Publications
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. 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.
. 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.
. Neural dynamics of hippocampal modulation of classical conditioning. In: Neural Network Models of Conditioning and Action. Neural Network Models of Conditioning and Action. Hillsdale, NJ: Lawrence Erlbaum Association; 1991.
. 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)
(1.74 MB). 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.
. 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)
(375.73 KB). 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.
. Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences. Biomedical Engineering Society. 1995.
. Is the “binding problem” a problem in inferiotemporal cortex?. Society for Neuroscience. 2005.
. 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.
. 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.
. 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 effect of visual experience on the position tolerance of primate object representations. Society for Neuroscience. 2004.
. Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance. Society for Neuroscience. 2006.
. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.
. 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.
. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
. A high-throughput screening approach to discovering good forms of visual representation. Computation and Systems Neuroscience (COSYNE). 2008.
. 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.
. 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.
. Inferotemporal representations underlying object recognition in the free viewing monkey. Society for Neuroscience. 2000.
. Laminar differences in spatiotemporal receptive field structure of neurons in area 3b of the awake macaque. Society for Neuroscience. 1996.
. A large-scale shape map in monkey inferior temporal cortex. Society for Neuroscience. 2006.
. Linear and non-linear processing of tactile spatial form in area 3b of the awake macaque. Society for Neuroscience. 1996.
. Mapping functional neuronal processing chains underlying sensory-motor tasks in the primate. Gordon Conference: Sensory coding and the natural environment. 2004.
. Multiple object response normalization in monkey inferotemporal cortex. Society for Neuroscience. 2005.
. 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.
. Neural dynamics of hippocampal modulation of classical conditioning. 12th Symposium on Models of Behavior: Neural Network Models of Conditioning and Action. 1989.
. Object recognition by selective spike and LFP data in macaque inferior temporal cortex. Society for Neuroscience. 2004.
. 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.
. The short-term memory regulation hypothesis of hippocampal function. Midwestern Psychology Association. 1990.
. 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.
. Spatial and temporal properties of neural receptive fields in area 3b of the awake monkey. Society for Neuroscience. 1997.
. A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.
. 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.
. Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex. Gordon Conference: Sensation and the Natural Environment. 2006.
. Transformation of tactile spatial form within a cortical column in area 3b of the macaque. Society for Neuroscience. 1994.
. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.
. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.
. 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.
. Using reaction time tasks to map sensory-motor chains in the monkey. Society for Neuroscience. 2002.
. Using ‘read-out’ of object identity to understand object coding in the macaque anterior inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2005.
. The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.
. 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.
. Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition. 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.
. 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)
(424.39 KB) Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.
. 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)
(6.95 MB). Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583. (2.64 MB)
(2.64 MB). 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)
(6.47 MB). 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.
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.
. 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.
. 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.
. 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.
. 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)
(209.48 KB) (1.26 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)
(3.92 MB). 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)
(225.75 KB). 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)
(1.25 MB). 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)
(538.96 KB) (141.46 KB). A hippocampal theory of schizophrenia. Behavioral and Brain Sciences. 1991;14:47-49. doi:10.1017/S0140525X00065353.
. 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)
(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)
(11.69 MB). 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)
(1.04 MB). 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.
. 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.
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)
(2.56 MB). 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)
(8.62 MB). 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.
. Multiple Object Response Normalization in Monkey Inferotemporal Cortex. Journal of Neuroscience. 2005;25(36):8150 - 8164. doi:10.1523/JNEUROSCI.2058-05.2005. (643.95 KB)
(643.95 KB). Neural Mechanisms Underlying Visual Object Recognition. Cold Spring Harbor Symposia on Quantitative Biology. 2014;79:99 - 107. doi:10.1101/sqb.2014.79.024729.
. A neural network approach to hippocampal function in classical conditioning. Behavioral Neuroscience. 1991;105(1):82 - 110. doi:10.1037/0735-7044.105.1.82. (3.46 MB)
(3.46 MB). Neural population control via deep image synthesis. Science. 2019;364(6439):eaav9436. doi:10.1126/science.aav9436.
. 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.
. 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)
(778.45 KB) An Open Resource for Non-human Primate Optogenetics. Neuron. 2020. doi:10.1016/j.neuron.2020.09.027.
. 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.
. 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.
. 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.
. 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.
. 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)
(692.91 KB). 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)
(1.58 MB). Stimulus configuration, classical conditioning, and hippocampal function. Psychological Review. 1992;99(2):268 - 305. doi:10.1037/0033-295X.99.2.268. (4.3 MB)
(4.3 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)
(1.33 MB) 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.
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv. 2020. Available at: https://arxiv.org/abs/2007.04954. (7.06 MB)
(7.06 MB). Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex. Science. 2008;321:1502 - 1507. doi:10.1126/science.1160028.
. 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.
Unsupervised Neural Network Models of the Ventral Visual Stream. bioRxiv. 2020. doi:10.1101/2020.06.16.155556. (2.7 MB)
(2.7 MB) 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)
(2.71 MB). Untangling invariant object recognition. Trends in Cognitive Sciences. 2007;11(8):333 - 341. doi:10.1016/j.tics.2007.06.010. (1.48 MB)
(1.48 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)
(949.25 KB) (2.3 MB). 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)
(847.96 KB). 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)
(773.41 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.