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Found 910 results
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Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022).PDF icon dellaferrera22a.pdf (909.91 KB)
Meanti*, G. et al. Estimating Koopman operators with sketching to provably learn large scale dynamical systems. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/f3d1e34a15c0af0954ae36a7f811c754-Paper-Conference.pdf>
Becker, L. A. et al. Eszopiclone and Zolpidem Produce Opposite Effects on Hippocampal Ripple DensityDataSheet1.docx. Frontiers in Pharmacology 12, (2022).
Gant, J., Banburski, A., Deza, A. & Poggio, T. Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>
Stemmann, H. & Freiwald, W. A. Evidence for an attentional priority map in inferotemporal cortex. Proceedings of the National Academy of Sciences 116, 23797 - 23805 (2019).
Kar, K., Kubilius, J., Schmidt, K., Issa, E. B. & DiCarlo, J. J. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019). doi:10.1038/s41593-019-0392-5PDF icon Author's last draft (1.74 MB)
Kar, K. & DiCarlo, J. J. Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . COSYNE (2020).
Kar, K. & DiCarlo, J. J. Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . Society for Neuroscience (2019).
Zaslavsky, N., Garvin, K., Kemp, C., Tishby, N. & Regier, T. The evolution of color naming reflects pressure for efficiency: Evidence from the recent pastAbstract. Journal of Language Evolution (2022). doi:10.1093/jole/lzac001
Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x
Ponce, C. R. et al. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell 177, 1009 (2019).PDF icon Author's last draft (20.26 MB)
Shalev-Shwartz, S. & Shashua, A. An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020).PDF icon CBMM-Memo-106.pdf (431.13 KB)
Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347 , 543-548 (2015).
Yildirim, I., Kulkarni, T., Freiwald, W. A. & Tenenbaum, J. B. Explaining Monkey Face Patch System as Efficient Analysis-by-Synthesis. (2014).PDF icon yildirimetal_cosyne15.pdf (313.57 KB)
Poggio, T. & Liao, Q. Explicit regularization and implicit bias in deep network classifiers trained with the square loss. arXiv (2020). at <https://arxiv.org/abs/2101.00072>
Deen, B., Kanwisher, N. & Saxe, R. Exploring the functional organization of the superior temporal sulcus with a broad set of naturalistic stimuli. (2014).
Peterson, M. F. et al. Eye movements and retinotopic tuning in developmental prosopagnosia. Journal of Vision 19, 7 (2019).
Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science 73, (2017).
Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science (2017).PDF icon eye_tracking_causality.pdf (8.04 MB)

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