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 174 results

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

2017

Dehghani, N. Design of the Artificial: lessons from the biological roots of general intelligence. (2017). at <https://arxiv.org/pdf/1703.02245>

DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)

Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211

srep40211.pdf (2.53 MB)

Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211

srep40211.pdf (2.53 MB)

Dasgupta, I., Bernstein, J., Rolnick, D. & Sompolinsky, H. Markov transitions between attractor states in a recurrent neural network. AAAI (2017).

aaai-abstract (1).pdf (357.72 KB)

Grossman, N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 169, 1029 - 1041.e16 (2017).

Deen, B. et al. Organization of high-level visual cortex in human infants. Nature Communications (2017). doi:10.1038/ncomms13995

Deen, B. et al. Organization of high-level visual cortex in human infants. Nature Communications (2017). doi:10.1038/ncomms13995

Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>

1707.05455.pdf (143.46 KB)

Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology 68, (2017).

GershmanDaw17.pdf (422.11 KB)

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

Dehghani, N. Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective. (2017). doi:10.1101/097618

StimComplexity.pdf (218.1 KB)

Jing, L. et al. Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNN. 34th International Conference on Machine Learning 70, 1733-1741 (2017).

1612.05231.pdf (2.3 MB)

Dillon, M. R. & Spelke, E. S. Young children's use of distance and angle information during map reading. SRCD (2017).

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)

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

CBMM-Memo-095.pdf (1.96 MB)

Dehghani, N. & Wimmer, R. A computational perspective of the role of Thalamus in cognition. arxiv (2018). at <https://arxiv.org/abs/1803.00997>

ThalamusComputationArxiv.pdf (5.12 MB)

Araya-Polo, M., Jennings, J., Adler, A. & Dahlke, T. Deep-learning tomography. The Leading Edge 37, 58 - 66 (2018).

TLE2018.pdf (1.9 MB)

Dillon, M. R. & Spelke, E. S. From Map Reading to Geometric Intuitions. Developmental Psychology (2018). doi:http://dx.doi.org/10.1037/dev0000509

Myanganbayar, B. et al. Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. (2018).

CBMM-Memo-097.pdf (8.53 MB)

Myanganbayar, B. et al. Partially Occluded Hands: A challenging new dataset for single-image hand pose estimation. The 14th Asian Conference on Computer Vision (ACCV 2018) (2018). at <http://accv2018.net/>

partially-occluded-hands-6.pdf (8.29 MB)

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)

Hart, Y. et al. The statistical shape of geometric reasoning. Scientific Reports 8, (2018).

Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).

Original, intermediate versions are available under request (2.67 MB)

CBMM Memo 90 v12.pdf (4.74 MB)

Theory_III_ver44.pdf Update Hessian (4.12 MB)

Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)

fixing errors and sharpening some proofs (2.45 MB)

2019

Jozwik, K. M., Lee, H., Kanwisher, N. & DiCarlo, J. J. Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).

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)

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