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

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

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>

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

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

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)

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>

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

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).

Poggio, T. How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse. (2022).

v1.0 (984.15 KB)

v5.7 adding in context learning etc (1.16 MB)

Leonard, J. A., Garcia, A. & Schulz, L. How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence. Child Development (2019). doi:10.1111/cdev.13305

Gan, Y. & Poggio, T. A Homogeneous Transformer Architecture. (2023).

CBMM Memo 143 v2 (1.1 MB)

Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. (2015).

holographic-embeddings.pdf (677.87 KB)

Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016).

1510.04935v2.pdf (360.65 KB)

Mutch, J. hmin: A Minimal HMAX Implementation. (2010).

Mutch, J. HMAX Package for CNS. (2012).

hmax.tar (210 KB)

Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal remapping as hidden state inference. eLife 9, (2020).

Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal Remapping as Hidden State Inference. (2019). doi:https://doi.org/10.1101/743260

CBMM-Memo-101.pdf (12.78 MB)

Feliciano-Ramos, P. A., Galazo, M., Penagos, H. & Wilson, M. Hippocampal memory reactivation during sleep is correlated with specific cortical states of the retrosplenial and prefrontal cortices. Learning & Memory 30, 221 - 236 (2023).

Khosla, M., N. Murty, A. Ratan & Kanwisher, N. A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition. Current Biology 32, 4159 - 4171.e9 (2022).

Le Van Quyen, M. et al. High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle. Proceedings of the National Academy of Sciences (2016). doi:10.1073/pnas.1523583113

BetaGammaSleepAwakeFull.pdf (3.68 MB)

Deza, A., Liao, Q., Banburski, A. & Poggio, T. Hierarchically Local Tasks and Deep Convolutional Networks. (2020).

CBMM_Memo_109.pdf (2.12 MB)

Bill, J., Pailian, H., Gershman, S. J. & Drugowitsch, J. Hierarchical structure is employed by humans during visual motion perception. Proceedings of the National Academy of Sciences 117, 24581 - 24589 (2020).

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