All Publications

2023

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
V. Subramaniam, Conwell, C., Wang, C., Kreiman, G., Katz, B., Cases, I., and Barbu, A., “Using Multimodal DNNs to Study Vision-Language Integration in the Brain”, in ICLR 2023, 2023.
CBMM Funded
M. S. Tomov, Tsividis, P. A., Pouncy, T., Tenenbaum, J. B., and Gershman, S. J., “The neural architecture of theory-based reinforcement learning”, Neuron, vol. 111, no. 8, pp. 1331 - 1344.e8, 2023.
CBMM Funded
A. Zador, Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., Botvinick, M., Chklovskii, D., Churchland, A., Clopath, C., DiCarlo, J., Ganguli, S., Hawkins, J., Körding, K., Koulakov, A., LeCun, Y., Lillicrap, T., Marblestone, A., Olshausen, B., Pouget, A., Savin, C., Sejnowski, T., Simoncelli, E., Solla, S., Sussillo, D., Tolias, A. S., and Tsao, D., “Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract”, Nature Communications, vol. 14, no. 1, 2023.
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
H. Azami, Moguilner, S., Penagos, H., Sarkis, R. A., Arnold, S. E., Gomperts, S. N., and Lam, A. D., “EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer’s Disease”, Journal of Alzheimer's Disease, vol. 91, no. 4, pp. 1557 - 1572, 2023.
CBMM Related
N. Kanwisher, Gupta, P., and Dobs, K., “CNNs reveal the computational implausibility of the expertise hypothesis”, iScience, vol. 26, no. 2, p. 105976, 2023.
CBMM Funded
G. Kreiman, “Neural coding: Stimulating cortex to alter visual perception”, Current Biology, vol. 33, no. 3, pp. R117 - R118, 2023.
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
R. Tejwani, Kuo, Y. - L., Shu, T., Stankovits, B., Gutfreund, D., Tenenbaum, J. B., Katz, B., and Barbu, A., “Zero-shot linear combinations of grounded social interactions with Linear Social MDPs”, in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 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
B. M. Woo and Spelke, E. S., “Infants and toddlers leverage their understanding of action goals to evaluate agents who help others”, Child Development, 2023.
CBMM Funded
W. Xiao, Sharma, S., Kreiman, G., and Livingstone, M. S., “Out of sight, out of mind: Responses in primate ventral visual cortex track individual fixations during natural vision”, bioRxiv, 2023.
CBMM Funded
Z. Sage Chen and Wilson, M. A., “How our understanding of memory replay evolves”, Journal of Neurophysiology, vol. 129, no. 3, pp. 552 - 580, 2023.
CBMM Related
M. J. Lee and DiCarlo, J. J., “An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models”, bioRxiv, 2023.
CBMM Related
X. Puig, Shu, T., Tenenbaum, J. B., and Torralba, A., “NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants”, arXiv, 2023.
CBMM Related
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
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 visionAbstract”, Scientific Reports, vol. 13, no. 1, 2023.
CBMM Funded
S. Villa, Matet, S., Vũ, B. Công, and Rosasco, L., “Implicit regularization with strongly convex bias: Stability and acceleration”, Analysis and Applications, vol. 21, no. 01, pp. 165 - 191, 2023.
CBMM Related
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

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