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Peters B, DiCarlo JJ, Gureckis T, et al. How does the primate brain combine generative and discriminative computations in vision?.  2024. doi: https://doi.org/10.48550/arXiv.2401.06005.\par \par Xie Y, Alter E, Schwartz J, DiCarlo JJ. Learning only a handful of latent variables produces neural-aligned CNN models of the ventral stream. In:  Computational and Systems Neuroscience (COSYNE) . Computational and Systems Neuroscience (COSYNE) . Lisbon, Portugal; 2024. Available at: https://hdl.handle.net/1721.1/153744.\par \par DiCarlo J, Bashivan P, Kar K. Software and Methods for Controlling Neural Responses in Deep Brain Regions.  2024. Available at: https://patents.google.com/patent/US20240082534A1/en.\par \par Zador A, Escola S, Richards B, et al. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications. 2023;14(1):1597. doi:10.1038/s41467-023-37180-x.\par \par Lee MJ, DiCarlo JJ. An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models. bioRxiv. 2023:2022.12.31.522402. doi:https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1.\par \par Mieczkowski E, Abate A, De Faria W, et al. fROI-level computational models enable broad-scale experimental testing and expose key divergences between models and brains. Journal of Vision. 2023;23(9):5788 - 5788. Available at: https://doi.org/10.1167/jov.23.9.5788.\par \par Lee MJ, DiCarlo JJ. How well do rudimentary plasticity rules predict adult visual object learning?. Kietzmann TChristian. PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713.\par \par Lee MJ, DiCarlo JJ. How well do rudimentary plasticity rules predict adult visual object learning?. Kietzmann TChristian. PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713.\par \par DiCarlo JJ, Yamins DLK, Ferguson ME, et al. Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human visionAbstract. Behavioral and Brain Sciences. 2023;4634. doi:10.1017/S0140525X23001607.\par \par Kuoch M, Chou C-N, Parthasarathy N, et al. Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds. In:  Conference on Parsimony and Learning (Proceedings Track). Conference on Parsimony and Learning (Proceedings Track). Hong Kong, China; 2023. Available at: https://openreview.net/forum?id=MxBS6aw5Gd.\par \par Kar K, DiCarlo JJ. The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates. arXiv. 2023. doi: https://doi.org/10.48550/arXiv.2312.05956.\par \par Gaziv G, Lee MJ, DiCarlo JJ. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv. 2023. doi: https://doi.org/10.48550/arXiv.2308.06887 Focus to learn more.\par \par Gaziv G, Lee MJ, DiCarlo JJ. Strong and Precise Modulation of Human Percepts via Robustified ANNs. In:  Neural Information Processing Systems. Neural Information Processing Systems. New Orleans, Louisiana; 2023. Available at: https://openreview.net/pdf?id=5GmTI4LNqX.\par \par Margalit E, Lee H, Finzi D, DiCarlo JJ, Grill-Spector K, Yamins DLK. A Unifying Principle for the Functional Organization of Visual Cortex. bioRxiv. 2023. doi: https://doi.org/10.1101/2023.05.18.541361.\par \par Guo C, Lee MJ, Leclerc G, et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv. 2022. doi:https://doi.org/10.48550/arXiv.2206.11228.\par \par Dapello J, Kar K, Schrimpf M, et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv. 2022. doi:https://doi.org/10.1101/2022.07.01.498495.\par \par Bagus AMarliawaty, Marques T, Sanghavi S, DiCarlo JJ, Schrimpf M. Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units. In:  SVHRM Workshop at Neural Information Processing Systems (NeurIPS). SVHRM Workshop at Neural Information Processing Systems (NeurIPS). Lisbon, Portugal; 2022. Available at: https://openreview.net/pdf?id=iPF7mhoWkOl.\par \par Geiger F, Schrimpf M, Marques T, DiCarlo JJ. Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. In:  International Conference on Learning Representations 2022 Spotlight. International Conference on Learning Representations 2022 Spotlight.; 2022. doi:10.1101/2020.06.08.140111.\par \par Kar K, Schrimpf M, Schmidt K, DiCarlo JJ. Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior. Journal of Vision. 2021;21(9):2489-2489. doi:https://doi.org/10.1167/jov.21.9.2489.\par \par Rajalingham R, Sorenson M, Azadi R, Bohn S, DiCarlo JJ, Afraz A. Chronically implantable LED arrays for behavioral optogenetics in primates. Nature Methods. 2021;18(9):1112 - 1116. doi:10.1038/s41592-021-01238-9.\par \par Baidya A, Dapello J, DiCarlo JJ, Marques T. Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. Shared Visual Representations in Human & Machine Intelligence - NeurIPS Workshop. 2021. Available at: https://arxiv.org/abs/2110.10645.\par \par Murty NAR, Bashivan P, Abate A, DiCarlo JJ, Kanwisher N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-25409-6.\par \par 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. 2021;109(1):164-167.e5. doi:https://doi.org/10.1016/j.neuron.2020.09.035.\par \par Marques T, Schrimpf M, DiCarlo JJ. Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior. bioRxiv. 2021. doi:10.1101/2021.03.01.433495.\par \par Dapello J, Feather J, Marques T, et al. Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. In:  Neural Information Processing Systems (NeurIPS). Neural Information Processing Systems (NeurIPS). Lisbon, Portugal; 2021. Available at: https://proceedings.neurips.cc/paper/2021/file/8383f931b0cefcc631f070480ef340e1-Paper.pdf.\par \par Gan C, Zhou S, Schwartz J, et al. The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI. arXiv. 2021. doi:arXiv:2103.14025.\par \par Schrimpf M, Grath PMc, DiCarlo JJ. Topographic ANNs Predict the Behavioral Effects of Causal Perturbations in Primate Visual Ventral Stream IT. Champalimaud Research Symposium (CRS21). 2021.\par \par Jia X, Hong H, DiCarlo JJ. Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex. eLife. 2021;10. doi:10.7554/eLife.60830.\par \par Zhuang C, Yan S, Nayebi A, et al. Unsupervised neural network models of the ventral visual stream. Proceedings of the National Academy of Sciences. 2021;118(3):e2014196118. doi:10.1073/pnas.2014196118.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par Schrimpf M, Kubilius J, Lee MJ, Murty NAR, Ajemian R, DiCarlo JJ. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron. 2020. doi:10.1016/j.neuron.2020.07.040.\par \par 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.\par \par 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. Neural Information Processing Systems (NeurIPS; spotlight). 2020. doi:10.1101/2020.06.16.154542.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par Lee MJ, DiCarlo JJ. Comparing novel object learning in humans, models, and monkeys. Journal of Vision. 2019;19(10):114b. doi:10.1167/19.10.114b.\par \par 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.\par \par 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.\par \par Bashivan P, Kar K, DiCarlo JJ. Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.\par \par 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.\par \par 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.\par \par Schrimpf M, Kubilius J, Hong H, et al. Using Brain-Score to Evaluate and Build Neural Networks for Brain-Like Object Recognition. In:  Computational and Systems Neuroscience (COSYNE). Computational and Systems Neuroscience (COSYNE). Denver, CO; 2019.\par \par 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.\par \par 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.\par \par Batista AP, DiCarlo JJ. Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par Bashivan P, Tensen M, DiCarlo JJ. Teacher Guided Architecture Search. arXiv. 2018. doi:https://arxiv.org/abs/1808.01405.\par \par Yamins DLK, DiCarlo JJ. Eight open questions in the computational modeling of higher sensory cortex. Current Opinion in Neurobiology. 2016;37:114 - 120. doi:10.1016/j.conb.2016.02.001.\par \par Hong H, Yamins DLK, Majaj NJ, DiCarlo JJ. Explicit information for category-orthogonal object properties increases along the ventral stream. Nature Neuroscience. 2016;19(4):613 - 622. doi:10.1038/nn.4247.