Publication

Found 230 results
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2024
Montagna, F. et al. Assumption violations in causal discovery and the robustness of score matching. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2024). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/93ed74938a54a73b5e4c52bbaf42ca8e-Paper-Conference.pdf>
Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).
Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).PDF icon CBMM-Memo-152.pdf (1.84 MB)
Mendoza-Halliday, D. et al. A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).
Mendoza-Halliday, D. et al. A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).
2023
Xiang, Y., Landy, J., Cushman, F. A., Vélez, N. & Gershman, S. J. Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks. Cognition 241, 105609 (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
Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (2023).
Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (2023).
Zhang, Y. et al. Decoding of human identity by computer vision and neuronal visionAbstract. Scientific Reports 13, (2023).
Zhang, Y. et al. Decoding of human identity by computer vision and neuronal vision. Scientific Reports 13, (2023).PDF icon s41598-022-26946-w.pdf (1.88 MB)
Xu, M., Rangamani, A., Liao, Q., Galanti, T. & Poggio, T. Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds. Research (2023). doi:10.34133/research.0024PDF icon research.0024.pdf (4.05 MB)
Azami, H. et al. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer’s Disease. Journal of Alzheimer's Disease 91, 1557 - 1572 (2023).
Lee, M. J. & DiCarlo, J. J. An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models. bioRxiv (2023). at <https://www.biorxiv.org/content/10.1101/2022.12.31.522402v1>
Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T. Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).PDF icon Feature_Learning_memo.pdf (2.16 MB)
Singh, P. et al. Learning to Learn: How to Continuously Teach Humans and Machines . International Conference on Computer Vision (ICCV), 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023/html/Singh_Learning_to_Learn_How_to_Continuously_Teach_Humans_and_Machines_ICCV_2023_paper.html>
Singh, P. et al. Learning to Learn: How to Continuously Teach Humans and Machines . International Conference on Computer Vision (ICCV), 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023/html/Singh_Learning_to_Learn_How_to_Continuously_Teach_Humans_and_Machines_ICCV_2023_paper.html>
Feather, J., Leclerc, G., Mądry, A. & McDermott, J. H. Model metamers reveal divergent invariances between biological and artificial neural networks. Nature Neuroscience (2023). doi:10.1038/s41593-023-01442-0
Xiao, W., Sharma, S., Kreiman, G. & Livingstone, M. S. Out of sight, out of mind: Responses in primate ventral visual cortex track individual fixations during natural vision. bioRxiv (2023). doi:10.1101/2023.02.08.527666
Lagomarsino-Oneto, D. et al. Physics informed machine learning for wind speed prediction. Energy 268, 126628 (2023).

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