Publication

Found 43 results
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2024
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>
2023
Caldarelli, E., Chatalic, A., Colom´e, A. `a, Rosasco, L. & Torras, C. Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees. 7th Conference on Robot Learning (CoRL 2023 (2023). at <https://proceedings.mlr.press/v229/caldarelli23a/caldarelli23a.pdf>
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).
Gan, Y. & Poggio, T. A Homogeneous Transformer Architecture. (2023).PDF icon CBMM Memo 143 v2 (1.1 MB)
Chen, Z. Sage & Wilson, M. A. How our understanding of memory replay evolves. Journal of Neurophysiology 129, 552 - 580 (2023).
Singhal, U. et al. How to Guess a Gradient. arXiv (2023). at <https://arxiv.org/abs/2312.04709>
2022
McPherson, M. J., Grace, R. C. & McDermott, J. H. Harmonicity aids hearing in noise. Attention, Perception, & Psychophysics (2022). doi:10.3758/s13414-021-02376-0
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).
Poggio, T. How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse. (2022).PDF icon v1.0 (984.15 KB)PDF icon v5.7 adding in context learning etc (1.16 MB)
2021
Sani, I. et al. The human endogenous attentional control network includes a ventro-temporal cortical node. Nature Communications 12, (2021).
Yang, S., Bill, J., Drugowitsch, J. & Gershman, S. J. Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports 11, (2021).
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.2104.02206PDF icon 2104.02206.pdf (2.25 MB)
2020
Marques, T., Schrimpf, M. & DiCarlo, J. J. Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).
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).
Deza, A., Liao, Q., Banburski, A. & Poggio, T. Hierarchically Local Tasks and Deep Convolutional Networks. (2020).PDF icon CBMM_Memo_109.pdf (2.12 MB)
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal remapping as hidden state inference. eLife 9, (2020).
2019
Liu, S., Cushman, F. A., Gershman, S. J., Kool, W. & Spelke, E. S. Hard choices: Children’s understanding of the cost of action selection. . Cognitive Science Society (2019).PDF icon phk_cogsci_2019_final.pdf (276.14 KB)
Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal Remapping as Hidden State Inference. (2019). doi:https://doi.org/10.1101/743260PDF icon CBMM-Memo-101.pdf (12.78 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
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).
Dobs, K., Isik, L., Pantazis, D. & Kanwisher, N. How face perception unfolds over time. Nature Communications 10, (2019).
Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).
2018
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).
Traer, J. & McDermott, J. H. Human recognition of environmental sounds is not always robust to reverberation. Annual Meeting of the Acoustical Society 143, (2018).
2017
Tsividis, P., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J. Human Learning in Atari. AAAI Spring Symposium Series (2017).PDF icon Tsividis et al - Human Learning in Atari.pdf (844.47 KB)

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