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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Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65-84 (2018).

Full interpretation of minimal images.pdf (4.55 MB)

Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65 - 84 (2018).

Dillon, M. R. & Spelke, E. S. From spatial symbols to Euclidean intuitions. Cognitive Development Society (CDS) (2015). at <http://cogdevsoc.org/sites/default/files/Official%20Full%20Conference%20Proceedings_10.10.pdf>

Dillon, M. R. & Spelke, E. S. From spatial symbols to Euclidean intuitions. Biennial Meeting of Cognitive Development Society (2015).

Poggio, T. From Marr’s Vision to the Problem of Human Intelligence. (2021).

CBMM-Memo-118.pdf (362.19 KB)

Dillon, M. R. & Spelke, E. S. From Map Reading to Geometric Intuitions. Developmental Psychology (2018). doi:http://dx.doi.org/10.1037/dev0000509

Poggio, T. From Associative Memories to Powerful Machines. (2021).

v1.0 (1.01 MB)

v1.3Section added August 6 on self attention (3.9 MB)

Sliwa, J. & Freiwald, W. A. From agents to actions to interactions: Uncovering multiple social networks in the primate brain. Society for Social and Affective Neuroscience (2017).

Sliwa, J. & Freiwald, W. A. From agents to actions to interactions: Uncovering multiple social networks in the primate brain. Society for Neuroscience (2016).

SFN2016.pdf (555.77 KB)

Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>

1912.04783.pdf (6.69 MB)

Linderman, S. W., Stock, C. & Adams, R. A framework for studying synaptic plasticity with neural spike train data. Neural Information Processing Systems (2014).

5274-a-framework-for-studying-synaptic-plasticity-with-neural-spike-train-data.pdf (4.6 MB)

Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).

cbmm_memo_044.pdf (11.48 MB)

Spokes, A. C. & Spelke, E. S. Four-year-old children favor kin when the stakes are higher. Cognitive Development Society (CDS) (2017). at <https://cogdevsoc.org/wp-content/uploads/2017/10/CDS2017AbstractBook.pdf>

Srinivasan, R. Francesco et al. Forward learning with top-down feedback: empirical and analytical characterization. arXiv (2023). at <https://arxiv.org/abs/2302.05440>

Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).

CBMM-Memo-150.pdf (4.03 MB)

Saxe, R. & Houlihan, S. Dae. Formalizing emotion concepts within a Bayesian model of theory of mind. Current Option in Psychology 17, 15-21 (2017).

1-s2.0-S2352250X17300283-main.pdf (613.77 KB)

Subirana, B., Bagiati, A. & Sarma, S. On the Forgetting of College Academics: at "Ebbinghaus Speed"?. (2017).

CBMM Memo 068-On Forgetting - June 18th 2017 v2.pdf (713.7 KB)

Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020).

CBMM_Memo_108.pdf (1015.14 KB)

Better bound (without inequalities!) (1.03 MB)

Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability. Analysis and Applications 21, 193 - 215 (2023).

Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).

CBMM-Memo-148.pdf (1.06 MB)

McNamee, D., Stachenfeld, K., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal-hippocampal system. Nature Neuroscience (2021). doi:10.1038/s41593-021-00831-7

Spokes, A. C., Venkatesan, T. & Spelke, E. S. Five-month-old infants attend to responsive caregivers. Cognitive Development Society (CDS) (2017). at <https://cogdevsoc.org/wp-content/uploads/2017/10/CDS2017AbstractBook.pdf>

Liang, T., Poggio, T., Rakhlin, A. & Stokes, J. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. arXiv.org (2017). at <https://arxiv.org/abs/1711.01530>

1711.01530.pdf (966.99 KB)

Wu, Y. & Schulz, L. A fine-grained understanding of emotions: Young children match within-valence emotional expressions to their causes. Cognitive Science Conference (CogSci) 2685-2690 (2015).

Cogsci Emotion pairings 2-4-15 Final version.pdf (729.07 KB)

Smith, K. A. et al. The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020 (Denison, S., Mack, M., Xu, Y. & Armstrong, B. C.) (2020). at <https://cogsci.mindmodeling.org/2020/papers/0761/index.html>

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