Publications
Export 156 results:
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 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)
. 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)
How Does the Brain Solve Visual Object Recognition?. Neuron. 2012;73(3):415 - 434. doi:10.1016/j.neuron.2012.01.010.
. A hippocampal theory of schizophrenia. Behavioral and Brain Sciences. 1991;14:47-49. doi:10.1017/S0140525X00065353.
. A high-throughput screening approach to discovering good forms of visual representation. Computation and Systems Neuroscience (COSYNE). 2008.
. 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)
. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
. 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)
. 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.
. 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)
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.
. 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.
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)
. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.
. 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.
. Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance. Society for Neuroscience. 2006.
. 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.
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)
. 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)
. Explicit information for category-orthogonal object properties increases along the ventral stream. Nature Neuroscience. 2016;19(4):613 - 622. doi:10.1038/nn.4247.
. 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.
. 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)
. 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.
. 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.
. The effect of visual experience on the position tolerance of primate object representations. Society for Neuroscience. 2004.
. 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.
. 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)
. 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.
. 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)
. 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.
Deep learning reaches the motor system. Nature Methods. 2018;15(10):772 - 773. doi:10.1038/s41592-018-0152-6.
. 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.
. CORnet: Modeling the Neural Mechanisms of Core Object Recognition. bioRxiv. 2018. doi:https://doi.org/10.1101/408385.
. 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.
. 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)
. Comparison of Object Recognition Behavior in Human and Monkey. Journal of Neuroscience. 2015;35(35):12127 - 12136. doi:10.1523/JNEUROSCI.0573-15.2015.
. 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.
. Comparing novel object learning in humans, models, and monkeys. Journal of Vision. 2019;19(10):114b. doi:10.1167/19.10.114b.
. 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)
. Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583. (2.64 MB)
. 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)
. 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.
. Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications. 2023;14(1):1597. doi:10.1038/s41467-023-37180-x. (749.44 KB)
'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)
. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.
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.
Is the “binding problem” a problem in inferiotemporal cortex?. Society for Neuroscience. 2005.
. 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.
. 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.
. 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)
. Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences. Biomedical Engineering Society. 1995.
. 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.
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)