All Publications

2023

Z. M. Aghajan, Kreiman, G., and Fried, I., “Minute-scale periodicity of neuronal firing in the human entorhinal cortex”, Cell Reports, vol. 42, no. 11, p. 113271, 2023.PDF icon 1-s2.0-S2211124723012834-main.pdf (5.33 MB)
CBMM Funded
I. Yildirim, Siegel, M. H., Soltani, A. A., Chaudhuri, S. Ray, and Tenenbaum, J. B., “Perception of 3D shape integrates intuitive physics and analysis-by-synthesis”, Nature Human Behaviour, 2023.
CBMM Funded
J. Feather, Leclerc, G., Mądry, A., and McDermott, J. H., “Model metamers reveal divergent invariances between biological and artificial neural networks”, Nature Neuroscience, 2023.
CBMM Funded
T. Poggio and Magrini, M., Cervelli menti algoritmi. Sperling & Kupfer, 2023, p. 272.
CBMM Funded
CBMM Memo No.
143
Y. Gan and Poggio, T. A., “A Homogeneous Transformer Architecture”. 2023.PDF icon CBMM-Memo-143.pdf (1.07 MB)
CBMM Funded
E. J. Bigelow, McCoy, J. P., and Ullman, T. D., “Non-commitment in mental imagery”, Cognition, vol. 238, p. 105498, 2023.
CBMM Funded
Y. Han, Poggio, T. A., and Cheung, B., “System Identification of Neural Systems: If We Got It Right, Would We Know?”, Proceedings of the 40th International Conference on Machine Learning, PMLR, vol. 202. pp. 12430-12444, 2023.PDF icon han23d.pdf (797.48 KB)
CBMM Funded
S. Dae Houlihan, Kleiman-Weiner, M., Hewitt, L. B., Tenenbaum, J. B., and Saxe, R., “Emotion prediction as computation over a generative theory of mind”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 381, no. 2251, 2023.PDF icon houlihan2023computedappraisals.pdf (2.37 MB)
CBMM Funded
CBMM Memo No.
142
Y. Xie, Li, Y., and Rangamani, A., “Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms”. 2023.PDF icon CBMM-Memo-142.pdf (1.64 MB)
CBMM Funded
D. Lagomarsino-Oneto, Meanti, G., Pagliana, N., Verri, A., Mazzino, A., Rosasco, L., and Seminara, A., “Physics informed machine learning for wind speed prediction”, Energy, vol. 268, p. 126628, 2023.
CBMM Funded
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
N. Kanwisher, Khosla, M., and Dobs, K., “Using artificial neural networks to ask ‘why’ questions of minds and brains”, Trends in Neurosciences, vol. 46, no. 3, pp. 240 - 254, 2023.
CBMM Related
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. 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 Related
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
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
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

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