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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Found 912 results

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] Year

Code

Meyers, E. The Neural Decoding Toolbox. (2013). at <http://www.readout.info/>

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

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

hmax.tar (210 KB)

Knoblich, U. hhpkg: Hodgkin-Huxley Package for CNS. (2012).

hhpkg.tar (380 KB)

Volokitin, A. & Roig, G. Do Deep Neural Networks Suffer from Crowding? [code]. (2017).

Mutch, J. & Turaga, S. cnpkg: 3-D Convolutional Network Package for CNS. (2012).

cnpkg.tar (50 KB)

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>

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

CBMM Memos

Dasgupta, I., Schulz, E. & Gershman, S. J. Where do hypotheses come from?. (2016).

CBMM-Memo-056-v2.pdf (733.35 KB)

Gao, T., Harari, D., Tenenbaum, J. B. & Ullman, S. When Computer Vision Gazes at Cognition. (2014).

CBMM-Memo-025.pdf (3.78 MB)

Zhang, M., Feng, J., Lim, J. Hwee, Zhao, Q. & Kreiman, G. What am I searching for?. (2018).

CBMM-Memo-096.pdf (1.74 MB)

Wang, J. et al. Visual concepts and compositional voting. (2018).

CBMM-Memo-087.pdf (3.37 MB)

Leibo, J. Z., Liao, Q., Freiwald, W. A., Anselmi, F. & Poggio, T. View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).

faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)

Lotter, W., Kreiman, G. & Cox, D. UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015).

CBMM Memo 040_rev1.pdf (1.92 MB)

Anselmi, F. et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. (2014).

CBMM Memo No. 001 (940.36 KB)

Liao, Q., Leibo, J. Z. & Poggio, T. Unsupervised learning of clutter-resistant visual representations from natural videos. (2014).

1409.3879v2.pdf (3.64 MB)

Berzak, Y. et al. Universal Dependencies for Learner English. (2016).

memo-52_rev1.pdf (472.67 KB)

Rangamani, A. & Xie, Y. Understanding the Role of Recurrent Connections in Assembly Calculus. (2022).

CBMM-Memo-137.pdf (1.49 MB)

Yamada, M., D'Amario, V., Takemoto, K., Boix, X. & Sasaki, T. Transformer Module Networks for Systematic Generalization in Visual Question Answering. (2022).

CBMM-Memo-121.pdf (1.06 MB)

version 2 (3/22/2023) (1.33 MB)

Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).

CBMM-Memo-132.pdf (1.15 MB)

Shrobe, H., Katz, B. & Davis, R. Towards a Programmer's Apprentice (Again). (2015).

CBMM-memo-030.pdf (294.27 KB)

Sakai, A. et al. Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations. (2022).

CBMM-Memo-119.pdf (31.08 MB)

Cano-Córdoba, F., Sarma, S. & Subirana, B. Theory of Intelligence with Forgetting: Mathematical Theorems Explaining Human Universal Forgetting using “Forgetting Neural Networks”. (2017).

CBMM-Memo-071.pdf (2.54 MB)

Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).

CBMM-Memo-073.pdf (2.65 MB)

CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)

CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)

CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)

Zhang, C. et al. Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).

CBMM-Memo-072.pdf (3.66 MB)

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