Publications
Export 156 results:
The Visual Cortex and GPUs. GPU Computing for Biomedical Research. 2009.
. Using ‘read-out’ of object identity to understand object coding in the macaque anterior inferior temporal cortex. Computation and Systems Neuroscience (COSYNE). 2005.
. Using reaction time tasks to map sensory-motor chains in the monkey. Society for Neuroscience. 2002.
. 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.
. Unlocking Brain-Inspired Computer Vision. GPU@BU. 2009.
. Unlocking Biologically-Inspired Computer Vision: a High-Throughput Approach. NVIDIA GPU Technology Conference. 2009.
. Transformation of tactile spatial form within a cortical column in area 3b of the macaque. Society for Neuroscience. 1994.
. Trade-off between shape selectivity and tolerance to identity-preserving transformations in monkey inferotemporal cortex. Gordon Conference: Sensation and the Natural Environment. 2006.
. 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.
. Topographic ANNs Predict the Behavioral Effects of Causal Perturbations in Primate Visual Ventral Stream IT. Champalimaud Research Symposium (CRS21). 2021. (3.47 MB)
. A systematic exploration of the relationship of fMRI signals and neuronal activity in the primate temporal lobe. Society for Neuroscience. 2009.
. Spatial and temporal properties of neural receptive fields in area 3b of the awake monkey. Society for Neuroscience. 1997.
. 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.
. The short-term memory regulation hypothesis of hippocampal function. Midwestern Psychology Association. 1990.
. 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.
. Object recognition by selective spike and LFP data in macaque inferior temporal cortex. Society for Neuroscience. 2004.
. Neural dynamics of hippocampal modulation of classical conditioning. 12th Symposium on Models of Behavior: Neural Network Models of Conditioning and Action. 1989.
. 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.
. Multiple object response normalization in monkey inferotemporal cortex. Society for Neuroscience. 2005.
. Mapping functional neuronal processing chains underlying sensory-motor tasks in the primate. Gordon Conference: Sensory coding and the natural environment. 2004.
. Linear and non-linear processing of tactile spatial form 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.
. Laminar differences in spatiotemporal receptive field structure of neurons in area 3b of the awake macaque. Society for Neuroscience. 1996.
. Inferotemporal representations underlying object recognition in the free viewing monkey. Society for Neuroscience. 2000.
. 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.
. 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.
. 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.
. A high-throughput screening approach to discovering good forms of visual representation. Computation and Systems Neuroscience (COSYNE). 2008.
. A High-Throughput Screening Approach to Biologically-Inspired Object Recognition. Learning Workshop-Computation and Systems Neuroscience (COSYNE). 2010.
. 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.
. Form processing in area 3b. International Symposium on Brain Mechanisms of Tactile Perception. 1999.
. Flexible and robust object recognition in inferior temporal cortex supported by neurons with limited position and clutter tolerance. Society for Neuroscience. 2006.
. 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.
. 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.
. 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.
. 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.
. 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)
. Is the “binding problem” a problem in inferiotemporal cortex?. Society for Neuroscience. 2005.
. Animals, Brain, Electrophysiology, Fluorescent Dyes, Macaca mulatta, Microelectrodes, Neurosciences. Biomedical Engineering Society. 1995.
. 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)
. 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.
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.
. 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)
. 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)
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)
. 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)
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)
. 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)
. 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.
. 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)
. 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.
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.
. 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)
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.
. 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.
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