Publication

Found 332 results
Author Title Type [ Year(Desc)]
Filters: First Letter Of Last Name is M  [Clear All Filters]
2022
Zheng, J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience 25, 358 - 368 (2022).
Bagus, A. Marliawaty, Marques, T., Sanghavi, S., DiCarlo, J. J. & Schrimpf, M. Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units. NeurIPS (2022). at <https://openreview.net/forum?id=iPF7mhoWkOl>
Montagna, F., Noceti, N., Rosasco, L., Zhang, K. & Locatello, F. Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>
Xiao, Y. et al. Task-specific neural processes underlying conflict resolution during cognitive control. BioRxiv (2022). doi:10.1101/2022.01.16.476535 PDF icon 2022.01.16.476535v1.full_.pdf (22.96 MB)
Sakai, A. et al. Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (2022).PDF icon CBMM-Memo-119.pdf (31.08 MB)
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).PDF icon CBMM-Memo-132.pdf (1.15 MB)
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928
Kamps, F. S., Richardson, H., N. Murty, A. Ratan, Kanwisher, N. & Saxe, R. Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years. Human Brain Mapping (2022). doi:10.1002/hbm.25815
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Madan, S. et al. When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nature Machine Intelligence 4, 146 - 153 (2022).
2023
Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).PDF icon journal.pone_.0268577.pdf (1.99 MB)
Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).PDF icon journal.pone_.0268577.pdf (1.99 MB)
Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).PDF icon journal.pone_.0268577.pdf (1.99 MB)
Consortium, C. et al. An adversarial collaboration to critically evaluate theories of consciousness. bioRxiv (2023). doi:https://doi.org/10.1101/2023.06.23.546249
Consortium, C. et al. An adversarial collaboration to critically evaluate theories of consciousness. bioRxiv (2023). doi:https://doi.org/10.1101/2023.06.23.546249
Consortium, C. et al. An adversarial collaboration to critically evaluate theories of consciousness. bioRxiv (2023). doi:https://doi.org/10.1101/2023.06.23.546249
Consortium, C. et al. An adversarial collaboration to critically evaluate theories of consciousness. bioRxiv (2023). doi:https://doi.org/10.1101/2023.06.23.546249
Dobs, K., Yuan, J., Martinez, J. & Kanwisher, N. Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition. Proceedings of the National Academy of Sciences 120, (2023).
Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (2023).
Poggio, T. & Magrini, M. Cervelli menti algoritmi. 272 (Sperling & Kupfer, 2023). at <https://www.sperling.it/libri/cervelli-menti-algoritmi-marco-magrini>
Xiao, Y. et al. Cross-task specificity and within-task invariance of cognitive control processes. Cell Reports 42, 111919 (2023).PDF icon PIIS2211124722018174.pdf (3.97 MB)

Pages