Publication

Found 906 results
[ Author(Desc)] Title Type Year
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C
Conwell, C. et al. Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex. (2021).PDF icon CBMM-Memo-131.pdf (9.37 MB)
Cormiea, S., Vaziri-Pashkam, M. & Nakayama, K. Unconscious perception of an opponent's goal. Vision Sciences Society Annual Meeting (VSS 2015) (2015). doi:10.1167/15.12.43
Coronel, S. Otero, Phillips-Jones, T., Sani, I. & Freiwald, W. A. Pupillary responses track changes in arousal and attention while exploring a virtual reality environment. The Rockefeller University 2019 Summer Undergraduate Research Fellowship (SURF) Program (2019).
Cusimano, M., Traer, J. & McDermott, J. H. Scrape, rub, and roll: causal inference in the perception of sustained contact sounds . Cognitive Science (2019).
D
Danhofer, D. A., D’Ascenzo, D., Dubach, R. & Poggio, T. Position: A Theory of Deep Learning Must Include Compositional Sparsity. (2025).PDF icon CBMM Memo 159.pdf (676.35 KB)
Dapello, J. et al. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/98b17f068d5d9b7668e19fb8ae470841-Abstract.html>
Dapello, J. et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).
Dasgupta, I., Schulz, E. & Gershman, S. J. Where do hypotheses come from?. (2016).PDF icon CBMM-Memo-056-v2.pdf (733.35 KB)
Dasgupta, I., Bernstein, J., Rolnick, D. & Sompolinsky, H. Markov transitions between attractor states in a recurrent neural network. AAAI (2017).PDF icon aaai-abstract (1).pdf (357.72 KB)
Dasgupta, I., Schulz, E., Tenenbaum, J. B. & Gershman, S. J. A theory of learning to infer. Psychological Review 127, 412 - 441 (2020).
Dasgupta, I., Guo, D., Gershman, S. J. & Goodman, N. D. Analyzing Machine‐Learned Representations: A Natural Language Case Study. Cognitive Science 44, (2020).
Dasgupta, I. & Gershman, S. J. Memory as a Computational Resource. Trends in Cognitive Sciences 25, 240 - 251 (2021).
de la Rosa, S. et al. Visual categorization of social interactions. Visual Cognition 22, (2015).
Deen, B., Kanwisher, N. & Saxe, R. Exploring the functional organization of the superior temporal sulcus with a broad set of naturalistic stimuli. (2014).
Deen, B. & Saxe, R. Parts‐based representations of perceived face movements in the superior temporal sulcus. Human Brain Mapping 40, 2499 - 2510 (2019).
Deen, B. & Saxe, R. Parts-based representations of perceived face movements in the superior temporal sulcus. Society for Neuroscience Annual Meeting (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Daily%20Books%202015/AM15Monday.ashx>
Deen, B., Kanwisher, N. & Saxe, R. Functional organization of the human superior temporal sulcus. Organization for Human Brain Mapping (OHBM 2015) (2015). at <https://ww4.aievolution.com/hbm1501/index.cfm?do=abs.viewAbs&abs=3635>
Deen, B. et al. Organization of high-level visual cortex in human infants. Nature Communications (2017). doi:10.1038/ncomms13995
Deen, B., Koldewyn, K., Kanwisher, N. & Saxe, R. Functional organization of social perception and cognition in the superior temporal sulcus. Cerebral Cortex 25, 4596-4609 (2015).
Dehaene-Lambertz, G. & Spelke, E. S. The Infancy of the Human Brain. Neuron 88, 93 - 109 (2015).
Dehaene-Lambertz, G. & Spelke, E. S. The infancy of the human brain. (2016). doi:http://dx.doi.org/10.1016/j.neuron.2015.09.026PDF icon CBMM-Memo-053.pdf (1.51 MB)
Dehghani, N. Dynamic balance of excitation and inhibition in human and monkey neocortex. Nature Scientific Reports (2016). doi:10.1038/srep23176PDF icon BalanceExcitationInhibition.pdf (2.1 MB)
Dehghani, N. & Wimmer, R. A computational perspective of the role of Thalamus in cognition. arxiv (2018). at <https://arxiv.org/abs/1803.00997>PDF icon ThalamusComputationArxiv.pdf (5.12 MB)
Dehghani, N. et al. Avalanche analysis from multielectrode ensemble recordings in cat, monkey, and human cerebral cortex during wakefulness and sleep. Frontiers in Physiology (2012). doi:10.3389/fphys.2012.00302PDF icon AvalancheDynamics.pdf (2.45 MB)
Dehghani, N. Design of the Artificial: lessons from the biological roots of general intelligence. (2017). at <https://arxiv.org/pdf/1703.02245>PDF icon DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)

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