Martin Schrimpf

CBMM Publications

S. Casper, Boix, X., D'Amario, V., Guo, L., Schrimpf, M., Vinken, K., and Kreiman, G., “Frivolous Units: Wider Networks Are Not Really That Wide”, in AAAI 2021, 2021.
T. Marques, Schrimpf, M., and DiCarlo, J. J., “Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior”, bioRxiv, 2021.
K. Kar, Schrimpf, M., Schmidt, K., and DiCarlo, J. J., “ Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior”, Journal of Vision, vol. 21, 2021.
M. Schrimpf, Sato, F., Sanghavi, S., and DiCarlo, J. J., “Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates ”, in COSYNE, Denver, CO, USA, 2020.
T. Marques, Schrimpf, M., and DiCarlo, J. J., “Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses ”, in COSYNE, Denver, Colorado, USA, 2020.
J. Schwartz, Alter, S., DiCarlo, J. J., McDermott, J. H., Tenenbaum, J. B., Yamins, D. L. K., Gutfreund, D., Gan, C., Traer, J., Kubilius, J., Schrimpf, M., Bhandwaldar, A., De Freitas, J., Mrowca, D., Lingelbach, M., Sano, M., Bear, D., Kim, K., Haber, N., and Fan, C., “ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation”. 2020.
C. Gen, Schwartz, J., Alter, S., Schrimpf, M., Traer, J., De Freitas, J., Kubilius, J., Bhandwaldar, A., Haber, N., Sano, M., Kim, K., Wang, E., Mrowca, D., Lingelbach, M., Curtis, A., Feigleis, K., Bear, D., Gutfreund, D., Cox, D., DiCarlo, J. J., McDermott, J. H., Tenenbaum, J. B., and Yamins, D. L. K., “ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation”, arXiv, 2020.
J. Dapello, Marques, T., Schrimpf, M., Geiger, F., Cox, D., and DiCarlo, J. J., “Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations”, Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). 2020.
J. Kubilius, Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N. J., Issa, E. B., Bashivan, P., Prescott-Roy, J., Schmidt, K., Nayebi, A., Bear, D., Yamins, D. L. K., and DiCarlo, J. J., “Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs”, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.
M. Schrimpf and Kubilius, J., “Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?”, bioRxiv preprint, 2018.
H. Tang, Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Caro, J. Ortega, Hardesty, W., Cox, D., and Kreiman, G., “Recurrent computations for visual pattern completion”, Proceedings of the National Academy of Sciences, 2018.
L. Arend, Han, Y., Schrimpf, M., Bashivan, P., Kar, K., Poggio, T., DiCarlo, J. J., and Boix, X., “Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results”. 2018.
N. Cheney, Schrimpf, M., and Kreiman, G., “On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations”. 2017.