All Publications

2023

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

2022

CBMM Memo No.
138
T. A. Poggio, “Foundations of Deep Learning: Compositional Sparsity of Computable Functions”. 2022.PDF icon v1.0 (984.15 KB)PDF icon v3.7 References added; also CoSp collaboration (18.81 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
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
A. Banburski and Rangamani, A., “Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay”, in IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 2022.
CBMM Funded
K. Dobs, Martinez-Trujillo, J., Kell, A. J. E., and Kanwisher, N., “Brain-like functional specialization emerges spontaneously in deep neural networks”, Science Advances, vol. 8, no. 11, 2022.
CBMM Funded
CBMM Memo No.
134
T. Galanti and Poggio, T., “SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks”. 2022.PDF icon Implicit Rank Regularization.pdf (1.42 MB)
CBMM Funded
T. Galanti and Galanti, L., “On the Implicit Bias Towards Minimal Depth of Deep Neural Networks”, arXiv, 2022.PDF icon 2202.09028.pdf (2 MB)
CBMM Funded
F. S. Kamps, Richardson, H., N. Murty, A. Ratan, Kanwisher, N., and Saxe, R., “Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years”, Human Brain Mapping, 2022.
CBMM Related
Y. Li, Wang, Y. Q., Boger, T., Smith, K. A., Gershman, S. J., and Ullman, T., “An approximate representation of objects underlies physical reasoning”, psyArXiv, 2022.
CBMM Related
J. Zheng, Schjetnan, A. G. P., Yebra, M., Gomes, B. A., Mosher, C. P., Kalia, S. K., Valiante, T. A., Mamelak, A. N., Kreiman, G., and Rutishauser, U., “Neurons detect cognitive boundaries to structure episodic memories in humans”, Nature Neuroscience, vol. 25, no. 3, pp. 358 - 368, 2022.
CBMM Funded
CBMM Memo No.
121
M. Yamada, D'Amario, V., Takemoto, K., Boix, X., and Sasaki, T., “Transformer Module Networks for Systematic Generalization in Visual Question Answering”. 2022.PDF icon CBMM-Memo-121.pdf (1.06 MB)
CBMM Funded
M. Armendariz, Xiao, W., Vinken, K., and Kreiman, G., “Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions”, Cognitive Neuropsychology, pp. 1 - 3, 2022.
CBMM Funded
A. Bardon, Xiao, W., Ponce, C. R., Livingstone, M. S., and Kreiman, G., “Face neurons encode nonsemantic features”, Proceedings of the National Academy of Sciences, vol. 119, no. 16, 2022.
CBMM Funded
S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations”, Nature Machine Intelligence, vol. 4, no. 2, pp. 146 - 153, 2022.
CBMM Funded
S. V. Norman-Haignere, Feather, J., Boebinger, D., Brunner, P., Ritaccio, A., McDermott, J. H., Schalk, G., and Kanwisher, N., “A neural population selective for song in human auditory cortex”, Current Biology, 2022.PDF icon mmc2.pdf (9.52 MB)
CBMM Funded
CBMM Memo No.
119
A. Sakai, Sunagawa, T., Madan, S., Suzuki, K., Katoh, T., Kobashi, H., Pfister, H., Sinha, P., Boix, X., and Sasaki, T., “Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations”. 2022.PDF icon CBMM-Memo-119.pdf (31.08 MB)
CBMM Funded
L. A. Becker, Penagos, H., Flores, F. J., Manoach, D. S., Wilson, M. A., and Varela, C., “Eszopiclone and Zolpidem Produce Opposite Effects on Hippocampal Ripple DensityDataSheet1.docx”, Frontiers in Pharmacology, vol. 12, 2022.
CBMM Funded

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