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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Adler, A., Wax, M. & Pantazis, D. Brain Signals Localization by Alternating Projections. arXiv (2019).

CBMM-Memo-099.pdf (421.67 KB)

Wang, C. et al. BrainBERT: Self-supervised representation learning for Intracranial Electrodes. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=xmcYx_reUn6>

985_brainbert_self_supervised_repr.pdf (9.71 MB)

Dobs, K., Martinez-Trujillo, J., Kell, A. J. E. & Kanwisher, N. Brain-like functional specialization emerges spontaneously in deep neural networks. Science Advances 8, (2022).

Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).

2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)

Muir, D., Fang, X. & Meyers, E. Brain-Observatory-Toolbox. (2018).

Schrimpf, M. & Kubilius, J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007

Brain-Score bioRxiv.pdf (789.83 KB)

Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).

CBMM Memo No. 047 (1.29 MB)

Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).

machines_that_think.pdf (3.45 MB)

Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. Behavioral and Brain Sciences 40, e253 (2017).

C

Liao, Q., Leibo, J. Z., Mroueh, Y. & Poggio, T. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).

CBMM-Memo-003.pdf (963.66 KB)

Jacquot, V., Ying, J. & Kreiman, G. Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).

Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).

CBMM-Memo-095.pdf (1.96 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)

Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nature Neuroscience 18, 1844 (2015).

Preprint (40.28 MB)

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)

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352

Manuscript (1.83 MB)

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>

Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>

Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (2023).

Yildirim, I. & Janner, M. Causal and compositional generative models in online perception. 39th Annual Conference of the Cognitive Science Society (Belledonne, M., Wallraven, C., Freiwald, W. A. & Tenenbaum, J. B.) (2017).

yildirim_janner_2_1.pdf (6.88 MB)

Yildirim, I. et al. Causal and compositional generative models in online perception. 39th Annual Meeting of the Cognitive Science Society - COGSCI 2017 (2017). at <https://mindmodeling.org/cogsci2017/papers/0266/index.html>

Gershman, S. J. & Ullman, T. D. Causal implicatures from correlational statements. PLOS ONE 18, e0286067 (2023).

Traer, J., Norman-Haignere, S. & McDermott, J. H. Causal inference in environmental sound recognition. Cognition (2021). doi:10.1016/j.cognition.2021.104627

Bramley, N., Mayrhofer, R., Gerstenberg, T. & Lagnado, D. A. Causal learning from interventions and dynamics in continuous time. Cognitive Science Conference (2017).

Bramley et al. - 2017 - Causal learning from interventions and dynamics in.pdf (1.78 MB)

Sadagopan, S., Zarco, W. & Freiwald, W. A. A Causal Relationship Between Face-Patch Activity and Face-Detection Behavior. eLife (2017). doi:https://doi.org/10.7554/eLife.18558.001

elife-18558-v1.pdf (813.71 KB)

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