\par \par Aparicio PL, Issa EB, DiCarlo JJ. Neurophysiological Organization of the Middle Face Patch in Macaque Inferior Temporal Cortex. The Journal of Neuroscience. 2016;36(50):12729 - 12745. doi:10.1523/JNEUROSCI.0237-16.2016.\par \par Yamins DLK, DiCarlo JJ. Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience. 2016;19(3):356 - 365. doi:10.1038/nn.4244.\par \par Rajalingham R, Schmidt K, DiCarlo JJ. Comparison of Object Recognition Behavior in Human and Monkey. Journal of Neuroscience. 2015;35(35):12127 - 12136. doi:10.1523/JNEUROSCI.0573-15.2015.\par \par Afraz A, Boyden ES, DiCarlo JJ. Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination. Proceedings of the National Academy of Sciences. 2015:6730 - 6735. doi:10.1073/pnas.1423328112.\par \par Majaj NJ, Hong H, Solomon EA, DiCarlo JJ. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance. Journal of Neuroscience. 2015;35(39):13402 - 13418. doi:10.1523/JNEUROSCI.5181-14.2015.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par DiCarlo \'a0J, Zoccolan D, Rust \'a0C. How Does the Brain Solve Visual Object Recognition?. Neuron. 2012;73(3):415 - 434. doi:10.1016/j.neuron.2012.01.010.\par \par 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.\par \par 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.\par \par 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.\par \par Pinto N, Barhomi Y, Cox DD, DiCarlo JJ. Comparing-State-of-the-Art Visual Features on Invariant Object Recognition Tasks. IEEE Workshop on Applications of Computer Vision (WACV). 2011:463-470. doi:10.1109/WACV.2011.5711540.\par \par Pagan M, Neissi AA, Baldassi C, Zecchina R, DiCarlo JJ, Zoccolan D. From luminance to semantics: how natural objects are represented in monkey inferotemporal cortex. Computational and Systems Neuroscience (COSYNE). 2011. Available at: http://www.cosyne.org/c/index.php?title=Cosyne_11_posters/I-89.\par \par 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.\par \par 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.\par \par Pinto N, DiCarlo JJ, Cox DD. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par Pinto N, Cox DD, DiCarlo JJ. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.\par \par Pinto N, Cox DD, DiCarlo JJ. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.\par \par Pinto N, Cox DD, DiCarlo JJ. The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par 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.\par \par DiCarlo JJ. Making faces in the brain. Nature. 2006;442:644 - 644. doi:10.1038/nature05000.\par \par 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.\par \par 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.\par \par Cox DD, DiCarlo JJ. Is the ?binding problem? a problem in inferiotemporal cortex?. Society for Neuroscience. 2005.\par \par Cox DD, Meier P, Oertelt N, DiCarlo JJ. 'Breaking' position-invariant object recognition. Nature Neuroscience. 2005;8(9):1145 - 1147. doi:10.1038/nn1519.\par \par Hung CP, Kreiman G, Poggio T, DiCarlo JJ. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex. Science. 2005;310:863 - 866. doi:10.1126/science.1117593.\par \par Zoccolan D, Cox DD, DiCarlo JJ. Multiple Object Response Normalization in Monkey Inferotemporal Cortex. Journal of Neuroscience. 2005;25(36):8150 - 8164. doi:10.1523/JNEUROSCI.2058-05.2005.\par \par Zoccolan D, Cox DD, DiCarlo JJ. Multiple object response normalization in monkey inferotemporal cortex. Society for Neuroscience. 2005.\par \par Hung CP, Kreiman G, Poggio T, DiCarlo JJ. Ultra-fast object recognition from few spikes. Cambridge, MA: MIT; 2005:1-31. Available at: https://dspace.mit.edu/handle/1721.1/30556.\par \par Hung CP, Kreiman G, Quiroga RQuian, Kraskov A, Poggio T, DiCarlo JJ. Using ?read-out? of object identity to understand object coding in the macaque anterior inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2005.\par \par Cox DD, DiCarlo JJ. The effect of visual experience on the position tolerance of primate object representations. Society for Neuroscience. 2004.