Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Idiart, M. A. P., Villavicencio, A., Katz, B., Rennó-Costa, C. & Lisman, J. How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms. Frontiers in Computational Neuroscience 13, (2019).

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>

Dobs, K., Isik, L., Pantazis, D. & Kanwisher, N. How face perception unfolds over time. Nature Communications 10, (2019).

Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016). at <https://cbmm.mit.edu/sites/default/files/publications/liao-leibo-poggio.pdf>

Liao, Q., Leibo, J. Z. & Poggio, T. How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).

liao-leibo-poggio.pdf (191.91 KB)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important is Weight Symmetry in Backpropagation?. (2015).

1510.05067v3.pdf (615.32 KB)

Spokes, A. C. How Infants Reason About Affective States and Social Interactions. International Conference on Infant Studies (ICIS) (2016).

Chen, Z. Sage & Wilson, M. A. How our understanding of memory replay evolves. Journal of Neurophysiology 129, 552 - 580 (2023).

Meyers, E. How PFC and LIP process single and multiple-object ‘pop-out’ displays. Society for Neuroscience (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Full%20Abstract%20PDFs%202015/SfN15_Abstracts_PDF_Nanos.ashx>

Singhal, U. et al. How to Guess a Gradient. arXiv (2023). at <https://arxiv.org/abs/2312.04709>

Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).

Gerstenberg, T., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. How, whether, why: Causal judgments as counterfactual contrasts. Annual Meeting of the Cognitive Science Society (CogSci) 782-787 (2015). at <https://mindmodeling.org/cogsci2015/papers/0142/index.html>

GerstenbergEtAl2015-Cogsci.pdf (2.16 MB)

Sani, I. et al. The human endogenous attentional control network includes a ventro-temporal cortical node. Nature Communications 12, (2021).

Traer, J. & McDermott, J. H. Human inference of force from impact sounds: Perceptual evidence for inverse physics. Annual Meeting of the Acoustical Society 143, (2018).

Tsividis, P., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J. Human Learning in Atari. AAAI Spring Symposium Series (2017).

Tsividis et al - Human Learning in Atari.pdf (844.47 KB)

Lifshitz, I., Fetaya, E. & Ullman, S. Human Pose Estimation Using Deep Consensus Voting. ECCV 2016 (2016).

1603.08212.pdf (6.05 MB)

Traer, J. & McDermott, J. H. Human recognition of environmental sounds is not always robust to reverberation. Annual Meeting of the Acoustical Society 143, (2018).

Yang, S., Bill, J., Drugowitsch, J. & Gershman, S. J. Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports 11, (2021).

Han, Y., Roig, G., Geiger, G. & Poggio, T. On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society (2017).

Han, Y., Roig, G., Geiger, G. & Poggio, T. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).

Lake, B. M., Salakhutdinov, R. & Tenenbaum, J. B. Human-level concept learning through probabilistic program induction. Science 350, 1332-1338 (2015).

Mottaghi, R., Fidler, S., Yuille, A., Urtasun, R. & Parikh, D. Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding. (2014).

CBMM-Memo-020.pdf (1.89 MB)

Zhang, M., Badkundri, R., Talbot, M. B., Zawar, R. & Kreiman, G. Hypothesis-driven Online Video Stream Learning with Augmented Memory. arXiv (2021). doi:10.48550/arXiv.2104.02206

2104.02206.pdf (2.25 MB)

Tsividis, P., Tenenbaum, J. B. & Schulz, L. Hypothesis-Space Constraints in Causal Learning. Annual Meeting of the Cognitive Science Society (CogSci) (2015). at <https://mindmodeling.org/cogsci2015/papers/0418/index.html>

hypothesis_space_constraints (1).pdf (1.54 MB)

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Harrod, J., Purdon, P. L., Brown, E. N. & Flores, F. J. Identification of vigilance states in freely behaving animals using thalamocortical activity and Deep Belief networks. Society for Neuroscience (2019).

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