All Publications

2023

T. Bricken, Davies, X., Singh, D., Krotov, D., and Kreiman, G., “Sparse distributed memory is a continual learner”, International Conference on Learning Representations. Kigali, Rwanda, Africa, 2023.PDF icon 6086_sparse_distributed_memory_is_a.pdf (13.3 MB)
CBMM Funded
H. L. Kosakowski, Norman‐Haignere, S., Mynick, A., Takahashi, A., Saxe, R., and Kanwisher, N., “Preliminary evidence for selective cortical responses to music in one‐month‐old infants”, Developmental Science, 2023.PDF icon Developmental Science - 2023 - Kosakowski - Preliminary evidence for selective cortical responses to music in one‐month‐old.pdf (2.6 MB)
CBMM Funded
M. Xu, Rangamani, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse, and Generalization Bounds”, Research, vol. 6, 2023.PDF icon research.0024.pdf (5.19 MB)
CBMM Funded
CBMM Memo No.
141
A. Rangamani, Lindegaard, M., Galanti, T., and Poggio, T., “Feature learning in deep classifiers through Intermediate Neural Collapse”. 2023.PDF icon Feature_Learning_memo.pdf (2.16 MB)
CBMM Funded
L. Melloni, Mudrik, L., Pitts, M., Bendtz, K., Ferrante, O., Gorska, U., Hirschhorn, R., Khalaf, A., Kozma, C., Lepauvre, A., Liu, L., Mazumder, D., Richter, D., Zhou, H., Blumenfeld, H., Boly, M., Chalmers, D. J., Devore, S., Fallon, F., de Lange, F. P., Jensen, O., Kreiman, G., Luo, H., Panagiotaropoulos, T. I., Dehaene, S., Koch, C., and Tononi, G., “An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory”, PLOS ONE, vol. 18, no. 2, p. e0268577, 2023.PDF icon journal.pone_.0268577.pdf (1.99 MB)
CBMM Related
R. Francesco Srinivasan, Mignacco, F., Sorbaro, M., Refinetti, M., Kreiman, G., and Dellaferrera, G., “Forward learning with top-down feedback: empirical and analytical characterization”, arXiv, 2023.
CBMM Funded
C. Wang, Subramaniam, V., Yaari, A. Uri, Kreiman, G., Katz, B., Cases, I., and Barbu, A., “BrainBERT: Self-supervised representation learning for Intracranial Electrodes”, International Conference on Learning Representations. Kigali, Rwanda, Africa, 2023.PDF icon 985_brainbert_self_supervised_repr.pdf (9.71 MB)
CBMM Funded
A. Rangamani, Rosasco, L., and Poggio, T., “For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability”, Analysis and Applications, vol. 21, no. 01, pp. 193 - 215, 2023.
CBMM Funded
Y. Xiao, Chou, C. - C., Cosgrove, G. Rees, Crone, N. E., Stone, S., Madsen, J. R., Reucroft, I., Shih, Y. - C., Weisholtz, D., Yu, H. - Y., Anderson, W. S., and Kreiman, G., “Cross-task specificity and within-task invariance of cognitive control processes”, Cell Reports, vol. 42, no. 1, p. 111919, 2023.PDF icon PIIS2211124722018174.pdf (3.97 MB)
CBMM Funded
M. Xu, Rangamani, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds”, Research, 2023.PDF icon research.0024.pdf (4.05 MB)
CBMM Funded
Y. Zhang, Aghajan, Z. M., Ison, M., Lu, Q., Tang, H., Kalender, G., Monsoor, T., Zheng, J., Kreiman, G., Roychowdhury, V., and Fried, I., “Decoding of human identity by computer vision and neuronal vision”, Scientific Reports, vol. 13, no. 1, 2023.PDF icon s41598-022-26946-w.pdf (1.88 MB)
CBMM Funded
CBMM Memo No.
140
T. Galanti, Siegel, Z., Gupte, A., and Poggio, T., “SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks”. 2023.PDF icon Low-rank bias.pdf (2.38 MB)
CBMM Funded
CBMM Memo No.
139
T. Galanti, Xu, M., Galanti, L., and Poggio, T., “Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks”. 2023.PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
CBMM Funded

2022

M. Zhang, Armendariz, M., Xiao, W., Rose, O., Bendtz, K., Livingstone, M., Ponce, C., and Kreiman, G., “Look twice: A generalist computational model predicts return fixations across tasks and species”, PLOS Computational Biology, vol. 18, no. 11, p. e1010654, 2022.PDF icon journal.pcbi_.1010654.pdf (4.51 MB)
CBMM Funded
CBMM Memo No.
138
T. A. Poggio, “How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse”. 2022.PDF icon v1.0 (984.15 KB)PDF icon v5.2 more fine tuning (1.14 MB)
CBMM Funded
S. Casper, Nadeau, M., Hadfield-Menell, D., and Kreiman, G., “Robust Feature-Level Adversaries are Interpretability Tools”, NeurIPS. New Orleans, Louisiana, 2022.PDF icon 8789_robust_feature_level_adversari.pdf (3.79 MB)
CBMM Funded
F. Anselmi and Poggio, T., “Representation Learning in Sensory Cortex: a theory”, IEEE Access, pp. 1 - 1, 2022.PDF icon Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)
CBMM Funded
K. Ellis, Albright, A., Solar-Lezama, A., Tenenbaum, J. B., and O’Donnell, T. J., “Synthesizing theories of human language with Bayesian program inductionAbstract”, Nature Communications, vol. 13, no. 1, 2022.PDF icon s41467-022-32012-w.pdf (2.19 MB)
CBMM Funded
CBMM Memo No.
137
A. Rangamani and Xie, Y., “Understanding the Role of Recurrent Connections in Assembly Calculus”. 2022.PDF icon CBMM-Memo-137.pdf (1.49 MB)
CBMM Funded
CBMM Memo No.
136
Y. Han, Poggio, T., and Cheung, B., “System identification of neural systems: If we got it right, would we know?”. 2022.PDF icon CBMM-Memo-136.pdf (1.75 MB)
CBMM Funded
G. Dellaferrera and 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, vol. 162. pp. 4937-4955, 2022.PDF icon dellaferrera22a.pdf (909.91 KB)
CBMM Funded
S. D. Houlihan, Ong, D., Cusimano, M., and Saxe, R., “Reasoning about the antecedents of emotions: Bayesian causal inference over an intuitive theory of mind”, Proceedings of the 44th Annual Conference of the Cognitive Science Society, vol. 44. Toronto, CA, pp. 854-861, 2022.PDF icon Houlihan 2022 Proceedings of the 44th Annual Conference of the Cognitive Science Society.pdf (687.98 KB)
CBMM Funded
N. Shaham, Chandra, J., Kreiman, G., and Sompolinsky, H., “Stochastic consolidation of lifelong memoryAbstract”, Scientific Reports, vol. 12, no. 1, 2022.PDF icon s41598-022-16407-9.pdf (2.54 MB)
CBMM Funded
K. Kar, “A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism”, The Journal of Neuroscience, pp. JN-RM-2229-21, 2022.
CBMM Related
P. Nilchian, Wilson, M. A., and Sanders, H., “Animal-to-Animal Variability in Partial Hippocampal Remapping in Repeated Environments”, The Journal of Neuroscience, vol. 42, no. 26, pp. 5268 - 5280, 2022.PDF icon 5268.full_.pdf (2.97 MB)
CBMM Funded

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