\par \par DiCarlo JJ, Maunsell JHR. Mapping functional neuronal processing chains underlying sensory-motor tasks in the primate. Gordon Conference: Sensory coding and the natural environment. 2004.\par \par Kreiman G, Hung CP, Poggio T, DiCarlo JJ. Object recognition by selective spike and LFP data in macaque inferior temporal cortex. Society for Neuroscience. 2004.\par \par Kreiman G, Hung CP, Poggio T, DiCarlo JJ. Selectivity of local field potentials in macaque inferior temporal cortex. Cambridge, M: MIT; 2004. Available at: https://dspace.mit.edu/handle/1721.1/30417.\par \par DiCarlo JJ, Maunsell JHR. Using Neuronal Latency to Determine Sensory?Motor Processing Pathways in Reaction Time Tasks. Journal of Neurophysiology. 2004;93(5):2974 - 2986. doi:10.1152/jn.00508.2004.\par \par DiCarlo JJ, Maunsell JHR. Anterior Inferotemporal Neurons of Monkeys Engaged in Object Recognition  Can be Highly Sensitive to Object Retinal Position. Journal of Neurophysiology. 2003;89(6):3264 - 3278. doi:10.1152/jn.00358.2002.\par \par DiCarlo JJ, Johnson KO. Receptive field structure in cortical area 3b of the alert monkey. Behavioural Brain Research. 2002;135(1-2):167 - 178. doi:10.1016/S0166-4328(02)00162-6.\par \par DiCarlo JJ, Maunsell JHR. Using reaction time tasks to map sensory-motor chains in the monkey. Society for Neuroscience. 2002.\par \par DiCarlo JJ, Maunsell JHR. Form representation in monkey inferotemporal cortex is virtually unaltered by free viewing. Nature Neuroscience. 2000;3(8):814 - 821. doi:10.1038/77722.\par \par DiCarlo JJ, Maunsell JHR. Inferotemporal representations underlying object recognition in the free viewing monkey. Society for Neuroscience. 2000.\par \par DiCarlo JJ, Johnson KO. Spatial and Temporal Structure of Receptive Fields in Primate Somatosensory Area 3b: Effects of Stimulus Scanning Direction and Orientation. The Journal of Neuroscience. 2000;20(1):495 - 510. doi:10.1523/JNEUROSCI.20-01-00495.2000.\par \par DiCarlo JJ, Johnson KO. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.\par \par 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.\par \par 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.\par \par DiCarlo JJ, Hsiao SS, Johnson KO. Spatial and temporal properties of neural receptive fields in area 3b of the awake monkey. Society for Neuroscience. 1997.\par \par Hsiao SS, Johnson KO, Twombly IA, DiCarlo JJ. Form processing and attention effects in somatosensory cortex. In: Franzen O, Johansson R, Terenius L Somesthesis and the Neurobiology of the Somatosensory Cortex. Somesthesis and the Neurobiology of the Somatosensory Cortex. Switzerland: Birkhauser Basel; 1996.\par \par DiCarlo JJ, Twombly IA, Hsiao SS, Johnson KO. Laminar differences in spatiotemporal receptive field structure of neurons in area 3b of the awake macaque. Society for Neuroscience. 1996.\par \par Twombly IA, DiCarlo JJ, Hsiao SS, Johnson KO. Linear and non-linear processing of tactile spatial form in area 3b of the awake macaque. Society for Neuroscience. 1996.\par \par DiCarlo JJ, Lane JW, Hsiao SS, Johnson KO. Marking microelectrode penetrations with fluorescent dyes. Journal of Neuroscience Methods. 1996;64(1):75 - 81. doi:10.1016/0165-0270(95)00113-1.\par \par Hsiao SS, DiCarlo JJ, Johnson KO. Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences. Biomedical Engineering Society. 1995.\par \par DiCarlo JJ, Hsiao SS, Johnson KO. Transformation of tactile spatial form within a cortical column in area 3b of the macaque. Society for Neuroscience. 1994.\par \par 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.\par \par Schmajuk NA, DiCarlo JJ. A hippocampal theory of schizophrenia. Behavioral and Brain Sciences. 1991;14:47-49. doi:10.1017/S0140525X00065353.\par \par 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.\par \par 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.\par \par Schmajuk NA, DiCarlo JJ. The short-term memory regulation hypothesis of hippocampal function. Midwestern Psychology Association. 1990.\par \par 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.\par \par }
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