravaca-Aguirre A, Piestun R, DiCarlo JJ. Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging. Biomedical Optics Express. 2018;9(4):1492-1509. doi:10.1364/BOE.9.001492.
Issa EB, Cadieu CF, DiCarlo JJ. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. eLife. 2018;7. doi:10.7554/eLife.42870.
Rajalingham R, DiCarlo JJ. Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits. bioRxiv. 2018. doi:https://doi.org/10.1101/390245.
Nayebi A, Bear D, Kubilius J, et al. Task-Driven Convolutional Recurrent Models of the Visual System. arXiv. 2018. doi:https://arxiv.org/abs/1807.00053.
Bashivan P, Tensen M, DiCarlo JJ. Teacher Guided Architecture Search. arXiv. 2018. doi:https://arxiv.org/abs/1808.01405.
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.
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.
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.
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.
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)
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)
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)
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.

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