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In Press
Freiwald, W., Deen, B., Sliwa, J. & Schwiedrzik, C. M. Specialized Networks for Social Cognition in the Primate Brain. (In Press).
2025
Pramod, R. T., Mieczkowski, E., Fang, C. X., Tenenbaum, J. B. & Kanwisher, N. Decoding predicted future states from the brain’s “physics engine”. Science Advances 11, (2025).
Poggio, T. A. On efficiently computable functions, deep networks and sparse compositionality. (2025).PDF icon CBMM-Memo-156.pdf (586.71 KB)PDF icon CBMM-Memo-156-v2.pdf (585.26 KB)
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>
Dubach, R., Abdallah, M. S. & Poggio, T. Multiplicative Regularization Generalizes Better Than Additive Regularization. (2025).PDF icon CBMM Memo 158.pdf (4.8 MB)
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)
Tiwary, K. et al. What if Eye..? Computationally Recreating Vision Evolution. arXiv (2025). at <https://arxiv.org/abs/2501.15001>PDF icon 2501.15001v1.pdf (5.2 MB)
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).
Poggio, T. & Fraser, M. Compositional sparsity of learnable functions. Bulletin of the American Mathematical Society 61, 438-456 (2024).
Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).PDF icon This is an update of the AMS paper (230.72 KB)
Mendoza-Halliday, D., Xu, H., Azevedo, F. A. C. & Desimone, R. Dissociable neuronal substrates of visual feature attention and working memory. Neuron 112, 850 - 863.e6 (2024).
Mahowald, K. et al. Dissociating language and thought in large language models. Trends in Cognitive Sciences 28, 517 - 540 (2024).
Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).PDF icon CBMM-Memo-148.pdf (1.06 MB)
Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).PDF icon CBMM-Memo-150.pdf (4.03 MB)
Pinto, A., Rangamani, A. & Poggio, T. On Generalization Bounds for Neural Networks with Low Rank Layers. (2024).PDF icon CBMM-Memo-151.pdf (697.31 KB)
Peters, B. et al. How does the primate brain combine generative and discriminative computations in vision?. arXiv (2024). at <https://arxiv.org/abs/2401.06005>
Meyers, E. M. NeuroDecodeR: a package for neural decoding in RData_Sheet_1.docx. Frontiers in Neuroinformatics 17, (2024).
Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).PDF icon CBMM-Memo-149.pdf (2.11 MB)
Ceola, F., Rosasco, L., Natale, L. & Ceola, F. RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4. IEEE Robotics and Automation Letters 9, 3045 - 3052 (2024).
Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).PDF icon CBMM-Memo-152.pdf (1.84 MB)
Alfano, P. Didier, Pastore, V. Paolo, Rosasco, L. & Odone, F. Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods. Image and Vision Computing 142, 104894 (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).
Gershman, S. J. What have we learned about artificial intelligence from studying the brain?. Biological Cybernetics 118, 1 - 5 (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).

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