Publication

Found 906 results
[ Author(Desc)] Title Type Year
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C
Conwell, C. et al. Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex. Cosyne (2021).
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. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).
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>
Dasgupta, I. & Gershman, S. J. Memory as a Computational Resource. Trends in Cognitive Sciences 25, 240 - 251 (2021).
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. & Gershman, S. J. Where do hypotheses come from?. (2016).PDF icon CBMM-Memo-056-v2.pdf (733.35 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).
de la Rosa, S. et al. Visual categorization of social interactions. Visual Cognition 22, (2015).
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., Kanwisher, N. & Saxe, R. Exploring the functional organization of the superior temporal sulcus with a broad set of naturalistic stimuli. (2014).
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).
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>
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. 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)
Dehghani, N. Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective. (2017). doi:10.1101/097618PDF icon StimComplexity.pdf (218.1 KB)
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)

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