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 912 results

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

S

Palmer, I., Rouditchenko, A., Barbu, A., Katz, B. & Glass, J. Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. Interspeech 2021 (2021). doi:10.21437/Interspeech.2021

Isik, L., Mynick, A., Pantazis, D. & Kanwisher, N. The speed of human social interaction perception. NeuroImage 116844 (2020). doi:10.1016/j.neuroimage.2020.116844

Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Speech Representations based on a Theory for Learning Invariances. (2014).

Freiwald, W., Deen, B., Sliwa, J. & Schwiedrzik, C. M. Specialized Networks for Social Cognition in the Primate Brain. (In Press).

Ben-Yosef, G., Kreiman, G. & Ullman, S. Spatiotemporal interpretation features in the recognition of dynamic images. (2018).

CBMM-Memo-094.pdf (1.21 MB)

CBMM-Memo-094-dynamic-figures.zip (1.8 MB)

fig1.ppsx (147.67 KB)

fig2.ppsx (419.72 KB)

fig4.ppsx (673.41 KB)

figS1.ppsx (587.88 KB)

figS2.ppsx (281.56 KB)

Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).

Peyrache, A. et al. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proceedings of the National Academy of Sciences (2012). doi:10.1073/pnas.1109895109

SpatiotemporalDynamic.pdf (2.56 MB)

Tacchetti, A., Isik, L. & Poggio, T. Spatio-temporal convolutional networks explain neural representations of human actions. (2016).

Dillon, M. R. & Spelke, E. S. Spatial cognition across development. SRCD (2017).

Bricken, T., Davies, X., Singh, D., Krotov, D. & Kreiman, G. Sparse distributed memory is a continual learner. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=JknGeelZJpHP>

6086_sparse_distributed_memory_is_a.pdf (13.3 MB)

Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).

CBMM-Memo-130.pdf (1.52 MB)

Freiwald, W. A. Social interaction networks in the primate brain. Current Opinion in Neurobiology 65, 49 - 58 (2020).

Xie, Y., Li, Y. & Rangamani, A. Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms. (2023).

CBMM-Memo-142.pdf (1.64 MB)

Golowich, N., Rakhlin, A. & Shamir, O. Size-Independent Sample Complexity of Neural Networks. (2017).

1712.06541.pdf (278.77 KB)

Liu, S. & Spelke, E. S. Six-month-old infants represent action efficiency on a continuous scale. 9th Biennial Meeting of the Cognitive Development Society (CDS) (2015).

Liu, S. & Spelke, E. S. Six-month-old infants expect agents to minimize the cost of their actions. Cognition 160, 35-42 (2017).

Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. (2018).

CBMM-Memo-084.pdf (1.92 MB)

Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>

Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).

CBMM-Memo-093.pdf (2.99 MB)

Fried, I., Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Human Brain. Probing Cognition. Probing cognition (MIT Press, 2014).

Prevedel, R. et al. Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nature Methods 11, 727 - 730 (2014).

Dapello, J. et al. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/98b17f068d5d9b7668e19fb8ae470841-Abstract.html>

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. (2014). at <http://klab.tch.harvard.edu/resources/singer_asynchrony.html>

Singer, J. & Kreiman, G. Short temporal asynchrony disrupts visual object recognition. J Vis 14, 7 (2014).

Pages