Publications
Export 160 results:
How does the primate brain combine generative and discriminative computations in vision?. 2024. doi: https://doi.org/10.48550/arXiv.2401.06005. (7.22 MB)
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. (2.57 MB)
. The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates. Annual Review of Vision Science. 2024. doi:10.1146/annurev-vision-112823-030616. (3.2 MB)
. Software and Methods for Controlling Neural Responses in Deep Brain Regions. 2024. Available at: https://patents.google.com/patent/US20240082534A1/en. (3.6 MB)
. A unifying framework for functional organization in early and higher ventral visual cortex. Neuron. 2024. doi:10.1016/j.neuron.2024.04.018.
. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications. 2023;14(1):1597. doi:10.1038/s41467-023-37180-x. (749.44 KB)
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.
. 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.
How well do rudimentary plasticity rules predict adult visual object learning?. . PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713. (11.69 MB)
. How well do rudimentary plasticity rules predict adult visual object learning?. . PLOS Computational Biology. 2023;19(12):e1011713. doi:10.1371/journal.pcbi.1011713. (11.69 MB)
. 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. (2.56 MB)
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. (2.75 MB)
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. (5.76 MB)
. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv. 2023. doi: https://doi.org/10.48550/arXiv.2308.06887 Focus to learn more. (3.53 MB)
. 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. (3.26 MB)
. A Unifying Principle for the Functional Organization of Visual Cortex. bioRxiv. 2023. doi: https://doi.org/10.1101/2023.05.18.541361. (6.57 MB)
. 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. (1.99 MB)
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.
Implementing Divisive Normalization in CNNs Improves Robustness to Common Image Corruptions. In: SVRHM 2022 Workshop@ NeurIPS. SVRHM 2022 Workshop@ NeurIPS. New Orleans, Louisiana; 2022. Available at: https://openreview.net/pdf?id=KAAbo44qhJV. (1.91 MB)
Measuring the Alignment of ANNs and Primate V1 on Luminance and Contrast Response Characteristics. In: SVRHM 2022 Workshop@ NeurIPS. SVRHM 2022 Workshop@ NeurIPS. New Orleans, Louisiana; 2022. Available at: https://openreview.net/pdf?id=XTPfeOoZD8. (1.17 MB)
. 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. (3.86 MB)
. 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. (1.45 MB)
. 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.
. Chronically implantable LED arrays for behavioral optogenetics in primates. Nature Methods. 2021;18(9):1112 - 1116. doi:10.1038/s41592-021-01238-9. (6.95 MB)
. 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. (1000.31 KB)
. 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. (6.47 MB)
. 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. (3.92 MB)
. 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. (3.12 MB)
. 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. (4.04 MB)
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI. arXiv. 2021. doi:arXiv:2103.14025. (6.79 MB)
Topographic ANNs Predict the Behavioral Effects of Causal Perturbations in Primate Visual Ventral Stream IT. Champalimaud Research Symposium (CRS21). 2021. (3.47 MB)
. Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex. eLife. 2021;10. doi:10.7554/eLife.60830.
. 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. (2.71 MB)
Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583. (2.64 MB)
. 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.
. 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.
. 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.
. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron. 2020. doi:10.1016/j.neuron.2020.07.040. (1.04 MB)
. An Open Resource for Non-human Primate Optogenetics. Neuron. 2020. doi:10.1016/j.neuron.2020.09.027.
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. (2.48 MB)
. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv. 2020. Available at: https://arxiv.org/abs/2007.04954. (7.06 MB)
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.
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.
. Unsupervised Neural Network Models of the Ventral Visual Stream. bioRxiv. 2020. doi:10.1101/2020.06.16.155556. (2.7 MB)
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.
Comparing novel object learning in humans, models, and monkeys. Journal of Vision. 2019;19(10):114b. doi:10.1167/19.10.114b.
. 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. (4.86 MB)
. Funcitional Properties of Circuits, Cellular Populations, and Areas. In: The Neocortex.Vol 27. The Neocortex. Cambridge, MA: The MIT Press; 2019:223-265. doi:10.7551/mitpress/12593.001.0001. (1.06 MB)
Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.
. 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.
. 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.
. 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.
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.
CORnet: Modeling the Neural Mechanisms of Core Object Recognition. bioRxiv. 2018. doi:https://doi.org/10.1101/408385.
. Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.
. 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.
. 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.
. 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.
. 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.
. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. eLife. 2018;7. doi:10.7554/eLife.42870.
. 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.
. Task-Driven Convolutional Recurrent Models of the Visual System. arXiv. 2018. doi:https://arxiv.org/abs/1807.00053.
Teacher Guided Architecture Search. arXiv. 2018. doi:https://arxiv.org/abs/1808.01405.
. 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.
. Explicit information for category-orthogonal object properties increases along the ventral stream. Nature Neuroscience. 2016;19(4):613 - 622. doi:10.1038/nn.4247.
. 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.
. Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience. 2016;19(3):356 - 365. doi:10.1038/nn.4244.
. Comparison of Object Recognition Behavior in Human and Monkey. Journal of Neuroscience. 2015;35(35):12127 - 12136. doi:10.1523/JNEUROSCI.0573-15.2015.
. 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.
. 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.
. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition. . PLoS Computational Biology. 2014;10(12):e1003963. doi:10.1371/journal.pcbi.1003963.
Neural Mechanisms Underlying Visual Object Recognition. Cold Spring Harbor Symposia on Quantitative Biology. 2014;79:99 - 107. doi:10.1101/sqb.2014.79.024729.
. 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.
. 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.
. 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.
. The Neural Representation Benchmark and its Evaluation on Brain and Machine. arXiv. 2013. doi:https://arxiv.org/abs/1301.3530.
. 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.
. 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.
. How Does the Brain Solve Visual Object Recognition?. Neuron. 2012;73(3):415 - 434. doi:10.1016/j.neuron.2012.01.010.
. 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.
. 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.
. 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.
. 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.
. 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.
. Do we have a strategy for understanding how the visual system accomplishes object recognition?. In: Object Categorization: Computer and Human Vision Perspectives. Object Categorization: Computer and Human Vision Perspectives. New York, NY, USA: Cambridge University Press; 2010.
. 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.
. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
. 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.
. 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.
. 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.
. 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.
. 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.
. 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.
. A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation. . PLoS Computational Biology. 2009;5(11):e1000579. doi:10.1371/journal.pcbi.1000579. (538.96 KB) (141.46 KB)
. 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)
. 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)
. 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.
. A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.
. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.
. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.
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