Publications
Export 156 results:
. 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.
. 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 systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.
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)
(7.22 MB). Why is real-world object recognition hard?: Establishing honest benchmarks and baselines for object recognition. Computation and Systems Neuroscience (COSYNE). 2008.
. The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.
. 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). 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.
. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.
. 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). 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). Why is Real-World Visual Object Recognition Hard?. . PLoS Computational Biology. 2008;4:e27. doi:10.1371/journal.pcbi.0040027. (1.93 MB)
(1.93 MB). 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.
. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.
. 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.
. 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.
. 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.
. 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). 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.
. Chronically implantable LED arrays for behavioral optogenetics in primates. bioRxiv. 2020. doi:10.1101/2020.09.10.291583. (2.64 MB)
(2.64 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.
. 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.
. 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.
. 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.
. 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.
. 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.
. 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.
. 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 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 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.
. A hippocampal theory of schizophrenia. Behavioral and Brain Sciences. 1991;14:47-49. doi:10.1017/S0140525X00065353.
. Neural dynamics of hippocampal modulation of classical conditioning. 12th Symposium on Models of Behavior: Neural Network Models of Conditioning and Action. 1989.
. 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). The short-term memory regulation hypothesis of hippocampal function. Midwestern Psychology Association. 1990.
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv. 2018. doi:https://doi.org/10.1101/407007.
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.
. Topographic ANNs Predict the Behavioral Effects of Causal Perturbations in Primate Visual Ventral Stream IT. Champalimaud Research Symposium (CRS21). 2021. (3.47 MB)
(3.47 MB). 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) 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)
(1.06 MB) An Open Resource for Non-human Primate Optogenetics. Neuron. 2020. doi:10.1016/j.neuron.2020.09.027.
. Linear and non-linear processing of tactile spatial form in area 3b of the awake macaque. Society for Neuroscience. 1996.
. 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)
(2.57 MB). 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.
. Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience. 2016;19(3):356 - 365. doi:10.1038/nn.4244.
. 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.
. 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.
Catalyzing next-generation Artificial Intelligence through NeuroAI. Nature Communications. 2023;14(1):1597. doi:10.1038/s41467-023-37180-x. (749.44 KB)
(749.44 KB) 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) Unsupervised Neural Network Models of the Ventral Visual Stream. bioRxiv. 2020. doi:10.1101/2020.06.16.155556. (2.7 MB)
(2.7 MB). Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex. Gordon Conference: Sensation and the Natural Environment. 2006.
. 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)
(730.6 KB). Multiple object response normalization in monkey inferotemporal cortex. Society for Neuroscience. 2005.
. 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. (758.94 KB)
(758.94 KB). 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). 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.