Publication

Found 265 results
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2026
Beneventano, P. et al. Agent Systems for Academic Research Automation. (2026).PDF icon Agent Systems for Academic Research Automation v. 2 (398.05 KB)PDF icon Agent Systems for Academic Research Automation v. 1 (396.53 KB)
Beneventano, P. et al. Agent Systems for Academic Research Automation. (2026).PDF icon Agent Systems for Academic Research Automation v. 2 (398.05 KB)PDF icon Agent Systems for Academic Research Automation v. 1 (396.53 KB)
Abdelmoneum, M., Beneventano, P. & Poggio, T. pAI/MSc: ML Theory Research with Humans on the Loop. (2026).PDF icon CBMM Memo 160.pdf (1.05 MB)
2025
Bono, S. et al. The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function. (2025). at <https://arxiv.org/abs/2401.15856>
Bono, S. et al. The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function. (2025). at <https://arxiv.org/abs/2401.15856>
2024
Mahowald, K. et al. Dissociating language and thought in large language models. Trends in Cognitive Sciences 28, 517 - 540 (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
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
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
O'Connell, T. P. et al. Approaching human 3D shape perception with neurally mappable models. arXiv (2023). at <https://arxiv.org/abs/2308.11300>
Wang, C. et al. BrainBERT: Self-supervised representation learning for Intracranial Electrodes. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=xmcYx_reUn6>PDF icon 985_brainbert_self_supervised_repr.pdf (9.71 MB)
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).
Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (2023).
Bricken, T., Schaeffer, R., Olshausen, B. & Kreiman, G. Emergence of Sparse Representations from Noise. ICML 2023 (2023). at <https://openreview.net/pdf?id=cxYaBAXVKg>
Allen, K. R. et al. Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experienceAbstract. Psychonomic Bulletin & Review (2023). doi:10.3758/s13423-023-02400-4
Tuckute, G., Feather, J., Boebinger, D. & McDermott, J. H. Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions. PLOS Biology 21, e3002366 (2023).
Schiatti, L. et al. Modeling Visual Impairments with Artificial Neural Networks: a Review. International Conference on Computer Vision 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023W/ACVR/html/Schiatti_Modeling_Visual_Impairments_with_Artificial_Neural_Networks_a_Review_ICCVW_2023_paper.html>

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