All Publications

2019

C. R. Ponce, Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., and Livingstone, M. S., “Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences”, Cell , vol. 177, p. 1009, 2019.PDF icon Author's last draft (20.26 MB)
CBMM Funded
J. Serrino, Kleiman-Weiner, M., Parkes, D. C., and Tenenbaum, J. B., “Finding Friend and Foe in Multi-Agent Games”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon Max KW paper.pdf (928.96 KB)
CBMM Funded
J. Zhang, Han, Y., Poggio, T., and Roig, G., “Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance ”, in Vision Science Society, Florida, USA, 2019.
CBMM Funded
K. Kar, Kubilius, J., Schmidt, K., Issa, E. B., and DiCarlo, J. J., “Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior”, Nature Neuroscience, 2019.PDF icon Author's last draft (1.74 MB)
CBMM Funded
O. Lewis and Hermann, K., “Data for free: Fewer-shot algorithm learning with parametricity data augmentation”, in ICLR 2019, 2019.
CBMM Related
Y. Vazquez, Ianni, G., and Freiwald, W. A., “Neural mechanisms supporting facial expressions ”, in unknown, New York, NY, USA, 2019.
CBMM Funded
A. Banburski, Liao, Q., Miranda, B., Rosasco, L., Hidary, J., and Poggio, T., “Dynamics & Generalization in Deep Networks -Minimizing the Norm”, in NAS Sackler Colloquium on Science of Deep Learning, Washington D.C., 2019.
CBMM Funded
M. A. P. Idiart, Villavicencio, A., Katz, B., Rennó-Costa, C., and Lisman, J., “How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms”, Frontiers in Computational Neuroscience, vol. 13, 2019.
CBMM Funded
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs”, Biological Psychiatry, vol. 85, no. 5, pp. 425 - 433, 2019.
CBMM Funded
B. Deen and Saxe, R., “Parts‐based representations of perceived face movements in the superior temporal sulcus”, Human Brain Mapping, vol. 40, no. 8, pp. 2499 - 2510, 2019.
CBMM Funded
S. J. Gershman, “How to never be wrong”, Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 13 - 28, 2019.
CBMM Funded
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “The transdiagnostic structure of mental effort avoidance”, Scientific Reports, vol. 9, no. 1, 2019.
CBMM Related
S. Ullman, “Using neuroscience to develop artificial intelligence”, Science, vol. 363, no. 6428, pp. 692 - 693, 2019.
CBMM Related
S. Ullman, Dorfman, N., and Harari, D., “A model for discovering ‘containment’ relations”, Cognition, vol. 183, pp. 67 - 81, 2019.
CBMM Related
A. Adler and Wax, M., “Blind Constant Modulus Multiuser Detection via Low-Rank Approximation”, IEEE Signal Processing Letters, pp. 1 - 1, 2019.
CBMM Funded
C. I. Calero, Shalom, D. E., Spelke, E. S., and Sigman, M., “Language, gesture, and judgment: Children’s paths to abstract geometry”, Journal of Experimental Child Psychology, vol. 177, pp. 70 - 85, 2019.
CBMM Funded
K. Dobs, Isik, L., Pantazis, D., and Kanwisher, N., “How face perception unfolds over time”, Nature Communications, vol. 10, no. 1, 2019.
CBMM Funded
N. Fazeli, Oller, M., Wu, J., Wu, Z., Tenenbaum, J. B., and Rodriguez, A., “See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion”, Science Robotics, vol. 4, no. 26, p. eaav3123, 2019.
CBMM Funded
A. J. E. Kell and McDermott, J. H., “Deep neural network models of sensory systems: windows onto the role of task constraints”, Current Opinion in Neurobiology, vol. 55, pp. 121 - 132, 2019.
CBMM Related
I. Yildirim, Wu, J., Kanwisher, N., and Tenenbaum, J. B., “An integrative computational architecture for object-driven cortex”, Current Opinion in Neurobiology, vol. 55, pp. 73 - 81, 2019.
CBMM Funded
M. Araya-Polo, Adler, A., Farris, S., and Jennings, J., “Fast and Accurate Seismic Tomography via Deep Learning”, in Deep Learning: Algorithms and Applications, SPRINGER-VERLAG, 2019.
CBMM Related
K. M. Jozwik, Schrimpf, M., Kanwisher, N., and DiCarlo, J. J., “To find better neural network models of human vision, find better neural network models of primate vision”, in BioRxiv, 2019.
CBMM Funded
K. M. Jozwik, Lee, H., Kanwisher, N., and DiCarlo, J. J., “Are topographic deep convolutional neural networks better models of the ventral visual stream?”, in Conference on Cognitive Computational Neuroscience, 2019.
CBMM Related
G. Kreiman, “What do neurons really want? The role of semantics in cortical representations.”, in Psychology of Learning and Motivation, vol. 70, 2019.
CBMM Funded
S. Liu, Cushman, F. A., Gershman, S. J., Kool, W., and Spelke, E. S., “Hard choices: Children’s understanding of the cost of action selection. ”, in Cognitive Science Society, 2019.PDF icon phk_cogsci_2019_final.pdf (276.14 KB)
CBMM Funded

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