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.

Videos
Support Us

Publication

Search

Show only items where

Author

Type

Term

Year

Keyword

Found 416 results

Author Title Type [ Year

]

Filters: First Letter Of Last Name is S [Clear All Filters]

2021

Bongiorno, C. et al. Vector-based pedestrian navigation in cities. Nature Computational Science 1, 678 - 685 (2021).

s43588-021-00130-y.pdf (1.96 MB)

2020

Kim, D. et al. The ability to predict actions of others from distributed cues is still developing in children. PsyArXiv Preprints (2020). doi:10.31234/osf.io/pu3tf

Action_prediction_in_children.pdf (427.84 KB)

Tomova, L. et al. Acute social isolation evokes midbrain craving responses similar to hunger. Nature Neuroscience 23, 1597 - 1605 (2020).

s41593-020-00742-z.pdf (5.47 MB)

Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences 1464, 222 - 241 (2020).

Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience 1464, 222-241 (2020).

gk7812.pdf (1.93 MB)

Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).

CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)

CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)

CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)

CBMM-Memo-104.pdf (425.12 KB)

Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).

CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)

CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)

CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)

CBMM-Memo-104.pdf (425.12 KB)

Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).

CBMM-Memo-111.pdf (9.76 MB)

Schulz, E., Quiroga, F. & Gershman, S. J. Communicating Compositional Patterns. Open Mind 4, 25 - 39 (2020).

Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).

CBMM-Memo-105.pdf (4.63 MB)

CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)

CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)

Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).

CBMM-Memo-105.pdf (4.63 MB)

CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)

CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)

Villalobos, K. M. et al. Do Neural Networks for Segmentation Understand Insideness?. (2020).

CBMM-Memo-105.pdf (4.63 MB)

CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)

CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)

Shalev-Shwartz, S. & Shashua, A. An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020).

CBMM-Memo-106.pdf (431.13 KB)

Shalev-Shwartz, S. & Shashua, A. An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020).

CBMM-Memo-106.pdf (431.13 KB)

Schaeffer, D. J. et al. Face selective patches in marmoset frontal cortexAbstract. Nature Communications 11, (2020).

Schaeffer, D. J. et al. Face selective patches in marmoset frontal cortexAbstract. Nature Communications 11, (2020).

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>

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>

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).

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

Thomas, A. J., Saxe, R. & Spelke, E. S. Infants represent 'like-kin' affiliation . Budapest Conference on Cognitive Development (2020).

Thomas, A. J., Saxe, R. & Spelke, E. S. Infants represent 'like-kin' affiliation . Budapest Conference on Cognitive Development (2020).

Dillon, M. R., Izard, V. & Spelke, E. S. Infants’ sensitivity to shape changes in 2D visual forms. Infancy 25, 618 - 639 (2020).

Rajalingham, R., Kar, K., Sanghavi, S., Dehaene, S. & DiCarlo, J. J. The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys. Nature Communications 11, (2020).

s41467-020-17714-3.pdf (25.01 MB)

Schrimpf, M. et al. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron 108, 413 - 423 (2020).

Pages