Publications
Export 155 results:
Neural Mechanisms Underlying Visual Object Recognition. Cold Spring Harbor Symposia on Quantitative Biology. 2014;79:99 - 107. doi:10.1101/sqb.2014.79.024729.
. 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.
. 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.
. 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.
. 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)
. 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)
. 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.
. Teacher Guided Architecture Search. arXiv. 2018. doi:https://arxiv.org/abs/1808.01405.
. Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.
. Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.
. 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.
The Neural Representation Benchmark and its Evaluation on Brain and Machine. arXiv. 2013. doi:https://arxiv.org/abs/1301.3530.
. 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)
. Is the “binding problem” a problem in inferiotemporal cortex?. Society for Neuroscience. 2005.
. 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)
. The effect of visual experience on the position tolerance of primate object representations. Society for Neuroscience. 2004.
. 'Breaking' position-invariant object recognition. Nature Neuroscience. 2005;8(9):1145 - 1147. doi:10.1038/nn1519. (175.59 KB) (49.96 KB) (87.63 KB)
. A high-throughput screening approach to discovering good forms of visual representation. Computation and Systems Neuroscience (COSYNE). 2008.
. 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.
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)
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)
. 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)
. A large-scale shape map in monkey inferior temporal cortex. Society for Neuroscience. 2006.
. 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)
. 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.
Marking microelectrode penetrations with fluorescent dyes. Journal of Neuroscience Methods. 1996;64(1):75 - 81. doi:10.1016/0165-0270(95)00113-1. (8.62 MB)
. 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. (949.25 KB) (2.3 MB)
. 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)
. 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. (692.91 KB)
. Untangling invariant object recognition. Trends in Cognitive Sciences. 2007;11(8):333 - 341. doi:10.1016/j.tics.2007.06.010. (1.48 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)
Mapping functional neuronal processing chains underlying sensory-motor tasks in the primate. Gordon Conference: Sensory coding and the natural environment. 2004.
. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.
. Using reaction time tasks to map sensory-motor chains in the monkey. Society for Neuroscience. 2002.
. Laminar differences in spatiotemporal receptive field structure of neurons in area 3b of the awake macaque. Society for Neuroscience. 1996.
. 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)
. How Does the Brain Solve Visual Object Recognition?. Neuron. 2012;73(3):415 - 434. doi:10.1016/j.neuron.2012.01.010.
. Transformation of tactile spatial form within a cortical column in area 3b of the macaque. Society for Neuroscience. 1994.
. 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. (382.63 KB)
. Inferotemporal representations underlying object recognition in the free viewing monkey. Society for Neuroscience. 2000.
. .
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.
. 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. (424.39 KB)
. Spatial and temporal properties of neural receptive fields in area 3b of the awake monkey. Society for Neuroscience. 1997.
. Form representation in monkey inferotemporal cortex is virtually unaltered by free viewing. Nature Neuroscience. 2000;3(8):814 - 821. doi:10.1038/77722. (225.75 KB)
. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv. 2020. Available at: https://arxiv.org/abs/2007.04954. (7.06 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)
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)
. 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)
. 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)
Explicit information for category-orthogonal object properties increases along the ventral stream. Nature Neuroscience. 2016;19(4):613 - 622. doi:10.1038/nn.4247.
. Form processing and attention effects in somatosensory cortex. In: Somesthesis and the Neurobiology of the Somatosensory Cortex. Somesthesis and the Neurobiology of the Somatosensory Cortex. Switzerland: Birkhauser Basel; 1996.
. Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences. Biomedical Engineering Society. 1995.
. Ultra-fast object recognition from few spikes. Cambridge, MA: MIT; 2005:1-31. Available at: https://dspace.mit.edu/handle/1721.1/30556.
. Fast Readout of Object Identity from Macaque Inferior Temporal Cortex. Science. 2005;310:863 - 866. doi:10.1126/science.1117593. (209.48 KB) (1.26 MB)
. Using ‘read-out’ of object identity to understand object coding in the macaque anterior inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2005.
. 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.
. 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.
. 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.
. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals. eLife. 2018;7. doi:10.7554/eLife.42870.
. 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 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.
. 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.
. 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)
. 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.
. 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)
. 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.
. 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.
. 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.
. 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)
. 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)
. Selectivity of local field potentials in macaque inferior temporal cortex. Cambridge, M: MIT; 2004. Available at: https://dspace.mit.edu/handle/1721.1/30417.
. Object recognition by selective spike and LFP data in macaque inferior temporal cortex. Society for Neuroscience. 2004.
. 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.
CORnet: Modeling the Neural Mechanisms of Core Object Recognition. bioRxiv. 2018. doi:https://doi.org/10.1101/408385.
. 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)
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)
. 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.
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)
. Comparing novel object learning in humans, models, and monkeys. Journal of Vision. 2019;19(10):114b. doi:10.1167/19.10.114b.
. 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.
. 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.
. 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.
. 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.
. 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.
. Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex. Science. 2008;321:1502 - 1507. doi:10.1126/science.1160028.
. 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.
. Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance. Society for Neuroscience. 2006.
. 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)
. 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.
. 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.
. 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.
. 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.
. 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)
. 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)
. 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.
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)
. Task-Driven Convolutional Recurrent Models of the Visual System. arXiv. 2018. doi:https://arxiv.org/abs/1807.00053.
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.
.