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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Dillon, M. R. & Spelke, E. S. Connecting core cognition, spatial symbols, and the abstract concepts of formal geometry. Cognitive Development Society Post-Conference, More on Development (2015).

Dillon, M. R. & Spelke, E. S. Young children’s automatic and alternating use of scene and object information in spatial symbols. Budapest CEU Conference on Cognitive Development (2015).

Deza, A., Liao, Q., Banburski, A. & Poggio, T. Hierarchically Local Tasks and Deep Convolutional Networks. (2020).

CBMM_Memo_109.pdf (2.12 MB)

Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022).

dellaferrera22a.pdf (909.91 KB)

A del Molino, G., Boix, X., Lim, J. & Tan, A. Active Video Summarization: Customized Summaries via On-line Interaction. AAAI Conference on Artificial Intelligence (2017).

21-Garcia-del-Molino-14856.pdf (413.77 KB)

Dehghani, N. Dynamic balance of excitation and inhibition in human and monkey neocortex. Nature Scientific Reports (2016). doi:10.1038/srep23176

BalanceExcitationInhibition.pdf (2.1 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)

Dehghani, N. et al. Avalanche analysis from multielectrode ensemble recordings in cat, monkey, and human cerebral cortex during wakefulness and sleep. Frontiers in Physiology (2012). doi:10.3389/fphys.2012.00302

AvalancheDynamics.pdf (2.45 MB)

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)

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)

Dehaene-Lambertz, G. & Spelke, E. S. The infancy of the human brain. (2016). doi:http://dx.doi.org/10.1016/j.neuron.2015.09.026

CBMM-Memo-053.pdf (1.51 MB)

Dehaene-Lambertz, G. & Spelke, E. S. The Infancy of the Human Brain. Neuron 88, 93 - 109 (2015).

Deen, B., Koldewyn, K., Kanwisher, N. & Saxe, R. Functional organization of social perception and cognition in the superior temporal sulcus. Cerebral Cortex 25, 4596-4609 (2015).

Deen, B. & Saxe, R. Parts-based representations of perceived face movements in the superior temporal sulcus. Society for Neuroscience Annual Meeting (2015). at <https://www.sfn.org/~/media/SfN/Documents/Annual%20Meeting/FinalProgram/NS2015/Daily%20Books%202015/AM15Monday.ashx>

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

Deen, B. & Saxe, R. Parts‐based representations of perceived face movements in the superior temporal sulcus. Human Brain Mapping 40, 2499 - 2510 (2019).

Deen, B., Kanwisher, N. & Saxe, R. Functional organization of the human superior temporal sulcus. Organization for Human Brain Mapping (OHBM 2015) (2015). at <https://ww4.aievolution.com/hbm1501/index.cfm?do=abs.viewAbs&abs=3635>

Deen, B., Kanwisher, N. & Saxe, R. Exploring the functional organization of the superior temporal sulcus with a broad set of naturalistic stimuli. (2014).

de la Rosa, S. et al. Visual categorization of social interactions. Visual Cognition 22, (2015).

Dasgupta, I., Schulz, E., Tenenbaum, J. B. & Gershman, S. J. A theory of learning to infer. Psychological Review 127, 412 - 441 (2020).

Dasgupta, I., Guo, D., Gershman, S. J. & Goodman, N. D. Analyzing Machine‐Learned Representations: A Natural Language Case Study. Cognitive Science 44, (2020).

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)

Dasgupta, I. & Gershman, S. J. Memory as a Computational Resource. Trends in Cognitive Sciences 25, 240 - 251 (2021).

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

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

Dapello, J. et al. Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. bioRxiv (2022).

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